Frozen cellets (cell pellets) were thawed on ice, then 30 mL lysis buffer (50 mM HEPES pH 7.5, 500 mM NaCl, 5 mM imidazole, 5% v/v glycerol, 2 mM DTT) was added. One Pierce EDTA-free protease inhibitor mini-tablet per cellet was also added, and resuspended in a vortexer. Cells in the slurry were then lysed by 3 passages through a cell homogenizer (Avestin) operating with 1000 bar peak. Lysate was then centrifuged for 45 min at 50,000×
A 5 mL Ni-NTA column (Cytiva) was equilibrated in freshly prepared low-imidazole buffer (50 mM HEPES pH 7.5, 500 mM NaCl, 30 mM imidazole, 5% v/v glycerol, 2 mM DTT). The lysate supernatant was applied to this column, washed with 2 column volumes (CV) of low-imidazole buffer, then gradient-eluted over 10 CV to 100% high-imidazole buffer (50 mM HEPES pH 7.5, 500 mM NaCl, 500 mM imidazole, 5% v/v glycerol, 2 mM DTT), collecting in 5 mL fractions. The STEP-containing fractions eluted around the 40% gradient mark were collected, concentrated using a 15 mL Centriprep 10 K spin-concentrator (Millipore) to a final volume of 5 mL, and filtered through a syringe-mount 0.22 μm filter to remove unidentified precipitate.
\",\n \"A Sephadex 20/200 column (Cytiva) was equilibrated with 2 CV of SEC buffer (50 mM HEPES pH 7.5, 500 mM NaCl, 5% v/v glycerol, 2 mM DTT). The concentrated, filtered Ni-binding fraction was injected onto a 5 mL loop, loaded onto the column, and fractionated over 2 CV, collecting 1 mL fractions. Two peaks were observed, and the fractions corresponding to the largest, STEP-containing peak were pooled.
\",\n \"A HiTrap Q HP anion-exchange column (Cytiva) was equilibrated with 2 CV of low-salt buffer (50 mM HEPES pH 7.5, 10 mM DTT). The pooled peak from size-exclusion chromatography was diluted to a final volume of 100 mL by addition of low-salt buffer, and filtered through a 0.22 μm bottle-top vacuum filter (Celltreat). This was then applied to the Q column, washed with 2 CV of low-salt buffer, and then gradient-eluted over 5 CV to 100% high-salt buffer (50 mM HEPES pH 7.5, 1000 mM NaCl, 10 mM DTT) collecting 5 mL fractions. A single STEP-containing peak was collected at 40% gradient mark.
\",\n \"This final STEP protein was concentrated in Centriprep 10 K spin-concentrators to 3 mL volume, and then further concentrated in Amicon 10 K spin-concentrators to a final concentration of 10 mg/mL, as measured by Nanodrop, and used fresh as the protein sample in crystallography. The identity of STEP vs. other proteins/contaminants was confirmed using SDS-PAGE gels at each step of the purification.
\",\n \"Precipitant well solution (30% PEG 3350, 200 mM Li2SO4, 100 mM bis-tris pH 5.65) was prepared fresh. A Mosquito (SPT Labtech) was used to prepare 96-well 3-drop Intelliplate Low-profile (Art Robbins Instruments) plates. 80 μL well solution was placed into the reservoir. Three 1 μL drops at a protein concentration of 10 mg/mL were placed per well, using 2:1, 1:1, and 1:2 ratios of well solution to protein sample. Crystallization drops were incubated at room temperature. Crystals nucleated within 3 days, mostly in 1:1 droplets, and grew over a week to around 80 × 80 x 20 μm.
\",\n \"For the ambient-pressure low-temperature 100 K (LoTP) dataset, the crystal was soaked in cryoprotectant (mother liquor + 15% v/v glycerol), and cryocooled with liquid nitrogen.
\",\n \"For the high-pressure (205 MPa) cryogenic-temperature dataset (HiP), the crystal was looped in a 100 μm loop, soaked in cryoprotectant (mother liquor + 15% v/v glycerol), and placed in a capillary with cryoprotectant at the end of the tube for shipping to CHESS. At CHESS, the capillary was removed, and the crystal was coated in NVH oil. The crystal was pressurized for 20 min at 205 MPa, cryocooled with liquid nitrogen under pressure, and stored under liquid nitrogen thereafter.
\",\n \"High-temperature diffraction required larger crystals, and so were prepared in Nextal EasyXtal 15-well hanging-drop trays. Precipitant well solution was prepared with the same composition as above (pH 5.5). In all, 400 μL well solution was placed into the reservoirs. Three 3 μL drops at a protein concentration of 10 mg/mL were placed per well, using 1:1 ratios of well solution to protein sample each. Crystallization drops were incubated at room temperature. Crystals nucleated within 3 days, and grew over a week to around 140 × 70 × 40 μm.
\",\n \"For the high-temperature ambient-pressure 310 K (HiT) dataset, the crystal was soaked in cryoprotectant (mother liquor + 15% v/v glycerol), coated in NVH oil, and placed in a capillary with cryoprotectant at the end of the tube for shipping to CHESS.
\",\n \"All X-ray diffraction datasets were collected at the ID7B2 (FlexX) beamline for macromolecular X-ray science at the Cornell High Energy Synchrotron Source (MacCHESS), Ithaca, New York, USA, using an X-ray beam energy of 12 keV and corresponding wavelength of 1.033 Å. The LoTP dataset was collected using beam dimensions of 30 × 20 µm, flux of 5 × 1011 ph/s, rotation rate of 2°/s, and no translation. The HiT dataset was collected using beam dimensions of 30 × 20 µm, flux flux of 1.6 × 1010 ph/s, rotation rate of 10°/s, and translation (i.e. helical/vector data collection) along the length of the approximately 140 × 70 x 40 µm crystal. The HiP dataset was collected using beam dimensions of 100 × 100 µm, flux of 2 × 1010 ph/s, rotation rate of 1°/s, and no translation.
\",\n \"Data reduction and modeling was performed similarly for all three datasets, with the data reduction pipeline DIALS##REF##34747533##60##. The LoTP dataset was trimmed to the first 130 (out of 180) frames due to increased ice inclusions in later frames. Resolution cutoffs were determined automatically by DIALS based on a combination of CC1/2, I/sigma(I), Rmerge, and completeness##REF##34747533##60##. Molecular replacement was performed via Dimple##UREF##11##61##, with subsequent refinement performed using REFMAC##REF##15299926##62## and phenix.refine##REF##22505256##63##, with models manually adjusted between rounds of refinement using COOT##REF##15572765##64##. Hydrogens were added using phenix.ready_set##REF##31588918##65##. X-ray/stereochemistry weight, X-ray ADP weight, and occupancies were all refined and optimized during the final rounds of refinement. Model validation statistics were generated using MolProbity##REF##29067766##66##. Solvent content was calculated via MATTPROB##REF##5700707##67##,##REF##24914969##68##. Ramachandran outliers (%) are 1.07, 0.36, and 0.71 for LoTP, HiT, and HiP, while Ramachandran favored (%) values are 95.00, 95.00, and 93.21, respectively. Data collection and refinement statistics can be found in Table ##TAB##0##1##.
\",\n \"To complement/validate the conclusions from the manually modeled waters in the deposited models, automated analysis of structural waters was performed by truncating our datasets to the same resolution (1.96 Å) in the PHENIX GUI reflection file editor. After removing all manually modeled heteroatoms including waters, our models were subjected to refinement with default settings, followed by both Cartesian and torsion angle simulated annealing refinement with a start temperature of 4000 K. Waters were automatically added based on real space density using the Coot “find waters” tool. Finally, the models were refined with phenix.refine, first without then with automatic water updating.
\",\n \"Polder maps and omit maps were calculated using the phenix.polder utility from the PHENIX GUI##REF##28177311##45##.
\",\n \"Cɑ distances between structures were calculated using VMD##UREF##12##69##. Rotamer names were calculated using phenix.rotalyze##REF##29067766##66## based on the latest rotamer distributions from MolProbity##REF##27018641##70##.
\",\n \"Protein volumes were calculated using the ProteinVolume software##UREF##13##71##. Values for the “total volume” output were nearly identical whether waters were included or not, and were similar (conclusions did not change) when state A vs. state B of the HiP structure were analyzed.
\",\n \"Ringer##REF##20499387##4## was run on models that only contained a single conformation for each residue, with alternate conformations removed using phenix.pdbtools. For HiP, each E-loop conformation was treated individually. The single-conformer models each underwent multiple cycles of refinement using phenix.refine. The refined models and maps were then used as input to Ringer. Plots were generated using
For RoPE analysis, all structures were pre-processed with PDB-REDO##UREF##14##72## to ensure consistent treatment, as recommended##UREF##9##42##. The different HiP points in Fig. ##FIG##5##6## correspond to different preparations of the model: run PDB-REDO for deposited model; extract each state, run PDB-REDO, then set all occupancies to unity; or extract each state, set all occupancies to unity, then run PDB-REDO.
\",\n \"Calculation of correlation coefficients for Ringer analysis in Fig. ##FIG##4##5## and Supplementary Figs. ##SUPPL##0##11## and ##SUPPL##0##12## was performed using the Pearson correlation method and Supplementary Data ##SUPPL##3##2##, and Supplementary Fig. ##SUPPL##0##4## was performed using Supplementary Data ##SUPPL##4##3##. The analysis of the effects on pockets and cavities in Supplementary Fig. ##SUPPL##0##10## was performed using a Welch’s two-sample
Further information on research design is available in the ##SUPPL##6##Nature Portfolio Reporting Summary## linked to this article.
\"\n]"},"results":{"kind":"list like","value":["To compare the effects of temperature vs. pressure on the STEP catalytic domain, we used similarly prepared crystals to obtain three complementary crystal structures: one at cryogenic temperature (100 K) and ambient pressure (0.1 MPa), one at physiological temperature (310 K) but ambient pressure, and one at high pressure (205 MPa) but cryogenic temperature via high-pressure cryocooling##REF##15983410##43##. For the remainder of this paper, we refer to the structure at low temperature and low pressure as LoTP, the structure at high temperature as HiT, and the structure at high pressure as HiP. The diffraction datasets and resulting refined structures were of high quality, including acceptably similar resolutions (Table ##TAB##0##1##). Therefore, these datasets can be directly compared to gain insights into the differential effects of temperature vs. pressure on the conformational ensemble of STEP.
","All three structures have the expected PTP catalytic domain architecture (Fig. ##FIG##0##1a, b##), including several key loops surrounding the active site region (Fig. ##FIG##0##1c##). Broadly speaking, they are similar to the 7 previously published structures of human STEP (8 including 1 structure of mouse STEP; Supplementary Fig. ##SUPPL##0##1##).
","However, the unit cell dimensions differed from the LoTP reference dataset in opposing ways: at HiT the unit cell volume expanded by 3.9%, whereas at HiP the unit cell was instead compressed by 2.1% (Table ##TAB##1##2##). Comparatively, the protein molecule itself was more robust, but was still affected by temperature and pressure: at HiT the protein volume expanded by 1.1%, whereas at HiP it still expanded, but by only 0.4%. Thus elevated temperature expands the unit cell and, to a lesser extent, the protein itself; by contrast, elevated pressure compresses the unit cell, but still allows the protein itself to slightly expand. These observations suggest that temperature vs. pressure has more complex effects on STEP than might be naïvely expected from the unit cell changes alone. Indeed, these distinct perturbations induce a variety of conformational changes distributed throughout the structure of STEP, including some with potential biological relevance, as shown below.
","One notable feature of our new structures differs from the previous STEP structures: the active site binds two sulfate molecules (Fig. ##FIG##0##1c, d## and Supplementary Fig. ##SUPPL##0##2a##). The top sulfate sits just beneath the catalytic WPD loop, where a lone sulfate has been observed previously in PDB ID 2bv5, 2bij##REF##16441242##39##, and 6h8r##REF##30207464##40##, and inhibitors with negatively charged moieties have been observed in PDB ID 5ovr, 5ovx, and 5ow1##REF##29116812##38## (Supplementary Fig. ##SUPPL##0##2b##).
","The bottom sulfate is well-coordinated by the catalytic P loop, analogous to the phosphate group in the phosphotyrosine (pTyr) substrate in PDB ID 2cjz##REF##19167335##41## (Supplementary Fig. ##SUPPL##0##2c##). A phosphocysteine reaction intermediate with a covalent bond to Cys472 is an unlikely explanation of our data, as refining such a putative model resulted in strong negative difference density peaks (Supplementary Fig. ##SUPPL##0##3##), our crystals contained high concentrations of lithium sulfate but not any phosphate-containing compounds, and previous intermediate-bound PTP structures used inactivating mutations to capture such intermediates##REF##19167335##41##,##REF##9553104##44## whereas our structure is wildtype. In our structures, as supported by strong electron density, Cys472 predominantly adopts a side-chain rotamer that points away from the sulfate, thus avoiding a steric clash. This rotamer in our new structures is rare for STEP: it was previously only seen as a partial-occupancy alternate conformation in an allosterically activated structure (PDB ID 6h8r) (Supplementary Fig. ##SUPPL##0##2b##). Further supporting this primary rotamer, Ringer curves##REF##20499387##4## for Cys472 for all three datasets have a dominant peak for the χ1 side-chain dihedral angle near 180°. In addition, Ringer curves from different model preparations for all three datasets also have a secondary χ1 peak near +60°, suggesting sensitivity of Ringer to precise input coordinates and/or subtle sensitivity to our global perturbations (Supplementary Fig. ##SUPPL##0##4##). This secondary Ringer peak is consistent with an alternate rotamer conformation for Cys472, which is further supported by unbiased Polder##REF##28177311##45## (Supplementary Fig. ##SUPPL##0##5##) and omit (Supplementary Fig. ##SUPPL##0##6##) electron density maps. Moreover, the backbone density for Cys472 is consistent with multiple positions, suggesting Cα displacements that are perpendicular to the chain direction (Supplementary Figs. ##SUPPL##0##5## and ##SUPPL##0##6##) and similar in magnitude (0.44–0.93 Å) to those seen between alternate conformations in a previous structure of STEP (0.73 Å for PDB ID 2bv5, although Cys472 is acetylated in that structure and does not change rotamer). Taken together, these observations support the interpretation that Cys472 samples both a rare primary rotamer and a low-occupancy alternate rotamer that is sterically mutually exclusive with a fortuitously observed sulfate bound at high but non-unity occupancy.
","Thus, although previous structures of STEP have sulfates or phosphate-like chemical groups independently in each of these sites, no previous structure has them in both sites simultaneously. The closest comparison is PDB ID 2bv5, in which the catalytic Cys472 is modeled as acetylated in the bottom site and a sulfate is in the top site (Supplementary Fig. ##SUPPL##0##2d##), but this arrangement differs in chemical character from what we observe.
","In addition to global changes to the crystal lattice, high temperature, and pressure have striking effects on the solvation layer surrounding the protein. In our manually modeled, deposited structures, LoTP has by far the most waters, HiT has by far the fewest, and HiP has an intermediate number (Supplementary Table ##SUPPL##0##1## and Fig. ##FIG##1##2##). To confirm that this result is not due to the small differences in resolution between datasets (Table ##TAB##0##1##), we truncated the LoTP and HiP diffraction data to match the resolution of the HiT dataset (1.96 Å), then performed fully automated, unbiased water placement for all three structures (see “Methods” section). The resulting water counts are similar to the counts of manually placed waters in our deposited structures (Supplementary Table ##SUPPL##0##1##), thus validating the conclusions derived from the latter.
","Turning to specific water positions in our deposited structures, 17 (24.6%) of the HiT waters and 23 (23.5%) of the HiP waters were distinct from any LoTP water (> 2 Å, accounting for crystal symmetry) (Fig. ##FIG##1##2##). Of these 40 new positions, only 1 (2.5%) is common to both HiT and HiP. This suggests that high temperature and high pressure do not merely retain a subset of ordered waters, but rather stabilize new water positions, resulting in a distinct pattern of solvation. As shown below, some of these unique waters are located at functional sites in STEP (Fig. ##FIG##2##3##). In total, we reveal 67 (LoTP) + 16 (HiT) + 22 (HiP) = 105 waters that are unique to one structure (Fig. ##FIG##1##2##), further underscoring the value of crystallography with different axes of perturbations for mapping accessible patterns of protein solvation.
","To explore differential effects of high temperature vs. pressure on the protein molecule itself, we examined Cɑ displacements in the HiT and HiP structures relative to the reference LoTP structure. A global plot of this Cɑ distance vs. amino acid sequence (Fig. ##FIG##2##3a##) reveals that most regions are similar in the three structures, with Cɑ distances <0.3 Å, but several local regions shift relative to the reference structure. These shifts tend to occur either only at high temperature or only at high pressure, suggesting that the protein responds to these different perturbations in distinct ways.
","Our structures were obtained in the same crystal form as PDB ID 2bv5, which, like our LoTP structure, is a cryogenic-temperature, ambient-pressure dataset. Cɑ distance analysis shows that for many key regions, 2bv5 is similar to our LoTP structure, whereas our HiT and HiP structures are more different (Supplementary Fig. ##SUPPL##0##7##). Thus the effects of temperature and pressure are generally greater than the variability inherent to determining structures of the same protein in similar conditions by different scientists at different times.
","Beyond 2bv5, all other previous human STEP structures were in a different crystal form (same space group but longer a and shorter c axes). These exhibit similar or greater Cɑ distances than do our HiT and HiP structures at several sites in STEP (Supplementary Fig. ##SUPPL##0##7b##). All previous STEP structures were determined at cryogenic temperature and ambient pressure. This indicates that, at least at a gross level, differences in crystal contacts may elicit protein structural variability##REF##21073878##46## that encompass much of the variability elicited by experimental perturbations such as temperature and pressure. Nonetheless, as shown below, temperature and pressure each induce unique conformational states of STEP.
","To explore the basis of these global structural differences, we examined several local areas with distinct conformations in the HiT vs. HiP structures. One local region that responds strongly to pressure — but not to temperature — is the E loop (Fig. ##FIG##2##3a## region (iii)). The E loop of PTPs, containing several Glu (E) residues, is located adjacent to the catalytic WPD loop and P loop (Fig. ##FIG##0##1##). Among previous structures of STEP, the E loop exhibited substantial variability (Supplementary Figs. ##SUPPL##0##1## and ##SUPPL##0##7##). The two main states previously modeled for this loop were the inactive-like state in 2bv5 (with an acetylated catalytic Cys472), and the active-like state in 6h8r (bound to a distal allosteric small-molecule activator). To validate these previous models, we inspected the electron density maps for all previous STEP crystal structures (7 human, 1 mouse). We determined that all of these structures besides 6h8r were either already modeled with a 2bv5-like conformation, or were unmodeled but could be better explained by a 2bv5-like conformation than by a 6h8r-like conformation. Thus, the active-like state of the E loop was only legitimately observed in the allosterically activated structure 6h8r, even though the density was somewhat noisy (Supplementary Fig. ##SUPPL##0##8##).
","In contrast to previous STEP structures, our HiP electron density for the E loop, albeit also noisy, is consistent with the presence of both an inactive-like state as in 2bv5 and an active-like state as in 6h8r. We therefore modeled both states as alternate conformations (Fig. ##FIG##3##4a, b##). Deletion of either of these conformations and calculation of omit maps results in positive Fo-Fc difference density peaks for the omitted model (Fig. ##FIG##3##4c, d##), suggesting both are present. The 6h8r-like conformation exists in our HiP structure despite having a different crystal form than 6h8r. By contrast to HiP, our crystallographically isomorphous LoTP and HiT structures are essentially identical to 2bv5 for the E loop. Therefore, high pressure appears to uniquely stabilize a conformation of a key active-site loop that is correlated with an allosterically activated state of human STEP.
","Another region that responds to pressure is residues 287-306, encompassing the pTyr loop, also known as the substrate-binding loop (SBL) (Fig. ##FIG##2##3a, b## region (ii)). Backbone shifts in this region play a crucial role in defining the depth of the catalytic pocket##REF##23860656##47##. In our models, the backbone of this region, particularly the N-terminal portions, shifts >1 Å from LoTP to HiP (Fig. ##FIG##2##3c##). In addition, the backbone of the C-terminal portions of this region, corresponding to the pTyr loop itself, shifts by up to ~0.7 Å from LoTP to HiT (Fig. ##FIG##2##3c##). Notably, the backbone for the pTyr loop residue Tyr304, whose side chain directly interacts with and helps position the pTyr substrate during catalysis, shifts at both HiP and HiT, yet its side chain remains in place. Overall, these observations suggest a degree of plasticity in the substrate-binding region, which we speculate may help accommodate different pTyr-containing substrates.
","In contrast to these regions that respond to pressure, other regions of STEP respond only to temperature. The backbone of the ɑ1′-ɑ2′ helical region near the N-terminus (residues 266-284) shifts by up to ~0.7 Å at HiT but not HiP (Fig. ##FIG##2##3a, b## region (i)). In addition, the junction between the active-site Q loop and the ɑ6 helix (residues 514-524) shifts by up to ~0.4 Å, also at HiT but not HiP (Fig. ##FIG##2##3a, b## region (v)). These new HiT conformations differ not only from our HiP and LoTP structures, but also from the only previous STEP structure with the same crystal form, 2bv5, which was at cryogenic temperature (Supplementary Fig. ##SUPPL##0##7a##).
","These backbone shifts are coupled to other notable changes to side-chain conformational ensembles (Fig. ##FIG##2##3c##). In concert with the Q loop backbone shift in this interface, the side chain of Cys518 (from the Q loop) switches from two rotamers to one. The disappearance of the alternate rotamer for Cys518 eliminates a hydrogen bond to the adjacent Glu519, causing the latter to switch to a new rotamer (see also Fig. ##FIG##4##5b##). The new Glu519 rotamer engages in a previously unseen interaction with Lys439 from the catalytic WPD loop, which coordinates the top sulfate (Fig. ##FIG##2##3c##). This conformation of Glu519 is not present in any previous STEP structures: it is unique to our HiT structure. Several of these changes are also correlated with shifts or disordering of nearby water molecules (Fig. ##FIG##2##3c##), illustrating an interplay between protein and solvent structure.
","The Q loop backbone shift is also correlated with an alternate side-chain rotamer for Gln516 (Fig. ##FIG##2##3c##) that has only been seen in two previous structures: bound to a pTyr substrate (2cjz; Supplementary Fig. ##SUPPL##0##2c##), and bound to a distal allosteric activator (6h8r; Supplementary Fig. ##SUPPL##0##9##). In particular, this new rotamer avoids what would otherwise be a steric clash with the pTyr substrate, which binds immediately adjacent to Gln516 (Fig. ##FIG##2##3c##). These observations suggest that HiT may capture an active-like conformation of STEP, even in the unliganded form, that is more compatible with formation of the Michaelis complex. Interestingly, Gln516 is immediately adjacent to Ile515, which is the only residue in STEP to have an unavoidable but real Ramachandran outlier — consistent with previous observations that validated, geometrically strained residues, while rare, occur preferentially at active sites##REF##12557186##48##.
","In the context of the crystal lattice, ɑ1′-ɑ2′ also abuts the distal S loop (residues 462-465), parts of which shift by >0.5 Å at HiT but not HiP (Supplementary Fig. ##SUPPL##0##9##). Interestingly, the S loop forms part of the binding site for a class of allosteric small-molecule activators that are unique to STEP##REF##30207464##40## (Supplementary Fig. ##SUPPL##0##9##). This coincidence of temperature-sensitive regions in 3D space suggests that subtle lattice expansion at elevated temperature can allow a protein “breathing room” to adopt subtly different conformations, including at functionally important regions.
","To assess how temperature vs. pressure affect various packing defects in the structure of STEP, we used the program CASTp##REF##29860391##49## to measure the volumes of all pockets and/or cavities in each structure (Supplementary Fig. ##SUPPL##0##10##). Interestingly, the largest pocket in each of the three structures was the allosteric activator site. Relative to the LoTP structure (128.7 Å3), the volume of this pocket increased at HiT (140.0 Å3) but decreased at HiP (74.8 Å3), consistent with general expectations of expansion with increasing temperature and compression with increasing pressure.
","Considering all pockets/cavities in each structure, the mean volume relative to LoTP (7.8 Å3) increases for HiT (11.3 Å3) and slightly decreases for HiP (7.5 Å3). However, relative to LoTP, we observe no statistically significant difference in the distribution of these volumes for either HiT or HiP (Welch’s two-sample
We next examined in detail how temperature vs. pressure affected conformations throughout the entirety of the STEP catalytic domain, using torsion angles in several ways. First, we performed Ringer analysis for each side chain in each structure by rotating around the Cα-Cβ vector (χ1 torsion angle) and measuring the 2Fo-Fc electron density value at each possible γ heavy atom position##REF##20499387##4##. For each residue, we then calculated a correlation coefficient (CC) between Ringer curves for each pair of datasets##UREF##5##15##. Relative to the reference LoTP dataset, a substantial number of residues had low CC for either HiT or for HiP (Supplementary Fig. ##SUPPL##0##11##), suggesting differences in side-chain conformational ensembles due to these perturbations. For example, 19 (6.7%) residues had CC < 0.5 in HiT, and 22 (7.8%) residues had CC < 0.5 in HiP. Excluding the flexible E loop (residues 375-383), 18 (6.6%) residues had CC < 0.5 in HiT, and 14 (5.1%) residues had CC < 0.5 in HiP. If high temperature vs. high pressure had similar structural effects, a similar set of residues would be expected to have low CC for both HiT and HiP (each relative to LoTP). However, relatively few residues fall into this category (purple bars in Supplementary Fig. ##SUPPL##0##11##), suggesting that temperature vs. pressure are complementary perturbations that affect different areas of the protein.
","To validate these quantitative Ringer results, we examined the models and density maps in detail for several examples. For most residues, the Ringer curves are indeed similar across all three datasets (Fig. ##FIG##4##5a##). For other residues, by contrast, the curves differ in one or more datasets, indicating perturbation-induced changes to side-chain conformations. For example, Glu519 adopts the same χ1 rotamer for LoTP and HiP, but a different χ1 rotamer at HiT (Fig. ##FIG##4##5b##; see also Fig. ##FIG##2##3c##), involving
As the Ringer curves above only account for the first side-chain torsion angle (χ1), we also compared rotamer names, which account for all side-chain torsion angles##REF##10861930##50## (see ”Methods” section). Excluding the flexible E loop, relatively few residues had different rotamers as alternate conformations in the same model: 7 for LoTP, 7 for HiT, and 7 for HiP. However, compared to LoTP, 50 residues (18%) had a different rotamer in HiT, and 38 residues (14%) had a different rotamer in HiP. Thus, by contrast to only the side-chain base as measured by Ringer, temperature, and pressure both have greater effects on the overall conformations of side chains, stabilizing distinct energy basins. Moreover, of the residues that differed from LoTP, 29 were unique to either only HiT or only HiP, indicating distinct conformational effects from temperature vs. pressure.
","Finally, beyond just torsion angles for individual side chains, we explored whether many torsion angles distributed throughout the protein structure may undergo correlated changes in response to temperature vs. pressure. A recent tool called RoPE showed that linear combinations of backbone and side-chain torsion angles in a reduced-dimensionality space can help reveal new insights into the key differences between sets of structural models##UREF##9##42##. Using RoPE analysis, we examined our three new structures relative to all previous STEP structures (Fig. ##FIG##5##6##), leading to several interesting observations.
","First, the STEP structures generally cluster based on resolution, as noted previously for other proteins##UREF##9##42##, with our new structures at intermediate-to-high resolution compared to prior structures. Second, each set of structures with a consistent crystal form clusters together: (i) our new structures plus 2bv5, (ii) most remaining structures, and (iii) the mouse STEP structure 6h8s. Third, most of the previous structures segregate into an active-like cluster (either allosterically activated or bound to a substrate peptide) or an inactive-like cluster (bound to orthosteric inhibitors), indicating that subtle signatures of the protein’s functional state are embedded in torsion-angle space. The allosterically activated structure (6h8s) is nearest to other active-like structures, despite it being mouse-derived (91% sequence identity to human STEP) and having a unique crystal form; hence, signatures of the protein’s inherent functional state appear to persist in this space despite differences in amino acid sequence and crystal lattice.
","Fourth, whereas our LoTP structure is near the most analogous previous structure (2bv5) in torsion-angle space as expected, our HiT and HiP structures move in distinct directions from this reference point (Fig. ##FIG##5##6##). Notably, HiT moves toward the active-like structures, whereas HiP moves away from all known STEP structures. This is despite only HiP featuring a conformation of the E loop resembling the allosterically activated structure 6h8r (Fig. ##FIG##3##4## and Supplementary Fig. ##SUPPL##0##8##), but consistent with only HiT featuring side-chain and backbone conformations of the active-site Q loop in a putatively active-like state (Figs. ##FIG##2##3c## and ##FIG##4##5b##). HiP models with significantly different E-loop conformations and prepared for analysis in different ways have similar positions in torsion-angle space, confirming that RoPE analysis highlights structurally distributed as opposed to localized features. Relative to LoTP, the coordinated torsion-angle changes in HiT and HiP detected by RoPE visually correspond to hinging of the first half of the primary structure (initial α-helices + loops) relative to the second half (β-sheet + α-helical bundle), albeit with apparently meaningful differences between them given their large separation in RoPE space. Overall, these results indicate that although high pressure induces an active-like conformation locally in the E loop, high temperature induces a more global, distributed active-like state of the protein.
"],"string":"[\n \"To compare the effects of temperature vs. pressure on the STEP catalytic domain, we used similarly prepared crystals to obtain three complementary crystal structures: one at cryogenic temperature (100 K) and ambient pressure (0.1 MPa), one at physiological temperature (310 K) but ambient pressure, and one at high pressure (205 MPa) but cryogenic temperature via high-pressure cryocooling##REF##15983410##43##. For the remainder of this paper, we refer to the structure at low temperature and low pressure as LoTP, the structure at high temperature as HiT, and the structure at high pressure as HiP. The diffraction datasets and resulting refined structures were of high quality, including acceptably similar resolutions (Table ##TAB##0##1##). Therefore, these datasets can be directly compared to gain insights into the differential effects of temperature vs. pressure on the conformational ensemble of STEP.
\",\n \"All three structures have the expected PTP catalytic domain architecture (Fig. ##FIG##0##1a, b##), including several key loops surrounding the active site region (Fig. ##FIG##0##1c##). Broadly speaking, they are similar to the 7 previously published structures of human STEP (8 including 1 structure of mouse STEP; Supplementary Fig. ##SUPPL##0##1##).
\",\n \"However, the unit cell dimensions differed from the LoTP reference dataset in opposing ways: at HiT the unit cell volume expanded by 3.9%, whereas at HiP the unit cell was instead compressed by 2.1% (Table ##TAB##1##2##). Comparatively, the protein molecule itself was more robust, but was still affected by temperature and pressure: at HiT the protein volume expanded by 1.1%, whereas at HiP it still expanded, but by only 0.4%. Thus elevated temperature expands the unit cell and, to a lesser extent, the protein itself; by contrast, elevated pressure compresses the unit cell, but still allows the protein itself to slightly expand. These observations suggest that temperature vs. pressure has more complex effects on STEP than might be naïvely expected from the unit cell changes alone. Indeed, these distinct perturbations induce a variety of conformational changes distributed throughout the structure of STEP, including some with potential biological relevance, as shown below.
\",\n \"One notable feature of our new structures differs from the previous STEP structures: the active site binds two sulfate molecules (Fig. ##FIG##0##1c, d## and Supplementary Fig. ##SUPPL##0##2a##). The top sulfate sits just beneath the catalytic WPD loop, where a lone sulfate has been observed previously in PDB ID 2bv5, 2bij##REF##16441242##39##, and 6h8r##REF##30207464##40##, and inhibitors with negatively charged moieties have been observed in PDB ID 5ovr, 5ovx, and 5ow1##REF##29116812##38## (Supplementary Fig. ##SUPPL##0##2b##).
\",\n \"The bottom sulfate is well-coordinated by the catalytic P loop, analogous to the phosphate group in the phosphotyrosine (pTyr) substrate in PDB ID 2cjz##REF##19167335##41## (Supplementary Fig. ##SUPPL##0##2c##). A phosphocysteine reaction intermediate with a covalent bond to Cys472 is an unlikely explanation of our data, as refining such a putative model resulted in strong negative difference density peaks (Supplementary Fig. ##SUPPL##0##3##), our crystals contained high concentrations of lithium sulfate but not any phosphate-containing compounds, and previous intermediate-bound PTP structures used inactivating mutations to capture such intermediates##REF##19167335##41##,##REF##9553104##44## whereas our structure is wildtype. In our structures, as supported by strong electron density, Cys472 predominantly adopts a side-chain rotamer that points away from the sulfate, thus avoiding a steric clash. This rotamer in our new structures is rare for STEP: it was previously only seen as a partial-occupancy alternate conformation in an allosterically activated structure (PDB ID 6h8r) (Supplementary Fig. ##SUPPL##0##2b##). Further supporting this primary rotamer, Ringer curves##REF##20499387##4## for Cys472 for all three datasets have a dominant peak for the χ1 side-chain dihedral angle near 180°. In addition, Ringer curves from different model preparations for all three datasets also have a secondary χ1 peak near +60°, suggesting sensitivity of Ringer to precise input coordinates and/or subtle sensitivity to our global perturbations (Supplementary Fig. ##SUPPL##0##4##). This secondary Ringer peak is consistent with an alternate rotamer conformation for Cys472, which is further supported by unbiased Polder##REF##28177311##45## (Supplementary Fig. ##SUPPL##0##5##) and omit (Supplementary Fig. ##SUPPL##0##6##) electron density maps. Moreover, the backbone density for Cys472 is consistent with multiple positions, suggesting Cα displacements that are perpendicular to the chain direction (Supplementary Figs. ##SUPPL##0##5## and ##SUPPL##0##6##) and similar in magnitude (0.44–0.93 Å) to those seen between alternate conformations in a previous structure of STEP (0.73 Å for PDB ID 2bv5, although Cys472 is acetylated in that structure and does not change rotamer). Taken together, these observations support the interpretation that Cys472 samples both a rare primary rotamer and a low-occupancy alternate rotamer that is sterically mutually exclusive with a fortuitously observed sulfate bound at high but non-unity occupancy.
\",\n \"Thus, although previous structures of STEP have sulfates or phosphate-like chemical groups independently in each of these sites, no previous structure has them in both sites simultaneously. The closest comparison is PDB ID 2bv5, in which the catalytic Cys472 is modeled as acetylated in the bottom site and a sulfate is in the top site (Supplementary Fig. ##SUPPL##0##2d##), but this arrangement differs in chemical character from what we observe.
\",\n \"In addition to global changes to the crystal lattice, high temperature, and pressure have striking effects on the solvation layer surrounding the protein. In our manually modeled, deposited structures, LoTP has by far the most waters, HiT has by far the fewest, and HiP has an intermediate number (Supplementary Table ##SUPPL##0##1## and Fig. ##FIG##1##2##). To confirm that this result is not due to the small differences in resolution between datasets (Table ##TAB##0##1##), we truncated the LoTP and HiP diffraction data to match the resolution of the HiT dataset (1.96 Å), then performed fully automated, unbiased water placement for all three structures (see “Methods” section). The resulting water counts are similar to the counts of manually placed waters in our deposited structures (Supplementary Table ##SUPPL##0##1##), thus validating the conclusions derived from the latter.
\",\n \"Turning to specific water positions in our deposited structures, 17 (24.6%) of the HiT waters and 23 (23.5%) of the HiP waters were distinct from any LoTP water (> 2 Å, accounting for crystal symmetry) (Fig. ##FIG##1##2##). Of these 40 new positions, only 1 (2.5%) is common to both HiT and HiP. This suggests that high temperature and high pressure do not merely retain a subset of ordered waters, but rather stabilize new water positions, resulting in a distinct pattern of solvation. As shown below, some of these unique waters are located at functional sites in STEP (Fig. ##FIG##2##3##). In total, we reveal 67 (LoTP) + 16 (HiT) + 22 (HiP) = 105 waters that are unique to one structure (Fig. ##FIG##1##2##), further underscoring the value of crystallography with different axes of perturbations for mapping accessible patterns of protein solvation.
\",\n \"To explore differential effects of high temperature vs. pressure on the protein molecule itself, we examined Cɑ displacements in the HiT and HiP structures relative to the reference LoTP structure. A global plot of this Cɑ distance vs. amino acid sequence (Fig. ##FIG##2##3a##) reveals that most regions are similar in the three structures, with Cɑ distances <0.3 Å, but several local regions shift relative to the reference structure. These shifts tend to occur either only at high temperature or only at high pressure, suggesting that the protein responds to these different perturbations in distinct ways.
\",\n \"Our structures were obtained in the same crystal form as PDB ID 2bv5, which, like our LoTP structure, is a cryogenic-temperature, ambient-pressure dataset. Cɑ distance analysis shows that for many key regions, 2bv5 is similar to our LoTP structure, whereas our HiT and HiP structures are more different (Supplementary Fig. ##SUPPL##0##7##). Thus the effects of temperature and pressure are generally greater than the variability inherent to determining structures of the same protein in similar conditions by different scientists at different times.
\",\n \"Beyond 2bv5, all other previous human STEP structures were in a different crystal form (same space group but longer a and shorter c axes). These exhibit similar or greater Cɑ distances than do our HiT and HiP structures at several sites in STEP (Supplementary Fig. ##SUPPL##0##7b##). All previous STEP structures were determined at cryogenic temperature and ambient pressure. This indicates that, at least at a gross level, differences in crystal contacts may elicit protein structural variability##REF##21073878##46## that encompass much of the variability elicited by experimental perturbations such as temperature and pressure. Nonetheless, as shown below, temperature and pressure each induce unique conformational states of STEP.
\",\n \"To explore the basis of these global structural differences, we examined several local areas with distinct conformations in the HiT vs. HiP structures. One local region that responds strongly to pressure — but not to temperature — is the E loop (Fig. ##FIG##2##3a## region (iii)). The E loop of PTPs, containing several Glu (E) residues, is located adjacent to the catalytic WPD loop and P loop (Fig. ##FIG##0##1##). Among previous structures of STEP, the E loop exhibited substantial variability (Supplementary Figs. ##SUPPL##0##1## and ##SUPPL##0##7##). The two main states previously modeled for this loop were the inactive-like state in 2bv5 (with an acetylated catalytic Cys472), and the active-like state in 6h8r (bound to a distal allosteric small-molecule activator). To validate these previous models, we inspected the electron density maps for all previous STEP crystal structures (7 human, 1 mouse). We determined that all of these structures besides 6h8r were either already modeled with a 2bv5-like conformation, or were unmodeled but could be better explained by a 2bv5-like conformation than by a 6h8r-like conformation. Thus, the active-like state of the E loop was only legitimately observed in the allosterically activated structure 6h8r, even though the density was somewhat noisy (Supplementary Fig. ##SUPPL##0##8##).
\",\n \"In contrast to previous STEP structures, our HiP electron density for the E loop, albeit also noisy, is consistent with the presence of both an inactive-like state as in 2bv5 and an active-like state as in 6h8r. We therefore modeled both states as alternate conformations (Fig. ##FIG##3##4a, b##). Deletion of either of these conformations and calculation of omit maps results in positive Fo-Fc difference density peaks for the omitted model (Fig. ##FIG##3##4c, d##), suggesting both are present. The 6h8r-like conformation exists in our HiP structure despite having a different crystal form than 6h8r. By contrast to HiP, our crystallographically isomorphous LoTP and HiT structures are essentially identical to 2bv5 for the E loop. Therefore, high pressure appears to uniquely stabilize a conformation of a key active-site loop that is correlated with an allosterically activated state of human STEP.
\",\n \"Another region that responds to pressure is residues 287-306, encompassing the pTyr loop, also known as the substrate-binding loop (SBL) (Fig. ##FIG##2##3a, b## region (ii)). Backbone shifts in this region play a crucial role in defining the depth of the catalytic pocket##REF##23860656##47##. In our models, the backbone of this region, particularly the N-terminal portions, shifts >1 Å from LoTP to HiP (Fig. ##FIG##2##3c##). In addition, the backbone of the C-terminal portions of this region, corresponding to the pTyr loop itself, shifts by up to ~0.7 Å from LoTP to HiT (Fig. ##FIG##2##3c##). Notably, the backbone for the pTyr loop residue Tyr304, whose side chain directly interacts with and helps position the pTyr substrate during catalysis, shifts at both HiP and HiT, yet its side chain remains in place. Overall, these observations suggest a degree of plasticity in the substrate-binding region, which we speculate may help accommodate different pTyr-containing substrates.
\",\n \"In contrast to these regions that respond to pressure, other regions of STEP respond only to temperature. The backbone of the ɑ1′-ɑ2′ helical region near the N-terminus (residues 266-284) shifts by up to ~0.7 Å at HiT but not HiP (Fig. ##FIG##2##3a, b## region (i)). In addition, the junction between the active-site Q loop and the ɑ6 helix (residues 514-524) shifts by up to ~0.4 Å, also at HiT but not HiP (Fig. ##FIG##2##3a, b## region (v)). These new HiT conformations differ not only from our HiP and LoTP structures, but also from the only previous STEP structure with the same crystal form, 2bv5, which was at cryogenic temperature (Supplementary Fig. ##SUPPL##0##7a##).
\",\n \"These backbone shifts are coupled to other notable changes to side-chain conformational ensembles (Fig. ##FIG##2##3c##). In concert with the Q loop backbone shift in this interface, the side chain of Cys518 (from the Q loop) switches from two rotamers to one. The disappearance of the alternate rotamer for Cys518 eliminates a hydrogen bond to the adjacent Glu519, causing the latter to switch to a new rotamer (see also Fig. ##FIG##4##5b##). The new Glu519 rotamer engages in a previously unseen interaction with Lys439 from the catalytic WPD loop, which coordinates the top sulfate (Fig. ##FIG##2##3c##). This conformation of Glu519 is not present in any previous STEP structures: it is unique to our HiT structure. Several of these changes are also correlated with shifts or disordering of nearby water molecules (Fig. ##FIG##2##3c##), illustrating an interplay between protein and solvent structure.
\",\n \"The Q loop backbone shift is also correlated with an alternate side-chain rotamer for Gln516 (Fig. ##FIG##2##3c##) that has only been seen in two previous structures: bound to a pTyr substrate (2cjz; Supplementary Fig. ##SUPPL##0##2c##), and bound to a distal allosteric activator (6h8r; Supplementary Fig. ##SUPPL##0##9##). In particular, this new rotamer avoids what would otherwise be a steric clash with the pTyr substrate, which binds immediately adjacent to Gln516 (Fig. ##FIG##2##3c##). These observations suggest that HiT may capture an active-like conformation of STEP, even in the unliganded form, that is more compatible with formation of the Michaelis complex. Interestingly, Gln516 is immediately adjacent to Ile515, which is the only residue in STEP to have an unavoidable but real Ramachandran outlier — consistent with previous observations that validated, geometrically strained residues, while rare, occur preferentially at active sites##REF##12557186##48##.
\",\n \"In the context of the crystal lattice, ɑ1′-ɑ2′ also abuts the distal S loop (residues 462-465), parts of which shift by >0.5 Å at HiT but not HiP (Supplementary Fig. ##SUPPL##0##9##). Interestingly, the S loop forms part of the binding site for a class of allosteric small-molecule activators that are unique to STEP##REF##30207464##40## (Supplementary Fig. ##SUPPL##0##9##). This coincidence of temperature-sensitive regions in 3D space suggests that subtle lattice expansion at elevated temperature can allow a protein “breathing room” to adopt subtly different conformations, including at functionally important regions.
\",\n \"To assess how temperature vs. pressure affect various packing defects in the structure of STEP, we used the program CASTp##REF##29860391##49## to measure the volumes of all pockets and/or cavities in each structure (Supplementary Fig. ##SUPPL##0##10##). Interestingly, the largest pocket in each of the three structures was the allosteric activator site. Relative to the LoTP structure (128.7 Å3), the volume of this pocket increased at HiT (140.0 Å3) but decreased at HiP (74.8 Å3), consistent with general expectations of expansion with increasing temperature and compression with increasing pressure.
\",\n \"Considering all pockets/cavities in each structure, the mean volume relative to LoTP (7.8 Å3) increases for HiT (11.3 Å3) and slightly decreases for HiP (7.5 Å3). However, relative to LoTP, we observe no statistically significant difference in the distribution of these volumes for either HiT or HiP (Welch’s two-sample
We next examined in detail how temperature vs. pressure affected conformations throughout the entirety of the STEP catalytic domain, using torsion angles in several ways. First, we performed Ringer analysis for each side chain in each structure by rotating around the Cα-Cβ vector (χ1 torsion angle) and measuring the 2Fo-Fc electron density value at each possible γ heavy atom position##REF##20499387##4##. For each residue, we then calculated a correlation coefficient (CC) between Ringer curves for each pair of datasets##UREF##5##15##. Relative to the reference LoTP dataset, a substantial number of residues had low CC for either HiT or for HiP (Supplementary Fig. ##SUPPL##0##11##), suggesting differences in side-chain conformational ensembles due to these perturbations. For example, 19 (6.7%) residues had CC < 0.5 in HiT, and 22 (7.8%) residues had CC < 0.5 in HiP. Excluding the flexible E loop (residues 375-383), 18 (6.6%) residues had CC < 0.5 in HiT, and 14 (5.1%) residues had CC < 0.5 in HiP. If high temperature vs. high pressure had similar structural effects, a similar set of residues would be expected to have low CC for both HiT and HiP (each relative to LoTP). However, relatively few residues fall into this category (purple bars in Supplementary Fig. ##SUPPL##0##11##), suggesting that temperature vs. pressure are complementary perturbations that affect different areas of the protein.
\",\n \"To validate these quantitative Ringer results, we examined the models and density maps in detail for several examples. For most residues, the Ringer curves are indeed similar across all three datasets (Fig. ##FIG##4##5a##). For other residues, by contrast, the curves differ in one or more datasets, indicating perturbation-induced changes to side-chain conformations. For example, Glu519 adopts the same χ1 rotamer for LoTP and HiP, but a different χ1 rotamer at HiT (Fig. ##FIG##4##5b##; see also Fig. ##FIG##2##3c##), involving
As the Ringer curves above only account for the first side-chain torsion angle (χ1), we also compared rotamer names, which account for all side-chain torsion angles##REF##10861930##50## (see ”Methods” section). Excluding the flexible E loop, relatively few residues had different rotamers as alternate conformations in the same model: 7 for LoTP, 7 for HiT, and 7 for HiP. However, compared to LoTP, 50 residues (18%) had a different rotamer in HiT, and 38 residues (14%) had a different rotamer in HiP. Thus, by contrast to only the side-chain base as measured by Ringer, temperature, and pressure both have greater effects on the overall conformations of side chains, stabilizing distinct energy basins. Moreover, of the residues that differed from LoTP, 29 were unique to either only HiT or only HiP, indicating distinct conformational effects from temperature vs. pressure.
\",\n \"Finally, beyond just torsion angles for individual side chains, we explored whether many torsion angles distributed throughout the protein structure may undergo correlated changes in response to temperature vs. pressure. A recent tool called RoPE showed that linear combinations of backbone and side-chain torsion angles in a reduced-dimensionality space can help reveal new insights into the key differences between sets of structural models##UREF##9##42##. Using RoPE analysis, we examined our three new structures relative to all previous STEP structures (Fig. ##FIG##5##6##), leading to several interesting observations.
\",\n \"First, the STEP structures generally cluster based on resolution, as noted previously for other proteins##UREF##9##42##, with our new structures at intermediate-to-high resolution compared to prior structures. Second, each set of structures with a consistent crystal form clusters together: (i) our new structures plus 2bv5, (ii) most remaining structures, and (iii) the mouse STEP structure 6h8s. Third, most of the previous structures segregate into an active-like cluster (either allosterically activated or bound to a substrate peptide) or an inactive-like cluster (bound to orthosteric inhibitors), indicating that subtle signatures of the protein’s functional state are embedded in torsion-angle space. The allosterically activated structure (6h8s) is nearest to other active-like structures, despite it being mouse-derived (91% sequence identity to human STEP) and having a unique crystal form; hence, signatures of the protein’s inherent functional state appear to persist in this space despite differences in amino acid sequence and crystal lattice.
\",\n \"Fourth, whereas our LoTP structure is near the most analogous previous structure (2bv5) in torsion-angle space as expected, our HiT and HiP structures move in distinct directions from this reference point (Fig. ##FIG##5##6##). Notably, HiT moves toward the active-like structures, whereas HiP moves away from all known STEP structures. This is despite only HiP featuring a conformation of the E loop resembling the allosterically activated structure 6h8r (Fig. ##FIG##3##4## and Supplementary Fig. ##SUPPL##0##8##), but consistent with only HiT featuring side-chain and backbone conformations of the active-site Q loop in a putatively active-like state (Figs. ##FIG##2##3c## and ##FIG##4##5b##). HiP models with significantly different E-loop conformations and prepared for analysis in different ways have similar positions in torsion-angle space, confirming that RoPE analysis highlights structurally distributed as opposed to localized features. Relative to LoTP, the coordinated torsion-angle changes in HiT and HiP detected by RoPE visually correspond to hinging of the first half of the primary structure (initial α-helices + loops) relative to the second half (β-sheet + α-helical bundle), albeit with apparently meaningful differences between them given their large separation in RoPE space. Overall, these results indicate that although high pressure induces an active-like conformation locally in the E loop, high temperature induces a more global, distributed active-like state of the protein.
\"\n]"},"discussion":{"kind":"list like","value":["While temperature is growing in use as an experimental perturbation in macromolecular crystallography, pressure has received less attention for such applications. Here we show that both temperature and pressure enact distinct and significant effects on the conformational ensemble of STEP, not only globally but also locally at several key functional areas.
","In our structures, high temperature increases both unit cell volume and protein molecular volume (Table ##TAB##1##2##) as seen previously##REF##21918110##9##. High pressure decreases unit cell volume as seen previously##REF##3586017##27##,##REF##11524565##51##,##REF##20516618##52##, yet still slightly increases protein molecular volume (Table ##TAB##1##2##). Thus, intriguingly, when subjected to pressure, the STEP protein molecule itself slightly expands, even as its environment is compressed. This differs from previous high-pressure crystal structures of other proteins with a decreased protein molecular volume##REF##3586017##27##,##REF##16269539##29##,##REF##25849385##31##, but agrees with a high-pressure NMR solution structure with a slightly increased protein molecular volume##REF##12930996##53##. The slight increase in molecular volume of the protein itself that we observe upon pressurization may be initially unintuitive, but can likely be explained by counterbalancing decreases in the volume of the bulk solvent (which is invisible to crystallography) within the crystal lattice, and is thus consistent with the thermodynamic expectation that pressurization decreases the molar volume of the protein-solvent system. Nonetheless, our observations suggest that the mechanisms by which pressure impacts the detailed conformational landscapes of different proteins are complex and potentially context-sensitive.
","Although many of the structural changes we see could be considered small, it is important to remember that sub-angstrom shifts can be directly relevant to protein function##REF##18768811##26##. This is consistent with our RoPE results, in which HiT vs. HiP have very distinct characteristics despite the overall structures being apparently similar. Crucially, the difference in the E loop at HiP does not dominate the signal (see Fig. ##FIG##5##6## and Methods), indicating that the differences between high temperature vs. pressure are driven by smaller, subtler conformational changes distributed throughout the tertiary structure.
","The largest conformational changes we observe in STEP are in the E loop (Fig. ##FIG##3##4a##), a conserved loop in PTPs that plays a critical role in regulation##REF##21094165##54##. Only at HiP do we see evidence in the electron density for a dual-conformation E loop (Fig. ##FIG##3##4b–d##). Both conformations were individually evident in previous structures of STEP with different chemical modifications or allosteric ligands (Supplementary Fig. ##SUPPL##0##8##). Our data indicate that applying a physical perturbation (pressure) is sufficient to induce these conformations to coexist in a single crystal of the unliganded protein, which has implications for accessing excited states of other proteins.
","Beyond the E loop, we observe new conformations not captured in previous structures of STEP. For instance, only at HiT, we see Glu519 of the active-site Q loop adopt a new side-chain rotamer that engages in an interaction with Lys439. Notably, Lys439 follows the WPD sequence, forming a WPDQK sequence. Recently, a conserved PDFG motif was proposed to underlie the ability of the WPD loop to toggle between discrete open vs. closed states in PTPs##UREF##10##55##. However, the corresponding residues in STEP are PDQK — and, as revealed by our HiT structure, the final K (Lys439) engages with the Q loop nearby. Perhaps not coincidentally given these idiosyncratic features, the WPD loop of STEP has not been observed in the usual open or closed states as with most other PTPs, but only in the atypically open state##REF##16441242##39##,##REF##19167335##41##. Together, these observations suggest that the STEP active site does not adhere to expectations from the rest of the PTP family, and points to specific amino acids and conformations that may encode its unusual behavior. We speculate that these unique structural properties of STEP likely underlie its substantially lower catalytic activity relative to other PTPs like PTP1B##UREF##2##11##,##REF##30207464##40##,##REF##23970698##56## and may be related to its unique physiological roles in neuronal development##REF##16806510##57##.
","It is plausible that this unresponsive, atypically open state could be modulated by binding of regulators or alterations in the cellular environment, creating a way to regulate catalysis. In this light, we observe two sulfates simultaneously bound within the active site. The biological significance of this observation for STEP is not immediately clear. It is likely that binding of sulfate-like moieties in the top site, as in several orthosteric inhibitors (Supplementary Fig. ##SUPPL##0##2b##), blocks closure of the WPD loop, effectively wedging it atypically open (Supplementary Fig. ##SUPPL##0##2e##). However, even with the top site free and substrate bound only to the bottom site, the WPD loop still remains atypically open in crystals##REF##19167335##41## (Supplementary Fig. ##SUPPL##0##2c##). Removing all tightly bound molecules from the active site of STEP in future crystallographic studies could provide more definitive answers about the conformational landscape of this functionally critical but unusual catalytic loop.
","Previously, based on computational simulations, a small-molecule allosteric activator for STEP was reported to enact its effects via a pair of allosteric pipelines##REF##30207464##40##. We do not observe obvious shifts along these pathways at high temperature or pressure. However, we do observe shifts in the activator binding pocket itself (Supplementary Fig. ##SUPPL##0##9##). Although subtle, these conformational shifts may be sufficient to influence ligand-binding energetics, and therefore may aid structure-based drug design efforts to improve upon the relatively weak reported activator.
","Beyond the reported allosteric activator site, we also observe perturbation-sensitive shifts at other known or putative ligand binding sites. First, at LoTP and HiP, an ordered glycerol molecule is bound near α2′ and the Q loop. By contrast, at HiT, ordered waters are present instead, and α1′-α2′ and the Q loop undergo conformational shifts (Fig. ##FIG##2##3c##). Importantly, all three structures are from crystals treated with similar glycerol-containing cryoprotectant solutions. It is thus plausible that crystal cryocooling induces the glycerol to bind##UREF##4##14##, preventing nearby conformations with potential functional relevance seen at physiological temperature (Fig. ##FIG##2##3c## and ##FIG##5##6##). Second, in the paralogous PTP SHP2, the α1′-α2′ region helps form the binding site for the potent allosteric inhibitor SHP099, although the mechanism also involves additional domains##REF##27362227##58##. The corresponding α1′-α2′ region in STEP is not known to be allosteric. However, the subtle but coordinated conformational changes we observe here at physiological temperature raise the enticing possibility that some aspects of the allosteric capacity demonstrated in SHP2 are also present in STEP, and perhaps even other PTPs.
","In general, allostery in STEP remains poorly understood, hindering efforts to elucidate this important protein’s endogenous regulatory mechanisms and to develop specific allosteric modulators. To address this important gap, several approaches should be considered. First, exploiting different crystal forms, including those in the allosteric-activator-bound structures for human and mouse STEP##REF##30207464##40##, may provide new windows into conformational mobility otherwise masked by crystal contacts. Second, higher pressures than those reported here##REF##35308861##32## may enable access to additional excited states. Third, X-ray diffraction at high pressure and physiological temperature simultaneously has the potential to reveal unique aspects of conformational landscapes not evident from a single perturbation alone. More broadly, the avant-garde crystallographic and computational methods outlined here should prove useful tools to investigate allosteric mechanisms in a variety of other proteins, including but not limited to other PTP family members that also exhibit an atypically open WPD loop such as LYP##REF##18056643##59##.
","Overall, the work reported here is consistent with the notion that proteins sample conformations from a multifaceted energy landscape, and that different physical perturbations such as temperature and pressure can access distinct, complementary features of this landscape, thus opening doors to elucidating fundamental connections between protein structural dynamics and function.
"],"string":"[\n \"While temperature is growing in use as an experimental perturbation in macromolecular crystallography, pressure has received less attention for such applications. Here we show that both temperature and pressure enact distinct and significant effects on the conformational ensemble of STEP, not only globally but also locally at several key functional areas.
\",\n \"In our structures, high temperature increases both unit cell volume and protein molecular volume (Table ##TAB##1##2##) as seen previously##REF##21918110##9##. High pressure decreases unit cell volume as seen previously##REF##3586017##27##,##REF##11524565##51##,##REF##20516618##52##, yet still slightly increases protein molecular volume (Table ##TAB##1##2##). Thus, intriguingly, when subjected to pressure, the STEP protein molecule itself slightly expands, even as its environment is compressed. This differs from previous high-pressure crystal structures of other proteins with a decreased protein molecular volume##REF##3586017##27##,##REF##16269539##29##,##REF##25849385##31##, but agrees with a high-pressure NMR solution structure with a slightly increased protein molecular volume##REF##12930996##53##. The slight increase in molecular volume of the protein itself that we observe upon pressurization may be initially unintuitive, but can likely be explained by counterbalancing decreases in the volume of the bulk solvent (which is invisible to crystallography) within the crystal lattice, and is thus consistent with the thermodynamic expectation that pressurization decreases the molar volume of the protein-solvent system. Nonetheless, our observations suggest that the mechanisms by which pressure impacts the detailed conformational landscapes of different proteins are complex and potentially context-sensitive.
\",\n \"Although many of the structural changes we see could be considered small, it is important to remember that sub-angstrom shifts can be directly relevant to protein function##REF##18768811##26##. This is consistent with our RoPE results, in which HiT vs. HiP have very distinct characteristics despite the overall structures being apparently similar. Crucially, the difference in the E loop at HiP does not dominate the signal (see Fig. ##FIG##5##6## and Methods), indicating that the differences between high temperature vs. pressure are driven by smaller, subtler conformational changes distributed throughout the tertiary structure.
\",\n \"The largest conformational changes we observe in STEP are in the E loop (Fig. ##FIG##3##4a##), a conserved loop in PTPs that plays a critical role in regulation##REF##21094165##54##. Only at HiP do we see evidence in the electron density for a dual-conformation E loop (Fig. ##FIG##3##4b–d##). Both conformations were individually evident in previous structures of STEP with different chemical modifications or allosteric ligands (Supplementary Fig. ##SUPPL##0##8##). Our data indicate that applying a physical perturbation (pressure) is sufficient to induce these conformations to coexist in a single crystal of the unliganded protein, which has implications for accessing excited states of other proteins.
\",\n \"Beyond the E loop, we observe new conformations not captured in previous structures of STEP. For instance, only at HiT, we see Glu519 of the active-site Q loop adopt a new side-chain rotamer that engages in an interaction with Lys439. Notably, Lys439 follows the WPD sequence, forming a WPDQK sequence. Recently, a conserved PDFG motif was proposed to underlie the ability of the WPD loop to toggle between discrete open vs. closed states in PTPs##UREF##10##55##. However, the corresponding residues in STEP are PDQK — and, as revealed by our HiT structure, the final K (Lys439) engages with the Q loop nearby. Perhaps not coincidentally given these idiosyncratic features, the WPD loop of STEP has not been observed in the usual open or closed states as with most other PTPs, but only in the atypically open state##REF##16441242##39##,##REF##19167335##41##. Together, these observations suggest that the STEP active site does not adhere to expectations from the rest of the PTP family, and points to specific amino acids and conformations that may encode its unusual behavior. We speculate that these unique structural properties of STEP likely underlie its substantially lower catalytic activity relative to other PTPs like PTP1B##UREF##2##11##,##REF##30207464##40##,##REF##23970698##56## and may be related to its unique physiological roles in neuronal development##REF##16806510##57##.
\",\n \"It is plausible that this unresponsive, atypically open state could be modulated by binding of regulators or alterations in the cellular environment, creating a way to regulate catalysis. In this light, we observe two sulfates simultaneously bound within the active site. The biological significance of this observation for STEP is not immediately clear. It is likely that binding of sulfate-like moieties in the top site, as in several orthosteric inhibitors (Supplementary Fig. ##SUPPL##0##2b##), blocks closure of the WPD loop, effectively wedging it atypically open (Supplementary Fig. ##SUPPL##0##2e##). However, even with the top site free and substrate bound only to the bottom site, the WPD loop still remains atypically open in crystals##REF##19167335##41## (Supplementary Fig. ##SUPPL##0##2c##). Removing all tightly bound molecules from the active site of STEP in future crystallographic studies could provide more definitive answers about the conformational landscape of this functionally critical but unusual catalytic loop.
\",\n \"Previously, based on computational simulations, a small-molecule allosteric activator for STEP was reported to enact its effects via a pair of allosteric pipelines##REF##30207464##40##. We do not observe obvious shifts along these pathways at high temperature or pressure. However, we do observe shifts in the activator binding pocket itself (Supplementary Fig. ##SUPPL##0##9##). Although subtle, these conformational shifts may be sufficient to influence ligand-binding energetics, and therefore may aid structure-based drug design efforts to improve upon the relatively weak reported activator.
\",\n \"Beyond the reported allosteric activator site, we also observe perturbation-sensitive shifts at other known or putative ligand binding sites. First, at LoTP and HiP, an ordered glycerol molecule is bound near α2′ and the Q loop. By contrast, at HiT, ordered waters are present instead, and α1′-α2′ and the Q loop undergo conformational shifts (Fig. ##FIG##2##3c##). Importantly, all three structures are from crystals treated with similar glycerol-containing cryoprotectant solutions. It is thus plausible that crystal cryocooling induces the glycerol to bind##UREF##4##14##, preventing nearby conformations with potential functional relevance seen at physiological temperature (Fig. ##FIG##2##3c## and ##FIG##5##6##). Second, in the paralogous PTP SHP2, the α1′-α2′ region helps form the binding site for the potent allosteric inhibitor SHP099, although the mechanism also involves additional domains##REF##27362227##58##. The corresponding α1′-α2′ region in STEP is not known to be allosteric. However, the subtle but coordinated conformational changes we observe here at physiological temperature raise the enticing possibility that some aspects of the allosteric capacity demonstrated in SHP2 are also present in STEP, and perhaps even other PTPs.
\",\n \"In general, allostery in STEP remains poorly understood, hindering efforts to elucidate this important protein’s endogenous regulatory mechanisms and to develop specific allosteric modulators. To address this important gap, several approaches should be considered. First, exploiting different crystal forms, including those in the allosteric-activator-bound structures for human and mouse STEP##REF##30207464##40##, may provide new windows into conformational mobility otherwise masked by crystal contacts. Second, higher pressures than those reported here##REF##35308861##32## may enable access to additional excited states. Third, X-ray diffraction at high pressure and physiological temperature simultaneously has the potential to reveal unique aspects of conformational landscapes not evident from a single perturbation alone. More broadly, the avant-garde crystallographic and computational methods outlined here should prove useful tools to investigate allosteric mechanisms in a variety of other proteins, including but not limited to other PTP family members that also exhibit an atypically open WPD loop such as LYP##REF##18056643##59##.
\",\n \"Overall, the work reported here is consistent with the notion that proteins sample conformations from a multifaceted energy landscape, and that different physical perturbations such as temperature and pressure can access distinct, complementary features of this landscape, thus opening doors to elucidating fundamental connections between protein structural dynamics and function.
\"\n]"},"conclusion":{"kind":"list like","value":[],"string":"[]"},"front":{"kind":"list like","value":["Protein function hinges on small shifts of three-dimensional structure. Elevating temperature or pressure may provide experimentally accessible insights into such shifts, but the effects of these distinct perturbations on protein structures have not been compared in atomic detail. To quantitatively explore these two axes, we report the first pair of structures at physiological temperature versus. high pressure for the same protein, STEP (PTPN5). We show that these perturbations have distinct and surprising effects on protein volume, patterns of ordered solvent, and local backbone and side-chain conformations. This includes interactions between key catalytic loops only at physiological temperature, and a distinct conformational ensemble for another active-site loop only at high pressure. Strikingly, in torsional space, physiological temperature shifts STEP toward previously reported active-like states, while high pressure shifts it toward a previously uncharted region. Altogether, our work indicates that temperature and pressure are complementary, powerful, fundamental macromolecular perturbations.
","A multi-perturbation crystallographic study on the atypical phosphatase STEP reveals that temperature and pressure are powerful macromolecular perturbations, with distinct effects on protein backbone, side-chain conformations, and ordered solvent.
","Protein function hinges on small shifts of three-dimensional structure. Elevating temperature or pressure may provide experimentally accessible insights into such shifts, but the effects of these distinct perturbations on protein structures have not been compared in atomic detail. To quantitatively explore these two axes, we report the first pair of structures at physiological temperature versus. high pressure for the same protein, STEP (PTPN5). We show that these perturbations have distinct and surprising effects on protein volume, patterns of ordered solvent, and local backbone and side-chain conformations. This includes interactions between key catalytic loops only at physiological temperature, and a distinct conformational ensemble for another active-site loop only at high pressure. Strikingly, in torsional space, physiological temperature shifts STEP toward previously reported active-like states, while high pressure shifts it toward a previously uncharted region. Altogether, our work indicates that temperature and pressure are complementary, powerful, fundamental macromolecular perturbations.
\",\n \"A multi-perturbation crystallographic study on the atypical phosphatase STEP reveals that temperature and pressure are powerful macromolecular perturbations, with distinct effects on protein backbone, side-chain conformations, and ordered solvent.
\",\n \"\n\n\n\n\n\n\n\n\n
"],"string":"[\n \"\\n\\n\\n\\n\\n\\n\\n\\n\\n
\"\n]"},"back":{"kind":"list like","value":["The online version contains supplementary material available at 10.1038/s42003-023-05609-0.
","DAK is supported by NIH R35 GM133769. We thank Helen Ginn for help with RoPE analysis, members of the CUNY Advanced Science Research Center (ASRC) Structural Biology Initiative (SBI) for helpful discussions, Akshay Raju and Shivani Sharma for help with PTP bioinformatics, and Marian Szebenyi for help with arranging our X-ray beamtime. This work is based upon research conducted at the Center for High Energy X-ray Sciences (CHEXS), which is supported by the National Science Foundation under award DMR-1829070, and the Macromolecular Diffraction at CHESS (MacCHESS) facility, which is supported by award 1-P30-GM124166-01A1 from the National Institute of General Medical Sciences, National Institutes of Health, and by New York State’s Empire State Development Corporation (NYSTAR).
","L.G. analyzed data and wrote the manuscript. A.E. analyzed data and wrote the manuscript. B.T.R. designed experiments, performed experiments, and edited the manuscript. M.K. designed experiments and performed experiments. Q.H. designed experiments and edited the manuscript. A.D.F. performed experiments and edited the manuscript. D.A.K. conceived the study, designed experiments, analyzed data, and wrote the manuscript.
","Coordinates and structure factors that were generated during the course of this study have been deposited in the Protein Data Bank with the accession codes 8sls (STEP at cryogenic temperature and ambient pressure), 8slt (STEP at physiological temperature and ambient pressure), and 8slu (STEP at cryogenic temperature and high pressure). The protein structure used as a search model for molecular replacement is accessible in the Protein Data Bank under accession codes 2bv5.
","The authors declare no competing interests.
"],"string":"[\n \"The online version contains supplementary material available at 10.1038/s42003-023-05609-0.
\",\n \"DAK is supported by NIH R35 GM133769. We thank Helen Ginn for help with RoPE analysis, members of the CUNY Advanced Science Research Center (ASRC) Structural Biology Initiative (SBI) for helpful discussions, Akshay Raju and Shivani Sharma for help with PTP bioinformatics, and Marian Szebenyi for help with arranging our X-ray beamtime. This work is based upon research conducted at the Center for High Energy X-ray Sciences (CHEXS), which is supported by the National Science Foundation under award DMR-1829070, and the Macromolecular Diffraction at CHESS (MacCHESS) facility, which is supported by award 1-P30-GM124166-01A1 from the National Institute of General Medical Sciences, National Institutes of Health, and by New York State’s Empire State Development Corporation (NYSTAR).
\",\n \"L.G. analyzed data and wrote the manuscript. A.E. analyzed data and wrote the manuscript. B.T.R. designed experiments, performed experiments, and edited the manuscript. M.K. designed experiments and performed experiments. Q.H. designed experiments and edited the manuscript. A.D.F. performed experiments and edited the manuscript. D.A.K. conceived the study, designed experiments, analyzed data, and wrote the manuscript.
\",\n \"Coordinates and structure factors that were generated during the course of this study have been deposited in the Protein Data Bank with the accession codes 8sls (STEP at cryogenic temperature and ambient pressure), 8slt (STEP at physiological temperature and ambient pressure), and 8slu (STEP at cryogenic temperature and high pressure). The protein structure used as a search model for molecular replacement is accessible in the Protein Data Bank under accession codes 2bv5.
\",\n \"The authors declare no competing interests.
\"\n]"},"figure":{"kind":"list like","value":["For each example residue, the following panels are shown: (
Our new structures (LoTP, HiT, HiP) are shown relative to all previous STEP structures from the PDB in RoPE reduced-dimensionality torsion-angle space##UREF##9##42##. Horizontal and vertical axes correspond to different combinations of the top principal component analysis (PCA) modes. Apparent inactive-like vs. active-like clusters are highlighted. Different icon shapes indicate distinct crystal forms (unit cell parameters). Resolution is shown by color. Arrows indicate the effects of high temperature vs. pressure relative to our reference structure. HiP* indicates several HiP models with the E loop prepared in different ways; see Methods. 6h8s: mouse STEP; all other structures: human STEP. All models were prepared for RoPE with PDB-REDO##UREF##14##72## to ensure consistent treatment##UREF##9##42##.
For each example residue, the following panels are shown: (
Our new structures (LoTP, HiT, HiP) are shown relative to all previous STEP structures from the PDB in RoPE reduced-dimensionality torsion-angle space##UREF##9##42##. Horizontal and vertical axes correspond to different combinations of the top principal component analysis (PCA) modes. Apparent inactive-like vs. active-like clusters are highlighted. Different icon shapes indicate distinct crystal forms (unit cell parameters). Resolution is shown by color. Arrows indicate the effects of high temperature vs. pressure relative to our reference structure. HiP* indicates several HiP models with the E loop prepared in different ways; see Methods. 6h8s: mouse STEP; all other structures: human STEP. All models were prepared for RoPE with PDB-REDO##UREF##14##72## to ensure consistent treatment##UREF##9##42##.
Crystallographic statistics.
| PDB ID | 8SLS | 8SLT | 8SLU |
|---|---|---|---|
| Dataset name | LoTP | HiT | HiP |
| Data collectiona | |||
| Temperature (K) | 100 | 310 | 100 |
| Pressure (MPa) | 0.1 (ambient) | 0.1 (ambient) | 205 |
| Space group | P212121 | P212121 | P212121 |
| Cell dimensions | |||
| | 39.67, 63.51, 135.16 | 39.98, 64.49, 137.21 | 39.14, 63.47, 134.20 |
| | 90, 90, 90 | 90, 90, 90 | 90, 90, 90 |
| Resolution (Å) | 1.71–67.58 | 1.96–68.61 | 1.84–57.37 |
| 0.108 (2.37)b | 0.170 (2.398) | 0.114 (2.127) | |
| 10.23 (0.59) | 7.79 (0.63) | 10.71 (0.64) | |
| Completeness (%) | 98.64 (93.42) | 99.80 (99.03) | 99.45 (99.12) |
| Redundancy | 4.7 (4.8) | 6.7 (6.9) | 6.6 (6.6) |
| Refinement | |||
| Resolution (Å) | 1.71–67.58 | 1.96–68.61 | 1.84–57.37 |
| No. reflections | 37,552 (3655) | 26,353 (2573) | 29,880 (2949) |
| 19.15/22.44 | 17.44/20.57 | 19.48/23.66 | |
| No. atoms | 4871 | 4721 | 4920 |
| Protein | 4700 | 4643 | 4799 |
| Ligand/ion | 23 | 10 | 23 |
| Water | 149 | 69 | 98 |
| 41.46 | 51.61 | 45.55 | |
| Protein | 41.45 | 51.71 | 45.63 |
| Ligand/ion | 34.43 | 43.79 | 41.57 |
| Water | 42.65 | 46.43 | 42.34 |
| R.M.S. deviations | |||
| Bond lengths (Å) | 0.01 | 0.02 | 0.01 |
| Bond angles (°) | 1.20 | 1.40 | 1.24 |
Change in unit cell and protein volume at high temperature vs. high pressure.
| LoTP | HiT | HiP | ||
|---|---|---|---|---|
| Unit Cell | a (Å) | 39.67 | 39.98 (+0.8%) | 39.15 (−1.3%) |
| b (Å) | 63.51 | 64.49 (+1.5%) | 63.45 (−0.1%) | |
| c (Å) | 135.16 | 137.21 (+1.5%) | 134.22 (−0.7%) | |
| Cell Volume (Å3) | 340527.7 | 353769.9 (+3.9%) | 333411.5 (−2.1%) | |
| Protein | Protein Volume (A3) | 37632.6 | 38039.1 (+1.1%) | 37783.3 (+0.4%) |
Crystallographic statistics.
| PDB ID | 8SLS | 8SLT | 8SLU |
|---|---|---|---|
| Dataset name | LoTP | HiT | HiP |
| Data collectiona | |||
| Temperature (K) | 100 | 310 | 100 |
| Pressure (MPa) | 0.1 (ambient) | 0.1 (ambient) | 205 |
| Space group | P212121 | P212121 | P212121 |
| Cell dimensions | |||
| | 39.67, 63.51, 135.16 | 39.98, 64.49, 137.21 | 39.14, 63.47, 134.20 |
| | 90, 90, 90 | 90, 90, 90 | 90, 90, 90 |
| Resolution (Å) | 1.71–67.58 | 1.96–68.61 | 1.84–57.37 |
| 0.108 (2.37)b | 0.170 (2.398) | 0.114 (2.127) | |
| 10.23 (0.59) | 7.79 (0.63) | 10.71 (0.64) | |
| Completeness (%) | 98.64 (93.42) | 99.80 (99.03) | 99.45 (99.12) |
| Redundancy | 4.7 (4.8) | 6.7 (6.9) | 6.6 (6.6) |
| Refinement | |||
| Resolution (Å) | 1.71–67.58 | 1.96–68.61 | 1.84–57.37 |
| No. reflections | 37,552 (3655) | 26,353 (2573) | 29,880 (2949) |
| 19.15/22.44 | 17.44/20.57 | 19.48/23.66 | |
| No. atoms | 4871 | 4721 | 4920 |
| Protein | 4700 | 4643 | 4799 |
| Ligand/ion | 23 | 10 | 23 |
| Water | 149 | 69 | 98 |
| 41.46 | 51.61 | 45.55 | |
| Protein | 41.45 | 51.71 | 45.63 |
| Ligand/ion | 34.43 | 43.79 | 41.57 |
| Water | 42.65 | 46.43 | 42.34 |
| R.M.S. deviations | |||
| Bond lengths (Å) | 0.01 | 0.02 | 0.01 |
| Bond angles (°) | 1.20 | 1.40 | 1.24 |
Change in unit cell and protein volume at high temperature vs. high pressure.
| LoTP | HiT | HiP | ||
|---|---|---|---|---|
| Unit Cell | a (Å) | 39.67 | 39.98 (+0.8%) | 39.15 (−1.3%) |
| b (Å) | 63.51 | 64.49 (+1.5%) | 63.45 (−0.1%) | |
| c (Å) | 135.16 | 137.21 (+1.5%) | 134.22 (−0.7%) | |
| Cell Volume (Å3) | 340527.7 | 353769.9 (+3.9%) | 333411.5 (−2.1%) | |
| Protein | Protein Volume (A3) | 37632.6 | 38039.1 (+1.1%) | 37783.3 (+0.4%) |
aOne crystal was used for each dataset.
bValues in parentheses are for the highest-resolution shell.
Absolute number given first (% change relative to LoTP given in parentheses. Protein total volume calculated by the ProteinVolume software##UREF##14##72##.
These authors contributed equally: Liliana Guerrero, Ali Ebrahim.
aOne crystal was used for each dataset.
bValues in parentheses are for the highest-resolution shell.
Absolute number given first (% change relative to LoTP given in parentheses. Protein total volume calculated by the ProteinVolume software##UREF##14##72##.
These authors contributed equally: Liliana Guerrero, Ali Ebrahim.
Supplementary Table and Figures
Description of Additional Supplementary Files
Supplementary Data 1
Supplementary Data 2
Supplementary Data 3
Supplementary Data 4
Reporting Summary
Peer Review File
Supplementary Table and Figures
Description of Additional Supplementary Files
Supplementary Data 1
Supplementary Data 2
Supplementary Data 3
Supplementary Data 4
Reporting Summary
Peer Review File
In recent years, the introduction of alien insect pests into new areas has caused major economic damage to agriculture. These intrusions are generally due to anthropic activities such as global trading and increased human travel##UREF##0##1##,##UREF##1##2##. On a worldwide scale, biological invasions are responsible for weighty economic losses estimated at approximately US$ 1.288 trillion (2017 US dollars), in addition to significant biodiversity declines##REF##33790468##3##.
In the present study, we examined the influence of three different temperatures (10 °C, 18 °C, and 25 °C) on restoring the vibrational calling activity and motility of
In recent years, the introduction of alien insect pests into new areas has caused major economic damage to agriculture. These intrusions are generally due to anthropic activities such as global trading and increased human travel##UREF##0##1##,##UREF##1##2##. On a worldwide scale, biological invasions are responsible for weighty economic losses estimated at approximately US$ 1.288 trillion (2017 US dollars), in addition to significant biodiversity declines##REF##33790468##3##.
In the present study, we examined the influence of three different temperatures (10 °C, 18 °C, and 25 °C) on restoring the vibrational calling activity and motility of
Adult insects at the verge of dormancy were collected throughout the end of November 2020 within the surrounding hedgerows of Fondazione Edmund Mach, in Northern Italy (46° 11' 34.8\" N 11° 08' 08.9\" E), using dark live traps as described in Suckling et al.##REF##31671778##42##. At the beginning of December 2020, they were transferred to artificial overwintering conditions (i.e., 9 °C; RH 65 ± 5%) for a 7-week period, which is essential for obtaining sexually mature
To guarantee spontaneous (i.e., without elicitation from the other sex)
Statistical analyses were all performed using R version 4.1.1 (R Core Team 2021, Vienna, Austria). The effect of copulation inhibition (JCg) on the vibrational calling and motility of
Overall survival (OS) was described using Kaplan-Meier curves##UREF##25##49## for the three temperature categories (10 °C, 18 °C, and 25 °C) for each sex. To simulate the time until insects’ death, Cox’s proportional hazards (PH) model (Cox, 1972) was performed only for males using the median survival time and temperature categories, with “10 °C” as the reference category (negative control). The Cox PH model was then adjusted considering the repetition of insects by cages, as well as a 2-sided 5% significance level. For females, the proportional hazard assumption was not met in the model, indicating that a single hazard ratio would not be adequate to represent the effect of temperature on their survival. Survival analyses were done following Moore##UREF##26##50## with packages ‘survival’##UREF##27##51## and ‘survminer’##UREF##28##52##. AIC##UREF##23##47## was used to select the best-fitting model.
"],"string":"[\n \"Adult insects at the verge of dormancy were collected throughout the end of November 2020 within the surrounding hedgerows of Fondazione Edmund Mach, in Northern Italy (46° 11' 34.8\\\" N 11° 08' 08.9\\\" E), using dark live traps as described in Suckling et al.##REF##31671778##42##. At the beginning of December 2020, they were transferred to artificial overwintering conditions (i.e., 9 °C; RH 65 ± 5%) for a 7-week period, which is essential for obtaining sexually mature
To guarantee spontaneous (i.e., without elicitation from the other sex)
Statistical analyses were all performed using R version 4.1.1 (R Core Team 2021, Vienna, Austria). The effect of copulation inhibition (JCg) on the vibrational calling and motility of
Overall survival (OS) was described using Kaplan-Meier curves##UREF##25##49## for the three temperature categories (10 °C, 18 °C, and 25 °C) for each sex. To simulate the time until insects’ death, Cox’s proportional hazards (PH) model (Cox, 1972) was performed only for males using the median survival time and temperature categories, with “10 °C” as the reference category (negative control). The Cox PH model was then adjusted considering the repetition of insects by cages, as well as a 2-sided 5% significance level. For females, the proportional hazard assumption was not met in the model, indicating that a single hazard ratio would not be adequate to represent the effect of temperature on their survival. Survival analyses were done following Moore##UREF##26##50## with packages ‘survival’##UREF##27##51## and ‘survminer’##UREF##28##52##. AIC##UREF##23##47## was used to select the best-fitting model.
\"\n]"},"results":{"kind":"list like","value":["The experimental differences resulting from the different cage state recording settings are summarized in Table ##TAB##0##1##.
","The temperature had various effects on the cumulative periods of calling and motility (measured as vibrational noise associated with walking). At 10 °C, no signaling activity of any type was registered, while a minimum amount of motility was observed (Figs. ##FIG##0##1##, ##FIG##1##2##, ##FIG##2##3##). In contrast, 18 °C and 25 °C were the temperatures at which
Regarding cage state treatment, our results indicated a significant effect of copulation inhibition (JCg) on the signaling and motility of
To describe insect survival period at each temperature, the Overall Survival (OS) was measured from the date of first observation (day 1) to the date of death (event) until the last follow-up date (day 34). Regarding the overall survival analysis, a total of 107 individuals were considered, with 100% deaths occurring until the end of the study. Looking at the total deaths separated by temperature and sex (Table ##SUPPL##0##S1##), the median overall survival was higher at 10 °C than at 18 °C and 25 °C. This was further confirmed in the Kaplan-Meier plot, where a lower survival probability was registered at higher temperatures (Fig. ##FIG##4##5##). Moreover, Table ##TAB##2##3## shows the results of the Cox model for males, with a significant difference between the survival at temperature 10 °C against 18 °C (HR = 5.28, 95% CI (2.28–12.2);
The experimental differences resulting from the different cage state recording settings are summarized in Table ##TAB##0##1##.
\",\n \"The temperature had various effects on the cumulative periods of calling and motility (measured as vibrational noise associated with walking). At 10 °C, no signaling activity of any type was registered, while a minimum amount of motility was observed (Figs. ##FIG##0##1##, ##FIG##1##2##, ##FIG##2##3##). In contrast, 18 °C and 25 °C were the temperatures at which
Regarding cage state treatment, our results indicated a significant effect of copulation inhibition (JCg) on the signaling and motility of
To describe insect survival period at each temperature, the Overall Survival (OS) was measured from the date of first observation (day 1) to the date of death (event) until the last follow-up date (day 34). Regarding the overall survival analysis, a total of 107 individuals were considered, with 100% deaths occurring until the end of the study. Looking at the total deaths separated by temperature and sex (Table ##SUPPL##0##S1##), the median overall survival was higher at 10 °C than at 18 °C and 25 °C. This was further confirmed in the Kaplan-Meier plot, where a lower survival probability was registered at higher temperatures (Fig. ##FIG##4##5##). Moreover, Table ##TAB##2##3## shows the results of the Cox model for males, with a significant difference between the survival at temperature 10 °C against 18 °C (HR = 5.28, 95% CI (2.28–12.2);
Knowledge of phenology is a key factor in the efficient control of invasive pests. Phenological modeling of
Regarding the vibrational activity of
Despite this spontaneous calling among females, this phenomenon might have been due to copulation abstinence for over a month after the end of the overwintering phase and/or reaching a certain age. According to Polajnar et al.##UREF##5##12##, females were never found to emit FS2 spontaneously; however, in that study, it was only reported that females were at least seven days old, without specifying whether there were any individuals aged 30 days or older among those tested. In addition, these individuals did not belong to the overwintering generations. In our experiment, the insects were not recorded singularly but in groups of 10 sexed insects per cage, which might have also played a role in eliciting this behavior. For example, the perception of incidental vibrations due to the walking activity could be potentially associated with the presence of conspecifics. The use of incidental vibrations to orientate towards possible prey is well documented in insects and spiders##UREF##15##33##, as well as it is known that aphids react to the approach of a predator by dropping##UREF##16##34##. However, little is known on the interpretation and reaction of phytophagous insects to the incidental vibrations of conspecifics, a theme that deserves further research.
","Notwithstanding, it is important to note that we cannot exclude an effect of pheromones because the sexed cages were kept within the same ventilated climatic chamber. In fact, even if pheromones cover an aggregation function in
Previous studies have suggested that the state of vitellogenesis in female
Given our interest in investigating the post-diapause temperature effect on
In view of the above, this study provides further insights into the post-diapause vibrational behavior of
Knowledge of phenology is a key factor in the efficient control of invasive pests. Phenological modeling of
Regarding the vibrational activity of
Despite this spontaneous calling among females, this phenomenon might have been due to copulation abstinence for over a month after the end of the overwintering phase and/or reaching a certain age. According to Polajnar et al.##UREF##5##12##, females were never found to emit FS2 spontaneously; however, in that study, it was only reported that females were at least seven days old, without specifying whether there were any individuals aged 30 days or older among those tested. In addition, these individuals did not belong to the overwintering generations. In our experiment, the insects were not recorded singularly but in groups of 10 sexed insects per cage, which might have also played a role in eliciting this behavior. For example, the perception of incidental vibrations due to the walking activity could be potentially associated with the presence of conspecifics. The use of incidental vibrations to orientate towards possible prey is well documented in insects and spiders##UREF##15##33##, as well as it is known that aphids react to the approach of a predator by dropping##UREF##16##34##. However, little is known on the interpretation and reaction of phytophagous insects to the incidental vibrations of conspecifics, a theme that deserves further research.
\",\n \"Notwithstanding, it is important to note that we cannot exclude an effect of pheromones because the sexed cages were kept within the same ventilated climatic chamber. In fact, even if pheromones cover an aggregation function in
Previous studies have suggested that the state of vitellogenesis in female
Given our interest in investigating the post-diapause temperature effect on
In view of the above, this study provides further insights into the post-diapause vibrational behavior of
Substrate-borne vibrational communication is common in pentatomids. Although several works exist on the vibrational communication of
Substrate-borne vibrational communication is common in pentatomids. Although several works exist on the vibrational communication of
\n
"],"string":"[\n \"\\n
\"\n]"},"back":{"kind":"list like","value":["The online version contains supplementary material available at 10.1038/s41598-023-50480-y.
","J.M.F.’s Ph.D. scholarship was funded by the Mediterranean Agronomic Institute of Bari (CIHEAM Bari). Gerardo Roselli’s help was crucial to collect the insects for the experiment.
","J.M.F. and M.S. designed and conceived the experiment; J.M.F. and M.S. conducted the experiment; J.M.F. and V.Z.C. analyzed the results; J.M.F. wrote the first draft; V.V., G.A., and V.M critically revised successive drafts of the manuscript and supervised the experiment. All authors reviewed and commented on the manuscript.
","All the relevant data are presented in the manuscript. The datasets generated and analyzed during the current study are available from the corresponding author upon request.
","The authors declare no competing interests.
"],"string":"[\n \"The online version contains supplementary material available at 10.1038/s41598-023-50480-y.
\",\n \"J.M.F.’s Ph.D. scholarship was funded by the Mediterranean Agronomic Institute of Bari (CIHEAM Bari). Gerardo Roselli’s help was crucial to collect the insects for the experiment.
\",\n \"J.M.F. and M.S. designed and conceived the experiment; J.M.F. and M.S. conducted the experiment; J.M.F. and V.Z.C. analyzed the results; J.M.F. wrote the first draft; V.V., G.A., and V.M critically revised successive drafts of the manuscript and supervised the experiment. All authors reviewed and commented on the manuscript.
\",\n \"All the relevant data are presented in the manuscript. The datasets generated and analyzed during the current study are available from the corresponding author upon request.
\",\n \"The authors declare no competing interests.
\"\n]"},"figure":{"kind":"list like","value":["Cumulative Signal 1 emission for each recording of male and female
Cumulative Signal 2 emission for each recording of male and female
Cumulative walking time for each recording of male and female
Barplots showing the mean signaling and walking differences when cages were recorded separately (SCg) or joined (JCg) vs the control (CCg). Recording condition groups indicated by different letters show significant differences (Wilks’ Lambda type non-parametric interference at
Kaplan–Meier plot describing the survival curves for
Scheme of the experimental setup. (
Cumulative Signal 1 emission for each recording of male and female
Cumulative Signal 2 emission for each recording of male and female
Cumulative walking time for each recording of male and female
Barplots showing the mean signaling and walking differences when cages were recorded separately (SCg) or joined (JCg) vs the control (CCg). Recording condition groups indicated by different letters show significant differences (Wilks’ Lambda type non-parametric interference at
Kaplan–Meier plot describing the survival curves for
Scheme of the experimental setup. (
Summary of experimental differences resulting from cage recording settings.
| Cage state treatment | Copulation | Vibrational signals | Pheromones |
|---|---|---|---|
| Control (CCg) | Can occur | Perceived | Perceived |
| Single (SCg) | Cannot occur | Not perceived | Not perceived |
| Joined (JCg) | Cannot occur | Perceived | Perceived |
Results of generalized linear mixed-effects model (GLMM) for signaling and walking.
| A | Model synthesis | AIC | BIC | LogLik | Dev | df | Distribution |
|---|---|---|---|---|---|---|---|
| (Signal 1) | Signal1 ~ temperature + sex + state + (1|cage) | 1358.3 | 1412.8 | − 666.1 | 1332.3 | 477 | Negative binomial |
| (Signal 2) | Signal2 ~ temperature + sex + state + (1|cage) | 1497.1 | 1547.5 | − 736.6 | 1437.1 | 477 | Tweedie |
| (Motility) | walking ~ temperature + state + (1|cage) | 2600.9 | 2651.2 | − 1288.5 | 2576.9 | 478 | Tweedie |
| B | Fixed effects | Estimate | Std. Error | Z value | Pr ( >|z|) |
|---|---|---|---|---|---|
| (Signal 1) | (Intercept) | 0.90958 | 0.98523 | 0.923 | 0.35589 |
| Temperature 18 °C | 2.61910 | 0.96350 | 2.718 | 0.00656 ** | |
| Temperature 24 °C | 2.14277 | 0.96513 | 2.220 | 0.02641 * | |
| Sex male | 1.09746 | 0.22270 | 4.928 | 8.31e−07 *** | |
| (Signal 2) | (Intercept) | − 3.695e+01 | 6.116e+03 | − 0.006 | 0.995 |
| Temperature 18 °C | 4.586 | 2.878e−01 | 15.935 | < 2e−16 *** | |
| Temperature 24 °C | 5.516 | 2.807e−01 | 19.653 | < 2e−16 *** | |
| Sex male | − 1.251 | 3.090e−01 | –4.049 | 5.13e−05 *** | |
| (Motility) | (Intercept) | 2.86618 | 0.36129 | 7.933 | 2.13e−15*** |
| Temperature 18 °C | 1.50367 | 0.34329 | 4.380 | 1.19e−05 *** | |
| Temperature 24 °C | 1.78674 | 0.35707 | 5.004 | 5.62e−07 *** |
Cox regression model for male—temperature; Hazard ratio and 95% confidence interval.
| Variables | Hazard ratio (CI 95%) | |
|---|---|---|
| Ref level 10 °C | ||
| 18 °C | 5.28 (2.3–12.2) | 0.000102*** |
| 25 °C | 27.7 (10–76.6) | 1.54e−10*** |
Summary of experimental differences resulting from cage recording settings.
| Cage state treatment | Copulation | Vibrational signals | Pheromones |
|---|---|---|---|
| Control (CCg) | Can occur | Perceived | Perceived |
| Single (SCg) | Cannot occur | Not perceived | Not perceived |
| Joined (JCg) | Cannot occur | Perceived | Perceived |
Results of generalized linear mixed-effects model (GLMM) for signaling and walking.
| A | Model synthesis | AIC | BIC | LogLik | Dev | df | Distribution |
|---|---|---|---|---|---|---|---|
| (Signal 1) | Signal1 ~ temperature + sex + state + (1|cage) | 1358.3 | 1412.8 | − 666.1 | 1332.3 | 477 | Negative binomial |
| (Signal 2) | Signal2 ~ temperature + sex + state + (1|cage) | 1497.1 | 1547.5 | − 736.6 | 1437.1 | 477 | Tweedie |
| (Motility) | walking ~ temperature + state + (1|cage) | 2600.9 | 2651.2 | − 1288.5 | 2576.9 | 478 | Tweedie |
| B | Fixed effects | Estimate | Std. Error | Z value | Pr ( >|z|) |
|---|---|---|---|---|---|
| (Signal 1) | (Intercept) | 0.90958 | 0.98523 | 0.923 | 0.35589 |
| Temperature 18 °C | 2.61910 | 0.96350 | 2.718 | 0.00656 ** | |
| Temperature 24 °C | 2.14277 | 0.96513 | 2.220 | 0.02641 * | |
| Sex male | 1.09746 | 0.22270 | 4.928 | 8.31e−07 *** | |
| (Signal 2) | (Intercept) | − 3.695e+01 | 6.116e+03 | − 0.006 | 0.995 |
| Temperature 18 °C | 4.586 | 2.878e−01 | 15.935 | < 2e−16 *** | |
| Temperature 24 °C | 5.516 | 2.807e−01 | 19.653 | < 2e−16 *** | |
| Sex male | − 1.251 | 3.090e−01 | –4.049 | 5.13e−05 *** | |
| (Motility) | (Intercept) | 2.86618 | 0.36129 | 7.933 | 2.13e−15*** |
| Temperature 18 °C | 1.50367 | 0.34329 | 4.380 | 1.19e−05 *** | |
| Temperature 24 °C | 1.78674 | 0.35707 | 5.004 | 5.62e−07 *** |
Cox regression model for male—temperature; Hazard ratio and 95% confidence interval.
| Variables | Hazard ratio (CI 95%) | |
|---|---|---|
| Ref level 10 °C | ||
| 18 °C | 5.28 (2.3–12.2) | 0.000102*** |
| 25 °C | 27.7 (10–76.6) | 1.54e−10*** |
A) The best explanatory model for each dependent variable. B) The significant independent variables of the chosen models. Dev (deviance), Est (estimate), df (df residuals). *
*** p < 0.001.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
A) The best explanatory model for each dependent variable. B) The significant independent variables of the chosen models. Dev (deviance), Est (estimate), df (df residuals). *
*** p < 0.001.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information.
Supplementary Information.
Rice (
The major consequences of climate change are droughts which are tragic threat to water supplies, agriculture crops, food production and causing ecological disturbance and famine in the World##UREF##3##7##. Climate change and global warming are intensively affecting the regional and Worldwide hydrological cycle leading to the frequency of drought events##UREF##4##8##. Rice (
At the same time salinity is an another environmental factor increasing in magnitude in the rice growing areas, due to the combine effects of high temperature, drought, sea level rising, and inferior agriculture practices##UREF##7##11##. In irrigated land, salt stress has been a serious threat to rice cultivation, and expected to be more than 20% in the near future, while it is estimated to reach 50% by 2050##UREF##8##12##. Salt stress reduce the rate of net carbon dioxide assimilation, growth of the leaf, enlargement of the leaf cell, accumulation of dry matter and relative growth##UREF##9##13##. The stress of salinity is dominated by sodium (Na+) and chloride (Cl–)##UREF##9##13##, negatively affecting rice growth and development due to creating ionic, osmotic and oxidative stress##UREF##9##13##–##REF##16690163##15##. High level of salt in rice plant increase the toxicity level, leading to early leaf senescence and ultimately resulting the decrease in the photosynthetic leaf area##REF##11841667##16##,##UREF##11##17##.
","Melatonin (N-acetyl-5-methoxytryptamine) is common biological hormone that plays an important role in biological functions##REF##25180262##18##, like circadian rhythms##REF##22034907##19##, immunomodulation##REF##23889107##20##, and oxidative stress reduction##REF##8272286##21##. Hence during the past half century, it received great attention due to its anti-aging properties##REF##7776176##22##. Recent studies showed that melatonin is present in a large number of vascular plants##REF##7776176##22##, having an important role in germination##REF##22747917##23##, lateral root formation, plant growth and defense against biotic and abiotic stresses##REF##22747917##23##,##REF##24350934##24##. Being a growth regulator, melatonin showed a great potential for enhancing plant drought resistance##REF##31248005##25##. Melatonin has attracted the researcher to have an effective strategy to induce the crop tolerance against drought, salt, heavy metals, high temperature, low temperature, nutritional deficiencies and different types of diseases##REF##33305363##26##. Melatonin can induce plant antioxidant system, help in plant photosynthesis improvement, enhance ion homeostasis and regulate plant hormone metabolism under the salt stress condition##REF##25156541##27##. Pretreatment of rice with melatonin showed improvement in tolerance to salt stress by increasing plant’s fresh and dry weight and minimize plasma membrane damage##UREF##12##28##. Exogenous melatonin enhanced catalase (CAT), peroxidase (POD), superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities in maize (
There is very limited information regarding the combined effects of salt and drought stress on rice plants, however salt and drought stress and its effects on rice were studied individually. A recent study on cotton (
Our study hypothesized that induction of melatonin enhances tolerance to individual as well as combined drought and salt stress in the rice plant. The aim of our study is to evaluate the role of melatonin in rice plants in response to drought and salt stress individually and when they are combined. We focused on the effects of melatonin on morphological parameters, antioxidants and transcriptional regulation of salt and drought responsive genes in response to salt and drought combined stress.
"],"string":"[\n \"Rice (
The major consequences of climate change are droughts which are tragic threat to water supplies, agriculture crops, food production and causing ecological disturbance and famine in the World##UREF##3##7##. Climate change and global warming are intensively affecting the regional and Worldwide hydrological cycle leading to the frequency of drought events##UREF##4##8##. Rice (
At the same time salinity is an another environmental factor increasing in magnitude in the rice growing areas, due to the combine effects of high temperature, drought, sea level rising, and inferior agriculture practices##UREF##7##11##. In irrigated land, salt stress has been a serious threat to rice cultivation, and expected to be more than 20% in the near future, while it is estimated to reach 50% by 2050##UREF##8##12##. Salt stress reduce the rate of net carbon dioxide assimilation, growth of the leaf, enlargement of the leaf cell, accumulation of dry matter and relative growth##UREF##9##13##. The stress of salinity is dominated by sodium (Na+) and chloride (Cl–)##UREF##9##13##, negatively affecting rice growth and development due to creating ionic, osmotic and oxidative stress##UREF##9##13##–##REF##16690163##15##. High level of salt in rice plant increase the toxicity level, leading to early leaf senescence and ultimately resulting the decrease in the photosynthetic leaf area##REF##11841667##16##,##UREF##11##17##.
\",\n \"Melatonin (N-acetyl-5-methoxytryptamine) is common biological hormone that plays an important role in biological functions##REF##25180262##18##, like circadian rhythms##REF##22034907##19##, immunomodulation##REF##23889107##20##, and oxidative stress reduction##REF##8272286##21##. Hence during the past half century, it received great attention due to its anti-aging properties##REF##7776176##22##. Recent studies showed that melatonin is present in a large number of vascular plants##REF##7776176##22##, having an important role in germination##REF##22747917##23##, lateral root formation, plant growth and defense against biotic and abiotic stresses##REF##22747917##23##,##REF##24350934##24##. Being a growth regulator, melatonin showed a great potential for enhancing plant drought resistance##REF##31248005##25##. Melatonin has attracted the researcher to have an effective strategy to induce the crop tolerance against drought, salt, heavy metals, high temperature, low temperature, nutritional deficiencies and different types of diseases##REF##33305363##26##. Melatonin can induce plant antioxidant system, help in plant photosynthesis improvement, enhance ion homeostasis and regulate plant hormone metabolism under the salt stress condition##REF##25156541##27##. Pretreatment of rice with melatonin showed improvement in tolerance to salt stress by increasing plant’s fresh and dry weight and minimize plasma membrane damage##UREF##12##28##. Exogenous melatonin enhanced catalase (CAT), peroxidase (POD), superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities in maize (
There is very limited information regarding the combined effects of salt and drought stress on rice plants, however salt and drought stress and its effects on rice were studied individually. A recent study on cotton (
Our study hypothesized that induction of melatonin enhances tolerance to individual as well as combined drought and salt stress in the rice plant. The aim of our study is to evaluate the role of melatonin in rice plants in response to drought and salt stress individually and when they are combined. We focused on the effects of melatonin on morphological parameters, antioxidants and transcriptional regulation of salt and drought responsive genes in response to salt and drought combined stress.
\"\n]"},"methods":{"kind":"list like","value":["Ilmi rice cultivar (
In this experiment, a total of eight groups of rice plants were involved. Each of these groups had three replicates, and within each replicate, there were five plants. The experimental groups were control plants (C), melatonin treated plants (M), salt treated plants (S), drought treated plants (D), salt
After 35 days of plant growth, shoot length, root length, height of shoot, and fresh weight (FW) of the rice plants were measured. For the determination of the dry weight (DW) of seedlings, the roots and shoots were dried by oven at 200 ℃ for 30 min and maintained at 60 ℃ for 48 h to obtain DW. The fresh leaves were put into a petri dish filled with distilled water and the petri dishes were placed in a dark environment for 4 h, and then their turgid weight (TW) was recorded, after that the leaves were oven dried to obtain DW. Finally, relative water content (RWC) was quantified using formula: ##UREF##16##43##.
","Chlorophyll contents were measured after 1 week of stress exposure by using portable chlorophyll meter (SPAD 502, Konica Minolta, Japan). The second last fully mature leaf was selected for chlorophyll measurement and the reading was taken from leaf base, middle and near the leaf tip. Five leaves were measured from each treatment group for chlorophyll contents and the average value was taken as SPAD value as mentioned previously##UREF##17##44##.
","For determination of electrolyte leakage, fresh leaves samples were cut into 5 mm and placed in test tubes containing 10 mL deionized water. The tubes were covered with plastic caps and placed in a water bath maintained at the constant temperature of 32 °C. The initial electrical conductivity (ECI) was measured after 2 h by electrical conductivity meter (CM-115, Kyoto Electronics, Kyoto, Japan). Then the samples were autoclaved at 121 °C for 20 min to release all electrolytes and kill the tissues. For measurement of final electrical conductivity (EC2), samples were cooled to 25 °C. Electrolyte leakage (EL) was find out using formula: .
","H2O2 contents were measured using previously described method##UREF##18##45##. Briefly, fresh leaves of 0.1 g were ground in liquid nitrogen, extracted in 5 mL of 0.1% TCA and centrifuged at 12,000×
Catalase activity was find out by the method of Ref.##UREF##20##47## briefly, crude enzymes was treated with 0.5 mL of 0.2 mM H2O2 using sodium phosphate buffer with 7 pH. The activity of catalase was determined by the decrease in the absorbance of H2O2 at 240 nm, and CAT one unit was defined as micromoles of hydrogen peroxide (H2O2) decomposed per minute per milligram of protein. Reduced glutathione contents were determined using the protocol of Ref.##REF##13650640##48##, in brief, fresh leaves were ground in liquid nitrogen, 2 mL 10% (v/v) trichloroacetic acid was added and centrifuged at 4 °C for 13 min at 10,000×
To determine expression level of
All experiments were performed three time. Data were analyzed using two-way ANOVA with Bonferroni post hoc tests (*shows p < 0.05 and **shows p < 0.01 significant difference). For the comparison of the mean values of different treatments completely randomized design was used. Data were graphically presented, and statistical analyses were calculated using the GraphPad Prism software (version 5.01, GraphPad, San Diego, CA, USA) and Statistical Analysis System (SAS 64 bit, developed by North Carolina State University, Raleigh, NC, USA) (Fig. ##FIG##0##1##).
"],"string":"[\n \"Ilmi rice cultivar (
In this experiment, a total of eight groups of rice plants were involved. Each of these groups had three replicates, and within each replicate, there were five plants. The experimental groups were control plants (C), melatonin treated plants (M), salt treated plants (S), drought treated plants (D), salt
After 35 days of plant growth, shoot length, root length, height of shoot, and fresh weight (FW) of the rice plants were measured. For the determination of the dry weight (DW) of seedlings, the roots and shoots were dried by oven at 200 ℃ for 30 min and maintained at 60 ℃ for 48 h to obtain DW. The fresh leaves were put into a petri dish filled with distilled water and the petri dishes were placed in a dark environment for 4 h, and then their turgid weight (TW) was recorded, after that the leaves were oven dried to obtain DW. Finally, relative water content (RWC) was quantified using formula: ##UREF##16##43##.
\",\n \"Chlorophyll contents were measured after 1 week of stress exposure by using portable chlorophyll meter (SPAD 502, Konica Minolta, Japan). The second last fully mature leaf was selected for chlorophyll measurement and the reading was taken from leaf base, middle and near the leaf tip. Five leaves were measured from each treatment group for chlorophyll contents and the average value was taken as SPAD value as mentioned previously##UREF##17##44##.
\",\n \"For determination of electrolyte leakage, fresh leaves samples were cut into 5 mm and placed in test tubes containing 10 mL deionized water. The tubes were covered with plastic caps and placed in a water bath maintained at the constant temperature of 32 °C. The initial electrical conductivity (ECI) was measured after 2 h by electrical conductivity meter (CM-115, Kyoto Electronics, Kyoto, Japan). Then the samples were autoclaved at 121 °C for 20 min to release all electrolytes and kill the tissues. For measurement of final electrical conductivity (EC2), samples were cooled to 25 °C. Electrolyte leakage (EL) was find out using formula: .
\",\n \"H2O2 contents were measured using previously described method##UREF##18##45##. Briefly, fresh leaves of 0.1 g were ground in liquid nitrogen, extracted in 5 mL of 0.1% TCA and centrifuged at 12,000×
Catalase activity was find out by the method of Ref.##UREF##20##47## briefly, crude enzymes was treated with 0.5 mL of 0.2 mM H2O2 using sodium phosphate buffer with 7 pH. The activity of catalase was determined by the decrease in the absorbance of H2O2 at 240 nm, and CAT one unit was defined as micromoles of hydrogen peroxide (H2O2) decomposed per minute per milligram of protein. Reduced glutathione contents were determined using the protocol of Ref.##REF##13650640##48##, in brief, fresh leaves were ground in liquid nitrogen, 2 mL 10% (v/v) trichloroacetic acid was added and centrifuged at 4 °C for 13 min at 10,000×
To determine expression level of
All experiments were performed three time. Data were analyzed using two-way ANOVA with Bonferroni post hoc tests (*shows p < 0.05 and **shows p < 0.01 significant difference). For the comparison of the mean values of different treatments completely randomized design was used. Data were graphically presented, and statistical analyses were calculated using the GraphPad Prism software (version 5.01, GraphPad, San Diego, CA, USA) and Statistical Analysis System (SAS 64 bit, developed by North Carolina State University, Raleigh, NC, USA) (Fig. ##FIG##0##1##).
\"\n]"},"results":{"kind":"list like","value":["In this experiment, we evaluated various growth parameters of rice plants in response to salt and drought combined and individual stress as shown in (Fig. ##FIG##1##2##A). Both salt and drought stress significantly reduced plant shoot length by 23% in (S), 20% in (D), and 32% in (S + D), and root length by 25% (S), 18% (D), and 32% (S + D) compared to (C). However, melatonin treated plants (M) resulted in a 22% increase in plant height compared to (S + M), 16% to (D + M), and 28% to (S + D + M) and increase in root length by 29% to (S + M), 22% to (D + M), and 33% to (S + D + M) respectively. Additionally, the application of exogenous melatonin notably raised plant shoot length by 53% in (S + M), 75% in (D + M), and 80% in (S + D + M), respectively. Likewise, melatonin showed an increase in plant root length by 21% in (S + M), 24% in (D + M), and 24% in (S + D + M) as compared to their respective stress (S), (D), and (S + D) conditions (Fig. ##FIG##1##2##B). Salt and drought stress significantly declined shoot fresh weight (43%, 31%, and 59%), shoot dry weight (48%, 40%, and 54%), root fresh weight (39%, 27%, and 25%), and root dry weight (60%, 53%, and 73%) in (S), (D), and (S + D) when compared to (C). Plants treated exclusively with melatonin (M) exhibited enhanced shoot fresh weight (36%, 24%, and 69%), shoot dry weight (31%, 36%, and 42%), root fresh weight (27%, 33%, and 45%), and root dry weight (23%, 31%, and 39%) in comparison to (S + M), (D + M), and (S + D + M). Similarly exogenous application of melatonin significantly induced increases in both fresh and dry weights of the shoot and root. Shoot fresh weight saw increments of 30.16% in (S + M), 34.25% in (D + M), and 82.05% in (S + D + M), while root fresh weight exhibited increases of 37.12% in (S), 9.35% in (D), and 26.80% in (S + D) compared to (S), (D), and (S + D) (Fig. ##FIG##1##2##C). Moreover, the dry weight of the shoot was enhanced by 37.83% in (S + M), 14.35% in (D + M), and 26.15% in (S + D + M), while the dry weight of the root experienced increments of 16.10% in (S + M), 41.30% in (D + M), and 42.85% in (S + D + M) as compared to (S), (D), and (S + D) (Fig. ##FIG##1##2##D).
","Salt and drought stress resulted in a significant decrease in chlorophyll contents by 31%, 22%, and 38% in (S), (D), and (S + D) compared to the control (C). However, 29%, 16%, and 27% of increment was observed in melatonin treated plants (M) as compared to (S + M), (D + M), and (S + D + M). Likewise, melatonin treatment led to a notable increase in chlorophyll contents, with increments of 20.33% in (S + M), 13.20% in (D + M), and 30.23% in (S + D + M) compared to (S), (D), and (S + D) conditions (Fig. ##FIG##2##3##A).
","Membrane permeability, as indicated by electrolyte leakage, is notably influenced by both salt and drought stress by 220% in (S), 190% in (D), and 410% in (S + D) as compared to (C). Although Melatonin treatment (M) markedly decreased electrolyte leakage by 65%, 68%, and 77% compared to (S + M), (D + M), and (S + D + M), respectively. Furthermore, exogenous melatonin treatment significantly decreased the electrolyte leakage by 44.51% in (S + M), 48.57% in (D + M) and 51.35% in (S + D + M) respectively as compared to (S), (D), and (S + D) (Fig. ##FIG##2##3##B).
","As a measure of plant water status, Relative Water Content (RWC) not only provides insights into the hydration level of a plant but also serves as a reflection of its metabolic activity##UREF##21##49##. RWC is closely related to physiological function of plants, and it also indicates the ability of plant to sustain its water contents and wilting degree of leaves##UREF##22##50##. A significant reduction of 24% under salt stress (S), 22% under drought stress (D), and 32% under combined salt and drought stress (S + D) was recorded compared to the control (C). While RLW contents were 16% 19% and 24% higher in melatonin-treated plants (M) as compared with (S + M), (D + M), and (S + D + M). Similarly, melatonin-treated plants exhibited a significant increase in relative water contents, registering a rise of 17.76% in (S + M), 11.92% in (D + M), and 22.82% in (S + D + M) when compared to (S), (D), and (S + D) conditions (Fig. ##FIG##2##3##C).
","Hydrogen peroxide serves as an indicator of the reactive oxygen species (ROS) scavenging capacity in plants under various stresses, and it is generated as a byproduct of cellular metabolism. The results show that H2O2 showed a substantial increase of 131% under salt stress (S), 112% under drought stress (D), and 206% under combined salt and drought stress (S + D) compared to the control (C). Melatonin-treated plants (M) exhibited a reduction in H2O2 contents by 43%, 45%, and 56% compared to the levels observed in plants under salt stress (S), drought stress (D), and combined salt and drought stress (S + D), respectively. Furthermore, exogenous application of melatonin demonstrated a substantial reduction in H2O2 accumulation, showing decreases by 37% in (S + M), 31% in (D + M), and 39% in (S + D + M) compared to (S), (D), and (S + D) (Fig. ##FIG##3##4##A).
","Salt and drought stress demonstrated a notable impact on Malondialdehyde (MDA) contents, elevating the levels by 210%, 250%, and 370% in salt-stressed (S), drought-stressed (D), and combined salt and drought-stressed (S + D) plants, respectively, compared to the control (C). In contrast, melatonin-treated plants (M) exhibited a significant reduction in MDA levels by 45%, 38%, and 55% compared to plants under salt and melatonin treatment (S + M), drought and melatonin treatment (D + M), and combined salt, drought, and melatonin treatment (S + D + M), respectively. Similarly, MDA contents were significantly decreased by melatonin treatment, recording reductions of 46% in (S + M), 56% in (D + M), and 55% in (S + D + M) compared to plants under salt stress (S), drought stress (D), and combined salt and drought stress (S + D) conditions, respectively (Fig. ##FIG##3##4##B).
","Melatonin provides protection to plants from oxidative damage through the activation of antioxidants. In this study, the effects of salt and drought stress on the antioxidant activities of glutathione (GSH) and catalase (CAT) in rice plants were investigated, with and without melatonin treatment. The results revealed a significant reduction in glutathione (GSH) by 56%, 53%, and 66% in salt-stressed (S), drought-stressed (D), and combined salt and drought-stressed (S + D) plants compared to the control (C). In contrast, melatonin-treated plants (M) exhibited a rise in glutathione (GSH) levels by 70%, 47%, and 80% compared to plants under salt and melatonin treatment (S + M), drought and melatonin treatment (D + M), and combined salt, drought, and melatonin treatment (S + D + M), respectively. Similarly, melatonin increased glutathione (GSH) activities by 40% in (S + M), 36% in (D + M), and 72% in (S + D + M) compared to plants under salt stress (S), drought stress (D), and combined salt and drought stress (S + D) (Fig. ##FIG##3##4##C).
","The catalase (CAT) activity showed a gradual increase due to salt and drought stress; however, melatonin treatment accelerated its activity. The catalase (CAT) activity increased by 16% under salt stress (S), 14% under drought stress (D), and 25% under combined salt and drought stress (S + D) compared to the control (C). Catalase (CAT) was also recorded higher by 26%, 27%, and 34% in salt and melatonin-treated (S + M), drought and melatonin-treated (D + M), and combined salt, drought, and melatonin-treated (S + D + M) conditions compared to melatonin-treated plants (M). Exogenous melatonin treatment significantly increased catalase (CAT) activity by 17.64% in (S), 21.80% in (D), and 20.40% in (S + D) respectively (Fig. ##FIG##3##4##D).
","The combined effects of salt and drought stress exert a considerable influence on the expression of genes associated with these stressors. The expression of
Similarly, the treatment of exogenous melatonin also induced the expression of drought responsive genes
In this experiment, we evaluated various growth parameters of rice plants in response to salt and drought combined and individual stress as shown in (Fig. ##FIG##1##2##A). Both salt and drought stress significantly reduced plant shoot length by 23% in (S), 20% in (D), and 32% in (S + D), and root length by 25% (S), 18% (D), and 32% (S + D) compared to (C). However, melatonin treated plants (M) resulted in a 22% increase in plant height compared to (S + M), 16% to (D + M), and 28% to (S + D + M) and increase in root length by 29% to (S + M), 22% to (D + M), and 33% to (S + D + M) respectively. Additionally, the application of exogenous melatonin notably raised plant shoot length by 53% in (S + M), 75% in (D + M), and 80% in (S + D + M), respectively. Likewise, melatonin showed an increase in plant root length by 21% in (S + M), 24% in (D + M), and 24% in (S + D + M) as compared to their respective stress (S), (D), and (S + D) conditions (Fig. ##FIG##1##2##B). Salt and drought stress significantly declined shoot fresh weight (43%, 31%, and 59%), shoot dry weight (48%, 40%, and 54%), root fresh weight (39%, 27%, and 25%), and root dry weight (60%, 53%, and 73%) in (S), (D), and (S + D) when compared to (C). Plants treated exclusively with melatonin (M) exhibited enhanced shoot fresh weight (36%, 24%, and 69%), shoot dry weight (31%, 36%, and 42%), root fresh weight (27%, 33%, and 45%), and root dry weight (23%, 31%, and 39%) in comparison to (S + M), (D + M), and (S + D + M). Similarly exogenous application of melatonin significantly induced increases in both fresh and dry weights of the shoot and root. Shoot fresh weight saw increments of 30.16% in (S + M), 34.25% in (D + M), and 82.05% in (S + D + M), while root fresh weight exhibited increases of 37.12% in (S), 9.35% in (D), and 26.80% in (S + D) compared to (S), (D), and (S + D) (Fig. ##FIG##1##2##C). Moreover, the dry weight of the shoot was enhanced by 37.83% in (S + M), 14.35% in (D + M), and 26.15% in (S + D + M), while the dry weight of the root experienced increments of 16.10% in (S + M), 41.30% in (D + M), and 42.85% in (S + D + M) as compared to (S), (D), and (S + D) (Fig. ##FIG##1##2##D).
\",\n \"Salt and drought stress resulted in a significant decrease in chlorophyll contents by 31%, 22%, and 38% in (S), (D), and (S + D) compared to the control (C). However, 29%, 16%, and 27% of increment was observed in melatonin treated plants (M) as compared to (S + M), (D + M), and (S + D + M). Likewise, melatonin treatment led to a notable increase in chlorophyll contents, with increments of 20.33% in (S + M), 13.20% in (D + M), and 30.23% in (S + D + M) compared to (S), (D), and (S + D) conditions (Fig. ##FIG##2##3##A).
\",\n \"Membrane permeability, as indicated by electrolyte leakage, is notably influenced by both salt and drought stress by 220% in (S), 190% in (D), and 410% in (S + D) as compared to (C). Although Melatonin treatment (M) markedly decreased electrolyte leakage by 65%, 68%, and 77% compared to (S + M), (D + M), and (S + D + M), respectively. Furthermore, exogenous melatonin treatment significantly decreased the electrolyte leakage by 44.51% in (S + M), 48.57% in (D + M) and 51.35% in (S + D + M) respectively as compared to (S), (D), and (S + D) (Fig. ##FIG##2##3##B).
\",\n \"As a measure of plant water status, Relative Water Content (RWC) not only provides insights into the hydration level of a plant but also serves as a reflection of its metabolic activity##UREF##21##49##. RWC is closely related to physiological function of plants, and it also indicates the ability of plant to sustain its water contents and wilting degree of leaves##UREF##22##50##. A significant reduction of 24% under salt stress (S), 22% under drought stress (D), and 32% under combined salt and drought stress (S + D) was recorded compared to the control (C). While RLW contents were 16% 19% and 24% higher in melatonin-treated plants (M) as compared with (S + M), (D + M), and (S + D + M). Similarly, melatonin-treated plants exhibited a significant increase in relative water contents, registering a rise of 17.76% in (S + M), 11.92% in (D + M), and 22.82% in (S + D + M) when compared to (S), (D), and (S + D) conditions (Fig. ##FIG##2##3##C).
\",\n \"Hydrogen peroxide serves as an indicator of the reactive oxygen species (ROS) scavenging capacity in plants under various stresses, and it is generated as a byproduct of cellular metabolism. The results show that H2O2 showed a substantial increase of 131% under salt stress (S), 112% under drought stress (D), and 206% under combined salt and drought stress (S + D) compared to the control (C). Melatonin-treated plants (M) exhibited a reduction in H2O2 contents by 43%, 45%, and 56% compared to the levels observed in plants under salt stress (S), drought stress (D), and combined salt and drought stress (S + D), respectively. Furthermore, exogenous application of melatonin demonstrated a substantial reduction in H2O2 accumulation, showing decreases by 37% in (S + M), 31% in (D + M), and 39% in (S + D + M) compared to (S), (D), and (S + D) (Fig. ##FIG##3##4##A).
\",\n \"Salt and drought stress demonstrated a notable impact on Malondialdehyde (MDA) contents, elevating the levels by 210%, 250%, and 370% in salt-stressed (S), drought-stressed (D), and combined salt and drought-stressed (S + D) plants, respectively, compared to the control (C). In contrast, melatonin-treated plants (M) exhibited a significant reduction in MDA levels by 45%, 38%, and 55% compared to plants under salt and melatonin treatment (S + M), drought and melatonin treatment (D + M), and combined salt, drought, and melatonin treatment (S + D + M), respectively. Similarly, MDA contents were significantly decreased by melatonin treatment, recording reductions of 46% in (S + M), 56% in (D + M), and 55% in (S + D + M) compared to plants under salt stress (S), drought stress (D), and combined salt and drought stress (S + D) conditions, respectively (Fig. ##FIG##3##4##B).
\",\n \"Melatonin provides protection to plants from oxidative damage through the activation of antioxidants. In this study, the effects of salt and drought stress on the antioxidant activities of glutathione (GSH) and catalase (CAT) in rice plants were investigated, with and without melatonin treatment. The results revealed a significant reduction in glutathione (GSH) by 56%, 53%, and 66% in salt-stressed (S), drought-stressed (D), and combined salt and drought-stressed (S + D) plants compared to the control (C). In contrast, melatonin-treated plants (M) exhibited a rise in glutathione (GSH) levels by 70%, 47%, and 80% compared to plants under salt and melatonin treatment (S + M), drought and melatonin treatment (D + M), and combined salt, drought, and melatonin treatment (S + D + M), respectively. Similarly, melatonin increased glutathione (GSH) activities by 40% in (S + M), 36% in (D + M), and 72% in (S + D + M) compared to plants under salt stress (S), drought stress (D), and combined salt and drought stress (S + D) (Fig. ##FIG##3##4##C).
\",\n \"The catalase (CAT) activity showed a gradual increase due to salt and drought stress; however, melatonin treatment accelerated its activity. The catalase (CAT) activity increased by 16% under salt stress (S), 14% under drought stress (D), and 25% under combined salt and drought stress (S + D) compared to the control (C). Catalase (CAT) was also recorded higher by 26%, 27%, and 34% in salt and melatonin-treated (S + M), drought and melatonin-treated (D + M), and combined salt, drought, and melatonin-treated (S + D + M) conditions compared to melatonin-treated plants (M). Exogenous melatonin treatment significantly increased catalase (CAT) activity by 17.64% in (S), 21.80% in (D), and 20.40% in (S + D) respectively (Fig. ##FIG##3##4##D).
\",\n \"The combined effects of salt and drought stress exert a considerable influence on the expression of genes associated with these stressors. The expression of
Similarly, the treatment of exogenous melatonin also induced the expression of drought responsive genes
In this study, both salt and drought stress, either independently or in combination, resulted in a substantial depletion in rice growth (Fig. ##FIG##1##2##). The inhibition of new leaf growth and the development of the root system due to drought and salt stress are widely acknowledged factors contributing to the reduction in biomass accumulation##REF##23800963##41##,##UREF##23##51##. Salinity and drought stress significantly reduce seed germination rates, shoot, and root length, as well as the overall biomass of rice seedlings, resulting in hindering plant growth##REF##27847508##52##,##REF##36292930##53##. This study confirms that both salinity and drought stress severely restricted the growth and development of rice, as shown in (Fig. ##FIG##1##2##A,B). And these stressors significantly reduced both above-ground fresh weight and dry weight, as depicted in (Fig. ##FIG##1##2##C,D). Moreover, the inhibitory effect on plant growth and biomass was more pronounced under salt stress compared to drought stress. Furthermore, chlorophyll plays essential roles in plant growth, development, and the synthesis of photosynthetic products. Salt stress hinders chlorophyll synthesis, directly impacting photosynthesis, retarding plant growth, and diminishing yield##UREF##24##54##,##UREF##25##55##. In this experiment, during salt and drought stress, chlorophyll contents were significantly reduced (Fig. ##FIG##2##3##A). This may be due to rise in the level of Na+, MDA, and H2O2, disrupting chloroplast membrane stability and causing degradation of the protein-pigment-lipid complex##UREF##26##56##. Exogenous application of melatonin reversed the downward trend and promoted plant growth, biomass, and chlorophyll contents (Fig. ##FIG##2##3##C,D). These findings align with prior research##UREF##27##57##, suggesting the potential impact of melatonin on enzymes contributes to the enhancement of chlorophyll level##REF##18691358##58##, thereby promoting plant growth and development.
","The findings from this study indicate that elevated salt and drought stress levels led to a decrease in RLWC (Relative Leaf Water Content). This decline in RLWC may have contributed to a reduction in various plant growth factors##UREF##28##59##. Prior treatment with melatonin notably enhanced Relative Leaf Water Content (RLWC) in rice plants under both salt and drought stress conditions (Fig. ##FIG##2##3##C). These outcomes are consistent with earlier findings from Ref.##UREF##29##60##, the observed rise in RLWC could be attributed to melatonin’s potential involvement in modulating stomatal behavior, effectively regulating their opening and closure to prevent undue water loss from leaves##UREF##30##61##. Electrolyte Leakage (EL) serves as an indicator of alterations in cell membrane structure during high salt and water deficit conditions. Our results show a notable increment in electrolyte leakage during salt and drought stress (Fig. ##FIG##2##3##B). Utilizing its relative conductivity allows for the assessment of damage to both the structure and function of cell membranes under various stresses##REF##30294219##62##. Melatonin pre-treatment significantly decreased electrolyte leakage during salt and drought stress in rice plants. Similar results were obtained by Ref.##REF##28633086##63## in drought and Ref.##UREF##31##64## in salt stress conditions. Consequently, the decrease in electrolyte leakage may be associated with elevated levels of CAT (catalase) and GSH (glutathione) by melatonin treatment during salt and drought stress conditions. This indicates that the utilization of melatonin might mitigate oxidative harm induced by salinity and drought stress.
","Both salt and drought stress in rice plants trigger the excessive production of reactive oxygen species (ROS), which then leads to damage within various biomolecules. This disruption in the equilibrium between ROS generation and elimination adds to the overall oxidative stress within the plant’s system##UREF##32##65##. Melatonin is believed to act as an antioxidant in plants, aiding in cellular redox regulation, scavenging reactive oxygen species (ROS), and stabilizing plant cell membranes, thus offering protection against various environmental stressors##REF##25156541##27##,##REF##11899100##66##. Our results show that melatonin pre-treatment in rice suppressed the accumulation of ROS during salt and drought stress (Fig. ##FIG##3##4##A,B). These findings align with previous observations indicating that melatonin reduces ROS accumulation in watermelon and cucumber subjected to salt stress##REF##28298921##67##,##REF##27999581##68## in maize and soybean subjected to drought stress##REF##31616591##40##,##UREF##27##57## with respect to non-treated plants.
","Moreover, Li et al.##UREF##33##69## reported that the application of exogenous melatonin enhanced plants’ tolerance to cold, drought, and salt stress. This effect was attributed to a reduction in reactive oxygen species (ROS) burst, maintenance of photosynthetic efficiency, decrease in malondialdehyde (MDA) levels, and enhancement of antioxidant activity in tea plants. Melatonin may have the capacity to enhance cellular redox homeostasis by stimulating the entire antioxidant system, encompassing both antioxidant enzymes (e.g., catalase, superoxide dismutase, peroxidase, ascorbate peroxidase, and monodehydroascorbate reductase) and non-enzymatic antioxidants (such as glutathione and ascorbate)##REF##16098085##70##, as well as elevating levels of polyphenols##REF##14740000##71##, carotenoids##UREF##34##72##, and anthocyanins##REF##27047496##73##, to protect plants from abiotic stress-induced oxidative stress. Nonetheless, the precise mechanisms underlying this stimulatory action remain unclear. It is yet to be determined whether melatonin’s effect results from a direct interaction with existing enzymes or if it involves signal transduction mechanisms that regulate gene expression, leading to increased enzyme production.
","During normal conditions, plants effectively neutralize reactive oxygen species (ROS) through both non-enzymatic and enzymatic antioxidants. However, under salt and drought conditions, the ROS production surpasses the capacity of the antioxidant defense systems, resulting in oxidative stress within the plant##UREF##35##74##. Catalase (CAT) and glutathione (GSH) are crucial antioxidants involved in vital processes within plant cells. Several studies on plants with altered levels of CAT and GSH proved the important roles of CAT and GSH in the tolerance of plants to environmental stresses##REF##15308753##75##. The results of our study show that salt and drought stress slightly increase the level of CAT as shown in the (Fig. ##FIG##3##4##D). This slight increase in CAT activity may be due to its activation to encounter the accumulation of H2O2 induced by water shortage and salinity stress##UREF##36##76##. The findings from this study align with previous research indicating that plants respond to oxidative damage induced by various stressors by deploying mechanisms to maintain cellular equilibrium and withstand abiotic stress##UREF##17##44##,##UREF##37##77##. When the levels of glutathione within cells become more oxidized or decrease due to environmental factors, it triggers a signaling process, which prompts cells to react as though their glutathione levels are persistently low, assisting in their adaptation to changes in the environment##REF##11368918##78##. The metabolism of glutathione (GSH) and the maintenance of the GSH pool are integral to plant responses to various abiotic stresses##UREF##38##79##. Several studies have highlighted a reduction in glutathione (GSH) levels in various plant species under stress conditions like salinity, extreme temperatures, and heavy metal exposure##UREF##39##80##–##REF##16668756##82##. The study’s findings indicate a decline in GSH levels during salt and drought stress, as depicted in (Fig. ##FIG##3##4##C). This trend might be attributed to the activation of NADPH oxidase, which has a direct correlation with both ROS production and GSH levels##REF##34725879##83##. However exogenous treatment of melatonin significantly increased the CAT and GSH activity during salt and drought stress (Fig. ##FIG##3##4##D). The results obtained in this study align with previous findings. Exogenous application of melatonin significantly boosted the activity of CAT in
Further we studied
Studies show that different transcription factors were identified which play an important role in the regulation of plants responses to different stresses##REF##12183182##96##. In rice cultivar AP2 transcription activators
In this study, both salt and drought stress, either independently or in combination, resulted in a substantial depletion in rice growth (Fig. ##FIG##1##2##). The inhibition of new leaf growth and the development of the root system due to drought and salt stress are widely acknowledged factors contributing to the reduction in biomass accumulation##REF##23800963##41##,##UREF##23##51##. Salinity and drought stress significantly reduce seed germination rates, shoot, and root length, as well as the overall biomass of rice seedlings, resulting in hindering plant growth##REF##27847508##52##,##REF##36292930##53##. This study confirms that both salinity and drought stress severely restricted the growth and development of rice, as shown in (Fig. ##FIG##1##2##A,B). And these stressors significantly reduced both above-ground fresh weight and dry weight, as depicted in (Fig. ##FIG##1##2##C,D). Moreover, the inhibitory effect on plant growth and biomass was more pronounced under salt stress compared to drought stress. Furthermore, chlorophyll plays essential roles in plant growth, development, and the synthesis of photosynthetic products. Salt stress hinders chlorophyll synthesis, directly impacting photosynthesis, retarding plant growth, and diminishing yield##UREF##24##54##,##UREF##25##55##. In this experiment, during salt and drought stress, chlorophyll contents were significantly reduced (Fig. ##FIG##2##3##A). This may be due to rise in the level of Na+, MDA, and H2O2, disrupting chloroplast membrane stability and causing degradation of the protein-pigment-lipid complex##UREF##26##56##. Exogenous application of melatonin reversed the downward trend and promoted plant growth, biomass, and chlorophyll contents (Fig. ##FIG##2##3##C,D). These findings align with prior research##UREF##27##57##, suggesting the potential impact of melatonin on enzymes contributes to the enhancement of chlorophyll level##REF##18691358##58##, thereby promoting plant growth and development.
\",\n \"The findings from this study indicate that elevated salt and drought stress levels led to a decrease in RLWC (Relative Leaf Water Content). This decline in RLWC may have contributed to a reduction in various plant growth factors##UREF##28##59##. Prior treatment with melatonin notably enhanced Relative Leaf Water Content (RLWC) in rice plants under both salt and drought stress conditions (Fig. ##FIG##2##3##C). These outcomes are consistent with earlier findings from Ref.##UREF##29##60##, the observed rise in RLWC could be attributed to melatonin’s potential involvement in modulating stomatal behavior, effectively regulating their opening and closure to prevent undue water loss from leaves##UREF##30##61##. Electrolyte Leakage (EL) serves as an indicator of alterations in cell membrane structure during high salt and water deficit conditions. Our results show a notable increment in electrolyte leakage during salt and drought stress (Fig. ##FIG##2##3##B). Utilizing its relative conductivity allows for the assessment of damage to both the structure and function of cell membranes under various stresses##REF##30294219##62##. Melatonin pre-treatment significantly decreased electrolyte leakage during salt and drought stress in rice plants. Similar results were obtained by Ref.##REF##28633086##63## in drought and Ref.##UREF##31##64## in salt stress conditions. Consequently, the decrease in electrolyte leakage may be associated with elevated levels of CAT (catalase) and GSH (glutathione) by melatonin treatment during salt and drought stress conditions. This indicates that the utilization of melatonin might mitigate oxidative harm induced by salinity and drought stress.
\",\n \"Both salt and drought stress in rice plants trigger the excessive production of reactive oxygen species (ROS), which then leads to damage within various biomolecules. This disruption in the equilibrium between ROS generation and elimination adds to the overall oxidative stress within the plant’s system##UREF##32##65##. Melatonin is believed to act as an antioxidant in plants, aiding in cellular redox regulation, scavenging reactive oxygen species (ROS), and stabilizing plant cell membranes, thus offering protection against various environmental stressors##REF##25156541##27##,##REF##11899100##66##. Our results show that melatonin pre-treatment in rice suppressed the accumulation of ROS during salt and drought stress (Fig. ##FIG##3##4##A,B). These findings align with previous observations indicating that melatonin reduces ROS accumulation in watermelon and cucumber subjected to salt stress##REF##28298921##67##,##REF##27999581##68## in maize and soybean subjected to drought stress##REF##31616591##40##,##UREF##27##57## with respect to non-treated plants.
\",\n \"Moreover, Li et al.##UREF##33##69## reported that the application of exogenous melatonin enhanced plants’ tolerance to cold, drought, and salt stress. This effect was attributed to a reduction in reactive oxygen species (ROS) burst, maintenance of photosynthetic efficiency, decrease in malondialdehyde (MDA) levels, and enhancement of antioxidant activity in tea plants. Melatonin may have the capacity to enhance cellular redox homeostasis by stimulating the entire antioxidant system, encompassing both antioxidant enzymes (e.g., catalase, superoxide dismutase, peroxidase, ascorbate peroxidase, and monodehydroascorbate reductase) and non-enzymatic antioxidants (such as glutathione and ascorbate)##REF##16098085##70##, as well as elevating levels of polyphenols##REF##14740000##71##, carotenoids##UREF##34##72##, and anthocyanins##REF##27047496##73##, to protect plants from abiotic stress-induced oxidative stress. Nonetheless, the precise mechanisms underlying this stimulatory action remain unclear. It is yet to be determined whether melatonin’s effect results from a direct interaction with existing enzymes or if it involves signal transduction mechanisms that regulate gene expression, leading to increased enzyme production.
\",\n \"During normal conditions, plants effectively neutralize reactive oxygen species (ROS) through both non-enzymatic and enzymatic antioxidants. However, under salt and drought conditions, the ROS production surpasses the capacity of the antioxidant defense systems, resulting in oxidative stress within the plant##UREF##35##74##. Catalase (CAT) and glutathione (GSH) are crucial antioxidants involved in vital processes within plant cells. Several studies on plants with altered levels of CAT and GSH proved the important roles of CAT and GSH in the tolerance of plants to environmental stresses##REF##15308753##75##. The results of our study show that salt and drought stress slightly increase the level of CAT as shown in the (Fig. ##FIG##3##4##D). This slight increase in CAT activity may be due to its activation to encounter the accumulation of H2O2 induced by water shortage and salinity stress##UREF##36##76##. The findings from this study align with previous research indicating that plants respond to oxidative damage induced by various stressors by deploying mechanisms to maintain cellular equilibrium and withstand abiotic stress##UREF##17##44##,##UREF##37##77##. When the levels of glutathione within cells become more oxidized or decrease due to environmental factors, it triggers a signaling process, which prompts cells to react as though their glutathione levels are persistently low, assisting in their adaptation to changes in the environment##REF##11368918##78##. The metabolism of glutathione (GSH) and the maintenance of the GSH pool are integral to plant responses to various abiotic stresses##UREF##38##79##. Several studies have highlighted a reduction in glutathione (GSH) levels in various plant species under stress conditions like salinity, extreme temperatures, and heavy metal exposure##UREF##39##80##–##REF##16668756##82##. The study’s findings indicate a decline in GSH levels during salt and drought stress, as depicted in (Fig. ##FIG##3##4##C). This trend might be attributed to the activation of NADPH oxidase, which has a direct correlation with both ROS production and GSH levels##REF##34725879##83##. However exogenous treatment of melatonin significantly increased the CAT and GSH activity during salt and drought stress (Fig. ##FIG##3##4##D). The results obtained in this study align with previous findings. Exogenous application of melatonin significantly boosted the activity of CAT in
Further we studied
Studies show that different transcription factors were identified which play an important role in the regulation of plants responses to different stresses##REF##12183182##96##. In rice cultivar AP2 transcription activators
Our study showed that both the salt and drought stresses induced oxidative damage by generation of ROS and membrane damage due to lipid peroxidation, which leads to reduction in rice plant growth and development. Exogenous melatonin application reduces salt, drought stress individually as well as in combine. Melatonin increased fresh and dry weight of rice under salt and drought stress. Similarly, melatonin treatment significantly reduced the accumulation of ROS and increased the antioxidant activity. Moreover, melatonin up-regulated the genes expression that are responsible for ion homeostasis. Future perspectives entail unraveling melatonin’s precise mechanisms, optimizing its application strategies, and validating its effectiveness in field trials for sustainable crop resilience under salt and drought stresses.
","The current study complies with relevant guidelines of IUCN Policy Statement on Research Involving Species at Risk of Extinction and Convention on the Trade in Endangered Species of Wild Fauna and Flora.
"],"string":"[\n \"Our study showed that both the salt and drought stresses induced oxidative damage by generation of ROS and membrane damage due to lipid peroxidation, which leads to reduction in rice plant growth and development. Exogenous melatonin application reduces salt, drought stress individually as well as in combine. Melatonin increased fresh and dry weight of rice under salt and drought stress. Similarly, melatonin treatment significantly reduced the accumulation of ROS and increased the antioxidant activity. Moreover, melatonin up-regulated the genes expression that are responsible for ion homeostasis. Future perspectives entail unraveling melatonin’s precise mechanisms, optimizing its application strategies, and validating its effectiveness in field trials for sustainable crop resilience under salt and drought stresses.
\",\n \"The current study complies with relevant guidelines of IUCN Policy Statement on Research Involving Species at Risk of Extinction and Convention on the Trade in Endangered Species of Wild Fauna and Flora.
\"\n]"},"front":{"kind":"list like","value":["Due to global climate change, crops are certainly confronted with a lot of abiotic and biotic stress factors during their growth that cause a serious threat to their development and overall productivity. Among different abiotic stresses, salt and drought are considered the most devastating stressors with serious impact on crop’s yield stability. Here, the current study aimed to elucidate how melatonin works in regulating plant biomass, oxidative stress, antioxidant defense system, as well as the expression of genes related to salt and drought stress in rice plants. Eight groups of rice plants (3 replicates, 5 plants each) underwent varied treatments: control, melatonin, salt, drought, salt + drought, salt + melatonin, drought + melatonin, and salt + drought + melatonin. Melatonin (100 µM) was alternately applied a week before stress exposure; salt stress received 100 mM NaCl every 3 days for 3 weeks, and drought stress involved 10% PEG. Young leaves were randomly sampled from each group. The results showed that melatonin treatment markedly reduces salt and drought stress damage by promoting root, shoot length, fresh and dry weight, increasing chlorophyll contents, and inhibiting excessive production of oxidative stress markers. Salt and drought stress significantly decreased the water balance, and damaged cell membrane by reducing relative water contents and increasing electrolyte leakage. However, melatonin treated rice plants showed high relative water contents and low electrolyte leakage. Under salt and drought stress conditions, exogenous application of melatonin boosted the expression level of salt and drought stress responsive genes like
Due to global climate change, crops are certainly confronted with a lot of abiotic and biotic stress factors during their growth that cause a serious threat to their development and overall productivity. Among different abiotic stresses, salt and drought are considered the most devastating stressors with serious impact on crop’s yield stability. Here, the current study aimed to elucidate how melatonin works in regulating plant biomass, oxidative stress, antioxidant defense system, as well as the expression of genes related to salt and drought stress in rice plants. Eight groups of rice plants (3 replicates, 5 plants each) underwent varied treatments: control, melatonin, salt, drought, salt + drought, salt + melatonin, drought + melatonin, and salt + drought + melatonin. Melatonin (100 µM) was alternately applied a week before stress exposure; salt stress received 100 mM NaCl every 3 days for 3 weeks, and drought stress involved 10% PEG. Young leaves were randomly sampled from each group. The results showed that melatonin treatment markedly reduces salt and drought stress damage by promoting root, shoot length, fresh and dry weight, increasing chlorophyll contents, and inhibiting excessive production of oxidative stress markers. Salt and drought stress significantly decreased the water balance, and damaged cell membrane by reducing relative water contents and increasing electrolyte leakage. However, melatonin treated rice plants showed high relative water contents and low electrolyte leakage. Under salt and drought stress conditions, exogenous application of melatonin boosted the expression level of salt and drought stress responsive genes like
Z.K., R.J., and K.-M.K.; designed the study; Z.K., R.J., S.A., and M.-F., performed the experiments; E.-G.K., Y.-H.J., and N.K., contributed to statistical analysis; Z.K., R.J., and K.-M.K.; wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
","This work was conducted with the support of “Cooperative Research Program for Agriculture Science and Technology Development (Project No. RS-2022-RD010034)” Rural Development Administration, Republic of Korea. The funding was also provided by Cooperative Research Program for Agriculture Science and Technology Rural Development Administration, Republic of Korea Development, Project No. RS-2023-00217583.
","The data presented in this study are available on request from the corresponding author.
","The authors declare no competing interests.
"],"string":"[\n \"Z.K., R.J., and K.-M.K.; designed the study; Z.K., R.J., S.A., and M.-F., performed the experiments; E.-G.K., Y.-H.J., and N.K., contributed to statistical analysis; Z.K., R.J., and K.-M.K.; wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
\",\n \"This work was conducted with the support of “Cooperative Research Program for Agriculture Science and Technology Development (Project No. RS-2022-RD010034)” Rural Development Administration, Republic of Korea. The funding was also provided by Cooperative Research Program for Agriculture Science and Technology Rural Development Administration, Republic of Korea Development, Project No. RS-2023-00217583.
\",\n \"The data presented in this study are available on request from the corresponding author.
\",\n \"The authors declare no competing interests.
\"\n]"},"figure":{"kind":"list like","value":["Experimental design of the experiment, indicating the eight experimental boxes and application of drought and salt stress. Box (
Evaluation of growth parameters in rice plants under salt and drought stress. Figures show that melatonin increased the root shoot length and plant biomass in salt and drought stress individually as well as in combined. (
Melatonin application reduces salt and drought stress effects on chlorophyll contents. (
Melatonin application alleviates salt and drought stress by scavenging ROS accumulation. (
Melatonin reduces salt and drougth stress via regulation of drought and salt stress responsive genes. (
Experimental design of the experiment, indicating the eight experimental boxes and application of drought and salt stress. Box (
Evaluation of growth parameters in rice plants under salt and drought stress. Figures show that melatonin increased the root shoot length and plant biomass in salt and drought stress individually as well as in combined. (
Melatonin application reduces salt and drought stress effects on chlorophyll contents. (
Melatonin application alleviates salt and drought stress by scavenging ROS accumulation. (
Melatonin reduces salt and drougth stress via regulation of drought and salt stress responsive genes. (
Primers and accession numbers of selected genes designed by NCBI for qRT-PCR.
| Gene | Forward primers | Reverse primers | Accession no |
|---|---|---|---|
| CTGCGGGTATCCATGAGACT | GGAGCAAGGCAGTGATCTTC | X16280.1 | |
| GCGAGAGAAGCTCAGCTAGG | CCCAGACGTAGAATCCGGTG | AK101182 | |
| AGGAGGGAGAAATCTGGCAC | CGCACTGAAAAGTGTGGACA | AK062422 | |
| GCGGTGCATTTTGCTCTCAA | CCTGCTTCAGATCAGGGTGG | AK104336 | |
| TCGCAGACAGGGTGTTTGAT | CGCTTTTGGGTGGAACACAC | AK101368 |
Primers and accession numbers of selected genes designed by NCBI for qRT-PCR.
| Gene | Forward primers | Reverse primers | Accession no |
|---|---|---|---|
| CTGCGGGTATCCATGAGACT | GGAGCAAGGCAGTGATCTTC | X16280.1 | |
| GCGAGAGAAGCTCAGCTAGG | CCCAGACGTAGAATCCGGTG | AK101182 | |
| AGGAGGGAGAAATCTGGCAC | CGCACTGAAAAGTGTGGACA | AK062422 | |
| GCGGTGCATTTTGCTCTCAA | CCTGCTTCAGATCAGGGTGG | AK104336 | |
| TCGCAGACAGGGTGTTTGAT | CGCTTTTGGGTGGAACACAC | AK101368 |
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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Climate change affects the physics and biology of marine ecosystems through warming, acidification, deoxygenation, and changes in productivity (e.g., chlorophyll concentration). Ocean observations suggest that these changes have taken place at a more rapid pace than previously expected##UREF##0##1## and can manifest in a range of ways. For example, there is evidence of increased frequencies of extreme heatwaves in the Pacific Ocean##REF##34912083##2## and expansion of deoxygenated zones, which can be attributed to eutrophication##REF##24706804##3##,##REF##18703733##4##, warming (decreased oxygen saturation), and stratification (decreased mixing and ventilation)##UREF##1##5##. Combined stressor impacts can change species distribution, phenology, survival, and growth, as well as habitat and spawning conditions. Effective decision-making on how to mitigate or adapt to climate change requires detailed data on future ocean temperatures, acidification, changes in oxygen, and ocean productivity.
","Scientific analysis and conclusions featured in the sixth assessment report of the IPCC are based largely on the critically important output from the Coupled Model Intercomparison Project (CMIP6) and historical observations. CMIP6 uses Earth System Models (ESMs) or General Circulation Models (GCMs) that can simulate the various physical, biological, and chemical processes within the atmosphere, ocean, ice, and land and how those processes combine to affect the global climate. These models provide possible climate trajectories for the future based on a range of greenhouse gas concentration scenarios and Shared Socioeconomic Pathways (SSP), which provide a wealth of information at large spatial scales##UREF##2##6##. Still, working with CMIP6 model outputs can be technically challenging##REF##37179171##7##. For example, the grid structure of CMIP6 models can be complex with non-uniform representation of longitude and latitude grid points, which are used to avoid singularities at the poles and to enhance resolution closer to the equator. In addition, a single model variable may be split into hundreds of files that need to be concatenated in time. Modelled variables may also be biased compared to observations. Working with these global datasets presents several logistical challenges. They require large storage space and cannot be sub-sampled prior to downloading from the original data archives, although both Google Cloud and Amazon Web Services now provide a subset of the model data improving their accessibility. CMIP6 associated terminology can also be impenetrable to non-experts##REF##37179171##7##. As a result, the outputs of CMIP6 across a range of spatial and temporal scales often prohibit the use by non-experts, limiting their use for further ecosystem impact analysis.
","The coarse resolution of the CMIP6 models (most models represent the ocean at roughly 1 × 1 resolution in longitude and latitude grid) does not resolve mesoscale features (e.g., eddies) essential to understanding dynamical processes in coastal regions. Higher resolution climate projections for coastal and shelf areas are increasingly needed as inputs to adaptation and mitigation planning as well as management of marine resources##UREF##3##8##. Downscaling global climate projections to a higher resolution can preserve the large-scale climate signal while capturing local variability and dynamics##UREF##4##9##. While several dynamical downscaling products exist for regional ocean domains##UREF##5##10##,##UREF##6##11##, these products lack the conceptual and standardized approach of the CMIP experiments, or the Coordinated REgional Downscaling Experiment (CORDEX) program for regional atmospheric models. Dynamically downscaled products often focus on a single or limited set of climate models and scenarios##UREF##7##12##,##UREF##8##13##. The lack of consistent ensembles with diversity in models and scenarios strongly limits the comparability of results across different systems and does not adequately quantify the uncertainty in the physical and biogeochemical projections##UREF##3##8##, which is critical for risk assessment, ecosystem-based management, and informing mitigation and adaptation policies.
","The EU-funded research project FutureMARES aims to provide socially and economically viable actions and strategies that support Nature-based Solutions (NBS) for climate change adaptation and mitigation across Europe. To meet these ambitious goals, the datasets developed here were explicitly designed to deliver consistent climate-driven projections of change in physical and biogeochemical factors at the spatial scales relevant for planning and management across European regional seas. To do this, we bias-corrected and statistically downscaled individual CMIP6 models to provide an ensemble of high-resolution climate projections for different IPCC scenarios. Here, we present the methodology, evaluation, and uncertainty analysis of the downscaled dataset products made publicly available through Zenodo (zenodo.org). The ensemble high-resolution climate dataset provides projections for 1993–2100 (monthly) at roughly 8 km (1/12th degree) resolution for four European regions: the North Sea, the Baltic Sea, the Bay of Biscay, and the Mediterranean Sea (Fig. ##FIG##0##1##).
"],"string":"[\n \"Climate change affects the physics and biology of marine ecosystems through warming, acidification, deoxygenation, and changes in productivity (e.g., chlorophyll concentration). Ocean observations suggest that these changes have taken place at a more rapid pace than previously expected##UREF##0##1## and can manifest in a range of ways. For example, there is evidence of increased frequencies of extreme heatwaves in the Pacific Ocean##REF##34912083##2## and expansion of deoxygenated zones, which can be attributed to eutrophication##REF##24706804##3##,##REF##18703733##4##, warming (decreased oxygen saturation), and stratification (decreased mixing and ventilation)##UREF##1##5##. Combined stressor impacts can change species distribution, phenology, survival, and growth, as well as habitat and spawning conditions. Effective decision-making on how to mitigate or adapt to climate change requires detailed data on future ocean temperatures, acidification, changes in oxygen, and ocean productivity.
\",\n \"Scientific analysis and conclusions featured in the sixth assessment report of the IPCC are based largely on the critically important output from the Coupled Model Intercomparison Project (CMIP6) and historical observations. CMIP6 uses Earth System Models (ESMs) or General Circulation Models (GCMs) that can simulate the various physical, biological, and chemical processes within the atmosphere, ocean, ice, and land and how those processes combine to affect the global climate. These models provide possible climate trajectories for the future based on a range of greenhouse gas concentration scenarios and Shared Socioeconomic Pathways (SSP), which provide a wealth of information at large spatial scales##UREF##2##6##. Still, working with CMIP6 model outputs can be technically challenging##REF##37179171##7##. For example, the grid structure of CMIP6 models can be complex with non-uniform representation of longitude and latitude grid points, which are used to avoid singularities at the poles and to enhance resolution closer to the equator. In addition, a single model variable may be split into hundreds of files that need to be concatenated in time. Modelled variables may also be biased compared to observations. Working with these global datasets presents several logistical challenges. They require large storage space and cannot be sub-sampled prior to downloading from the original data archives, although both Google Cloud and Amazon Web Services now provide a subset of the model data improving their accessibility. CMIP6 associated terminology can also be impenetrable to non-experts##REF##37179171##7##. As a result, the outputs of CMIP6 across a range of spatial and temporal scales often prohibit the use by non-experts, limiting their use for further ecosystem impact analysis.
\",\n \"The coarse resolution of the CMIP6 models (most models represent the ocean at roughly 1 × 1 resolution in longitude and latitude grid) does not resolve mesoscale features (e.g., eddies) essential to understanding dynamical processes in coastal regions. Higher resolution climate projections for coastal and shelf areas are increasingly needed as inputs to adaptation and mitigation planning as well as management of marine resources##UREF##3##8##. Downscaling global climate projections to a higher resolution can preserve the large-scale climate signal while capturing local variability and dynamics##UREF##4##9##. While several dynamical downscaling products exist for regional ocean domains##UREF##5##10##,##UREF##6##11##, these products lack the conceptual and standardized approach of the CMIP experiments, or the Coordinated REgional Downscaling Experiment (CORDEX) program for regional atmospheric models. Dynamically downscaled products often focus on a single or limited set of climate models and scenarios##UREF##7##12##,##UREF##8##13##. The lack of consistent ensembles with diversity in models and scenarios strongly limits the comparability of results across different systems and does not adequately quantify the uncertainty in the physical and biogeochemical projections##UREF##3##8##, which is critical for risk assessment, ecosystem-based management, and informing mitigation and adaptation policies.
\",\n \"The EU-funded research project FutureMARES aims to provide socially and economically viable actions and strategies that support Nature-based Solutions (NBS) for climate change adaptation and mitigation across Europe. To meet these ambitious goals, the datasets developed here were explicitly designed to deliver consistent climate-driven projections of change in physical and biogeochemical factors at the spatial scales relevant for planning and management across European regional seas. To do this, we bias-corrected and statistically downscaled individual CMIP6 models to provide an ensemble of high-resolution climate projections for different IPCC scenarios. Here, we present the methodology, evaluation, and uncertainty analysis of the downscaled dataset products made publicly available through Zenodo (zenodo.org). The ensemble high-resolution climate dataset provides projections for 1993–2100 (monthly) at roughly 8 km (1/12th degree) resolution for four European regions: the North Sea, the Baltic Sea, the Bay of Biscay, and the Mediterranean Sea (Fig. ##FIG##0##1##).
\"\n]"},"methods":{"kind":"list like","value":["The methodology applied to statistically bias-correct and downscale large-scale CMIP6 climate projections to the regional level was conducted in four steps: (1) preparing the input data, (2) bias-correcting with respect to observations, (3) statistically downscaling the bias-corrected fields to higher resolution, and (4) creating ensemble statistics of the downscaled models.
","The raw data from global climate models (GCM) and Earth System Models (ESMs) can be represented on various global ocean grids. Some of these grids have higher resolution in one part of the world, e.g., around the equator, while others can have three poles to avoid the singularity in the ocean at 90° N. To be able to work consistently with these model outputs, we interpolated the data to a uniform cartesian grid of 0.5° × 0.5° longitude-latitude. We employed the Earth System Modelling Framework (ESMF)##UREF##9##14## to allow fast interpolation within a tested framework. We also use the Python xesmf interface##UREF##10##15## to the ESMF package, which further simplified the conversion from the native to a uniform grid. We used the xMIP package for pre-processing the CMIP6 data##UREF##11##16##. Once the GCM/ESM data were converted to a standard grid, we performed a bias-corrected statistical downscaling of the data, a two-step process: (1) bias-correction and (2) statistical downscaling.
","The GCMs and ESMs within CMIP6 were designed to represent the probability distributions, variability, and observed trends in physical and biological variables and not to exactly replicate individual features in time and space as reanalysis systems would do for the past or forecasting systems for the near future. For this reason, GCMs and ESMs are inherently biased from historical observations. To correct the offset in the global models, we performed a bias-correction where the large-scale climate signal was constrained to observed values of the range and variability using detrended quantile mapping (DQM) transformations##UREF##12##17##,##UREF##13##18##. The DQM is designed to remove biases across all quantiles, effectively aligning the data distribution of the model data relative to the observed values##UREF##13##18##. Here, we use the ocean reanalysis GLORYS12V1##UREF##5##10## as observations to quantify the biases present in the GCM/ESMs. The DQM method was trained with the historical (1993–2020) GLORYS12V1 reanalysis and applied to the historical GCM/ESM data to calculate the transform function which was used to adjust the detrended quantiles for the future projections (2020–2100). Once the timeseries had been adjusted using the DQM methodology, the trend was added back to the timeseries##UREF##4##9##. This approach ensures that the trend from the GCMs and ESMs is preserved in the downscaled product. The bias-correction was performed at the resolution of the interpolated GCM or ESM, which is 0.5° × 0.5° latitude-longitude. The global ocean physics of the GLORYS12V1 reanalysis at 1/12th degrees resolution have been thoroughly validated against observations##UREF##5##10##,##UREF##14##19##. The GLORYS12V1 reanalysis assimilates available historical data (e.g., satellite, CTD, XBT, buoys) for 1993-01-01 to 2019-12-31 and represents state-of-the-art hydrodynamic modeling. GLORYS12V1##UREF##14##19## is developed by Mercator Ocean and is an operational service from the Copernicus Marine Service Center (marine.copernicus.eu).
","Historical biogeochemical data such as oxygen, chlorophyll, and pH were obtained from the biological model Global Ocean Biogeochemistry hindcast (GOBH) from Mercator Ocean distributed via the Copernicus Marine Service. The GOBH model##UREF##15##20## uses the PISCES model to represent biogeochemistry and physics from the FREEGLORYS2V4 model, which is a non-assimilative version of the GLORYS2V4 reanalysis model. The GOBH and FREEGLORYS2V4 models were run at a resolution of 1/4th degree. To align the downscaled physical and biogeochemical results directly, we interpolated (bilinear) and extrapolated these model data onto the physical model grid, allowing the final biological downscaled data to be at 1/12th degree resolution. As the bathymetry along the coastline of the biological model is coarser than the physical model, we extrapolated to the destination point by using the weighted average of the eight nearest source points. The weight is the reciprocal of the distance of the source point from the destination point raised to a power 2 (the inverse weighted distance method)##UREF##9##14##. The documentation for GOBH states that the model holds a global bias in pH of 0.02, making it slightly more acidic compared to observations. The model is able to reproduce observed surface and sub-surface oxygen concentrations including the oxygen minimum zone##UREF##15##20##. However, being a hindcast, as all dynamic ocean models, this dataset contains some biases with respect to the real world which will be inherited by the downscaled products.
","The statistical downscaling (SD) allowed us to establish an empirical relationship between high-resolution historical and large-scale climate indicators and apply these statistics to produce local climate projections. The bias-corrected fields at 0.5° × 0.5° longitude-latitude resolution were used as input to the DQM statistical downscaling algorithm together with the high-resolution GLORYS12V1 reanalysis to provide ESM sub-grid variability. This involved (1) determine a scaling factor that allow the mean of the historical bias-corrected CMIP6 projection to be equal to the mean of the historical GLORYS12V1/GOBH timeseries, (2) remove the trend from both timeseries (3) calculate the adjustment factors between the quantiles of the two timeseries, (4) apply the scaling factor to the future projections, (5) match the quantiles of the detrended projections and apply the adjustment factor, and (6) add back the trend to the projections##UREF##13##18##. The GLORYS12V1 and GOBH models have 50 and 75 vertical depth levels, respectively, which were linearly interpolated if the bias correction and downscaling were performed at an intermediate depth level. Linear interpolation was also performed on the global climate model outputs as downscaling was done at individual, fixed depth levels (e.g., 5 m, 25 m). The exception was the bottom depth, where each grid point had a unique depth level, and the ESMs were interpolated to the GLORYS bathymetry.
","This downscaling was performed for a range of CMIP6 models (3 to 7) per variable per climate scenario (see Table ##TAB##0##1## for an overview), and the final product to be used by researchers was the ensemble of these individual downscaled models. The ensemble provides datasets for five indicators of marine habitat conditions (temperature (°C), salinity, pH, dissolved oxygen (ml/l), chlorophyll (kg/m##REF##24706804##3##)). These data were provided at three distinct depth levels (surface (5 m), sub-surface (25 m), and seafloor) for 1993–2100 under three different future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Within the datasets, the ensemble mean is provided along with standard deviations and 2.5, 50, and 97.5 percentiles, depicting the spread of the ensemble at each point in space and time. Although some of the CMIP6 models were downscaled for multiple model realizations (Table ##TAB##0##1##), each downscaled realization held equal weight when calculating the ensemble product which may favour these models results compared to single variant models, a weakness that can potentially be addressed by ensemble weighting based on model independence and performance##UREF##16##21##. This approach will be attempted in a future iteration of this dataset. The downscaled results were stored as compressed NetCDF4 files containing self-describing metadata of the downscaled variable.
","Global climate models are complex tools that allow researchers to explore how combinations of stressors interact and affect the Earths’ climate system. These models use global greenhouse gas concentrations emerging for the radiative forcing targets of the Representative Concentration Pathways—RCPs, under different shared socioeconomic pathways (SSPs) up to 2100 according to the ScenarioMIP protocol##UREF##17##22##. For the sixth Intergovernmental Panel on Climate Change (IPCC) report, five narratives provided alternative socio-economic developments for the world, including sustainable development (SSP1), regional rivalry (SSP3), regional inequality (SSP4), fossil-fuelled development (SSP5), and middle-of-the-road development (SSP2). While, in principle, the two development streams of climate and socio-economic scenarios are independent, some combinations are more likely than others. This dataset focuses on the combinations SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5 which are part of the ScenarioMIP Tier 1 simulations and available across various ESMs. The selection of CMIP6 models and variants was made based on each model's overall performance and skill##REF##35835803##23##, and model availability across variables and scenarios.
","Several techniques and datasets were applied to validate the ensemble downscaled climate projections. This involved comparing the ensemble results with comprehensive observational datasets, focusing on temperature, and oxygen as key variables. First, we compared the ensemble and its members against the spatially continuous World Ocean Atlas (WOA) climatology for surface temperature (WOA23, 1/4° degree resolution) and dissolved oxygen (WOA18, 1° degree resolution) to obtain a spatially continuous gapless comparison##UREF##18##24##. The World Ocean Atlas is a collection of objectively analysed profile data (e.g., temperature, oxygen) from the World Ocean Database. The performance of the downscaled product with respect to the original GCMs and ESMs was assessed using several standard metrics. Each metric was computed for the spatial fields of the seasonal climatology of surface temperature and surface dissolved oxygen. The seasonal climatologies were compared against the WOA climatology and the metrics averaged over seasons. These datasets come at a resolution that is comparable to the original ESM data and allows us to compare the SD with the raw ESM outputs and identify the improvements of the downscaled product beyond the added value of increased resolution.
","The metrics calculated and compared were: the ratio of the model mean over the mean of observations (α) which assessed the overall bias of the model fields for each season; the ratio of the model standard deviation over the standard deviation of observations (β) which assessed the overall spread of the model fields for each season; the Pearson correlation coefficient (
These three metrics were also combined into a summary metric providing a single number for model skill. Several approaches have been previously used to report model skill##UREF##19##25##–##UREF##21##27##. We chose the Liu-mean efficiency skill score (LSE), which provides a more balanced representation of the individual components that have been shown to yield superior results to the previous methods when used in model optimizations##UREF##21##27##. This skill score combines the individual metrics according to:
","A Liu skill score of 1 represents a perfect comparison with observations, while values below 1 indicate a diminishing level of comparison between ensemble and observations. Specifically, the first component of the skill score combining the correlation coefficient and the ratio of the standard deviations evaluates the distance of the linear regression slope between the ensemble dataset and the reference observations to 1. The second component is the non-dimensional measure of the overall bias of the two datasets. To illustrate the numerical values of the skill score, here are a few examples: a completely uncorrelated relationship of the two datasets would yield 0 for the first component of the skill score leading to negative values of the skill score for any further deficiency in the second component. Similarly, if the standard deviations or the mean of the ensemble would reach twice that of the observations, the skill would become 0 or smaller even for perfect correlation between model and observations.
","In addition, to best validate surface and bottom values of temperature, and oxygen, we compared the ensemble data with in-situ observations obtained from a variety of platforms (e.g., buoys, profiles, and shipboard CTD, pump data, mooring data,) available from the ICES online database (
The uncertainties in the downscaled ensemble product were assessed by evaluating the three principal categories of uncertainty in these types of simulations##UREF##24##30##. Scenario uncertainty is the uncertainty related to the different greenhouse gas concentrations and shared socioeconomic pathways affecting the global climate; Model uncertainty, the uncertainty related to the different structures, and parametrization of the GCMs and ESMs; Internal variability, the uncertainty related to the natural variability of the climate system in the absence of external forcing caused by intrinsic processes of the ocean, atmosphere, and land.
These three components have different relative importance at different lead times of a climate projection, with the latter two generally dominating at shorter time scales. The scenario uncertainty tends to become increasingly important as the projection evolves with time due to the increasing spread in greenhouse gas forcing among the scenario pathways##UREF##25##31##.
","Our uncertainty assessment illustrated the spatial distribution of changes in three key ecosystem indicators induced by anthropogenic greenhouse gas emissions, relating them to each source of uncertainty via their ratio (change/uncertainty). Changes are considered significant where the ratio exceeds 1.
","In our assessment, future changes were computed from the ensemble mean for the middle of the road Scenario (SSP2-4.5) for the mid- and long-term IPCC assessment periods (2041–2060 and 2081–2100, respectively) by subtracting the mean conditions of the present-day time slice (1995–2014) from the mean conditions of the future time slice.
","The uncertainty fields used to compute the significance ratios for each source of uncertainty were computed as follows: Scenario uncertainty: changes were computed for each scenario as for the baseline scenario SSP2-4.5 described above. The uncertainty was then determined as the min–max range of all scenarios in each spatial pixel. Model uncertainty: changes were computed for each individual model realization of the baseline scenario SSP2-4.5 as for the ensemble mean described above. The uncertainty was then determined as the min–max range of all model realizations in each spatial pixel. Internal variability was estimated by the difference between the maximum and minimum value of the annual mean time series of the ensemble mean of the baseline scenario projection with long-term trends removed, which was achieved by applying a running average filter with a 21-year window over the original time series.
The results from the uncertainty assessment were provided as spatial maps of the change in each variable and its magnitude relative to each source of uncertainty. This spatial representation illustrates the differences in importance of the three sources of uncertainty at different locations.
"],"string":"[\n \"The methodology applied to statistically bias-correct and downscale large-scale CMIP6 climate projections to the regional level was conducted in four steps: (1) preparing the input data, (2) bias-correcting with respect to observations, (3) statistically downscaling the bias-corrected fields to higher resolution, and (4) creating ensemble statistics of the downscaled models.
\",\n \"The raw data from global climate models (GCM) and Earth System Models (ESMs) can be represented on various global ocean grids. Some of these grids have higher resolution in one part of the world, e.g., around the equator, while others can have three poles to avoid the singularity in the ocean at 90° N. To be able to work consistently with these model outputs, we interpolated the data to a uniform cartesian grid of 0.5° × 0.5° longitude-latitude. We employed the Earth System Modelling Framework (ESMF)##UREF##9##14## to allow fast interpolation within a tested framework. We also use the Python xesmf interface##UREF##10##15## to the ESMF package, which further simplified the conversion from the native to a uniform grid. We used the xMIP package for pre-processing the CMIP6 data##UREF##11##16##. Once the GCM/ESM data were converted to a standard grid, we performed a bias-corrected statistical downscaling of the data, a two-step process: (1) bias-correction and (2) statistical downscaling.
\",\n \"The GCMs and ESMs within CMIP6 were designed to represent the probability distributions, variability, and observed trends in physical and biological variables and not to exactly replicate individual features in time and space as reanalysis systems would do for the past or forecasting systems for the near future. For this reason, GCMs and ESMs are inherently biased from historical observations. To correct the offset in the global models, we performed a bias-correction where the large-scale climate signal was constrained to observed values of the range and variability using detrended quantile mapping (DQM) transformations##UREF##12##17##,##UREF##13##18##. The DQM is designed to remove biases across all quantiles, effectively aligning the data distribution of the model data relative to the observed values##UREF##13##18##. Here, we use the ocean reanalysis GLORYS12V1##UREF##5##10## as observations to quantify the biases present in the GCM/ESMs. The DQM method was trained with the historical (1993–2020) GLORYS12V1 reanalysis and applied to the historical GCM/ESM data to calculate the transform function which was used to adjust the detrended quantiles for the future projections (2020–2100). Once the timeseries had been adjusted using the DQM methodology, the trend was added back to the timeseries##UREF##4##9##. This approach ensures that the trend from the GCMs and ESMs is preserved in the downscaled product. The bias-correction was performed at the resolution of the interpolated GCM or ESM, which is 0.5° × 0.5° latitude-longitude. The global ocean physics of the GLORYS12V1 reanalysis at 1/12th degrees resolution have been thoroughly validated against observations##UREF##5##10##,##UREF##14##19##. The GLORYS12V1 reanalysis assimilates available historical data (e.g., satellite, CTD, XBT, buoys) for 1993-01-01 to 2019-12-31 and represents state-of-the-art hydrodynamic modeling. GLORYS12V1##UREF##14##19## is developed by Mercator Ocean and is an operational service from the Copernicus Marine Service Center (marine.copernicus.eu).
\",\n \"Historical biogeochemical data such as oxygen, chlorophyll, and pH were obtained from the biological model Global Ocean Biogeochemistry hindcast (GOBH) from Mercator Ocean distributed via the Copernicus Marine Service. The GOBH model##UREF##15##20## uses the PISCES model to represent biogeochemistry and physics from the FREEGLORYS2V4 model, which is a non-assimilative version of the GLORYS2V4 reanalysis model. The GOBH and FREEGLORYS2V4 models were run at a resolution of 1/4th degree. To align the downscaled physical and biogeochemical results directly, we interpolated (bilinear) and extrapolated these model data onto the physical model grid, allowing the final biological downscaled data to be at 1/12th degree resolution. As the bathymetry along the coastline of the biological model is coarser than the physical model, we extrapolated to the destination point by using the weighted average of the eight nearest source points. The weight is the reciprocal of the distance of the source point from the destination point raised to a power 2 (the inverse weighted distance method)##UREF##9##14##. The documentation for GOBH states that the model holds a global bias in pH of 0.02, making it slightly more acidic compared to observations. The model is able to reproduce observed surface and sub-surface oxygen concentrations including the oxygen minimum zone##UREF##15##20##. However, being a hindcast, as all dynamic ocean models, this dataset contains some biases with respect to the real world which will be inherited by the downscaled products.
\",\n \"The statistical downscaling (SD) allowed us to establish an empirical relationship between high-resolution historical and large-scale climate indicators and apply these statistics to produce local climate projections. The bias-corrected fields at 0.5° × 0.5° longitude-latitude resolution were used as input to the DQM statistical downscaling algorithm together with the high-resolution GLORYS12V1 reanalysis to provide ESM sub-grid variability. This involved (1) determine a scaling factor that allow the mean of the historical bias-corrected CMIP6 projection to be equal to the mean of the historical GLORYS12V1/GOBH timeseries, (2) remove the trend from both timeseries (3) calculate the adjustment factors between the quantiles of the two timeseries, (4) apply the scaling factor to the future projections, (5) match the quantiles of the detrended projections and apply the adjustment factor, and (6) add back the trend to the projections##UREF##13##18##. The GLORYS12V1 and GOBH models have 50 and 75 vertical depth levels, respectively, which were linearly interpolated if the bias correction and downscaling were performed at an intermediate depth level. Linear interpolation was also performed on the global climate model outputs as downscaling was done at individual, fixed depth levels (e.g., 5 m, 25 m). The exception was the bottom depth, where each grid point had a unique depth level, and the ESMs were interpolated to the GLORYS bathymetry.
\",\n \"This downscaling was performed for a range of CMIP6 models (3 to 7) per variable per climate scenario (see Table ##TAB##0##1## for an overview), and the final product to be used by researchers was the ensemble of these individual downscaled models. The ensemble provides datasets for five indicators of marine habitat conditions (temperature (°C), salinity, pH, dissolved oxygen (ml/l), chlorophyll (kg/m##REF##24706804##3##)). These data were provided at three distinct depth levels (surface (5 m), sub-surface (25 m), and seafloor) for 1993–2100 under three different future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Within the datasets, the ensemble mean is provided along with standard deviations and 2.5, 50, and 97.5 percentiles, depicting the spread of the ensemble at each point in space and time. Although some of the CMIP6 models were downscaled for multiple model realizations (Table ##TAB##0##1##), each downscaled realization held equal weight when calculating the ensemble product which may favour these models results compared to single variant models, a weakness that can potentially be addressed by ensemble weighting based on model independence and performance##UREF##16##21##. This approach will be attempted in a future iteration of this dataset. The downscaled results were stored as compressed NetCDF4 files containing self-describing metadata of the downscaled variable.
\",\n \"Global climate models are complex tools that allow researchers to explore how combinations of stressors interact and affect the Earths’ climate system. These models use global greenhouse gas concentrations emerging for the radiative forcing targets of the Representative Concentration Pathways—RCPs, under different shared socioeconomic pathways (SSPs) up to 2100 according to the ScenarioMIP protocol##UREF##17##22##. For the sixth Intergovernmental Panel on Climate Change (IPCC) report, five narratives provided alternative socio-economic developments for the world, including sustainable development (SSP1), regional rivalry (SSP3), regional inequality (SSP4), fossil-fuelled development (SSP5), and middle-of-the-road development (SSP2). While, in principle, the two development streams of climate and socio-economic scenarios are independent, some combinations are more likely than others. This dataset focuses on the combinations SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5 which are part of the ScenarioMIP Tier 1 simulations and available across various ESMs. The selection of CMIP6 models and variants was made based on each model's overall performance and skill##REF##35835803##23##, and model availability across variables and scenarios.
\",\n \"Several techniques and datasets were applied to validate the ensemble downscaled climate projections. This involved comparing the ensemble results with comprehensive observational datasets, focusing on temperature, and oxygen as key variables. First, we compared the ensemble and its members against the spatially continuous World Ocean Atlas (WOA) climatology for surface temperature (WOA23, 1/4° degree resolution) and dissolved oxygen (WOA18, 1° degree resolution) to obtain a spatially continuous gapless comparison##UREF##18##24##. The World Ocean Atlas is a collection of objectively analysed profile data (e.g., temperature, oxygen) from the World Ocean Database. The performance of the downscaled product with respect to the original GCMs and ESMs was assessed using several standard metrics. Each metric was computed for the spatial fields of the seasonal climatology of surface temperature and surface dissolved oxygen. The seasonal climatologies were compared against the WOA climatology and the metrics averaged over seasons. These datasets come at a resolution that is comparable to the original ESM data and allows us to compare the SD with the raw ESM outputs and identify the improvements of the downscaled product beyond the added value of increased resolution.
\",\n \"The metrics calculated and compared were: the ratio of the model mean over the mean of observations (α) which assessed the overall bias of the model fields for each season; the ratio of the model standard deviation over the standard deviation of observations (β) which assessed the overall spread of the model fields for each season; the Pearson correlation coefficient (
These three metrics were also combined into a summary metric providing a single number for model skill. Several approaches have been previously used to report model skill##UREF##19##25##–##UREF##21##27##. We chose the Liu-mean efficiency skill score (LSE), which provides a more balanced representation of the individual components that have been shown to yield superior results to the previous methods when used in model optimizations##UREF##21##27##. This skill score combines the individual metrics according to:
\",\n \"A Liu skill score of 1 represents a perfect comparison with observations, while values below 1 indicate a diminishing level of comparison between ensemble and observations. Specifically, the first component of the skill score combining the correlation coefficient and the ratio of the standard deviations evaluates the distance of the linear regression slope between the ensemble dataset and the reference observations to 1. The second component is the non-dimensional measure of the overall bias of the two datasets. To illustrate the numerical values of the skill score, here are a few examples: a completely uncorrelated relationship of the two datasets would yield 0 for the first component of the skill score leading to negative values of the skill score for any further deficiency in the second component. Similarly, if the standard deviations or the mean of the ensemble would reach twice that of the observations, the skill would become 0 or smaller even for perfect correlation between model and observations.
\",\n \"In addition, to best validate surface and bottom values of temperature, and oxygen, we compared the ensemble data with in-situ observations obtained from a variety of platforms (e.g., buoys, profiles, and shipboard CTD, pump data, mooring data,) available from the ICES online database (
The uncertainties in the downscaled ensemble product were assessed by evaluating the three principal categories of uncertainty in these types of simulations##UREF##24##30##. Scenario uncertainty is the uncertainty related to the different greenhouse gas concentrations and shared socioeconomic pathways affecting the global climate; Model uncertainty, the uncertainty related to the different structures, and parametrization of the GCMs and ESMs; Internal variability, the uncertainty related to the natural variability of the climate system in the absence of external forcing caused by intrinsic processes of the ocean, atmosphere, and land.
These three components have different relative importance at different lead times of a climate projection, with the latter two generally dominating at shorter time scales. The scenario uncertainty tends to become increasingly important as the projection evolves with time due to the increasing spread in greenhouse gas forcing among the scenario pathways##UREF##25##31##.
\",\n \"Our uncertainty assessment illustrated the spatial distribution of changes in three key ecosystem indicators induced by anthropogenic greenhouse gas emissions, relating them to each source of uncertainty via their ratio (change/uncertainty). Changes are considered significant where the ratio exceeds 1.
\",\n \"In our assessment, future changes were computed from the ensemble mean for the middle of the road Scenario (SSP2-4.5) for the mid- and long-term IPCC assessment periods (2041–2060 and 2081–2100, respectively) by subtracting the mean conditions of the present-day time slice (1995–2014) from the mean conditions of the future time slice.
\",\n \"The uncertainty fields used to compute the significance ratios for each source of uncertainty were computed as follows: Scenario uncertainty: changes were computed for each scenario as for the baseline scenario SSP2-4.5 described above. The uncertainty was then determined as the min–max range of all scenarios in each spatial pixel. Model uncertainty: changes were computed for each individual model realization of the baseline scenario SSP2-4.5 as for the ensemble mean described above. The uncertainty was then determined as the min–max range of all model realizations in each spatial pixel. Internal variability was estimated by the difference between the maximum and minimum value of the annual mean time series of the ensemble mean of the baseline scenario projection with long-term trends removed, which was achieved by applying a running average filter with a 21-year window over the original time series.
The results from the uncertainty assessment were provided as spatial maps of the change in each variable and its magnitude relative to each source of uncertainty. This spatial representation illustrates the differences in importance of the three sources of uncertainty at different locations.
\"\n]"},"results":{"kind":"list like","value":["The statistical downscaling improved model skill for oxygen and temperature when comparing SD products and the original ESMs (Tables ##TAB##1##2##, ##TAB##2##3##; see Tables ##SUPPL##0##S1##–##SUPPL##0##S10## for more details) across all regions. Temperature shows a higher skill than oxygen, with a difference of around 0.2–0.4 points between the ESM Liu-mean efficiency skill score and the SD. It is worth noting that the SD significantly reduced model differences in performance, while for the original ESMs, the inter-model differences can be substantial.
","The decomposition of the performance analysis into its components (Tables ##SUPPL##0##S1##–##SUPPL##0##S10##) suggests that, for the SD products, reduced model skill was attributable to the spatial correlation coefficient (Table ##SUPPL##0##S1##–##SUPPL##0##S2##), while the ratio of means and ratio of standard deviations showed close to perfect matches for these products (Table ##SUPPL##0##S3##–##SUPPL##0##S5##). For the original GCM and ESM simulations, the ratio of means was also generally very close to 1 (Tables ##SUPPL##0##S9##, ##SUPPL##0##S10##). Still, both the Pearson correlation and the ratio of standard deviation indicate substantial shortcomings in skill (Table ##SUPPL##0##S6##, ##SUPPL##0##S9##). For the SD products, the main driver for lack of skill was a weak correlation which is significantly stronger than the mismatches of standard deviations.
","The relatively coarse resolution of the WOA data, particularly oxygen, is a challenge for representing enclosed regions such as the Baltic Sea. For that reason, the ensemble downscaled data for bottom temperature (Fig. ##FIG##1##2##) and oxygen (Fig. ##FIG##2##3##) were compared with observations from the ICES database for the Baltic Sea, the Bay of Biscay and for the North Sea, while data for the Mediterranean were compared against the GLODAP database. In total, we extracted a large number of observations totaling 39,797 data points for the Baltic Sea, 126,401 for the North Sea, 9,385 for the Bay of Biscay, and 6,306 for the Mediterranean (Fig. ##SUPPL##0##S1##). The depth and range distributions of bottom temperature (Fig. ##FIG##1##2##) are comparable to the observations for all regions. For the Mediterranean, the bifurcation between the Eastern and Western Mediterranean basins can be identified in both the temperature and oxygen data. When compared with observations, the downscaled temperature data realistically captured the value range as a function of depth (Fig. ##FIG##1##2##) for all regions. The oxygen data in the ensemble exhibited a narrower range across all depths compared to the observed values in the Baltic and North Sea (Fig. ##FIG##2##3##). Conversely, in the Bay of Biscay, the available oxygen observations were limited and dispersed over a wider area, making direct comparison with the ensemble data more challenging. In the Baltic Sea, the ensemble data show higher oxygen concentrations than the observations, especially in the waters shallower than 100 m (Fig. ##FIG##2##3##). This difference is also seen in the upper 100 m in the North Sea where values below 1.4 ml/L is typically classified as hypoxic conditions can be found in the observations (Fig. ##FIG##2##3##). The bottom pH from the ensemble products were compared with the ICES and GLODAP databases but very few observations matched our filtering criteria, although the ones that did compared reasonably well (Fig. ##SUPPL##0##S2##).
","In this section, we illustrate the changes induced by anthropogenic greenhouse gas emissions for the three variables previously evaluated, sea surface temperature representing warming, surface pH representing acidification, and bottom dissolved oxygen representing deoxygenation. The significance of the induced changes was further analysed by comparing them to three separate sources of uncertainty in climate projections: (1) internal variability, (2) model uncertainty, and (3) scenario uncertainty. In the following subsections, we present the results of this analysis by region.
","Figure ##FIG##3##4## shows the Mediterranean Sea basin average time series of the three ecosystem indicators from 1993 up to the end of this century for the three scenarios represented by the SD ensemble product. The patterns are qualitatively comparable to those observed for the global mean trajectories##UREF##26##32##,##UREF##27##33##. For the no-mitigation scenario SSP5-8.5, the bulk surface temperature of the Mediterranean Sea gradually increases to 5 °C higher than the present day. For the middle of the road scenario SSP2-4.5, the increase in temperature from present-day is lower, reaching approximately 2 °C. For the strongly mitigated scenario SSP1-2.6, the temperature initially increases and then stabilizes towards the middle of the century at around 1.5 °C of warming. The model spread is moderately high (2.5 to 3.0 °C), so the differences between the two scenarios producing weaker warming partially overlap. In contrast, for the high emissions scenario (SSP5-8.5), there is stronger warming and the difference in temperature emerges from the model uncertainty. Interannual variability is low compared to long-term changes. Regarding ocean acidification, in SSP5-8.5 a strong, gradual decrease in ocean pH occurs to about 0.4 units from present-day conditions, while the decline in SSP2-4.5 is less marked, and pH stabilizes by the end of the century in SSP2-4.5, after a 0.15 unit decrease. The SSP1-2.6 scenario suggests a slightly reversing trend, limiting the overall decrease in pH to less than 0.1 units. Uncertainty for this pressure is inherently low, and differences in the changes among the scenarios are clear. For bottom oxygen, uncertainty is highest relative to the changes observed among the three scenarios. For all three scenarios, oxygen decreases by approximately 0.5, 0.2, and 0.1 ml/l for SSP5-8.5, SSP2-4.5, and SSP1-2.6, respectively.
","To give a clearer picture of the relative importance of the different sources of uncertainty and their role in different locations, Figs. ##FIG##4##5## and ##FIG##5##6## show maps of the changes of the three indicators for the ensemble average of scenario SSP2-4.5 as absolute values and relative to the uncertainties. The increase in surface temperature is strongest in the Adriatic and Aegean Seas, with higher changes in the Eastern compared to the Western Basin of the Mediterranean Sea. Warming almost doubles from mid to the end of the century with no major difference in the spatial distribution of change. Mid-century interannual variability and model uncertainty are of the order of the changes across the basin, while the differences between the scenarios are significantly lower than the changes induced. This situation reverses for long-term changes, which become more significant with respect to interannual variability. At the same time, the difference between the scenarios has become larger in relative terms and is comparable to the magnitude of the change.
","Acidification in the Mediterranean Sea is strongest in the Northern Adriatic, with a generally slightly higher decrease in pH in the Northern parts compared to the Southern coast of the basin. As can be inferred from the basin mean time series, changes are strongly significant for both time slices with respect to interannual variability and model uncertainty. At the same time, the difference between the mitigation pathways is on the order of the changes at mid-century and is much larger towards the end of the century.
","For bottom dissolved oxygen, the situation is much less clear. While, on average, a decrease in seafloor oxygen is visible from the time series, some areas show an increase in oxygen for the ensemble mean (most evident in the Aegean Sea); interannual variability and, particularly, model uncertainty is high in these areas. Areas of oxygen decrease, on the contrary, emerge from interannual variability with changes slightly higher than the model uncertainty, although with some regional exceptions. Similarly, changes in scenario play a much more important role in areas of oxygen increase compared to areas of decrease. In the latter areas, differences among the scenarios were relatively small compared to the magnitude of the induced decrease in oxygen.
","The basin-scale mean evolution of greenhouse gas-induced changes in physical and biogeochemical pressures of the wider North Sea area (Fig. ##FIG##6##7##) is comparable to the Mediterranean Sea. Warming is, however, less accentuated in the North Sea compared to the Mediterranean Sea, with only a 3.0 °C increase projected at the end of the century for the no-mitigation scenario SSP5-8.5 and < 1.0 °C for the moderate and strong mitigation scenarios. Acidification ranges from slightly less than 0.1 units (SSP1-2.6) to around 0.5 units of decrease in surface pH (SPP5-8.5), while seafloor oxygen decrease by ~ 0.1–0.3 ml/l) with interannual variability up to ~ 0.2 ml/l).
","Considering the spatial distribution of changes and uncertainties (Figs. ##FIG##7##8##, ##FIG##8##9##) warming is strongest towards the Eastern parts of the European shelf and comparatively weak towards the open Atlantic Ocean. On most of the continental shelf, however, these trends are comparatively weak with respect to interannual variability and model uncertainty (ratio is only slightly > 1). The differences between the scenario pathways are only of minor importance at mid-century (approximately 1/3 of the change signal across the basin) but eventually reach about the same order of magnitude as those induced by long-term changes. Seafloor oxygen in the ensemble average changes noticeably only in the open ocean areas along the shelf break, where dissolved oxygen declines by up to 1 ml/l. These changes begin to emerge at mid-century but only become significant towards the end of the century. The difference in changes between greenhouse gas scenarios is minor, even towards the end of the century.
","In the area around the Bay of Biscay, the domain averages roughly followed the patterns observed in the two previous regions with strong continuous warming up to 3.0 °C by 2100 and acidification of 0.4 pH units for SSP5-8.5 (Fig. ##FIG##9##10##). These changes are attenuated in the other two (moderate to strong mitigation) scenarios. For example, pH does not decrease but increases somewhat in the second half of the century for SSP1-2.6. Acidification trends were strongly significant, while the warming trends emerged less clearly due to considerable model uncertainty (~ 1.5° to 3.5°), particularly for the two pathways producing weaker changes. Deoxygenation shows considerable model (~ 0.4–0.5 ml/l) and interannual (up to 0.2 ml/l) variability. The trend in deoxygenation are minimal or absent for the strongest (SSP5-8.5) scenario through 2040, followed by a few years of strong interannual variability and a rapid decline that continues through the end of this century. There is a weaker, more continuous deoxygenation in the other two scenarios, similar to patterns in the North and Mediterranean Seas. The unexpected observed climate variability across scenarios could be indicative of infrequent and quasi-stochastic regime shifts which are an expected component of natural variability##UREF##28##34##. Still, filtering out the inter-annual variability suggests that the trend is consistent across the scenarios.
","The spatial distribution of these average patterns in the domain (Figs. ##FIG##10##11##, ##FIG##11##12##) indicated the Northern coast of the Iberian Peninsula and the northern coast of Brittany as hotspots of surface warming, with the former particularly strong in the mid-term (up to 1°) and the latter particularly strong in the long-term (almost 2°). Relative to interannual variability and model uncertainty, however, this warming is only slightly emergent in the mid-term, while both interannual and model uncertainties are high in the deeper Atlantic waters. In the long-term, changes become significant with respect to interannual variability, while model uncertainty remains persistent through the end of the century. Consistent with patterns in the other regional seas, differences in scenario pathways in warming are small in the mid-term, while in the long-term, the different mitigation strategies will lead to differences in warming of the same order of magnitude as the change itself. Oxygen is expected to increase on the Celtic and Armorican shelfs although the three sources of uncertainty remain high for both mid and long term. While in the deeper Atlantic waters and the coastal waters of northern Spain and the western coast of Portugal oxygen will decrease, and changes are significant with respect to interannual variability. For model and scenario uncertainty changes in oxygen are pre-dominantly significant with exceptions such as the inner coastal domain of Portugal which is dominated by upwelling and more complex oceanographic processes. Acidification trends are comparatively homogeneous across the domain, with somewhat stronger trends in the off-shelf areas of the North-Eastern Atlantic. This pattern is consistent between the two time slices; however, acidification is about 50% higher at the end of the century compared to the mid-century. The trends are strongly significant with respect to model uncertainty and interannual variability for both time slices. The difference between scenarios is of the same order of magnitude as the induced changes at mid-century, while at the end of the century, the mitigation pathways become increasingly important as the difference between scenarios reaches twice the magnitude of the change signal.
","While the basin mean warming, acidification, and deoxygenation trends are also present in the Baltic Sea, their behaviour and relation to uncertainty are substantially different in this coastal, semi-enclosed basin compared to the other regions. A fundamental difference is the large model uncertainty (up to 0.8 pH units) and increased interannual variability (up to 0.05 units) of surface pH with respect to the induced changes (0.1–0.5 pH units) (Fig. ##FIG##12##13##). In addition, the deoxygenation change (~ 0.2 ml/l) here is significantly weaker, and interannual variability is significantly higher (up to 0.5 ml/l). Warming trends (2–5 °C), by contrast, are comparable to the other basins.
","Looking at regional differences in these trends (Figs. ##FIG##13##14##, ##FIG##14##15##), warming in the ensemble average of the Baltic Sea for scenario SSP2-4.5 at mid-century is strongest in the Bothnian Sea and the Gulf of Riga and weakest at the margins of the Bothnian Bay and the Southern Baltic Proper. This pattern also persists at the end of the century with the two warming hotspots spreading into the Northern Baltic Proper. Compared to interannual variability and model uncertainty, the trends only weakly emerge across the basin. Differences between mitigation pathways are negligible at mid-century but reach the order of magnitude of the induced changes by 2100. For acidification, which is strongest in the Bothnian Bay, there is a clear distinction in the impact of interannual variability and model uncertainty on the significance of the mid-and long-term trend. While trends clearly emerge from interannual variability, they are subject to model uncertainty, as was visible already in the basin average time series that reaches more than twice the level of the trend.
","The bottom oxygen concentration at mid-century reveals deoxygenation across the whole basin except for a small region of increase in oxygen north of Gotland that extends to the whole Gotland Basin at the end of the century. It should be noted, however, that these changes are comparatively uncertain with respect to interannual variability and model uncertainty across the entire Baltic Sea and are particularly uncertain in oxygen increase. This area is also the only area in which scenario differences in oxygen trends are larger than the actual oxygen trend, making it an area of uncertain outcome in all aspects.
"],"string":"[\n \"The statistical downscaling improved model skill for oxygen and temperature when comparing SD products and the original ESMs (Tables ##TAB##1##2##, ##TAB##2##3##; see Tables ##SUPPL##0##S1##–##SUPPL##0##S10## for more details) across all regions. Temperature shows a higher skill than oxygen, with a difference of around 0.2–0.4 points between the ESM Liu-mean efficiency skill score and the SD. It is worth noting that the SD significantly reduced model differences in performance, while for the original ESMs, the inter-model differences can be substantial.
\",\n \"The decomposition of the performance analysis into its components (Tables ##SUPPL##0##S1##–##SUPPL##0##S10##) suggests that, for the SD products, reduced model skill was attributable to the spatial correlation coefficient (Table ##SUPPL##0##S1##–##SUPPL##0##S2##), while the ratio of means and ratio of standard deviations showed close to perfect matches for these products (Table ##SUPPL##0##S3##–##SUPPL##0##S5##). For the original GCM and ESM simulations, the ratio of means was also generally very close to 1 (Tables ##SUPPL##0##S9##, ##SUPPL##0##S10##). Still, both the Pearson correlation and the ratio of standard deviation indicate substantial shortcomings in skill (Table ##SUPPL##0##S6##, ##SUPPL##0##S9##). For the SD products, the main driver for lack of skill was a weak correlation which is significantly stronger than the mismatches of standard deviations.
\",\n \"The relatively coarse resolution of the WOA data, particularly oxygen, is a challenge for representing enclosed regions such as the Baltic Sea. For that reason, the ensemble downscaled data for bottom temperature (Fig. ##FIG##1##2##) and oxygen (Fig. ##FIG##2##3##) were compared with observations from the ICES database for the Baltic Sea, the Bay of Biscay and for the North Sea, while data for the Mediterranean were compared against the GLODAP database. In total, we extracted a large number of observations totaling 39,797 data points for the Baltic Sea, 126,401 for the North Sea, 9,385 for the Bay of Biscay, and 6,306 for the Mediterranean (Fig. ##SUPPL##0##S1##). The depth and range distributions of bottom temperature (Fig. ##FIG##1##2##) are comparable to the observations for all regions. For the Mediterranean, the bifurcation between the Eastern and Western Mediterranean basins can be identified in both the temperature and oxygen data. When compared with observations, the downscaled temperature data realistically captured the value range as a function of depth (Fig. ##FIG##1##2##) for all regions. The oxygen data in the ensemble exhibited a narrower range across all depths compared to the observed values in the Baltic and North Sea (Fig. ##FIG##2##3##). Conversely, in the Bay of Biscay, the available oxygen observations were limited and dispersed over a wider area, making direct comparison with the ensemble data more challenging. In the Baltic Sea, the ensemble data show higher oxygen concentrations than the observations, especially in the waters shallower than 100 m (Fig. ##FIG##2##3##). This difference is also seen in the upper 100 m in the North Sea where values below 1.4 ml/L is typically classified as hypoxic conditions can be found in the observations (Fig. ##FIG##2##3##). The bottom pH from the ensemble products were compared with the ICES and GLODAP databases but very few observations matched our filtering criteria, although the ones that did compared reasonably well (Fig. ##SUPPL##0##S2##).
\",\n \"In this section, we illustrate the changes induced by anthropogenic greenhouse gas emissions for the three variables previously evaluated, sea surface temperature representing warming, surface pH representing acidification, and bottom dissolved oxygen representing deoxygenation. The significance of the induced changes was further analysed by comparing them to three separate sources of uncertainty in climate projections: (1) internal variability, (2) model uncertainty, and (3) scenario uncertainty. In the following subsections, we present the results of this analysis by region.
\",\n \"Figure ##FIG##3##4## shows the Mediterranean Sea basin average time series of the three ecosystem indicators from 1993 up to the end of this century for the three scenarios represented by the SD ensemble product. The patterns are qualitatively comparable to those observed for the global mean trajectories##UREF##26##32##,##UREF##27##33##. For the no-mitigation scenario SSP5-8.5, the bulk surface temperature of the Mediterranean Sea gradually increases to 5 °C higher than the present day. For the middle of the road scenario SSP2-4.5, the increase in temperature from present-day is lower, reaching approximately 2 °C. For the strongly mitigated scenario SSP1-2.6, the temperature initially increases and then stabilizes towards the middle of the century at around 1.5 °C of warming. The model spread is moderately high (2.5 to 3.0 °C), so the differences between the two scenarios producing weaker warming partially overlap. In contrast, for the high emissions scenario (SSP5-8.5), there is stronger warming and the difference in temperature emerges from the model uncertainty. Interannual variability is low compared to long-term changes. Regarding ocean acidification, in SSP5-8.5 a strong, gradual decrease in ocean pH occurs to about 0.4 units from present-day conditions, while the decline in SSP2-4.5 is less marked, and pH stabilizes by the end of the century in SSP2-4.5, after a 0.15 unit decrease. The SSP1-2.6 scenario suggests a slightly reversing trend, limiting the overall decrease in pH to less than 0.1 units. Uncertainty for this pressure is inherently low, and differences in the changes among the scenarios are clear. For bottom oxygen, uncertainty is highest relative to the changes observed among the three scenarios. For all three scenarios, oxygen decreases by approximately 0.5, 0.2, and 0.1 ml/l for SSP5-8.5, SSP2-4.5, and SSP1-2.6, respectively.
\",\n \"To give a clearer picture of the relative importance of the different sources of uncertainty and their role in different locations, Figs. ##FIG##4##5## and ##FIG##5##6## show maps of the changes of the three indicators for the ensemble average of scenario SSP2-4.5 as absolute values and relative to the uncertainties. The increase in surface temperature is strongest in the Adriatic and Aegean Seas, with higher changes in the Eastern compared to the Western Basin of the Mediterranean Sea. Warming almost doubles from mid to the end of the century with no major difference in the spatial distribution of change. Mid-century interannual variability and model uncertainty are of the order of the changes across the basin, while the differences between the scenarios are significantly lower than the changes induced. This situation reverses for long-term changes, which become more significant with respect to interannual variability. At the same time, the difference between the scenarios has become larger in relative terms and is comparable to the magnitude of the change.
\",\n \"Acidification in the Mediterranean Sea is strongest in the Northern Adriatic, with a generally slightly higher decrease in pH in the Northern parts compared to the Southern coast of the basin. As can be inferred from the basin mean time series, changes are strongly significant for both time slices with respect to interannual variability and model uncertainty. At the same time, the difference between the mitigation pathways is on the order of the changes at mid-century and is much larger towards the end of the century.
\",\n \"For bottom dissolved oxygen, the situation is much less clear. While, on average, a decrease in seafloor oxygen is visible from the time series, some areas show an increase in oxygen for the ensemble mean (most evident in the Aegean Sea); interannual variability and, particularly, model uncertainty is high in these areas. Areas of oxygen decrease, on the contrary, emerge from interannual variability with changes slightly higher than the model uncertainty, although with some regional exceptions. Similarly, changes in scenario play a much more important role in areas of oxygen increase compared to areas of decrease. In the latter areas, differences among the scenarios were relatively small compared to the magnitude of the induced decrease in oxygen.
\",\n \"The basin-scale mean evolution of greenhouse gas-induced changes in physical and biogeochemical pressures of the wider North Sea area (Fig. ##FIG##6##7##) is comparable to the Mediterranean Sea. Warming is, however, less accentuated in the North Sea compared to the Mediterranean Sea, with only a 3.0 °C increase projected at the end of the century for the no-mitigation scenario SSP5-8.5 and < 1.0 °C for the moderate and strong mitigation scenarios. Acidification ranges from slightly less than 0.1 units (SSP1-2.6) to around 0.5 units of decrease in surface pH (SPP5-8.5), while seafloor oxygen decrease by ~ 0.1–0.3 ml/l) with interannual variability up to ~ 0.2 ml/l).
\",\n \"Considering the spatial distribution of changes and uncertainties (Figs. ##FIG##7##8##, ##FIG##8##9##) warming is strongest towards the Eastern parts of the European shelf and comparatively weak towards the open Atlantic Ocean. On most of the continental shelf, however, these trends are comparatively weak with respect to interannual variability and model uncertainty (ratio is only slightly > 1). The differences between the scenario pathways are only of minor importance at mid-century (approximately 1/3 of the change signal across the basin) but eventually reach about the same order of magnitude as those induced by long-term changes. Seafloor oxygen in the ensemble average changes noticeably only in the open ocean areas along the shelf break, where dissolved oxygen declines by up to 1 ml/l. These changes begin to emerge at mid-century but only become significant towards the end of the century. The difference in changes between greenhouse gas scenarios is minor, even towards the end of the century.
\",\n \"In the area around the Bay of Biscay, the domain averages roughly followed the patterns observed in the two previous regions with strong continuous warming up to 3.0 °C by 2100 and acidification of 0.4 pH units for SSP5-8.5 (Fig. ##FIG##9##10##). These changes are attenuated in the other two (moderate to strong mitigation) scenarios. For example, pH does not decrease but increases somewhat in the second half of the century for SSP1-2.6. Acidification trends were strongly significant, while the warming trends emerged less clearly due to considerable model uncertainty (~ 1.5° to 3.5°), particularly for the two pathways producing weaker changes. Deoxygenation shows considerable model (~ 0.4–0.5 ml/l) and interannual (up to 0.2 ml/l) variability. The trend in deoxygenation are minimal or absent for the strongest (SSP5-8.5) scenario through 2040, followed by a few years of strong interannual variability and a rapid decline that continues through the end of this century. There is a weaker, more continuous deoxygenation in the other two scenarios, similar to patterns in the North and Mediterranean Seas. The unexpected observed climate variability across scenarios could be indicative of infrequent and quasi-stochastic regime shifts which are an expected component of natural variability##UREF##28##34##. Still, filtering out the inter-annual variability suggests that the trend is consistent across the scenarios.
\",\n \"The spatial distribution of these average patterns in the domain (Figs. ##FIG##10##11##, ##FIG##11##12##) indicated the Northern coast of the Iberian Peninsula and the northern coast of Brittany as hotspots of surface warming, with the former particularly strong in the mid-term (up to 1°) and the latter particularly strong in the long-term (almost 2°). Relative to interannual variability and model uncertainty, however, this warming is only slightly emergent in the mid-term, while both interannual and model uncertainties are high in the deeper Atlantic waters. In the long-term, changes become significant with respect to interannual variability, while model uncertainty remains persistent through the end of the century. Consistent with patterns in the other regional seas, differences in scenario pathways in warming are small in the mid-term, while in the long-term, the different mitigation strategies will lead to differences in warming of the same order of magnitude as the change itself. Oxygen is expected to increase on the Celtic and Armorican shelfs although the three sources of uncertainty remain high for both mid and long term. While in the deeper Atlantic waters and the coastal waters of northern Spain and the western coast of Portugal oxygen will decrease, and changes are significant with respect to interannual variability. For model and scenario uncertainty changes in oxygen are pre-dominantly significant with exceptions such as the inner coastal domain of Portugal which is dominated by upwelling and more complex oceanographic processes. Acidification trends are comparatively homogeneous across the domain, with somewhat stronger trends in the off-shelf areas of the North-Eastern Atlantic. This pattern is consistent between the two time slices; however, acidification is about 50% higher at the end of the century compared to the mid-century. The trends are strongly significant with respect to model uncertainty and interannual variability for both time slices. The difference between scenarios is of the same order of magnitude as the induced changes at mid-century, while at the end of the century, the mitigation pathways become increasingly important as the difference between scenarios reaches twice the magnitude of the change signal.
\",\n \"While the basin mean warming, acidification, and deoxygenation trends are also present in the Baltic Sea, their behaviour and relation to uncertainty are substantially different in this coastal, semi-enclosed basin compared to the other regions. A fundamental difference is the large model uncertainty (up to 0.8 pH units) and increased interannual variability (up to 0.05 units) of surface pH with respect to the induced changes (0.1–0.5 pH units) (Fig. ##FIG##12##13##). In addition, the deoxygenation change (~ 0.2 ml/l) here is significantly weaker, and interannual variability is significantly higher (up to 0.5 ml/l). Warming trends (2–5 °C), by contrast, are comparable to the other basins.
\",\n \"Looking at regional differences in these trends (Figs. ##FIG##13##14##, ##FIG##14##15##), warming in the ensemble average of the Baltic Sea for scenario SSP2-4.5 at mid-century is strongest in the Bothnian Sea and the Gulf of Riga and weakest at the margins of the Bothnian Bay and the Southern Baltic Proper. This pattern also persists at the end of the century with the two warming hotspots spreading into the Northern Baltic Proper. Compared to interannual variability and model uncertainty, the trends only weakly emerge across the basin. Differences between mitigation pathways are negligible at mid-century but reach the order of magnitude of the induced changes by 2100. For acidification, which is strongest in the Bothnian Bay, there is a clear distinction in the impact of interannual variability and model uncertainty on the significance of the mid-and long-term trend. While trends clearly emerge from interannual variability, they are subject to model uncertainty, as was visible already in the basin average time series that reaches more than twice the level of the trend.
\",\n \"The bottom oxygen concentration at mid-century reveals deoxygenation across the whole basin except for a small region of increase in oxygen north of Gotland that extends to the whole Gotland Basin at the end of the century. It should be noted, however, that these changes are comparatively uncertain with respect to interannual variability and model uncertainty across the entire Baltic Sea and are particularly uncertain in oxygen increase. This area is also the only area in which scenario differences in oxygen trends are larger than the actual oxygen trend, making it an area of uncertain outcome in all aspects.
\"\n]"},"discussion":{"kind":"list like","value":["Many governments have made Nature-based Solutions ecosystem-based management, habitat restoration, and adaptive marine spatial planning a core part of their climate adaptation and planning##UREF##29##35##. However, such efforts demand accounting for impacts of costal biological and physical ocean dynamics and variability which further requires biophysical model results that are at a finer resolution than the CMIP outputs. Statistical downscaling can provide the necessary data to perform rapid analysis including estimates of the uncertainties involved while ensuring high skill when compared with historical observations. Our results suggest warming is evident across all regions, fully emerging from the background uncertainties related to internal variability and model differences in the second half of the century, with substantial differences between the mitigation pathways. Acidification significantly emerges from model uncertainty and internal variability in the historical climate, while the different climate mitigation scenarios lead to distinct trajectories in surface pH before mid-century. Although deoxygenation appears to be present across all domains, the signal is weaker compared to temperature and pH in terms of model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is much less distinct. These qualitative characteristics vary considerably in extent and exhibit substantial local heterogeneity within each domain, underlining the importance of a spatially explicit and high-resolution approach to providing projections of the impacts of anthropogenic climate change on marine ecosystem components, functions, and services.
","Statistically downscaled ensemble products, such as the one presented here, can be useful for calculating exposure terms for climate risk assessments performed across large areas, such as that conducted for threatened species within marine protected areas throughout the Mediterranean##UREF##30##36## or for fisheries across European regional seas##REF##34583987##37##. Ongoing studies along the European coastline are utilizing downscaled climate data to better understand how preservation of natural habitat-forming species such as mangroves, seagrasses, kelp forests, or coral reefs can buffer impacts from storms, and sea-level rise, as well as contribute as carbon sinks. In some cases, first-hand knowledge of the oceanography and marine biogeochemistry of an area is essential to decide whether an ensemble climate product is representative of an area. Here we use a model reanalysis, the GLORYS12V1 at 1/12th degree resolution, for the ocean physics and a model hindcast, the GOBH at ¼ degree resolution, for the biogeochemistry as a baseline for the bias-correction and downscaling, which particularly in the Baltic Sea is too coarse to resolve many of the local features. In addition, the performance of the downscaling will be limited by the skill of the hindcast GOBH model. Still, we argue that, for biogeochemical variables, model hindcasts are currently the only comprehensive datasets consistently available across the regions and vertical layers of this study, even though efforts to fill this gap are ongoing, e.g., via reanalysis products and extrapolation of observational datasets using artificial intelligence. We believe that the downscaled product nevertheless substantially improves upon the original CMIP6 models for the same region. Future improvements can be made if an enhanced biological hindcast, reanalysis or observational products are developed at sufficient resolution and time coverage.
","Generally, the utilization of ocean biogeochemical models for understanding the fluctuations and transformations in marine environments, arising from both natural and human-induced influences, has surged in recent decades. The growth in the use of these tools can be attributed to the emergence of computers capable of executing trillions of calculations within seconds, resulting in global high-resolution ocean reanalyses such as GLORYS12V1, providing researchers detailed information on the marine environment. Still, to perform global future projections at high spatial resolution, the cost and time required is considerable. In fact, an increase in horizontal resolution by a factor of two increases the computational cost by ten##UREF##31##38##. Instead, we rely on global coarse resolution models that provide less detail but are faster to run, which can be further downscaled locally to hold a proper resolution useful for projecting coastal processes. Most often, these downscaled models are dynamic, meaning that they calculate the full set of hydrodynamic equations for a limited domain and use the coarse-resolution global models as boundary forcing. Using dynamical models for simulating one model domain is time-consuming and expensive. As a result, dynamical models are often constrained to downscale a few, or a small subset, of global climate models and scenarios, which limits their flexibility and hampers an assessment of sources of uncertainty. Alternatively, the statistical downscaling described in this study, provides a more rapid way of assessing local coastal impacts of climate change for a subset of relevant variables. While both dynamic and statistical approaches have their advantages and disadvantages, they are complementary. For example, one can apply a statistical downscaling approach to effectively gain an understanding of expected coastal climate impacts across a range of scenarios and climate models as has been done here for European regional seas. These findings can inform the selection of locations that require a more refined dynamical model for a comprehensive assessment of the three-dimensional effects of climate change on local ecosystems. This knowledge facilitates the optimal utilization of models and strategic application to enhance scientific efforts in understanding and addressing future ecological impacts.
","Statistical downscaling has shortcomings that must be recognized when using the ensemble data. First, for the dataset presented here, we limit downscaling to individual depth levels. This limits analysis to two dimensions instead of three, which, as one example, is acceptable if you are analysing shellfish distributions under changing environmental conditions##UREF##32##39##, but inadequate if you need to understand the vertical distribution of a diel vertical migrator like krill. The DQM methodology also assumes that the biases at quantiles are stationary in time i.e., the functional relationship between the observed values and the GCM/ESM for the historical time period holds for the future##UREF##4##9##. This assumption can cause problems with extreme values as their historical distribution is expected to continue in the future when we know that climate change can lead to novel non-linear states##REF##35973999##40##. In a recent paper the DQM approach was a favoured methodology as it preserved the climate change signal and trend##UREF##33##41## compared to other methods like traditional Quantile Mapping (QM##UREF##4##9##) where the mean of the raw climate change signal (CCS) is not preserved but altered to correct for biases in the GCM/ESM. Choosing DQM suggests that you have confidence in the GCM/ESM circulation pattern and skill, as the resulting downscaling will maintain the inherent CCS##UREF##4##9##. Challenges with statistical downscaling, such as inflating or deflating extreme values during downscaling, and considerations as what should be regarded as best practice is an ongoing process where new developments and approaches are published frequently##UREF##34##42##–##UREF##36##44##. In addition, dynamical downscaling becomes fundamental when dynamic consistency across multiple variables is required in the downstream applications, such as subsequent modelling studies that require the coherent representation of dynamic features that may be lost across variables applying statistical approaches.
","Regional downscaling provides detailed local climate projections for researchers, stakeholders, and management entities to understand, mitigate, and adapt to climate change. Choosing between dynamical and statistical downscaling depends on the application and research question(s). A dynamical model is effective for understanding a region's structure and dynamics under climate change but is subject to bias##UREF##3##8## and lacks the breadth and coordinated protocols of global experiments. These characteristics hinder the ability to conduct a thorough evaluation of sources for proper uncertainty assessment, thereby limiting confidence in the projections. Statistical downscaling is a good alternative, particularly when applied to individual variables or depth levels, but lacks dynamic consistency and probably will not predict unobserved phenomena (e.g., synergistic, or antagonistic multivariate processes). Despite its widespread use in atmospheric science##UREF##34##42##,##UREF##35##43##,##UREF##37##45##, statistical downscaling is rarely applied to ocean models##UREF##38##46##. In fact, to our knowledge, this is the first ocean downscaling that uses a detrended quantile mapping approach to downscale both physical and biogeochemical ocean properties. This downscaling provided climate projections at a regional scale across all European waters across a range of climate models and scenarios at reasonable computational cost.
"],"string":"[\n \"Many governments have made Nature-based Solutions ecosystem-based management, habitat restoration, and adaptive marine spatial planning a core part of their climate adaptation and planning##UREF##29##35##. However, such efforts demand accounting for impacts of costal biological and physical ocean dynamics and variability which further requires biophysical model results that are at a finer resolution than the CMIP outputs. Statistical downscaling can provide the necessary data to perform rapid analysis including estimates of the uncertainties involved while ensuring high skill when compared with historical observations. Our results suggest warming is evident across all regions, fully emerging from the background uncertainties related to internal variability and model differences in the second half of the century, with substantial differences between the mitigation pathways. Acidification significantly emerges from model uncertainty and internal variability in the historical climate, while the different climate mitigation scenarios lead to distinct trajectories in surface pH before mid-century. Although deoxygenation appears to be present across all domains, the signal is weaker compared to temperature and pH in terms of model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is much less distinct. These qualitative characteristics vary considerably in extent and exhibit substantial local heterogeneity within each domain, underlining the importance of a spatially explicit and high-resolution approach to providing projections of the impacts of anthropogenic climate change on marine ecosystem components, functions, and services.
\",\n \"Statistically downscaled ensemble products, such as the one presented here, can be useful for calculating exposure terms for climate risk assessments performed across large areas, such as that conducted for threatened species within marine protected areas throughout the Mediterranean##UREF##30##36## or for fisheries across European regional seas##REF##34583987##37##. Ongoing studies along the European coastline are utilizing downscaled climate data to better understand how preservation of natural habitat-forming species such as mangroves, seagrasses, kelp forests, or coral reefs can buffer impacts from storms, and sea-level rise, as well as contribute as carbon sinks. In some cases, first-hand knowledge of the oceanography and marine biogeochemistry of an area is essential to decide whether an ensemble climate product is representative of an area. Here we use a model reanalysis, the GLORYS12V1 at 1/12th degree resolution, for the ocean physics and a model hindcast, the GOBH at ¼ degree resolution, for the biogeochemistry as a baseline for the bias-correction and downscaling, which particularly in the Baltic Sea is too coarse to resolve many of the local features. In addition, the performance of the downscaling will be limited by the skill of the hindcast GOBH model. Still, we argue that, for biogeochemical variables, model hindcasts are currently the only comprehensive datasets consistently available across the regions and vertical layers of this study, even though efforts to fill this gap are ongoing, e.g., via reanalysis products and extrapolation of observational datasets using artificial intelligence. We believe that the downscaled product nevertheless substantially improves upon the original CMIP6 models for the same region. Future improvements can be made if an enhanced biological hindcast, reanalysis or observational products are developed at sufficient resolution and time coverage.
\",\n \"Generally, the utilization of ocean biogeochemical models for understanding the fluctuations and transformations in marine environments, arising from both natural and human-induced influences, has surged in recent decades. The growth in the use of these tools can be attributed to the emergence of computers capable of executing trillions of calculations within seconds, resulting in global high-resolution ocean reanalyses such as GLORYS12V1, providing researchers detailed information on the marine environment. Still, to perform global future projections at high spatial resolution, the cost and time required is considerable. In fact, an increase in horizontal resolution by a factor of two increases the computational cost by ten##UREF##31##38##. Instead, we rely on global coarse resolution models that provide less detail but are faster to run, which can be further downscaled locally to hold a proper resolution useful for projecting coastal processes. Most often, these downscaled models are dynamic, meaning that they calculate the full set of hydrodynamic equations for a limited domain and use the coarse-resolution global models as boundary forcing. Using dynamical models for simulating one model domain is time-consuming and expensive. As a result, dynamical models are often constrained to downscale a few, or a small subset, of global climate models and scenarios, which limits their flexibility and hampers an assessment of sources of uncertainty. Alternatively, the statistical downscaling described in this study, provides a more rapid way of assessing local coastal impacts of climate change for a subset of relevant variables. While both dynamic and statistical approaches have their advantages and disadvantages, they are complementary. For example, one can apply a statistical downscaling approach to effectively gain an understanding of expected coastal climate impacts across a range of scenarios and climate models as has been done here for European regional seas. These findings can inform the selection of locations that require a more refined dynamical model for a comprehensive assessment of the three-dimensional effects of climate change on local ecosystems. This knowledge facilitates the optimal utilization of models and strategic application to enhance scientific efforts in understanding and addressing future ecological impacts.
\",\n \"Statistical downscaling has shortcomings that must be recognized when using the ensemble data. First, for the dataset presented here, we limit downscaling to individual depth levels. This limits analysis to two dimensions instead of three, which, as one example, is acceptable if you are analysing shellfish distributions under changing environmental conditions##UREF##32##39##, but inadequate if you need to understand the vertical distribution of a diel vertical migrator like krill. The DQM methodology also assumes that the biases at quantiles are stationary in time i.e., the functional relationship between the observed values and the GCM/ESM for the historical time period holds for the future##UREF##4##9##. This assumption can cause problems with extreme values as their historical distribution is expected to continue in the future when we know that climate change can lead to novel non-linear states##REF##35973999##40##. In a recent paper the DQM approach was a favoured methodology as it preserved the climate change signal and trend##UREF##33##41## compared to other methods like traditional Quantile Mapping (QM##UREF##4##9##) where the mean of the raw climate change signal (CCS) is not preserved but altered to correct for biases in the GCM/ESM. Choosing DQM suggests that you have confidence in the GCM/ESM circulation pattern and skill, as the resulting downscaling will maintain the inherent CCS##UREF##4##9##. Challenges with statistical downscaling, such as inflating or deflating extreme values during downscaling, and considerations as what should be regarded as best practice is an ongoing process where new developments and approaches are published frequently##UREF##34##42##–##UREF##36##44##. In addition, dynamical downscaling becomes fundamental when dynamic consistency across multiple variables is required in the downstream applications, such as subsequent modelling studies that require the coherent representation of dynamic features that may be lost across variables applying statistical approaches.
\",\n \"Regional downscaling provides detailed local climate projections for researchers, stakeholders, and management entities to understand, mitigate, and adapt to climate change. Choosing between dynamical and statistical downscaling depends on the application and research question(s). A dynamical model is effective for understanding a region's structure and dynamics under climate change but is subject to bias##UREF##3##8## and lacks the breadth and coordinated protocols of global experiments. These characteristics hinder the ability to conduct a thorough evaluation of sources for proper uncertainty assessment, thereby limiting confidence in the projections. Statistical downscaling is a good alternative, particularly when applied to individual variables or depth levels, but lacks dynamic consistency and probably will not predict unobserved phenomena (e.g., synergistic, or antagonistic multivariate processes). Despite its widespread use in atmospheric science##UREF##34##42##,##UREF##35##43##,##UREF##37##45##, statistical downscaling is rarely applied to ocean models##UREF##38##46##. In fact, to our knowledge, this is the first ocean downscaling that uses a detrended quantile mapping approach to downscale both physical and biogeochemical ocean properties. This downscaling provided climate projections at a regional scale across all European waters across a range of climate models and scenarios at reasonable computational cost.
\"\n]"},"conclusion":{"kind":"list like","value":[],"string":"[]"},"front":{"kind":"list like","value":["Climate change impact studies need climate projections for different scenarios and at scales relevant to planning and management, preferably for a variety of models and realizations to capture the uncertainty in these models. To address current gaps, we statistically downscaled (SD) 3–7 CMIP6 models for five key indicators of marine habitat conditions: temperature, salinity, pH, oxygen, and chlorophyll across European waters for three climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results provide ensemble averages and uncertainty estimates that can serve as input data for projecting the potential success of a range of Nature-based Solutions, including the restoration of habitat-forming species such as seagrass in the Mediterranean and kelp in coastal areas of Portugal and Norway. Evaluation of the ensemble with observations from four European regions (North Sea, Baltic Sea, Bay of Biscay, and Mediterranean Sea) indicates that the SD projections realistically capture the climatological conditions of the historical period 1993–2020. Model skill (Liu-mean efficiency, Pearson correlation) clearly improves for both surface temperature and oxygen across all regions with respect to the original ESMs demonstrating a higher skill for temperature compared to oxygen. Warming is evident across all areas and large differences among scenarios fully emerge from the background uncertainties related to internal variability and model differences in the second half of the century. Scenario-specific differences in acidification significantly emerge from model uncertainty and internal variability leading to distinct trajectories in surface pH starting before mid-century (in some cases starting from present day). Deoxygenation is also present across all domains, but the climate signal was significantly weaker compared to the other two indicators when compared to model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is less distinct. The substantial regional and local heterogeneity in these three abiotic indicators underscores the need for highly spatially resolved physical and biogeochemical projections to understand how climate change may impact marine ecosystems.
","Climate change impact studies need climate projections for different scenarios and at scales relevant to planning and management, preferably for a variety of models and realizations to capture the uncertainty in these models. To address current gaps, we statistically downscaled (SD) 3–7 CMIP6 models for five key indicators of marine habitat conditions: temperature, salinity, pH, oxygen, and chlorophyll across European waters for three climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results provide ensemble averages and uncertainty estimates that can serve as input data for projecting the potential success of a range of Nature-based Solutions, including the restoration of habitat-forming species such as seagrass in the Mediterranean and kelp in coastal areas of Portugal and Norway. Evaluation of the ensemble with observations from four European regions (North Sea, Baltic Sea, Bay of Biscay, and Mediterranean Sea) indicates that the SD projections realistically capture the climatological conditions of the historical period 1993–2020. Model skill (Liu-mean efficiency, Pearson correlation) clearly improves for both surface temperature and oxygen across all regions with respect to the original ESMs demonstrating a higher skill for temperature compared to oxygen. Warming is evident across all areas and large differences among scenarios fully emerge from the background uncertainties related to internal variability and model differences in the second half of the century. Scenario-specific differences in acidification significantly emerge from model uncertainty and internal variability leading to distinct trajectories in surface pH starting before mid-century (in some cases starting from present day). Deoxygenation is also present across all domains, but the climate signal was significantly weaker compared to the other two indicators when compared to model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is less distinct. The substantial regional and local heterogeneity in these three abiotic indicators underscores the need for highly spatially resolved physical and biogeochemical projections to understand how climate change may impact marine ecosystems.
\",\n \"\n
"],"string":"[\n \"\\n
\"\n]"},"back":{"kind":"list like","value":["The online version contains supplementary material available at 10.1038/s41598-024-51160-1.
","We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. The SD dataset was produced as an EU deliverable from the FutureMARES—Climate Change and Future Marine Ecosystem Services and Biodiversity (869300) European Commission grant. The dataset was generated using E.U. Copernicus Marine Service Information; 10.48670/moi-00021, 10.48670/moi-00019. Version 1 was distributed on May 24, 2022, and version 2 is planned to be distributed in spring 2024. The datasets are distributed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
","We declare that all the authors contributed to preparing the manuscript. T.K. prepared the downscaled data, T.K., M.B., and M.P. analyzed the data, prepared the figures, wrote the manuscript, and formatted the final NetCDF files.
","The ensemble projections datasets covering European waters for three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, are available on Zenodo DOI: 10.5281/zenodo.6523925##UREF##39##47## and from individual DOIs for each region: (1) North Sea: 10.5281/zenodo.6523926 (2) Mediterranean Sea: 10.5281/zenodo.6523899 (3) Baltic Sea: 10.5281/zenodo.6524111 (4) Bay of Biscay: 10.5281/zenodo.6524142.
","TK is a co-founder and the CTO of Actea Inc (
The online version contains supplementary material available at 10.1038/s41598-024-51160-1.
\",\n \"We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. The SD dataset was produced as an EU deliverable from the FutureMARES—Climate Change and Future Marine Ecosystem Services and Biodiversity (869300) European Commission grant. The dataset was generated using E.U. Copernicus Marine Service Information; 10.48670/moi-00021, 10.48670/moi-00019. Version 1 was distributed on May 24, 2022, and version 2 is planned to be distributed in spring 2024. The datasets are distributed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
\",\n \"We declare that all the authors contributed to preparing the manuscript. T.K. prepared the downscaled data, T.K., M.B., and M.P. analyzed the data, prepared the figures, wrote the manuscript, and formatted the final NetCDF files.
\",\n \"The ensemble projections datasets covering European waters for three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, are available on Zenodo DOI: 10.5281/zenodo.6523925##UREF##39##47## and from individual DOIs for each region: (1) North Sea: 10.5281/zenodo.6523926 (2) Mediterranean Sea: 10.5281/zenodo.6523899 (3) Baltic Sea: 10.5281/zenodo.6524111 (4) Bay of Biscay: 10.5281/zenodo.6524142.
\",\n \"TK is a co-founder and the CTO of Actea Inc (
Map showing the four European focus regions of FutureMARES where statistical downscaling of CMIP6 projections was applied: the Baltic Sea, the North Sea, the Bay of Biscay, and the Mediterranean Sea. The Black Sea is not included in the Mediterranean region.
Comparison between the ensemble (blue) downscaled bottom temperature (thetao (°C)) and observations (orange) extracted from the ICES (
Comparison between the ensemble (blue) downscaled bottom oxygen (O2 (ml/l)) and shipboard observations (orange) extracted from the ICES (
Time series of Mediterranean Sea average surface temperature (°C), pH, and bottom oxygen (ml/l) over the historical time period and the three scenarios. Solid lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distributions.
The magnitude of mid-term changes in the Mediterranean Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Strength of long-term climate-driven changes in the Mediterranean Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem variables. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Time series of North Sea average surface temperature (°C), pH, and bottom oxygen (ml/l) over the historical time period and the three scenarios. Solid lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distributions.
Significance of mid-term changes in the North Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Significance of long-term changes in the North Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Time series of the Bay of Biscay average surface temperature (°C), pH, and bottom oxygen (ml/l) over the historical time period and the three scenarios. Solid lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distributions.
Significance of mid-term changes in the Bay of Biscay under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Significance of long-term changes in the Bay of Biscay under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Time series of Baltic Sea average surface temperature (॰C), pH, and bottom oxygen (ml/l) over the historical time slice and the three scenarios. Full lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distribution.
Significance of mid-term changes in the Baltic Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Significance of long-term changes in the Baltic Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Map showing the four European focus regions of FutureMARES where statistical downscaling of CMIP6 projections was applied: the Baltic Sea, the North Sea, the Bay of Biscay, and the Mediterranean Sea. The Black Sea is not included in the Mediterranean region.
Comparison between the ensemble (blue) downscaled bottom temperature (thetao (°C)) and observations (orange) extracted from the ICES (
Comparison between the ensemble (blue) downscaled bottom oxygen (O2 (ml/l)) and shipboard observations (orange) extracted from the ICES (
Time series of Mediterranean Sea average surface temperature (°C), pH, and bottom oxygen (ml/l) over the historical time period and the three scenarios. Solid lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distributions.
The magnitude of mid-term changes in the Mediterranean Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Strength of long-term climate-driven changes in the Mediterranean Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem variables. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Time series of North Sea average surface temperature (°C), pH, and bottom oxygen (ml/l) over the historical time period and the three scenarios. Solid lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distributions.
Significance of mid-term changes in the North Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Significance of long-term changes in the North Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Time series of the Bay of Biscay average surface temperature (°C), pH, and bottom oxygen (ml/l) over the historical time period and the three scenarios. Solid lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distributions.
Significance of mid-term changes in the Bay of Biscay under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Significance of long-term changes in the Bay of Biscay under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Time series of Baltic Sea average surface temperature (॰C), pH, and bottom oxygen (ml/l) over the historical time slice and the three scenarios. Full lines show the ensemble, and shaded areas show the ensemble spread based on the 2.5 and 97.5 percentiles of the model distribution.
Significance of mid-term changes in the Baltic Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between mid-term conditions (2041–2060 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Significance of long-term changes in the Baltic Sea under SSP2-4.5, against three sources of uncertainty for three ecosystem indicators. From left to right: changes between long-term conditions (2081–2100 mean) and present-day conditions (1995–2014); changes relative to internal variability; changes relative to model uncertainty; changes relative to scenario uncertainty. Top to bottom: Surface Temperature (K); surface pH; bottom dissolved oxygen (ml/l).
Climate scenarios, realizations, and variables downscaled for each CMIP6 model used to create the ensembles.
| Model name | Realization | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| O2 | Temperature | Chlorophyll | pH | Salinity | ||||||||||||
IPSL-CM6A-LR (Boucher et al., 2020) | r1i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | |||
| r3i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | ||||
MPI-ESM1-2-LR (Mauritsen et al., 2019) | r1i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | x | x | |
| r2i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | |
GFDL-ESM4 (Dunne et al., 2020) | r1i1p1f1 | x | x | x | x | x | x | |||||||||
CMCC-ESM2 (Lovato et al., 2022) | r1i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
CMCC-CM2-SR5 (Cherchi et al., 2018) | r1i1p1f1 | x | x | x | x | x | x |
Liu-mean efficiency of the statistical downscaling products for surface temperature (thetao (°C)) and surface oxygen (O2 (ml/l)) for each basin.
Liu-mean efficiency of the original earth system models for surface temperature (thetao (°C)) and surface oxygen (O2 (ml/l)) for each basin.
Climate scenarios, realizations, and variables downscaled for each CMIP6 model used to create the ensembles.
| Model name | Realization | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| O2 | Temperature | Chlorophyll | pH | Salinity | ||||||||||||
IPSL-CM6A-LR (Boucher et al., 2020) | r1i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | |||
| r3i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | ||||
MPI-ESM1-2-LR (Mauritsen et al., 2019) | r1i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | x | x | |
| r2i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | |
GFDL-ESM4 (Dunne et al., 2020) | r1i1p1f1 | x | x | x | x | x | x | |||||||||
CMCC-ESM2 (Lovato et al., 2022) | r1i1p1f1 | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
CMCC-CM2-SR5 (Cherchi et al., 2018) | r1i1p1f1 | x | x | x | x | x | x |
Liu-mean efficiency of the statistical downscaling products for surface temperature (thetao (°C)) and surface oxygen (O2 (ml/l)) for each basin.
Liu-mean efficiency of the original earth system models for surface temperature (thetao (°C)) and surface oxygen (O2 (ml/l)) for each basin.
The evaluation is based on present-day seasonal averages against WOA climatology.
The evaluation is based on present-day seasonal averages against WOA climatology.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The evaluation is based on present-day seasonal averages against WOA climatology.
The evaluation is based on present-day seasonal averages against WOA climatology.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information.
Supplementary Information.
Free convection relies on inherent buoyancy forces generated by thermal gradients to drive fluid motion and enable efficient heat transfer. Free convection optimizes the heat transfer processes in energy systems like solar collectors and power plants, reducing energy consumption and increasing system effectiveness. Industrial drying, cooling, and casting operations are made more efficient by free convection, in which molten metal is cast and solidified under better control. The electronics industry takes advantage of free convection by providing an innovative solution for reliable thermal management, effectively regulating the temperature of electronic components. Moreover, free convection is essential to the design and operation of heat exchangers and ventilation systems and contributes to our comprehension of complex environmental and geophysical phenomena##UREF##0##1##–##UREF##3##4##. Magnetohydrodynamics (MHD) represents an intriguing scientific field that improves the regulation of heat transmission, particularly in liquid metal cooling. MHD enables the use of magnetic fields to control electrically conducting fluids and enhance heat transfer processes. By applying a magnetic field, MHD induces electric currents that interact with the magnetic field, generating Lorentz forces that regulate fluid motion. The effective heat transfer made possible by this controlled fluid motion makes MHD a crucial component of advanced cooling systems for high-temperature applications. In several fields, such as nuclear power, aeronautical engineering, and advanced materials processing, where conventional cooling techniques may have limitations, MHD-based cooling systems have found applications##UREF##4##5##,##UREF##5##6##. On the other hand, the applications of thermal radiation span numerous sectors and industries. For the power sector, high-temperature operation, and aerospace applications, it is essential. Thermal radiation also plays a critical role in regulating energy transport, especially in polymer manufacturing. Furthermore, in solar energy-based industries, thermal radiation is employed in many applications, like solar energy collectors. Given the vast array of possibilities arising from the applications of free convection as well as the crucial functions of magnetic fields and thermal radiation in the realm of energy transfer, a multitude of numerical studies have delved into this issue. The findings of Sheikholeslami et al.’s##UREF##6##7## study demonstrated that an improvement in energy transport has a direct relationship with the radiation parameter. Additionally, the study found that the radiation parameter has a positive correlation with the Nusselt number. The results of El-Kabeir et al.##UREF##7##8## confirmed that as the magnetic force increases, both the skin-friction coefficient and heat transport rate decrease, while a contrasting pattern emerges when it comes to thermal radiation. As thermal radiation intensifies, an increase in the skin-friction coefficient and heat transport rate occurs. In a numerical study performed by Lone et al.##UREF##8##9##, it was revealed that an increase in the magnetic parameter amplifies velocity profiles in the x-direction while simultaneously diminishing them in the z-direction. The study also observed a correlation between an escalation in the magnetic field parameter and a decrease in skin friction, specifically along the x-direction. Additionally, the study found that the Nusselt number experienced a notable increase with an elevation in the thermal radiation parameter. See these intriguing numerical studies##UREF##9##10##–##UREF##12##13##.
","Polar microfluids are frequently characterized as polar, isotropic liquids with no consideration for molecular deformation. Erringen##UREF##13##14## introduced the micropolar theory in 1966. His simple model, commonly referred to as the “micropolar model”, has gained considerable acceptance and has been adopted to describe the thermal behavior of actual liquids with an internal structure. The flow behavior of liquid crystals, suspension solutions, animal blood, and many other fluids can be characterized by a micropolar fluid model. In recent years, numerous research projects have focused on energy transport characteristics using the micropolar model. Nazar et al.##UREF##14##15##,##UREF##15##16## relied on the micropolar model to predict and analyze the thermal behavior of the fluid moving around a spherical object, considering constant wall temperature and heat flux. The findings of their studies indicate that as the micropolar factor increases, both wall temperature and skin friction exhibit a rising trend. Swalmeh et al.##UREF##16##17##,##UREF##17##18## extended the studies of Nazar et al. by considering nanofluid issues through the single-phase model. Their findings reveal that the temperature and velocity of Al2O3-H2O surpass those of Al2O3-kerosene oil. Furthermore, the energy transport rate of Cu-H2O exhibits a noticeable decline compared to Al2O3-H2O as the micro-rotation factor escalates. Nabwey et al.##UREF##18##19## examine the influence of Newtonian heating on magnetohydrodynamic heat transfer induced by natural means of polar nanoliquids across a spherical object. Their validated findings support the notion that the presence of the micropolar factor diminishes skin friction and the energy transport rate. Likewise, their observations indicate that incorporating the Newtonian heating factor enhances both skin friction and the energy transport rate. Other related studies can be found in Refs.##UREF##19##20##–##UREF##21##22##.
","Control and management of energy and their related issues are increasingly recognized as one of mankind’s greatest challenges in the coming years to keep pace with the surge in industrialization and technology##UREF##22##23##,##UREF##23##24##. One of the innovative proposals is to optimize the performance of energy-transport fluids through the incorporation of metallic and ceramic ultrafine particles into the original fluid to form nanofluid. It all began with the study of Choi and Eastman##UREF##24##25##, who theoretically confirmed that the thermal conductivity of H2O can be markedly enhanced by including copper nanosolids. Afterwards, experimental and numerical studies continued, confirming that the thermal behavior of the reference fluid is significantly affected by nanosolids##UREF##25##26##–##UREF##31##32##. At present, nanofluids are evidently employed in a wide array of manufacturing and engineering applications, such as solar energy, heat exchangers, and cooling systems##UREF##32##33##–##UREF##36##37##. Hybrid nanomaterials are a developed class of nanomaterials fabricated from two nanoparticles to obtain the properties of their constituent materials. That is, the main objective of their synthesis is to create a compound with properties that combine thermal and rheological efficiency, as no single nanosolid can possess these properties##UREF##37##38##–##REF##36823230##43##. To acquire features that are more integrated, ternary hybrid nanosolids have been fabricated. Several studies have shown the thermal advantages of these upgraded nanocomposites over the previous class##UREF##42##44##–##UREF##45##47##. For numerical studies, Mahmood et al.##UREF##46##48## computationally simulated the unsteady magneto-flow of polymer trihybrid nanofluid around a sphere under the impact of ohmic heating. According to their findings, the magnetic factor and nanosolids concentration enhance heat distribution, while unsteadiness and rotation factors reduce it. In comparison to hybrid and original nanoliquids, tri-hybrid nanoliquids transport energy more rapidly. AlBaidani et al.##UREF##47##49## conducted a computational simulation to predict the enhancement of fin performance due to the use of tri-hybrid nanosolids, considering the shape factor of nanosolids and free convection. Their key findings indicate that the efficiency of energy performance is significantly influenced by thermal conductivity and free convection. Utilizing magnetic fields and thermal radiation proves to be effective in cooling fins. Tri-hybrid nanosolids enhance the efficiency of fins as opposed to hybrid nanosolids. See also##UREF##48##50##,##UREF##49##51##.
","As a control parameter, the shape of the suspended nanoparticles is among the critical parameters that affect the thermophysical features of nanoliquids. Numerous earlier experimental and numerical publications have highlighted the influence of nanosolid shapes. A numerical study was carried out by Kumar et al.##UREF##50##52## to explore the flow and thermal features of nanoliquid in a thermally driven cavity. It was found that an increment in the values of the shape factor was accompanied by a significant enhancement in thermal conductivity. Sheikholeslami and Shamlooei##UREF##51##53## examined the flow of magnetized iron oxide–H2O nanoliquid in a permeable medium, considering the shape factor. Their study showed that platelet-shaped iron oxide nanoparticles achieved the maximum energy transfer rate. Khashi’ie et al.##UREF##52##54## analyzed the thermal characteristics of Cu–Al2O3/H2O hybrid nanoliquid flow past an EMHD sheet, considering the impact of radiation. Their results supported the idea that as the volume fraction factor values rise, blade-shaped nanosolids exhibit the maximum energy transport rate, while spherical nanosolids exhibit the lowest energy transport rate. Ghobadi and Hassankolaei##UREF##53##55## carried out a numerical simulation of magnetohydrodynamic hybrid nanoliquid flow across a stretching cylinder. They observed that lamina nanomaterials have a greater effect on the Nusselt number than hexagonal nanomaterials. Shanmugapriya et al.##UREF##54##56## presented a numerical simulation to explore the efficiency of energy transfer in MHD tri-hybrid nanoliquid on a radiative moving wedge. In their study, they compared the efficiency of energy transfer between different shapes of nanosolids. See##UREF##55##57##–##UREF##57##59## for more related studies.
","By drawing upon the insights gained from previous studies. This work represents a natural progression from the investigations conducted by Nazar et al.##UREF##14##15##,##UREF##15##16## on micropolar fluid flow around a sphere to the more recent advances made by Swalmeh et al.##UREF##16##17##,##UREF##17##18## on micropolar nanofluids, along with the expansion that takes into account the micropolar hybrid nanofluid examined by Alkasasbeh et al.##UREF##58##60##. The novelty of the current study is to expand upon these findings by investigating the new problem of a micropolar tri-hybrid nanoliquid moving around a radiative spherical object with the application of a magnetic field. In addition to considering the impacts of a nanosolid’s shape on flow properties and energy transport and highlighting the influences of control factors on some physical groups associated with energy transit. Furthermore, this consideration plays an essential role in numerous physical and engineering applications that rely on heat transmission primarily via electrically conductive fluids. Its applications are considerably obvious, with biomedical applications and flow control around hypersonic and re-entry vehicles. Also, its outcomes could provide new insights into the design and optimization of energy transport systems that use ternary nanoliquids with tailored shapes of the nanosolids. It is anticipated and hoped that the results of this analysis will be beneficial for upcoming academic studies and, additionally, for engineering and practical applications. More precisely, this investigation will demonstrate the following issues: How do the magnetohydrodynamics (MHD) and micropolar tri-hybrid nanoliquid models construct the problem of free convection flow moving around a radiative spherical object? How can a mathematical model for the problem of MHD micropolar tri-hybrid nanoliquid be derived over a radiative spherical object? How does the MHD micropolar tri-hybrid nanoliquid model compare with the published natural heat transfer flow problems? How does the analysis of the numerical outcomes that can be obtained from the effects of MHD micropolar tri-hybrid parameters on the interested engineering physical quantities? How do the heat transfer behaviors of the utilized nanoparticles suspended in the original fluid change under the influence of the studied parameters?
Employing Hamilton and Crosser’s extended Maxwell model##UREF##59##61##, mono nanoliquids’ thermal conductivity, containing similar nanosolids of any shape, is calculated:
","The mathematical expression for the viscosity of mono-nanoliquids, which takes into account the shape of nanosolids, is as follows (see##UREF##60##62##):where is the empirical shape factor, and is the particle’s sphericity, which is defined as the ratio of its spherical surface area to another shape’s surface area, considering both shapes have the same volumes. are the vicosity coefficients, which are calculated experimentally at room temperature. The coefficients of viscosity and shape factor of the nanoparticles employed in the current study are listed in Table ##TAB##0##1##.\n
","The density, specific heat capacity, and thermal expansion of tri-hybrid nanoliquids can be evaluated based on the model presented by Refs.##UREF##57##59##,##UREF##62##64## as follows:
","Using the interpolation method, the viscosity, thermal conductivity, and electrical conductivity of tri-hybrid nanoliquids can be calculated by employing the following formulas (see##UREF##57##59##):
","The subscriptions 1 and 2 indicate Al2O3 and Cu, respectively, while subscription 3 indicates graphene or MWCNT. is the accumulation nanoparticle volume fraction factor. The thermophysical features of the original fluid and the nanosolids utilized in the current study are presented in Table ##TAB##1##2##.\n
","Figure ##FIG##0##1## presents a visualization of the relationship between the nanosolids shape and the thermal conductivity ratio. The thermal conductivity ratio exhibits an upward trend as the surface area of nanosolids grows. Blade-shaped nanosolids demonstrate the highest thermal conductivity ratio, whilst spherical nanoparticles exhibit the lowest ratio. This confirms that the higher shape factor of nanosolids produces the highest ratio of thermal conductivity. Figure ##FIG##1##2## presents a visualization of the relationship between the nanosolids shape and the dynamic viscosity ratio. It is noted that nanosolids with larger elongations (like platelets and cylinders) give kerosene oil the maximum dynamic viscosity ratio due to the structure of these shapes. Therefore, relying on these nanosolid shapes gives the original fluid a higher boiling point, which, of course, enhances its energy-carrying capacity.
","Suppose we have a two-dimensional free convection boundary layer flow of kerosene oil containing Al2O3 + Cu + graphene or Al2O3 + Cu + MWCNT around a solid sphere of radius
In light of the previous considerations and the Boussinesq boundary layer approximations, as well as employing the ternary hybrid nanofluids model, regarding magnetic, thermal radiation, and micropolar impacts, the continuity, momentum, energy, and micropolar equations are developed##UREF##19##20##,##UREF##58##60##,##UREF##66##68##:
","It is noted that the vector g (gravity acceleration), that exists in Eq. (##FORMU##19##6##), is implicitly expressed in (
The mathematical model (##FORMU##33##11##)–(##FORMU##36##14##) can be reduced using the following non-similar transformation (stream function ):where .
","Utilizing the non-similar transformations (##FORMU##42##15##) and using Eqs. (##FORMU##0##1##)–(##FORMU##1##4##) yields: subject to:
","The skin friction
Using the Eqs. (##FORMU##49##21##), (##FORMU##29##10##), and (##FORMU##42##15##), we get:
","In this section, the hybrid linearization spectral collocation technique (HLSC) combined Newton’s linearization method (NLM) with Chebyshev spectral collocation method (CSCM) in -direction. Firstly, NLM is utilized to linearize and decouple the nonlinear PDEs which are solved using Chebyshev spectral method (see##UREF##67##69##–##REF##31913322##71##).
","System (##FORMU##44##16##)–(##FORMU##47##19##) can be written as:with the boundary conditions:where , , \n, , \n.
","Applying, NLM##UREF##69##72## to the nonlinear PDEs (##FORMU##52##23##)–(##FORMU##56##27##) results in:where and BCs are:where the coefficients in the system (##FORMU##64##28##)–(##FORMU##67##31##) are defined as:where
","In Eqs. (##FORMU##64##28##)–(##FORMU##65##33##) are a decoupled linear PDEs system where the terms subscripted by n are known from the previous iteration level, and the terms subscripted by n + 1 are the current approximation. The linearized system (##FORMU##64##28##)–(##FORMU##70##33##) is solved by CSCM in -direction and the two-point implicit finite difference approach in -direction, where Chebyshev polynomials are typically selected with their corresponding collocation points in the interval [− 1
The first order derivatives with respect to are discretized using or or . The following system for each line is obtained by applying CSCM to Eqs. (##FORMU##64##28##)–(##FORMU##70##33##):
","Subject to boundary conditions:
","Here, Eq. (##FORMU##105##38##)s coefficients are the coefficients stated in system (##FORMU##70##33##) expressed in vectors form. The system (##FORMU##105##38##) and (##FORMU##107##39##) is solved iteratively at , . The above Eqs. (##FORMU##105##38##) and (##FORMU##107##39##), at the point (), , can be determined as the following:
","Subject to boundary conditions where the coefficients in system (##FORMU##113##40##) are defined as:
","The iterative process of each the systems (##FORMU##113##40##), (##FORMU##114##41##) and (##FORMU##105##38##), (##FORMU##107##39##) is terminated if there is a difference of less than between the outcomes of two successive iterations. Subject to the BCs (32) hence, suitable initial approximations are:
","Once the MATLAB program has been established, we need to set the convergence standards. This requires identifying some important calculations: the proper step sizes (∆
Free convection relies on inherent buoyancy forces generated by thermal gradients to drive fluid motion and enable efficient heat transfer. Free convection optimizes the heat transfer processes in energy systems like solar collectors and power plants, reducing energy consumption and increasing system effectiveness. Industrial drying, cooling, and casting operations are made more efficient by free convection, in which molten metal is cast and solidified under better control. The electronics industry takes advantage of free convection by providing an innovative solution for reliable thermal management, effectively regulating the temperature of electronic components. Moreover, free convection is essential to the design and operation of heat exchangers and ventilation systems and contributes to our comprehension of complex environmental and geophysical phenomena##UREF##0##1##–##UREF##3##4##. Magnetohydrodynamics (MHD) represents an intriguing scientific field that improves the regulation of heat transmission, particularly in liquid metal cooling. MHD enables the use of magnetic fields to control electrically conducting fluids and enhance heat transfer processes. By applying a magnetic field, MHD induces electric currents that interact with the magnetic field, generating Lorentz forces that regulate fluid motion. The effective heat transfer made possible by this controlled fluid motion makes MHD a crucial component of advanced cooling systems for high-temperature applications. In several fields, such as nuclear power, aeronautical engineering, and advanced materials processing, where conventional cooling techniques may have limitations, MHD-based cooling systems have found applications##UREF##4##5##,##UREF##5##6##. On the other hand, the applications of thermal radiation span numerous sectors and industries. For the power sector, high-temperature operation, and aerospace applications, it is essential. Thermal radiation also plays a critical role in regulating energy transport, especially in polymer manufacturing. Furthermore, in solar energy-based industries, thermal radiation is employed in many applications, like solar energy collectors. Given the vast array of possibilities arising from the applications of free convection as well as the crucial functions of magnetic fields and thermal radiation in the realm of energy transfer, a multitude of numerical studies have delved into this issue. The findings of Sheikholeslami et al.’s##UREF##6##7## study demonstrated that an improvement in energy transport has a direct relationship with the radiation parameter. Additionally, the study found that the radiation parameter has a positive correlation with the Nusselt number. The results of El-Kabeir et al.##UREF##7##8## confirmed that as the magnetic force increases, both the skin-friction coefficient and heat transport rate decrease, while a contrasting pattern emerges when it comes to thermal radiation. As thermal radiation intensifies, an increase in the skin-friction coefficient and heat transport rate occurs. In a numerical study performed by Lone et al.##UREF##8##9##, it was revealed that an increase in the magnetic parameter amplifies velocity profiles in the x-direction while simultaneously diminishing them in the z-direction. The study also observed a correlation between an escalation in the magnetic field parameter and a decrease in skin friction, specifically along the x-direction. Additionally, the study found that the Nusselt number experienced a notable increase with an elevation in the thermal radiation parameter. See these intriguing numerical studies##UREF##9##10##–##UREF##12##13##.
\",\n \"Polar microfluids are frequently characterized as polar, isotropic liquids with no consideration for molecular deformation. Erringen##UREF##13##14## introduced the micropolar theory in 1966. His simple model, commonly referred to as the “micropolar model”, has gained considerable acceptance and has been adopted to describe the thermal behavior of actual liquids with an internal structure. The flow behavior of liquid crystals, suspension solutions, animal blood, and many other fluids can be characterized by a micropolar fluid model. In recent years, numerous research projects have focused on energy transport characteristics using the micropolar model. Nazar et al.##UREF##14##15##,##UREF##15##16## relied on the micropolar model to predict and analyze the thermal behavior of the fluid moving around a spherical object, considering constant wall temperature and heat flux. The findings of their studies indicate that as the micropolar factor increases, both wall temperature and skin friction exhibit a rising trend. Swalmeh et al.##UREF##16##17##,##UREF##17##18## extended the studies of Nazar et al. by considering nanofluid issues through the single-phase model. Their findings reveal that the temperature and velocity of Al2O3-H2O surpass those of Al2O3-kerosene oil. Furthermore, the energy transport rate of Cu-H2O exhibits a noticeable decline compared to Al2O3-H2O as the micro-rotation factor escalates. Nabwey et al.##UREF##18##19## examine the influence of Newtonian heating on magnetohydrodynamic heat transfer induced by natural means of polar nanoliquids across a spherical object. Their validated findings support the notion that the presence of the micropolar factor diminishes skin friction and the energy transport rate. Likewise, their observations indicate that incorporating the Newtonian heating factor enhances both skin friction and the energy transport rate. Other related studies can be found in Refs.##UREF##19##20##–##UREF##21##22##.
\",\n \"Control and management of energy and their related issues are increasingly recognized as one of mankind’s greatest challenges in the coming years to keep pace with the surge in industrialization and technology##UREF##22##23##,##UREF##23##24##. One of the innovative proposals is to optimize the performance of energy-transport fluids through the incorporation of metallic and ceramic ultrafine particles into the original fluid to form nanofluid. It all began with the study of Choi and Eastman##UREF##24##25##, who theoretically confirmed that the thermal conductivity of H2O can be markedly enhanced by including copper nanosolids. Afterwards, experimental and numerical studies continued, confirming that the thermal behavior of the reference fluid is significantly affected by nanosolids##UREF##25##26##–##UREF##31##32##. At present, nanofluids are evidently employed in a wide array of manufacturing and engineering applications, such as solar energy, heat exchangers, and cooling systems##UREF##32##33##–##UREF##36##37##. Hybrid nanomaterials are a developed class of nanomaterials fabricated from two nanoparticles to obtain the properties of their constituent materials. That is, the main objective of their synthesis is to create a compound with properties that combine thermal and rheological efficiency, as no single nanosolid can possess these properties##UREF##37##38##–##REF##36823230##43##. To acquire features that are more integrated, ternary hybrid nanosolids have been fabricated. Several studies have shown the thermal advantages of these upgraded nanocomposites over the previous class##UREF##42##44##–##UREF##45##47##. For numerical studies, Mahmood et al.##UREF##46##48## computationally simulated the unsteady magneto-flow of polymer trihybrid nanofluid around a sphere under the impact of ohmic heating. According to their findings, the magnetic factor and nanosolids concentration enhance heat distribution, while unsteadiness and rotation factors reduce it. In comparison to hybrid and original nanoliquids, tri-hybrid nanoliquids transport energy more rapidly. AlBaidani et al.##UREF##47##49## conducted a computational simulation to predict the enhancement of fin performance due to the use of tri-hybrid nanosolids, considering the shape factor of nanosolids and free convection. Their key findings indicate that the efficiency of energy performance is significantly influenced by thermal conductivity and free convection. Utilizing magnetic fields and thermal radiation proves to be effective in cooling fins. Tri-hybrid nanosolids enhance the efficiency of fins as opposed to hybrid nanosolids. See also##UREF##48##50##,##UREF##49##51##.
\",\n \"As a control parameter, the shape of the suspended nanoparticles is among the critical parameters that affect the thermophysical features of nanoliquids. Numerous earlier experimental and numerical publications have highlighted the influence of nanosolid shapes. A numerical study was carried out by Kumar et al.##UREF##50##52## to explore the flow and thermal features of nanoliquid in a thermally driven cavity. It was found that an increment in the values of the shape factor was accompanied by a significant enhancement in thermal conductivity. Sheikholeslami and Shamlooei##UREF##51##53## examined the flow of magnetized iron oxide–H2O nanoliquid in a permeable medium, considering the shape factor. Their study showed that platelet-shaped iron oxide nanoparticles achieved the maximum energy transfer rate. Khashi’ie et al.##UREF##52##54## analyzed the thermal characteristics of Cu–Al2O3/H2O hybrid nanoliquid flow past an EMHD sheet, considering the impact of radiation. Their results supported the idea that as the volume fraction factor values rise, blade-shaped nanosolids exhibit the maximum energy transport rate, while spherical nanosolids exhibit the lowest energy transport rate. Ghobadi and Hassankolaei##UREF##53##55## carried out a numerical simulation of magnetohydrodynamic hybrid nanoliquid flow across a stretching cylinder. They observed that lamina nanomaterials have a greater effect on the Nusselt number than hexagonal nanomaterials. Shanmugapriya et al.##UREF##54##56## presented a numerical simulation to explore the efficiency of energy transfer in MHD tri-hybrid nanoliquid on a radiative moving wedge. In their study, they compared the efficiency of energy transfer between different shapes of nanosolids. See##UREF##55##57##–##UREF##57##59## for more related studies.
\",\n \"By drawing upon the insights gained from previous studies. This work represents a natural progression from the investigations conducted by Nazar et al.##UREF##14##15##,##UREF##15##16## on micropolar fluid flow around a sphere to the more recent advances made by Swalmeh et al.##UREF##16##17##,##UREF##17##18## on micropolar nanofluids, along with the expansion that takes into account the micropolar hybrid nanofluid examined by Alkasasbeh et al.##UREF##58##60##. The novelty of the current study is to expand upon these findings by investigating the new problem of a micropolar tri-hybrid nanoliquid moving around a radiative spherical object with the application of a magnetic field. In addition to considering the impacts of a nanosolid’s shape on flow properties and energy transport and highlighting the influences of control factors on some physical groups associated with energy transit. Furthermore, this consideration plays an essential role in numerous physical and engineering applications that rely on heat transmission primarily via electrically conductive fluids. Its applications are considerably obvious, with biomedical applications and flow control around hypersonic and re-entry vehicles. Also, its outcomes could provide new insights into the design and optimization of energy transport systems that use ternary nanoliquids with tailored shapes of the nanosolids. It is anticipated and hoped that the results of this analysis will be beneficial for upcoming academic studies and, additionally, for engineering and practical applications. More precisely, this investigation will demonstrate the following issues: How do the magnetohydrodynamics (MHD) and micropolar tri-hybrid nanoliquid models construct the problem of free convection flow moving around a radiative spherical object? How can a mathematical model for the problem of MHD micropolar tri-hybrid nanoliquid be derived over a radiative spherical object? How does the MHD micropolar tri-hybrid nanoliquid model compare with the published natural heat transfer flow problems? How does the analysis of the numerical outcomes that can be obtained from the effects of MHD micropolar tri-hybrid parameters on the interested engineering physical quantities? How do the heat transfer behaviors of the utilized nanoparticles suspended in the original fluid change under the influence of the studied parameters?
Employing Hamilton and Crosser’s extended Maxwell model##UREF##59##61##, mono nanoliquids’ thermal conductivity, containing similar nanosolids of any shape, is calculated:
\",\n \"The mathematical expression for the viscosity of mono-nanoliquids, which takes into account the shape of nanosolids, is as follows (see##UREF##60##62##):where is the empirical shape factor, and is the particle’s sphericity, which is defined as the ratio of its spherical surface area to another shape’s surface area, considering both shapes have the same volumes. are the vicosity coefficients, which are calculated experimentally at room temperature. The coefficients of viscosity and shape factor of the nanoparticles employed in the current study are listed in Table ##TAB##0##1##.\\n
\",\n \"The density, specific heat capacity, and thermal expansion of tri-hybrid nanoliquids can be evaluated based on the model presented by Refs.##UREF##57##59##,##UREF##62##64## as follows:
\",\n \"Using the interpolation method, the viscosity, thermal conductivity, and electrical conductivity of tri-hybrid nanoliquids can be calculated by employing the following formulas (see##UREF##57##59##):
\",\n \"The subscriptions 1 and 2 indicate Al2O3 and Cu, respectively, while subscription 3 indicates graphene or MWCNT. is the accumulation nanoparticle volume fraction factor. The thermophysical features of the original fluid and the nanosolids utilized in the current study are presented in Table ##TAB##1##2##.\\n
\",\n \"Figure ##FIG##0##1## presents a visualization of the relationship between the nanosolids shape and the thermal conductivity ratio. The thermal conductivity ratio exhibits an upward trend as the surface area of nanosolids grows. Blade-shaped nanosolids demonstrate the highest thermal conductivity ratio, whilst spherical nanoparticles exhibit the lowest ratio. This confirms that the higher shape factor of nanosolids produces the highest ratio of thermal conductivity. Figure ##FIG##1##2## presents a visualization of the relationship between the nanosolids shape and the dynamic viscosity ratio. It is noted that nanosolids with larger elongations (like platelets and cylinders) give kerosene oil the maximum dynamic viscosity ratio due to the structure of these shapes. Therefore, relying on these nanosolid shapes gives the original fluid a higher boiling point, which, of course, enhances its energy-carrying capacity.
\",\n \"Suppose we have a two-dimensional free convection boundary layer flow of kerosene oil containing Al2O3 + Cu + graphene or Al2O3 + Cu + MWCNT around a solid sphere of radius
In light of the previous considerations and the Boussinesq boundary layer approximations, as well as employing the ternary hybrid nanofluids model, regarding magnetic, thermal radiation, and micropolar impacts, the continuity, momentum, energy, and micropolar equations are developed##UREF##19##20##,##UREF##58##60##,##UREF##66##68##:
\",\n \"It is noted that the vector g (gravity acceleration), that exists in Eq. (##FORMU##19##6##), is implicitly expressed in (
The mathematical model (##FORMU##33##11##)–(##FORMU##36##14##) can be reduced using the following non-similar transformation (stream function ):where .
\",\n \"Utilizing the non-similar transformations (##FORMU##42##15##) and using Eqs. (##FORMU##0##1##)–(##FORMU##1##4##) yields: subject to:
\",\n \"The skin friction
Using the Eqs. (##FORMU##49##21##), (##FORMU##29##10##), and (##FORMU##42##15##), we get:
\",\n \"In this section, the hybrid linearization spectral collocation technique (HLSC) combined Newton’s linearization method (NLM) with Chebyshev spectral collocation method (CSCM) in -direction. Firstly, NLM is utilized to linearize and decouple the nonlinear PDEs which are solved using Chebyshev spectral method (see##UREF##67##69##–##REF##31913322##71##).
\",\n \"System (##FORMU##44##16##)–(##FORMU##47##19##) can be written as:with the boundary conditions:where , , \\n, , \\n.
\",\n \"Applying, NLM##UREF##69##72## to the nonlinear PDEs (##FORMU##52##23##)–(##FORMU##56##27##) results in:where and BCs are:where the coefficients in the system (##FORMU##64##28##)–(##FORMU##67##31##) are defined as:where
\",\n \"In Eqs. (##FORMU##64##28##)–(##FORMU##65##33##) are a decoupled linear PDEs system where the terms subscripted by n are known from the previous iteration level, and the terms subscripted by n + 1 are the current approximation. The linearized system (##FORMU##64##28##)–(##FORMU##70##33##) is solved by CSCM in -direction and the two-point implicit finite difference approach in -direction, where Chebyshev polynomials are typically selected with their corresponding collocation points in the interval [− 1
The first order derivatives with respect to are discretized using or or . The following system for each line is obtained by applying CSCM to Eqs. (##FORMU##64##28##)–(##FORMU##70##33##):
\",\n \"Subject to boundary conditions:
\",\n \"Here, Eq. (##FORMU##105##38##)s coefficients are the coefficients stated in system (##FORMU##70##33##) expressed in vectors form. The system (##FORMU##105##38##) and (##FORMU##107##39##) is solved iteratively at , . The above Eqs. (##FORMU##105##38##) and (##FORMU##107##39##), at the point (), , can be determined as the following:
\",\n \"Subject to boundary conditions where the coefficients in system (##FORMU##113##40##) are defined as:
\",\n \"The iterative process of each the systems (##FORMU##113##40##), (##FORMU##114##41##) and (##FORMU##105##38##), (##FORMU##107##39##) is terminated if there is a difference of less than between the outcomes of two successive iterations. Subject to the BCs (32) hence, suitable initial approximations are:
\",\n \"Once the MATLAB program has been established, we need to set the convergence standards. This requires identifying some important calculations: the proper step sizes (∆
In this section, the hybrid linearization spectral collocation technique (HLSC) combined Newton’s linearization method (NLM) with Chebyshev spectral collocation method (CSCM) in -direction. Firstly, NLM is utilized to linearize and decouple the nonlinear PDEs which are solved using Chebyshev spectral method (see##UREF##67##69##–##REF##31913322##71##).
","System (##FORMU##44##16##)–(##FORMU##47##19##) can be written as:with the boundary conditions:where , , \n, , \n.
","Applying, NLM##UREF##69##72## to the nonlinear PDEs (##FORMU##52##23##)–(##FORMU##56##27##) results in:where and BCs are:where the coefficients in the system (##FORMU##64##28##)–(##FORMU##67##31##) are defined as:where
","In Eqs. (##FORMU##64##28##)–(##FORMU##65##33##) are a decoupled linear PDEs system where the terms subscripted by n are known from the previous iteration level, and the terms subscripted by n + 1 are the current approximation. The linearized system (##FORMU##64##28##)–(##FORMU##70##33##) is solved by CSCM in -direction and the two-point implicit finite difference approach in -direction, where Chebyshev polynomials are typically selected with their corresponding collocation points in the interval [− 1
The first order derivatives with respect to are discretized using or or . The following system for each line is obtained by applying CSCM to Eqs. (##FORMU##64##28##)–(##FORMU##70##33##):
","Subject to boundary conditions:
","Here, Eq. (##FORMU##105##38##)s coefficients are the coefficients stated in system (##FORMU##70##33##) expressed in vectors form. The system (##FORMU##105##38##) and (##FORMU##107##39##) is solved iteratively at , . The above Eqs. (##FORMU##105##38##) and (##FORMU##107##39##), at the point (), , can be determined as the following:
","Subject to boundary conditions where the coefficients in system (##FORMU##113##40##) are defined as:
","The iterative process of each the systems (##FORMU##113##40##), (##FORMU##114##41##) and (##FORMU##105##38##), (##FORMU##107##39##) is terminated if there is a difference of less than between the outcomes of two successive iterations. Subject to the BCs (32) hence, suitable initial approximations are:
","Once the MATLAB program has been established, we need to set the convergence standards. This requires identifying some important calculations: the proper step sizes (∆
In this section, the hybrid linearization spectral collocation technique (HLSC) combined Newton’s linearization method (NLM) with Chebyshev spectral collocation method (CSCM) in -direction. Firstly, NLM is utilized to linearize and decouple the nonlinear PDEs which are solved using Chebyshev spectral method (see##UREF##67##69##–##REF##31913322##71##).
\",\n \"System (##FORMU##44##16##)–(##FORMU##47##19##) can be written as:with the boundary conditions:where , , \\n, , \\n.
\",\n \"Applying, NLM##UREF##69##72## to the nonlinear PDEs (##FORMU##52##23##)–(##FORMU##56##27##) results in:where and BCs are:where the coefficients in the system (##FORMU##64##28##)–(##FORMU##67##31##) are defined as:where
\",\n \"In Eqs. (##FORMU##64##28##)–(##FORMU##65##33##) are a decoupled linear PDEs system where the terms subscripted by n are known from the previous iteration level, and the terms subscripted by n + 1 are the current approximation. The linearized system (##FORMU##64##28##)–(##FORMU##70##33##) is solved by CSCM in -direction and the two-point implicit finite difference approach in -direction, where Chebyshev polynomials are typically selected with their corresponding collocation points in the interval [− 1
The first order derivatives with respect to are discretized using or or . The following system for each line is obtained by applying CSCM to Eqs. (##FORMU##64##28##)–(##FORMU##70##33##):
\",\n \"Subject to boundary conditions:
\",\n \"Here, Eq. (##FORMU##105##38##)s coefficients are the coefficients stated in system (##FORMU##70##33##) expressed in vectors form. The system (##FORMU##105##38##) and (##FORMU##107##39##) is solved iteratively at , . The above Eqs. (##FORMU##105##38##) and (##FORMU##107##39##), at the point (), , can be determined as the following:
\",\n \"Subject to boundary conditions where the coefficients in system (##FORMU##113##40##) are defined as:
\",\n \"The iterative process of each the systems (##FORMU##113##40##), (##FORMU##114##41##) and (##FORMU##105##38##), (##FORMU##107##39##) is terminated if there is a difference of less than between the outcomes of two successive iterations. Subject to the BCs (32) hence, suitable initial approximations are:
\",\n \"Once the MATLAB program has been established, we need to set the convergence standards. This requires identifying some important calculations: the proper step sizes (∆
In this part, the simulation results are graphically presented, elaborated upon, and analyzed in order to offer a comprehensive grasp on the issue. In addition to providing physical explanations for the responses and behaviors of physical groups when affected by the key factors and analyzing their reflections on flow characteristics and energy transport by natural means. Al2O3 + Cu + Graphene/KO and Al2O3 + Cu + MWCNT/KO are the used ternary hybrid nanofluids, assuming the graphene nanosolids are shaped like platelets, MWCNT is cylindrical, and the other nanosolids are spherical. Figure ##FIG##3##4## describes the influence of augmentation of the volume fraction factor of nanosolids on the Nusselt number. The rise in the factor ameliorates the Nusselt number in response to the remarkable improvement in the thermal conductivity of kerosene oil when the values of this factor are increased. This means the augmentation of the volume fraction factor enhances the convective heat transfer process in the kerosene oil. Likewise, skin friction adopts the same behavior when affected by increasing the volume fraction factor, as shown in Fig. ##FIG##4##5##. This implies that there is a stronger resistance to the flow of fluid over a surface, indicating a higher drag force or frictional force acting on the fluid. Figure ##FIG##5##6## shows a visualization of the relationship between the increase in magnetic field strength and the Nusselt number. Augmentation of the magnetic factor triggers a brake in fluid motion, which is followed by a diminished convective heat transport, and this means that the Nusselt number will minimize. In consideration of the fact that the magnetic factor is inversely related to the motion of fluids, the drag forces experienced by the fluid also diminish, which negatively affects the values of skin friction, which in turn tends to reduce; this behavior is clearly shown in Fig. ##FIG##6##7##. Figure ##FIG##7##8## depicts the extent of the change in the Nusselt number if the micropolar factor values are raised. An increase in the polar factor raises the viscosity of the tri-hybrid nanoliquid, which inhibits its motion and, as a result, reduces its ability to transmit heat. Figure ##FIG##8##9## clarifies the opposite response of skin friction caused by elevated micropolar factor values. In situations where the micropolar factor is elevated, the result is a liquid with a higher viscosity, as previously stated. This restricts liquid motion and actually weakens frictional forces. Figures ##FIG##9##10## and ##FIG##10##11## illustrate how the Nusselt number and drag force depend on the radiation factor. The radiation factor serves as an auxiliary energy source, enhancing the efficacy of both heat transmission and frictional forces. Thereby, it can be indicated that the energy transport and frictional forces of the tri-hybrid polar liquid increase as the amount of emitted thermal radiation increases. The dependence of velocity profiles, angular velocity profiles, and temperature profiles on the magnetic factor is shown in Figs. ##FIG##11##12##, ##FIG##12##13## and ##FIG##13##14##, respectively. The increment in the magnetic factor means that the magnetic field strength will increase, and this causes a brake in the flow process, or, in other words, it strengthens the resistance of the tri-hybrid liquid’s particles to movement, which will diminish its velocity and angular velocity, while raising its temperature. Figures ##FIG##14##15##, ##FIG##15##16## and ##FIG##16##17## are plotted to explore the behaviors of velocity profiles, angular velocity profiles, and temperature profiles under the impact of the volume fraction parameter, respectively. According to the results of the current study and previous studies, elevating the volume fraction values increases the thermal conductivity of the original fluid. This enhances the fluid’s heat-transporting efficiency. Consequently, its velocity and temperature will increase. On the contrary, Fig. ##FIG##15##16## reveals a negative relationship between the angular velocity of the original fluid and the volume fraction factor. Figures ##FIG##17##18##, ##FIG##18##19## and ##FIG##19##20## visualize the trends of the velocity profiles, angular velocity profiles, and temperature profiles under the influence of the polar factor. By increasing the polar factor values, the temperature and angular velocity contours tend to rise, whereas the velocity contours tend to decline. This generally happens since increasing the polar factor enhances nanofluid viscosity. The positive effect of the thermal radiation factor on velocity and temperature and its negative effect on angular velocity are clearly shown in Figs. ##FIG##20##21##, ##FIG##21##22## and ##FIG##22##23##. This behavior may be explained by the fact that an increase in the amount of radiation emitted results in adding additional energy sources to the micropolar liquid, which in turn enhances its velocity and temperature.
"],"string":"[\n \"In this part, the simulation results are graphically presented, elaborated upon, and analyzed in order to offer a comprehensive grasp on the issue. In addition to providing physical explanations for the responses and behaviors of physical groups when affected by the key factors and analyzing their reflections on flow characteristics and energy transport by natural means. Al2O3 + Cu + Graphene/KO and Al2O3 + Cu + MWCNT/KO are the used ternary hybrid nanofluids, assuming the graphene nanosolids are shaped like platelets, MWCNT is cylindrical, and the other nanosolids are spherical. Figure ##FIG##3##4## describes the influence of augmentation of the volume fraction factor of nanosolids on the Nusselt number. The rise in the factor ameliorates the Nusselt number in response to the remarkable improvement in the thermal conductivity of kerosene oil when the values of this factor are increased. This means the augmentation of the volume fraction factor enhances the convective heat transfer process in the kerosene oil. Likewise, skin friction adopts the same behavior when affected by increasing the volume fraction factor, as shown in Fig. ##FIG##4##5##. This implies that there is a stronger resistance to the flow of fluid over a surface, indicating a higher drag force or frictional force acting on the fluid. Figure ##FIG##5##6## shows a visualization of the relationship between the increase in magnetic field strength and the Nusselt number. Augmentation of the magnetic factor triggers a brake in fluid motion, which is followed by a diminished convective heat transport, and this means that the Nusselt number will minimize. In consideration of the fact that the magnetic factor is inversely related to the motion of fluids, the drag forces experienced by the fluid also diminish, which negatively affects the values of skin friction, which in turn tends to reduce; this behavior is clearly shown in Fig. ##FIG##6##7##. Figure ##FIG##7##8## depicts the extent of the change in the Nusselt number if the micropolar factor values are raised. An increase in the polar factor raises the viscosity of the tri-hybrid nanoliquid, which inhibits its motion and, as a result, reduces its ability to transmit heat. Figure ##FIG##8##9## clarifies the opposite response of skin friction caused by elevated micropolar factor values. In situations where the micropolar factor is elevated, the result is a liquid with a higher viscosity, as previously stated. This restricts liquid motion and actually weakens frictional forces. Figures ##FIG##9##10## and ##FIG##10##11## illustrate how the Nusselt number and drag force depend on the radiation factor. The radiation factor serves as an auxiliary energy source, enhancing the efficacy of both heat transmission and frictional forces. Thereby, it can be indicated that the energy transport and frictional forces of the tri-hybrid polar liquid increase as the amount of emitted thermal radiation increases. The dependence of velocity profiles, angular velocity profiles, and temperature profiles on the magnetic factor is shown in Figs. ##FIG##11##12##, ##FIG##12##13## and ##FIG##13##14##, respectively. The increment in the magnetic factor means that the magnetic field strength will increase, and this causes a brake in the flow process, or, in other words, it strengthens the resistance of the tri-hybrid liquid’s particles to movement, which will diminish its velocity and angular velocity, while raising its temperature. Figures ##FIG##14##15##, ##FIG##15##16## and ##FIG##16##17## are plotted to explore the behaviors of velocity profiles, angular velocity profiles, and temperature profiles under the impact of the volume fraction parameter, respectively. According to the results of the current study and previous studies, elevating the volume fraction values increases the thermal conductivity of the original fluid. This enhances the fluid’s heat-transporting efficiency. Consequently, its velocity and temperature will increase. On the contrary, Fig. ##FIG##15##16## reveals a negative relationship between the angular velocity of the original fluid and the volume fraction factor. Figures ##FIG##17##18##, ##FIG##18##19## and ##FIG##19##20## visualize the trends of the velocity profiles, angular velocity profiles, and temperature profiles under the influence of the polar factor. By increasing the polar factor values, the temperature and angular velocity contours tend to rise, whereas the velocity contours tend to decline. This generally happens since increasing the polar factor enhances nanofluid viscosity. The positive effect of the thermal radiation factor on velocity and temperature and its negative effect on angular velocity are clearly shown in Figs. ##FIG##20##21##, ##FIG##21##22## and ##FIG##22##23##. This behavior may be explained by the fact that an increase in the amount of radiation emitted results in adding additional energy sources to the micropolar liquid, which in turn enhances its velocity and temperature.
\"\n]"},"discussion":{"kind":"list like","value":["In this part, the simulation results are graphically presented, elaborated upon, and analyzed in order to offer a comprehensive grasp on the issue. In addition to providing physical explanations for the responses and behaviors of physical groups when affected by the key factors and analyzing their reflections on flow characteristics and energy transport by natural means. Al2O3 + Cu + Graphene/KO and Al2O3 + Cu + MWCNT/KO are the used ternary hybrid nanofluids, assuming the graphene nanosolids are shaped like platelets, MWCNT is cylindrical, and the other nanosolids are spherical. Figure ##FIG##3##4## describes the influence of augmentation of the volume fraction factor of nanosolids on the Nusselt number. The rise in the factor ameliorates the Nusselt number in response to the remarkable improvement in the thermal conductivity of kerosene oil when the values of this factor are increased. This means the augmentation of the volume fraction factor enhances the convective heat transfer process in the kerosene oil. Likewise, skin friction adopts the same behavior when affected by increasing the volume fraction factor, as shown in Fig. ##FIG##4##5##. This implies that there is a stronger resistance to the flow of fluid over a surface, indicating a higher drag force or frictional force acting on the fluid. Figure ##FIG##5##6## shows a visualization of the relationship between the increase in magnetic field strength and the Nusselt number. Augmentation of the magnetic factor triggers a brake in fluid motion, which is followed by a diminished convective heat transport, and this means that the Nusselt number will minimize. In consideration of the fact that the magnetic factor is inversely related to the motion of fluids, the drag forces experienced by the fluid also diminish, which negatively affects the values of skin friction, which in turn tends to reduce; this behavior is clearly shown in Fig. ##FIG##6##7##. Figure ##FIG##7##8## depicts the extent of the change in the Nusselt number if the micropolar factor values are raised. An increase in the polar factor raises the viscosity of the tri-hybrid nanoliquid, which inhibits its motion and, as a result, reduces its ability to transmit heat. Figure ##FIG##8##9## clarifies the opposite response of skin friction caused by elevated micropolar factor values. In situations where the micropolar factor is elevated, the result is a liquid with a higher viscosity, as previously stated. This restricts liquid motion and actually weakens frictional forces. Figures ##FIG##9##10## and ##FIG##10##11## illustrate how the Nusselt number and drag force depend on the radiation factor. The radiation factor serves as an auxiliary energy source, enhancing the efficacy of both heat transmission and frictional forces. Thereby, it can be indicated that the energy transport and frictional forces of the tri-hybrid polar liquid increase as the amount of emitted thermal radiation increases. The dependence of velocity profiles, angular velocity profiles, and temperature profiles on the magnetic factor is shown in Figs. ##FIG##11##12##, ##FIG##12##13## and ##FIG##13##14##, respectively. The increment in the magnetic factor means that the magnetic field strength will increase, and this causes a brake in the flow process, or, in other words, it strengthens the resistance of the tri-hybrid liquid’s particles to movement, which will diminish its velocity and angular velocity, while raising its temperature. Figures ##FIG##14##15##, ##FIG##15##16## and ##FIG##16##17## are plotted to explore the behaviors of velocity profiles, angular velocity profiles, and temperature profiles under the impact of the volume fraction parameter, respectively. According to the results of the current study and previous studies, elevating the volume fraction values increases the thermal conductivity of the original fluid. This enhances the fluid’s heat-transporting efficiency. Consequently, its velocity and temperature will increase. On the contrary, Fig. ##FIG##15##16## reveals a negative relationship between the angular velocity of the original fluid and the volume fraction factor. Figures ##FIG##17##18##, ##FIG##18##19## and ##FIG##19##20## visualize the trends of the velocity profiles, angular velocity profiles, and temperature profiles under the influence of the polar factor. By increasing the polar factor values, the temperature and angular velocity contours tend to rise, whereas the velocity contours tend to decline. This generally happens since increasing the polar factor enhances nanofluid viscosity. The positive effect of the thermal radiation factor on velocity and temperature and its negative effect on angular velocity are clearly shown in Figs. ##FIG##20##21##, ##FIG##21##22## and ##FIG##22##23##. This behavior may be explained by the fact that an increase in the amount of radiation emitted results in adding additional energy sources to the micropolar liquid, which in turn enhances its velocity and temperature.
"],"string":"[\n \"In this part, the simulation results are graphically presented, elaborated upon, and analyzed in order to offer a comprehensive grasp on the issue. In addition to providing physical explanations for the responses and behaviors of physical groups when affected by the key factors and analyzing their reflections on flow characteristics and energy transport by natural means. Al2O3 + Cu + Graphene/KO and Al2O3 + Cu + MWCNT/KO are the used ternary hybrid nanofluids, assuming the graphene nanosolids are shaped like platelets, MWCNT is cylindrical, and the other nanosolids are spherical. Figure ##FIG##3##4## describes the influence of augmentation of the volume fraction factor of nanosolids on the Nusselt number. The rise in the factor ameliorates the Nusselt number in response to the remarkable improvement in the thermal conductivity of kerosene oil when the values of this factor are increased. This means the augmentation of the volume fraction factor enhances the convective heat transfer process in the kerosene oil. Likewise, skin friction adopts the same behavior when affected by increasing the volume fraction factor, as shown in Fig. ##FIG##4##5##. This implies that there is a stronger resistance to the flow of fluid over a surface, indicating a higher drag force or frictional force acting on the fluid. Figure ##FIG##5##6## shows a visualization of the relationship between the increase in magnetic field strength and the Nusselt number. Augmentation of the magnetic factor triggers a brake in fluid motion, which is followed by a diminished convective heat transport, and this means that the Nusselt number will minimize. In consideration of the fact that the magnetic factor is inversely related to the motion of fluids, the drag forces experienced by the fluid also diminish, which negatively affects the values of skin friction, which in turn tends to reduce; this behavior is clearly shown in Fig. ##FIG##6##7##. Figure ##FIG##7##8## depicts the extent of the change in the Nusselt number if the micropolar factor values are raised. An increase in the polar factor raises the viscosity of the tri-hybrid nanoliquid, which inhibits its motion and, as a result, reduces its ability to transmit heat. Figure ##FIG##8##9## clarifies the opposite response of skin friction caused by elevated micropolar factor values. In situations where the micropolar factor is elevated, the result is a liquid with a higher viscosity, as previously stated. This restricts liquid motion and actually weakens frictional forces. Figures ##FIG##9##10## and ##FIG##10##11## illustrate how the Nusselt number and drag force depend on the radiation factor. The radiation factor serves as an auxiliary energy source, enhancing the efficacy of both heat transmission and frictional forces. Thereby, it can be indicated that the energy transport and frictional forces of the tri-hybrid polar liquid increase as the amount of emitted thermal radiation increases. The dependence of velocity profiles, angular velocity profiles, and temperature profiles on the magnetic factor is shown in Figs. ##FIG##11##12##, ##FIG##12##13## and ##FIG##13##14##, respectively. The increment in the magnetic factor means that the magnetic field strength will increase, and this causes a brake in the flow process, or, in other words, it strengthens the resistance of the tri-hybrid liquid’s particles to movement, which will diminish its velocity and angular velocity, while raising its temperature. Figures ##FIG##14##15##, ##FIG##15##16## and ##FIG##16##17## are plotted to explore the behaviors of velocity profiles, angular velocity profiles, and temperature profiles under the impact of the volume fraction parameter, respectively. According to the results of the current study and previous studies, elevating the volume fraction values increases the thermal conductivity of the original fluid. This enhances the fluid’s heat-transporting efficiency. Consequently, its velocity and temperature will increase. On the contrary, Fig. ##FIG##15##16## reveals a negative relationship between the angular velocity of the original fluid and the volume fraction factor. Figures ##FIG##17##18##, ##FIG##18##19## and ##FIG##19##20## visualize the trends of the velocity profiles, angular velocity profiles, and temperature profiles under the influence of the polar factor. By increasing the polar factor values, the temperature and angular velocity contours tend to rise, whereas the velocity contours tend to decline. This generally happens since increasing the polar factor enhances nanofluid viscosity. The positive effect of the thermal radiation factor on velocity and temperature and its negative effect on angular velocity are clearly shown in Figs. ##FIG##20##21##, ##FIG##21##22## and ##FIG##22##23##. This behavior may be explained by the fact that an increase in the amount of radiation emitted results in adding additional energy sources to the micropolar liquid, which in turn enhances its velocity and temperature.
\"\n]"},"conclusion":{"kind":"list like","value":["Centralizing on filling the research gap by considering the effect of MHD micropolar ternary hybrid nanofluids, the current study considers the nanosolids’ shapes via a mathematical model of the flow of the magnetized micropolar ternary nanoliquid around a spherical shape with thermal radiation effects, which was successfully constructed. On the other hand, the spectral collocation technique (HLSC) has been employed to solve the PDEs and get new numerical outcomes that combine the effects of MHD micropolar ternary hybrid nanofluid parameters that were not studied in the same model. Consequently, we obtained new results that were compared with previous literature and came to an excellent agreement. Moreover, it can contribute to the establishment of future studies based on this study. Depending on that, this study has drawn the following key conclusions: Blade nanosolids give the maximal thermal conductivity ratio, while spherical nanosolids give the minimal ratio. Nanosolids with larger elongations offer kerosene oil the greatest dynamic viscosity ratio. The fluid velocity, frictional forces, and energy transport rate are all suppressed when the micropolar or magnetic factor values rise. As the volume fraction factor values get higher, temperature, velocity, and angular velocity all rise. All examined physical quantities elevate due to the augmentation in radiation factor values. As the volume fraction factor increases, the average percentage improvement in convective heat transfer for Al2O3 + Cu + MWCNT/kerosene oil compared to Al2O3 + Cu + graphene/kerosene oil approximately ranges from 0.8 to 2.6%.
Depending on this investigation, there is a lot of future research that can be examined for coming studies. The same problem can be expanded in future work utilizing other mathematical models, such as the Casson model, and it can also develop to comprise ternary hybrid nanofluids with viscous dissipation and Joule heating impacts and incorporated.
"],"string":"[\n \"Centralizing on filling the research gap by considering the effect of MHD micropolar ternary hybrid nanofluids, the current study considers the nanosolids’ shapes via a mathematical model of the flow of the magnetized micropolar ternary nanoliquid around a spherical shape with thermal radiation effects, which was successfully constructed. On the other hand, the spectral collocation technique (HLSC) has been employed to solve the PDEs and get new numerical outcomes that combine the effects of MHD micropolar ternary hybrid nanofluid parameters that were not studied in the same model. Consequently, we obtained new results that were compared with previous literature and came to an excellent agreement. Moreover, it can contribute to the establishment of future studies based on this study. Depending on that, this study has drawn the following key conclusions: Blade nanosolids give the maximal thermal conductivity ratio, while spherical nanosolids give the minimal ratio. Nanosolids with larger elongations offer kerosene oil the greatest dynamic viscosity ratio. The fluid velocity, frictional forces, and energy transport rate are all suppressed when the micropolar or magnetic factor values rise. As the volume fraction factor values get higher, temperature, velocity, and angular velocity all rise. All examined physical quantities elevate due to the augmentation in radiation factor values. As the volume fraction factor increases, the average percentage improvement in convective heat transfer for Al2O3 + Cu + MWCNT/kerosene oil compared to Al2O3 + Cu + graphene/kerosene oil approximately ranges from 0.8 to 2.6%.
Depending on this investigation, there is a lot of future research that can be examined for coming studies. The same problem can be expanded in future work utilizing other mathematical models, such as the Casson model, and it can also develop to comprise ternary hybrid nanofluids with viscous dissipation and Joule heating impacts and incorporated.
\"\n]"},"front":{"kind":"list like","value":["The control and management of energy and their associated issues are increasingly recognized as one of mankind’s greatest challenges in the coming years to keep pace with the surge in industrialization and technology. Free convection optimizes the heat transfer processes in energy systems like solar collectors and power plants, reducing energy consumption and increasing system effectiveness. Further, studying and analyzing critical factors like magnetic fields, thermal radiation, and the shape of nanoparticles can assist in the control of fluid motion and improve the efficiency of heat transfer processes in a wide range of real-world applications, such as the power sector, aerospace applications, molten metal, nuclear power, and aeronautical engineering. This study aims to scrutinize the thermal performance of a magneto tri-hybrid polar nanoliquid flowing over a radiative sphere, considering the nanosolids’ shape. The single-phase model is developed to acquire the problems governing equations, and the hybrid linearization spectral collection approach is utilized to approximate the solution. The present findings reveal that blade-shaped nanosolids exhibit the highest thermal conductivity ratio when incorporated into the base fluid, whereas spherical nanosolids exhibit the lowest ratio. Volume fraction and thermal radiation factors have an effective role in raising fluid velocity and thermal performance. The magnetic and microapolar factors significantly suppress fluid velocity and energy transfer. As the volume fraction factor increases, the average percentage improvement in convective heat transfer for Al2O3 + Cu + MWCNT/kerosene oil compared to Al2O3 + Cu + graphene/kerosene oil approximately ranges from 0.8 to 2.6%.
","The control and management of energy and their associated issues are increasingly recognized as one of mankind’s greatest challenges in the coming years to keep pace with the surge in industrialization and technology. Free convection optimizes the heat transfer processes in energy systems like solar collectors and power plants, reducing energy consumption and increasing system effectiveness. Further, studying and analyzing critical factors like magnetic fields, thermal radiation, and the shape of nanoparticles can assist in the control of fluid motion and improve the efficiency of heat transfer processes in a wide range of real-world applications, such as the power sector, aerospace applications, molten metal, nuclear power, and aeronautical engineering. This study aims to scrutinize the thermal performance of a magneto tri-hybrid polar nanoliquid flowing over a radiative sphere, considering the nanosolids’ shape. The single-phase model is developed to acquire the problems governing equations, and the hybrid linearization spectral collection approach is utilized to approximate the solution. The present findings reveal that blade-shaped nanosolids exhibit the highest thermal conductivity ratio when incorporated into the base fluid, whereas spherical nanosolids exhibit the lowest ratio. Volume fraction and thermal radiation factors have an effective role in raising fluid velocity and thermal performance. The magnetic and microapolar factors significantly suppress fluid velocity and energy transfer. As the volume fraction factor increases, the average percentage improvement in convective heat transfer for Al2O3 + Cu + MWCNT/kerosene oil compared to Al2O3 + Cu + graphene/kerosene oil approximately ranges from 0.8 to 2.6%.
\",\n \"This project was fully implemented at Suez University Egypt.
","E.A.E. methodology, software and validation F.A.A. and F.A. formal analysis, investigation, and resources, M.Z.S. writing—original draft preparation. All authors reviewed the final version of the manuscript.
","The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
","The authors declare no competing interests.
"],"string":"[\n \"This project was fully implemented at Suez University Egypt.
\",\n \"E.A.E. methodology, software and validation F.A.A. and F.A. formal analysis, investigation, and resources, M.Z.S. writing—original draft preparation. All authors reviewed the final version of the manuscript.
\",\n \"The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
\",\n \"The authors declare no competing interests.
\"\n]"},"figure":{"kind":"list like","value":["Thermal conductivity versus shape factor.
Dynamic viscosity versus shape factor.
Physical configuration.
Nusselt number vs. volume fraction factor.
Skin friction vs. volume fraction factor.
Nusselt number vs magnetic factor.
Skin friction vs magnetic factor.
Nusselt number vs micropolar factor.
Skin friction vs micropolar factor.
Nusselt number vs radiation factor.
Skin friction vs radiation factor.
Velocity vs magnetic factor.
Angular velocity vs magnetic factor.
Temperature vs magnetic factor.
Velocity vs volume fraction factor.
Angular velocity vs volume fraction factor.
Temperature vs volume fraction factor.
Velocity vs micropolar factor.
Angular velocity vs micropolar factor.
Temperature vs micropolar factor.
Velocity vs radiation factor.
Angular velocity vs radiation factor.
Temperature vs radiation factor.
Thermal conductivity versus shape factor.
Dynamic viscosity versus shape factor.
Physical configuration.
Nusselt number vs. volume fraction factor.
Skin friction vs. volume fraction factor.
Nusselt number vs magnetic factor.
Skin friction vs magnetic factor.
Nusselt number vs micropolar factor.
Skin friction vs micropolar factor.
Nusselt number vs radiation factor.
Skin friction vs radiation factor.
Velocity vs magnetic factor.
Angular velocity vs magnetic factor.
Temperature vs magnetic factor.
Velocity vs volume fraction factor.
Angular velocity vs volume fraction factor.
Temperature vs volume fraction factor.
Velocity vs micropolar factor.
Angular velocity vs micropolar factor.
Temperature vs micropolar factor.
Velocity vs radiation factor.
Angular velocity vs radiation factor.
Temperature vs radiation factor.
Coefficients of viscosity and shape factor##UREF##60##62##,##UREF##61##63##.
| Shape of nanosolid | Viscosity coefficient | Sphericity \n | Shape factor (\n | |
|---|---|---|---|---|
| \n\n | \n\n | |||
| Platelets | 37.1 | 612.6 | 0.52 | 5.7 |
| Blades | 14.6 | 123.3 | 0.36 | 8.6 |
| Cylinders | 13.5 | 904.4 | 0.62 | 4.9 |
| Bricks | 1.9 | 471.4 | 0.81 | 3.7 |
| Sphere | 2.5 | 6.2 | 1 | 3 |
Thermophysical features of original fluid and nanosolids##UREF##57##59##,##UREF##63##65##–##UREF##65##67##.
| Thermo-physical feature | Kerosene Oil (KO) | Al2O3 | Cu | Graphene | MWCNT |
|---|---|---|---|---|---|
| \n(J/kg K) | 2090 | 773 | 385 | 790 | 740 |
| \n×10−5 (K−1) | 22.85 | 0.85 | 1.67 | − 0.8 | 44 |
| 783 | 3970 | 8933 | 2200 | 2600 | |
| 0.15 | 40 | 401 | 5000 | 3000 | |
| 5 × 10–11 | 1.12 105 | 3.5 × 107 | 1 × 10–7 | 1.9 10–4 | |
| Pr | 22.85 | … | … | … | … |
Comparison of Nazar et al.##UREF##15##16## results with the current results for .
| Nazar et al. ##UREF##15##16## results Pr = 7 | Present results Pr = 7 | |
|---|---|---|
| 0° | 0.9595 | 0.9575 |
| 10° | 0.9572 | 0.9553 |
| 20° | 0.9506 | 0.9489 |
| 30° | 0.9397 | 0.9382 |
| 40° | 0.9243 | 0.9230 |
| 50° | 0.9045 | 0.9034 |
| 60° | 0.8801 | 0.8793 |
| 70° | 0.8510 | 0.8503 |
| 80° | 0.8168 | 0.8163 |
| 90° | 0.7792 | 0.7770 |
| 100° | – | 0.7321 |
| 110° | – | 0.6808 |
| 120° | – | 0.6226 |
Coefficients of viscosity and shape factor##UREF##60##62##,##UREF##61##63##.
| Shape of nanosolid | Viscosity coefficient | Sphericity \\n | Shape factor (\\n | |
|---|---|---|---|---|
| \\n\\n | \\n\\n | |||
| Platelets | 37.1 | 612.6 | 0.52 | 5.7 |
| Blades | 14.6 | 123.3 | 0.36 | 8.6 |
| Cylinders | 13.5 | 904.4 | 0.62 | 4.9 |
| Bricks | 1.9 | 471.4 | 0.81 | 3.7 |
| Sphere | 2.5 | 6.2 | 1 | 3 |
Thermophysical features of original fluid and nanosolids##UREF##57##59##,##UREF##63##65##–##UREF##65##67##.
| Thermo-physical feature | Kerosene Oil (KO) | Al2O3 | Cu | Graphene | MWCNT |
|---|---|---|---|---|---|
| \\n(J/kg K) | 2090 | 773 | 385 | 790 | 740 |
| \\n×10−5 (K−1) | 22.85 | 0.85 | 1.67 | − 0.8 | 44 |
| 783 | 3970 | 8933 | 2200 | 2600 | |
| 0.15 | 40 | 401 | 5000 | 3000 | |
| 5 × 10–11 | 1.12 105 | 3.5 × 107 | 1 × 10–7 | 1.9 10–4 | |
| Pr | 22.85 | … | … | … | … |
Comparison of Nazar et al.##UREF##15##16## results with the current results for .
| Nazar et al. ##UREF##15##16## results Pr = 7 | Present results Pr = 7 | |
|---|---|---|
| 0° | 0.9595 | 0.9575 |
| 10° | 0.9572 | 0.9553 |
| 20° | 0.9506 | 0.9489 |
| 30° | 0.9397 | 0.9382 |
| 40° | 0.9243 | 0.9230 |
| 50° | 0.9045 | 0.9034 |
| 60° | 0.8801 | 0.8793 |
| 70° | 0.8510 | 0.8503 |
| 80° | 0.8168 | 0.8163 |
| 90° | 0.7792 | 0.7770 |
| 100° | – | 0.7321 |
| 110° | – | 0.6808 |
| 120° | – | 0.6226 |
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