A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base.pdf
RESEARCH Open Access A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base Marcia Glaze Wyatt1* and John M Peters2 Abstract Results of previous studies support the existence of a spatially coherent, secularly varying climate signal, propagating through a network of synchronized climate indices across the Northern Hemisphere during the 20th century. The signal was identified in both instrumental and proxy data sets. In this present study, we seek to detect this same low-frequency signal propagating hemispherically through networks of model-simulated climate indices. These simulated climate indices were reconstructed from a data set generated by models of the third Coupled Model Intercomparison Project (CMIP3). Methods used in the earlier studies on climate-signal propagation guide the strategy for this companion study, for which 60 network analyses were performed. Most analyses focused on 20th century behavior, several on pre-industrial conditions. None succeeded in reproducing a hemispherically propagating signal. In light of previous results, we offer possible reasons for this finding. Among them is speculation on whether mechanisms relevant to signal propagation might be missing from this suite of general circulation models. Keywords: Climate, Network, Synchronize, CMIP3, Stadium-wave, Signal propagation 1 Introduction Recent work using instrumental data of the 20th century suggests that a spatially coherent, low-frequency climate signal propagates across the Northern Hemisphere (Wyatt et al. 2011). Authors of this 2011 paper analyzed a lagged covariance structure of a network of eight cli- mate indices. Their results detailed the transmission of a multidecadal-scale climate signal propagating through- out the Northern Hemisphere through a sequence of synchronizeda atmospheric and lagged oceanic telecon- nections. The authors termed this signal propagation the ‘stadium wave’ - a term alluding to the behavior often seen in a sports arena, where successive groups of spec- tators stand with arms raised, and then sit, giving the visual impression of a wave passing through the crowd. Subsequent dissertation work by Wyatt ((2012) and sub- mitted manuscript (2013)) probes both spatially expanded data sets of geophysical indices and temporally expanded data sets of proxies (1700 to 2000). Hemispheric signal propagation is found in all sets. All network combinations of twentieth-century data, both proxy and instrumental, reflect consistent results of apparent quasi-periodicity and signal propagation. Prior to 1850, signal propagation is evident; yet time scale of variability in the proxy-index networks differs slightly from the time scale of variability exhibited by the signal seen in all data sets post-1850. This latter observation brings up an important point. The significant finding regarding the ‘stadium-wave’ signal identified in these diverse index sets is its propagating sig- nature. Timescale of its variability can be characterized as low-frequency, but we stop short of claiming periodicity, or even quasi-periodicity. This we cannot statistically as- sess. While all 20th-century data sets - the originally used eight-member instrumental set, the 20th century portion of the proxy set, and the spatially expanded instrumental set - reflect similar secular-scaleb variability centered at ~64 years over this century-scale interval; a one-hundred- year time series is too short a quantity over which to claim identification of a statistically significant multi-decadal- scale periodicity. In addition, proxy data sets, while longer, * Correspondence: marciawyatt@earthlink.net 1Department of Geologic Sciences, CIRES/INSTAAR, Benson Earth Sciences Building, University of Colorado-Boulder, Boulder, CO 80309, USA Full list of author information is available at the end of the article a SpringerOpen Journal © 2012 Wyatt and Peters; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Wyatt and Peters SpringerPlus 2012, 1:68 http://www.springerplus.com/content/1/1/68 are inherently noisy, thereby adding challenge to statistical significance assessment. These points made, it is hard to ignore the pervasiveness of multidecadal (~50 to 70-year) fluctuations identified in records of diverse, and perhaps indirectly related indices: from numerous and varied climate-related parameters (e.g. Kushnir 1994; Minobe 1997, Minobe 1999; Klyashtorin and Lyubushin 2007; Frolov et al. 2009 and references therein; Nowak et al. 2011; Chambers Don et al. 2012) to similarly cadenced variations in commercial-fish populations (Beamish and Bouillon 1993; Beamish et al. 1997, 1999; Chavez et al. 2003; Klyashtorin 1998; Klyashtorin and Lyubushin 2007; Klyashtorin et al. 2009), cosmogenic nuclide accumulations (Ogurtsov et al. 2002; Patterson et al. 2004), geomagnetic- field intensity (Courtillot et al. 2007, Roberts et al. 2007), Earth’s rotational-rate anomalies (Beamish et al. 1999; Sidorenkov et al. 2005; Sidorenkov 2005, Sidorenkov 2009), and solar-related aurora records of the mid-latitudes (Scafetta 2011). While the authors of the previous stadium-wave studies were unable to assign statistical significance to a quasi-oscillatory nature of the identified signal in observed and proxy data, they were able to quantify the likelihood that a low-frequency signal, characterized by delayed alignment of spatially and dynamically diverse indices, i.e. a hemispherically propagating signal, could be due to mere random chance. That likelihood was found to be less than 5% in observational data sets for the 20th century. Beyond instrumental and proxy data, the last realm of data available to us is model-generated data. We want to know if further support for the stadium-wave signal can be identified by applying our statistical methods to simu- lated indices reconstructed from model-generated raw variables. Section 2 details the methods and data; section 3 pro- vides the results; section 4 offers discussion of results; and 5 presents the conclusion. 2 Approach, data sets, and methods 2.1 Approach The strategy underlying all stadium-wave studies involves evaluating collective behavior within a network of syn- chronized interacting nodes. In the case of climate, these nodes represent climate indices - regional patterns of ocean, atmosphere, or ice dynamics, for examples. View- ing systems as networks is common in many disciplines, from biology to electronics to social sciences. Funda- mental to networks is the observation that behavior of a system does not equal merely a sum of component parts. The difference between the collective behavior of interacting parts versus a collection of behaviors of individual parts can be traced to how those parts (nodes) are linked (coupled). The latter determines communication. Communication is at the core of a net- work’s breadth and stability (e.g. see Pikovsky et al. 2003). This present model-based inquiry is an extension of pre- vious instrumental and proxy work on the ‘stadium-wave’ climate signal. We repeat here the methodology followed in those studies and use those results to guide our inter- pretation of results derived from the model data. 2.2 Data sets Five steps were involved in data preparation. Details follow. 2.2.1 Acquiring the raw model-simulated data Data sets of model-generated raw variables - e.g. sea- surface temperature (SST), sea-level pressure (SLP), sea-surface height (SSH), wind strength and direction, etc. - were obtained from the third Coupled Model Intercomparison Project (CMIP3: Meehl et al. 2007) web site (https://esg.llnl.gov:8443/about/registration.do) - a site comprising a vast collection of data sets generated by atmospheric-oceanic general circulation models used in Intergovernmental Panel on Climate Change (IPCC)- related projects. Acquisition of data from this site is available to all researchers, requiring only registration for its use. Twenty-two models are represented by CMIP3. Doz- ens of experiments have been performed by each model, most with several runs each. Two experiments were of interest to us: 1) 20th-century runs and 2) control runs. Twentieth-century CMIP-model runs generally cover the historical period 1850 to 2000. Such model runs in- corporate observed radiative forcings (greenhouse gases (with CO2 increases of 1% per year), aerosols, volcanic eruptions, etc.) throughout the period, the exact levels and proportions of forcings differing among models (Reichler and Kim 2007). In contrast, control experi- ments, also termed “long controls”, generally cover 500-plus years. Incorporated in the long controls are at- mospheric forcings that are held constant, in particular, that of CO2, which is held at pre-industrial levels of 280 parts per million (ppm). We considered all 22 models participating in the CMIP data base. For most model runs, data are available on daily, monthly, and annual bases. We were interested in monthly records. Data were extracted via varied codes custom-tailored to the format and size of data files. Once downloaded, these data were compiled in variable- specific files for subsequent use. Upon completion of this data-acquisition step, we had successfully extracted data from 21 of 22 models. Information from one model was unavailable. We evaluated at least one 20th- century experiment for each of the 21 models. For several of these, analysis of a second run of the 20th- century experiment was performed. In addition, for six Wyatt and Peters SpringerPlus 2012, 1:68 Page 2 of 25 http://www.springerplus.com/content/1/1/68 impacts of these winds are a function of, among other things, the geographical placement of the polar-high. This placement indirectly scripts regional sea-ice inven- tory, distribution, spatial pattern of ice thickness, and sea-ice export. Kwok examined groups of models. He found the overall CMIP3 simulations of sea-ice dynam- ics and related features to be poor, with some models performing better than others. He suggests the culprit is the significant displacement of the mean high-pressure pattern in the southern Beaufort region. Its modeled position tends to be skewed toward the central Arctic Basin rather than its observed mean position in the southern Beaufort Sea. Misplacement of related large- scale mean features of the circulation pattern follows. Other influences on the Arctic freshwater balance derive from sea-ice extent north of the Bering Strait. This, too, has been linked to non-static geographical placement of the Aleutian Low (Niebauer 1998). And according to (Jun et al. 2008), errors related to sea-ice north of the Bering Strait are common among models: GFDL, GISS, NCAR, and UKMO. Geographical placement of centers-of-action and dy- namics of western-boundary currents are but two identi- fied features that appear to determine whether a regional circulation pattern’s reach remains regional or extends be- yond, via direct or indirect means. These features are examples of small variations begetting disproportionately large results. The classic work on network theory by soci- ologist Mark Granovetter (1973) points to such small links yielding profound consequences. In his seminal work, he describes the crucial role of weak ties in enlarging and sta- bilizing a network. He describes the phenomenon in terms of societal behavior; yet this concept applies to any network. We suggest the “weak-tie” details of inter- connectivity within the climate network may be a neces- sary ingredient for hemispheric signal propagation. It may be that regional patterns are well modeled. But is their connectivity equally well modeled, be that through geo- graphical positioning of oceanic and atmospheric centers- of-action; western-boundary currents, their extensions, and their relationship to overlying jet-stream tracks; or other such “small-scale” features? And finally, along that same vein, (Van den Berge et al. 2011) have considered connectivity among nodes when modeling climate. They invoke the influential work done by (Pecora and Carroll 1990) on non-linear systems, applying principles of synchronized theory to modeling climate. In essence, they have found that with a limited amount of information exchanged, a system’s behavior can be reconstructed. This information ex- change is accomplished by connecting each variable of a model to each variable of two other models. By linking chaotic systems, synchronization of the network of sys- tems follows (Pecora and Carroll 1990). Here, consistent with what we see in stadium-wave dynamics, links be- tween nodes are critical to capturing the full spatio- temporal signature of the climate network. 5 Conclusion Analyses performed on indices reconstructed from data generated by models archived in the CMIP3 database failed to detect a statistically significant stadium-wave climate signal. Results were the same for both 20th-cen- tury experiments and long-control runs of pre-industrial experiments. We cannot offer an explanation for this, only speculation. In previous stadium-wave studies, this signal was identified for the 20th century in a wide variety of geographically and dynamically diverse instrumental and proxy-reconstructed geophysical indices. Ocean- ice-atmospheric coupling is hypothesized as lying at the heart of signal propagation (Wyatt 2012; submitted manuscript (2013). Weak ties in network behavior are critical to network stability and function. Weak ties within the climate net- work appear to include, among other things, geographical positioning of oceanic and atmospheric centers-of-action. These features have been shown to be poorly represented in many models. This may offer insight to a physical mechanism that may be relevant to signal propagation that appears to be missing from this suite of models. Endnotes aSynchronization refers to the matching of rhythms of self-sustained quasi-oscillators (our intrinsically variable climate indices); whereas synchronous is distinct from synchronization. Synchronous means “same timing”. The stadium-wave signal involves a network of synchro- nized climate indices. bOccurring one or fewer times per century. c(NHT, AMO, NAO, NINO3.4, NPO, PDO, and ALPI) dIn the original study, we used M=20, but in addition, M was varied. No changes in outcome resulted; thus we justify use of only M=20 for the window size in this study. eThe majority of other indices in this network had much lower auto-correlation values, suggesting our esti- mated degrees-of-freedom may be on the low side, im- plying the statistical-significance of the stadium-wave signal in observed data may be larger than is stated here. fThe term ‘centers-of-action’ (COA) refers to circula- tion centers. In the atmosphere, the Aleutian Low and Icelandic Low are examples of COAs. In the ocean, the subpolar and subtropical gyres are COAs. Competing interests The authors declare they have no competing interests. Wyatt and Peters SpringerPlus 2012, 1:68 Page 23 of 25 http://www.springerplus.com/content/1/1/68 Authors’ contributions JP wrote Matlab codes for reading and preparing downloaded data. MW carried out the network analyses, collated results, and drafted the paper. All authors read and approved the final manuscript. Acknowledgements We thank Sergey Kravtsov for his tremendous support throughout this project. We also thank Anastasios Tsonis, Peter Molnar, and Roger Pielke, Sr. for their useful feedback, encouragement, and editorial suggestions. Author details 1Department of Geologic Sciences, CIRES/INSTAAR, Benson Earth Sciences Building, University of Colorado-Boulder, Boulder, CO 80309, USA. 2Department of Mathematical Sciences, Atmospheric Sciences Group, University of Wisconsin-Milwaukee, Milwaukee, WI 53201-0413, USA. 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Ph.D. Dissertation. University of Colorado, Boulder, CO, 201 pp. Available from UMI ProQuest. Publication #UMI3527373 Ann Arbor, MI. doi:10.1186/2193-1801-1-68 Cite this article as: Wyatt and Peters: A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base. SpringerPlus 2012 1:68. Submit your manuscript to a  journal and beneﬁ t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the ﬁ eld 7 Retaining the copyright to your article     Submit your next manuscript at 7 springeropen.com Wyatt and Peters SpringerPlus 2012, 1:68 Page 25 of 25 http://www.springerplus.com/content/1/1/68
Title: A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base ​
Authors: Marcia Glaze Wyatt and John M Peters ​
Publisher: SpringerPlus
Date: December 15, 2012 ​
Abstract: Results of previous studies support the existence of a spatially coherent, secularly varying climate signal, propagating through a network of synchronized climate indices across the Northern Hemisphere during the 20th century. ​ The signal was identified in both instrumental and proxy data sets. ​ In this present study, we seek to detect this same low-frequency signal propagating hemispherically through networks of model-simulated climate indices. ​ These simulated climate indices were reconstructed from a data set generated by models of the third Coupled Model Intercomparison Project (CMIP3). ​ Methods used in the earlier studies on climate-signal propagation guide the strategy for this companion study, for which 60 network analyses were performed. ​ Most analyses focused on 20th century behavior, several on pre-industrial conditions. ​ None succeeded in reproducing a hemispherically propagating signal. ​ In light of previous results, we offer possible reasons for this finding. ​ Among them is speculation on whether mechanisms relevant to signal propagation might be missing from this suite of general circulation models. ​
Keywords: Climate, Network, Synchronize, CMIP3, Stadium-wave, Signal propagation ​