models parameterizations operate, satellite simulators greatly increase the credibility of climate model-observation comparisons. On a more pragmatic note, the availability of gridded datasets in a commonly used format with appropriate metadata (e.g., netcdf) will greatly facilitate the use of any dataset in the climate model evaluation context, a point also emphasized by Massonnet and Jahn 2012. b. Different uses, different needs: from the "process scale" to the "climate scale" Climate modelers use observations in different ways and for different purposes. Here, we distinguish between the use of "process scale" and "climate scale" observations recognizing that these definitions are somewhat loose (see Table 1). We also discuss if observations are "climate representative", i.e., if the observations can be used to evaluate a long-term average state of a climate model or the climatic significance of a process. Temporal Spatial coverage/ Primary use Examples resolutions resolution "process seconds, minutes, adequate to parameterization SHEBA, scale" hours, daily capture process development MPACE observations under consideration "climate Hourly, daily, regional to global. evaluation of the CERES-EBAF monthly, seasonal, For evaluation of mean state, observations annual means spatial variability temporal, and atmosphere based on at least 10 these data are spatial variability fluxes (Loeb years of data preferably gridded et al. 2009) (preferably more, at least 5x5 note: the number of degrees. years needed to be “climate representative” is something we could use CESM to help quantify) Table 1. Observations used by climate modelers Equations representing physical processes and observations are both key ingredients for parameterization development. Improving the representation of key physical processes in climate models requires that observations address key uncertainties in existing parameterizations (e.g., in process in functional dependencies) or help identify important processes that are not considered (e.g., biogeochemical processes in sea ice, clouds not forming from aerosols, ridging of sea ice). Many flavors of observations can be useful for physical parameterization development, including data from individual field campaigns and laboratory experiments. "Process scale" observations provide detailed information about these process rates and key relationships. "Process scale" observations are also critical for identifying processes that may be missing from a climate model. As a
models parameterizations operate, satellite simulators greatly increase the credibility of climate model-observation comparisons. On a more pragmatic note, the availability of gridded datasets in a commonly used format with appropriate metadata (e.g., netcdf) will greatly facilitate the use of any dataset in the climate model evaluation context, a point also emphasized by Massonnet and Jahn 2012. b. Different uses, different needs: from the "process scale" to the "climate scale" Climate modelers use observations in different ways and for different purposes. Here, we distinguish between the use of "process scale" and "climate scale" observations recognizing that these definitions are somewhat loose (see Table 1). We also discuss if observations are "climate representative", i.e., if the observations can be used to evaluate a long-term average state of a climate model or the climatic significance of a process. Temporal resolutions Spatial coverage/ resolution Primary use Examples "process scale" observations seconds, minutes, hours, daily adequate to capture process under consideration parameterization development SHEBA, MPACE "climate observations Hourly, daily, monthly, seasonal, annual means based on at least 10 years of data (preferably more, note: the number of years needed to be “climate representative” is something we could use CESM to help quantify) regional to global. For evaluation of spatial variability these data are preferably gridded at least 5x5 degrees. evaluation of the mean state, temporal, and spatial variability CERES-EBAF fluxes (Loeb et al. 2009) Table 1. Observations used by climate modelers Equations representing physical processes and observations are both key ingredients for parameterization development. Improving the representation of key physical processes in climate models requires that observations address key uncertainties in existing parameterizations (e.g., in process in functional dependencies) or help identify important processes that are not considered (e.g., biogeochemical processes in sea ice, clouds not forming from aerosols, ridging of sea ice). Many flavors of observations can be useful for physical parameterization development, including data from individual field campaigns and laboratory experiments. "Process scale" observations provide detailed information about these process rates and key relationships. "Process scale" observations are also critical for identifying processes that may be missing from a climate model. As a atmosphere
PCWG_workingdoc_obs4modelers_march13,2012.docx