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Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change on ESMs for climate projections beyond a couple of decades, and it creates two problems. These models, though scientifically cutting-edge, are not as well tested as the traditional AOGCMs, and are typically run at lower resolutions. The indication is that roughly half the long-term climate projections in CMIP5 will come from ESMs and half from AOGCMs (Gerald Meehl, National Center for Atmospheric Research, personal communication, July 2009). The second gap is the insufficient number of ensemble members planned for the “Core” climate projections in CMIP5. As in CMIP3, this would lead to the inability to cleanly distinguish model-to-model differences in the climate signal from the model’s natural variability. Finally, the higher-resolution models used for decadal prediction do not typically have a parallel set of climate projections for comparison. This report provides recommendations to address both of these deficiencies. 3.1.4 How are GCMs managed? The development of GCMs is managed at over 15 modeling centers around the world (Meehl et al., 2007a, 2007b). Four of these centers are located in the United States (Table 3.1). Modeling centers exercise a large degree of organizational autonomy in developing climate models. However, some of the component models are developed in partnership with other institutions. The academic research community, through individual or collaborative scientific grants, plays an important role in developing new mechanistic process models and parameterizations. Even though the centers are organizationally autonomous, model development at each center does not occur in a scientific vacuum, as scientific ideas are constantly exchanged. As a result, the individual GCMs are not entirely independent of one another (Knutti, 2008). Table 3.1. Major climate models and modeling centers in the United States participating in CMIP3 or CMIP5 Model Institution Web site/contact Community Climate System Model (CCSM 4) Geophysical Fluid Dynamics Laboratory (GFDL) CM2.5, CM3 ESM 2.x GISS Model E NASA GEOS5/AO (decadal only) NCAR [funded by National Science Foundation and U.S. Department of Energy (DOE) Office of Biological and Environmental Research] NOAA GFDL National Aeronautics and Space Administration (NASA)/GISS NASA/Goddard Space Flight Center http://www.ccsm.ucar.edu Dr. Peter Gent gent@ucar.edu http://www.gfdl.noaa.gov gfdl.climate.model.info@noaa.gov http://www.giss.nasa.gov/tools/modelE/ Dr. Gavin Schmidt gschmidt@giss.nasa.gov http://gmao.gsfc.nasa.gov/systems/geos5/ Dr. Max Suarez max.j.suarez@nasa.gov Page 3-9
Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change Among modeling groups, the management of the NCAR Community Climate System Model (CCSM) stands apart from the others because it is based on a large community of scientists that extends beyond the lead institution and is governed by a diverse scientific steering committee. 3.1.5 How are climate projection intercomparison runs decided? The CMIP5 intercomparison project will be the most significant set of GCM runs for the next 5– 10 years for making comprehensive assessments of climate change and its impacts. These modeling efforts are coordinated by the Working Group on Coupled Models (WGCM, http://www.clivar.org/organization/wgcm/wgcm.php). The WGCM is part of CLIVAR (climate variability and predictability), an arm of the World Climate Research Programme (WCRP, http://wcrp.wmo.int/wcrp-index.html). It is up to the individual participating institutions to carry out the model development and perform the runs. Therefore, the CMIP5 standards often represent the least common denominator – institutions are free to exceed the recommendations. Decadal prediction efforts are organized by WGCM and coordinated through the WCRP Decadal Prediction Cross-Cut (http://www.clivar.org/organization/decadal/prediction.php). The US- CLIVAR Working Group on Decadal Prediction (http://www.usclivar.org/wgdp.php) will help facilitate the analysis of decadal prediction runs. More details of the CMIP process can be found in Meehl et al. (2007b). 3.1.6 How are the models evaluated for accuracy? Weather prediction models are evaluated for accuracy by repeatedly comparing forecasts with what actually transpired. This is far more difficult to do with climate models because the models are used to project long-term climate trends, not short-term weather events. Climate models are evaluated on how well they can reproduce the climate of the present and the past by comparing the statistics of historical observations with those from a GCM’s historical climate simulation. Often the comparison involves using gridded observational data, where nearby individual observing stations have been combined to form averages over grid cells that are of comparable scale to the GCM output. 4 A simple statistical comparison of observations and model simulations is limited, in part, because we have imperfect information about the past climate. This is particularly true for the interior of the ocean, for sea ice, and even for many hydrologic quantities such as soil moisture. In addition, there is no guarantee that a good simulation of the past will result in an accurate prediction of the future because we may be getting the right answer for the wrong reasons. In practice we draw on multiple lines of evidence. Knutti (2008), for example, lists seven lines of 4. Therefore, it is not appropriate to attempt to analyze the skill of a GCM by comparing observed data from a single weather station in a grid box with the GCM’s estimate of climate in that grid box. Page 3-10
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