IMPROVEMENT VIA TESTS IN NUMERICAL WEATHER PREDICTION MODE

There are no fundamental differences in formulation between the global models used for climate simulation and those used for numerical weather prediction (NWP). Models used in operational NWP are evaluated daily for their ability to simulate the evolution of particular weather systems. The statistics of such forecasts have been analyzed to identify systematic errors that emerge in the first few days of simulation. In many cases these systematic forecast errors are closely related to the errors that the same models produce in simulations of the present climate. These considerations led the ECMWF (1999) and the World Climate Research Program (2001) to advocate the use of NWP as a means of evaluating climate models. The idea is to use climate models to produce weather forecasts for real cases, identify the forecast errors, and trace those errors back to weaknesses in the models’ formulations—in other words, to do what NWP centers do as a matter of course.

There are precedents for this. The United Kingdom’s Met Office has been using a single modeling system for both climate simulation and NWP for years now. In addition, the Max Planck Institute for Meteorology in Hamburg, Germany, developed its climate model by starting from a version of ECMWF’s NWP model. Until recently, however, the United States lagged behind in this area. This has now been corrected through a program called CAPT, the CCPP-ARM (Atmospheric Radiation Measurements) Parameterization Testbed, where CCPP stands for the Climate Change Prediction Program funded by the U.S. Department of Energy. CAPT is using analyses of global weather from NWP centers, in conjunction with field observations such as those provided by ARM, to evaluate parameterizations of subgrid-scale processes in global climate models (Williamson et al., 2005; Boyle et al., 2005). CAPT’s methods were first applied to the community atmosphere model (CAM) and are gradually being used with a wider variety of models, including the GFDL’s Atmospheric Model 2 (AM2). Workshop participants thought that much could be gained if these methods were widely adopted by the U.S. climate modeling community.



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