2004: Beyond the means: Validating climate models with higher-order statistics
Large-scale climate models are validated by comparing the model’s mean and variability to observations. New applications are placing more demands on such models, which can be addressed by examining the models ’ distributions of daily quantities such as temperature and precipitation. What determines...
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| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.581 http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf |
| Summary: | Large-scale climate models are validated by comparing the model’s mean and variability to observations. New applications are placing more demands on such models, which can be addressed by examining the models ’ distributions of daily quantities such as temperature and precipitation. What determines the climate where you live? Why does it vary, so that some years have unusually cold winters, or particularly hot summers? What will next winter be like? What will the climate be in coming years, and is it affected by emissions of gasses such as CO2? These are just a few of the questions that are examined with coupled ocean-atmosphere general circulation models (O-A GCMs). Such models are complicated, incorporating the equations of motion for air and water masses, the properties of sea ice, parameterizations for cloud processes, schemes for river flow, and the effects of soil moisture and ground cover. The projections given by such models might influence decisions ranging from whether someone’s aging roof should be repaired before the coming winter to what future technologies the automobile industry should pursue. How are such models validated, so that we understand what confidence should be placed in their predictions? This is typically done by comparing the model’s behavior to that of the real world. The assumption |
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