. "2 Development and Testing of Model Parameterizations: Some Examples." Improving the Scientific Foundation for Atmosphere-Land-Ocean Simulations: Report of a Workshop. Washington, DC: The National Academies Press, 2005.
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Improving the Scientific Foundation for Atmosphere-Land-Ocean Simulations: Report of A Workshop
field projects under way to test theoretical predictions. This has led to the desirable outcome that there is very little variation in representations of surface fluxes over the oceans in large-scale models.
At the other end of the spectrum is the representation of clouds and convection in regional and global models. Parameterizations of clouds and convection purport to describe the higher-order statistics of ensembles of clouds (e.g., the variance of relative humidity or the vertical flux of energy due to cloud-scale motions) in terms of lower-order statistics (e.g., mean temperature, humidity, vertical velocity). Here all statistics are assumed to be based on averages over scales that are large relative to individual clouds.
Parameterizing clouds and moist convection in models presents several unique challenges to modelers. Layer clouds may span many grid cells horizontally, but may be thin compared to typical vertical layer thicknesses; conversely, convective clouds usually occupy only a small fraction of a grid cell, yet span many model levels. Convective clouds represent major local sources of enthalpy and water vapor, and layer clouds have large effects on both shortwave and longwave radiative transfer. Representing both is crucial to most atmospheric and climate models, yet doing so has proven notoriously difficult. Transfer of enthalpy and water substance by convective clouds is sensitive to very small-scale processes such as turbulent entrainment and cloud microphysics, but evaluating parameterizations of clouds and moist convection against direct observations of these processes is a formidable undertaking. As discussed in Chapter 3, the difficulty of directly testing cumulus and cloud parameterizations has led to some undesirable practices in developing and testing such schemes. However, a new U.S. Climate Variability and Predictability (CLIVAR)-based activity, Climate Process and Modeling Teams (CPTs1), was established in 2003 to provide a thorough, efficient forum for improving model parameterizations by bringing together theoreticians, field observationalists, process modelers, and scientists at the large modeling centers (Bretherton et al., 2004; http://www.usclivar.org/CPT/index-newcpt.html). Three pilot CPTs are
CPTs are sponsored by the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA). The key objective of CPTs is to speed development of global coupled climate models and reduce uncertainties in climate models by building a new holistic community that exchanges knowledge, ideas, and needs. CPTs are driven directly by the scientific needs perceived within the modeling centers.