- Related Research Areas
- Climate Variability & Change, Water & Energy Cycles, Weather
Present-day shortcomings in the representation of clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a large source of uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of high-quality, global observations targeting clouds and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often-disjointed nature between model representation and the characteristics of the observed values have hampered GCM development and validation efforts from making effective use of the observations that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice and liquid processes have, in some cases, become more sophisticated recently, this development has largely occurred independently of the global-scale observations. This has led to serious deficiencies in the representation of cumulus convection and cirrus anvils, mid-level cumulus congestus, stratocumulus, and boundary-layer stratus clouds. These deficiencies range from the considerable uncertainties in their modeled radiative feedbacks in conjunction with climate change to the most basic properties such as representing the mass of liquid and ice water in clouds correct to within an order of magnitude. With the relatively recent addition of satellite-derived products from CloudSat, CALIPSO, Aura/MLS, etc., along with longer running products from MODIS, CERES and ISCCP there are now considerably more resources with new and unique capabilities to evaluate and improve cloud representations in GCMs. In this proposal, we will develop judicious approaches for making model-data evaluations and use these evaluations to improve cloud-related parameterizations in conventional and Multi-Scale GCMs. This includes accounting for sensor sensitivities and algorithm assumptions and the spatial/temporal sampling constraints of the instruments, as well as accounting for the physical assumptions in the model’s parameterized hydrometeor representation. Specific processes/parameterizations to be addressed include extensions of our ongoing work with upper-tropospheric ice clouds and analogous studies on lower-tropospheric liquid clouds, primarily using CloudSat and MLS cloud-retrievals and available instrument simulators. These data will be augmented by AIRS and GPS temperature and moisture soundings, and ISCCP, MODIS/CERES, CALIPSO and SSMI/AMSR-E cloud-property characterizations. Specific models to be examined - with a focus on improving cloud ice and water content representation - include the NASA GEOS5 and GISS GCMs, the NASA fvMMF and Harvard DARE Multi-Scale GCMs, and the ECMWF and MERRA analyses. In addition, we will lead the participation, on behalf of GEOS5, in two GEWEX cloud-modeling projects, the Cloud Feedback Model Intercomparison Project (CFMIP-2) and the Pacific Cross-section Intercomparison (GPCI) project. The outcome of this effort will be: I) the development of a number of robust and complementary methods for making model-data comparisons using the above satellite observations of clouds and related processes, II) quantitative evaluations of the representations of ice and liquid clouds in a number of contemporary GCMs and, III) through our own efforts and the close collaboration with the modeling teams, develop/implement sorely needed improvements in our GCM representations of the mass, size distribution, and floating/falling characteristics of liquid and frozen hydrometeors. These outcomes will demonstrate the value of the synergistic use of measurements from a number of contemporary satellites. It will also lead to more realistic treatments of cloud properties in our weather and climate prediction models, and in regards to the latter, has the potential to reduce the uncertainties in future suites of global change projections through improved cloud (feedback) representations.
Project PI: Duane Waliser/Jet Propulsion Laboratoory
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