“In this investigation, we will evaluate microphysical processes of warm liquid clouds in global climate models with multi-sensor satellite observations, in response to the Modeling, Analysis, Prediction and Projection (MAPP) competition of “Process-oriented evaluation of climate and Earth system models and derived projections” within Area A “Metrics for climate and Earth system model development” and of Type 2 “Research teams developing process-oriented metrics.” Warm cloud microphysics is one of the most uncertain components in global climate models and is also a major pathway through which aerosols influence the clouds and climate, referred to as the aerosol indirect effect. The objective of the research is to: (i) develop observation-based metrics that dictate key signatures of the warm rain microphysical processes with a combined use of the CloudSat/A-Train multi-sensor satellite observation products, (ii) apply the methodologies to climate models to identify fundamental biases in the representation of key microphysical processes that are crucial for estimates of the aerosol indirect radiative forcing and thus for climate projections, and (iii) propose improvements of microphysical parameterizations in such models for a better representation of warm cloud processes and more reliable estimates of the aerosol indirect radiative forcing.
Previous studies by the PI devised new methodologies for analyzing the CloudSat/A-Train multi-sensor satellite observations to “fingerprint” warm cloud microphysical processes and also applied them to cloud-resolving and climate models to identify fundamental model biases in representing the processes. This investigation will extend such model diagnostic approaches to systematic analysis of global climate models. For this purpose, we plan to: (i) integrate the new PI-developed methodologies as a unified set of observation-based, process-oriented metrics that “fingerprint” the fundamental process signatures of warm cloud microphysics, and (ii) apply the metrics to results from multiple climate models including GFDL Climate Model version 3 (CM3) and NCAR Community Atmosphere Model version 5 (CAM5) for their process-oriented evaluations. Furthermore, (iii) systematic sensitivity experiments with GFDL CM3 or its atmospheric component, AM3, will be conducted to examine how different assumptions and configurations in microphysics parameterization schemes influence the model representation of the process in the form of the new metrics. Through these analyses, we intend to offer a process-based constraint on fundamental uncertainty in climate models in an attempt to improve the microphysical process representations. A particular emphasis will be placed on mitigation of the dichotomy found in the investigators’ previous study between such a process-based model constraint and the historical temperature reproducibility in GFDL CM3.
The proposed research will directly contribute to the specific objective of the Competition aiming at “process-oriented evaluations of climate and Earth system models and derived projections”. In particular, we intend to substantially mitigate the uncertainties in aerosol indirect radiative forcing arising from fundamental uncertainties in model representation of cloud microphysical processes for more reliable climate projections of global temperature and precipitation. The proposed research will thus contribute to NOAA’s long-term climate goal through addressing the core activities of “understanding and modeling” and “predictions and projections” and the societal challenges of “climate impacts on water resources”.”