Tuesday, 10 July 2018: 4:30 PM
Regency D/E/F (Hyatt Regency Vancouver)
There is a growing interest in the climate research community toward novel “process-oriented” evaluations of global climate models from traditional “performance-oriented” evaluations to substantially advance our capability of climate modeling. This emerging trend of research is particularly accelerated by recent progress in satellite observations, which enabled us to explore key processes relevant to cloud and precipitation. Exploiting this observational capability and motivated by the community’s interest, the authors and collaborators have developed a novel approach for diagnosing the warm rain process, a key determinant of climate effect of low clouds including their interactions with aerosols, through combining satellite observables obtained from multiple sensors and platforms. The methodology is to construct the particular statistics from the observables that probe the process. The statistics also serve as a diagnostic tool for evaluating global models in their representations of the process. The authors applied the methodology to multiple state-of-the-art global models, including traditional climate models and a global cloud-resolving model, to identify a key common bias of the “too fast, too frequent” rain formation in the models, which is further traced to uncertainty in parameterizations of water conversion process. Satellite-based information in the form of the statistics constructed provides a process-level constraint on the uncertainty that have been regarded as “tunable knobs” in climate models. The models thus constrained for the warm rain process, however, turned out to result in the aerosol indirect forcing that is too large negative to offset the greenhouse-gas-induced warming. This is caused by pronounced perturbations to cloud water budget, which appear to be amplified due to mutual interplay between aerosols and clouds through processes such as auto-conversion and wet-scavenging represented in the models. The apparent “dichotomy” identified between the process-based model constraint and the energy-balance requirement implies a fundamental gap in our understanding of linkage of the key processes to the global climate system and also underscores the importance of better model constraints on these processes. Recent studies by the authors that implemented sophisticated representations of precipitation into a climate model illustrate how the cloud water increase in response to increased aerosols tends to be muted when precipitation is treated as a prognostic variable. This acts to mitigate the “dichotomy” above through modulating the relative contributions from two major water conversion processes (i.e. auto-conversion and accretion) and therefore magnitudes of the aerosol indirect forcing. This presentation highlights these recent findings to discuss how to bridge the gap in our understanding between the process-oriented and energy-based perspectives of the aerosol-cloud interaction.
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