Dr. Anthony D. Del Genio Profile

Dr. Anthony D. Del Genio

at NASA Goddard Institute for Space Studies

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Proceedings Article | 23 October 2018 Paper

Constraining the models' response of tropical clouds to SST forcings using CALIPSO observations

Gregory Cesana, Anthony Del Genio, Andrew Ackerman

Proceedings Volume 10782, 107820A (2018) https://doi.org/10.1117/12.2324800

KEYWORDS: Clouds, Climatology, Climate change, Turbulence, LIDAR, Atmospheric modeling, Satellites

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Here we present preliminary results from the analysis of the low cloud cover (LCC) and cloud radiative effect (CRE) interannual changes in response to sea surface temperature (SST) forcings in two GISS climate models, and 12 other climate models. We further classify them as a function of their ability to reproduce the vertical structure of the cloud response to SST change against 10 years of CALIPSO observations: “the constrained models, which match the observation constraint, and the unconstrained models”. The constrained models replicate the observed interannual LCC change particularly well (ΔLCC_con=-3.49 ±1.01 %/K vs. ΔLCC_obs=-3.59 ±0.28 %/K) as opposed to the unconstrained models, which largely underestimate it (ΔLCC_unc = -1.32 ± 1.28 %/K). As a result, the amount of short-wave warming simulated by the constrained models (ΔCRE_con=2.60 ±1.13 W/m² /K) is in better agreement with the observations (ΔCRE_obs=3.05 ± 0.28 W/m² /K) than the unconstrained models (ΔCRE_con=0.87 ±2.63 W/m² /K). Depending on the type of low cloud, the observed relationship between cloud/radiation and surface temperature varies. Over the stratocumulus regions, increasing SSTs generate higher cloud top height along with a large decrease of the cloud fraction below as opposed to a slight decrease of the cloud fraction at each level over the trade cumulus regions. Our results suggest that the models must generate sustainable stratocumulus decks and moist processes in the planetary boundary layer to reproduce these observed features. Future work will focus on defining a method to objectively discriminate these cloud types that can be applied consistently in both the observations and the models.

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