Evaluating models' response of tropical low clouds to SST forcings using CALIPSO observations

Abstract

Abstract. Recent studies have shown that, in response to a surface warming, the marine tropical low-cloud cover (LCC) as observed by passive-sensor satellites substantially decreases, therefore generating a smaller negative value of the top-of-the-atmosphere (TOA) cloud radiative effect (CRE). Here we study the LCC and CRE interannual changes in response to sea surface temperature (SST) forcings in the GISS model E2 climate model, a developmental version of the GISS model E3 climate model, and in 12 other climate models, as a function of their ability to represent the vertical structure of the cloud response to SST change against 10 years of CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) observations. The more realistic models (those that satisfy the observational constraint) capture the observed interannual LCC change quite well (ΔLCC/ΔSST=-3.49±1.01 % K−1 vs. ΔLCC/ΔSSTobs=-3.59±0.28 % K−1) while the others largely underestimate it (ΔLCC/ΔSST=-1.32±1.28 % K−1). Consequently, the more realistic models simulate more positive shortwave (SW) feedback (ΔCRE/ΔSST=2.60±1.13 W m−2 K−1) than the less realistic models (ΔCRE/ΔSST=0.87±2.63 W m−2 K−1), in better agreement with the observations (ΔCRE/ΔSSTobs=3±0.26 W m−2 K−1), although slightly underestimated. The ability of the models to represent moist processes within the planetary boundary layer (PBL) and produce persistent stratocumulus (Sc) decks appears crucial to replicating the observed relationship between clouds, radiation and surface temperature. This relationship is different depending on the type of low clouds in the observations. Over stratocumulus regions, cloud-top height increases slightly with SST, accompanied by a large decrease in cloud fraction, whereas over trade cumulus (Cu) regions, cloud fraction decreases everywhere, to a smaller extent.

Causes of Higher Climate Sensitivity in CMIP6 Models

“…As more output from CMIP6 models becomes available, more detailed analysis of the cloud feedback will be possible (Cesana et al, 2019;Gordon & Klein, 2014;Terai et al, 2016;Tsushima et al, 2016;Zelinka et al, 2012aZelinka et al, , 2012bZelinka et al, , 2013Zelinka et al, , 2016, permitting better understanding of why the low cloud feedback has strengthened. Output from additional models might also refine the statistical significance of several intriguing results reported here, which based on the limited sample of models fall just short of being statistically significant.…”

Solar UV Radiation in Saint-Denis, La Réunion and Cape Town, South Africa: 10 years Climatology and Human Exposure Assessment at Altitude

“…Furthermore, within the context of climate change, there is a direct link between increasing sea surface temperatures and the distribution of cloud cover in the tropics [39]. The negative trends obtained for the Cape Town site could result from a combination of many processes at different scales.…”

The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

“…Finally, by documenting the global geographical distribution of Sc and Cu clouds for the first time, the CASCCAD datasets make it possible to evaluate the shallow convection (Cu type) and boundary layer (Sc type) clouds in state-of-the art climate models, which are typically generated by distinct parametrizations (i.e., Cesana et al, 2019a). By doing so, one could also assess the radiative contribution of Sc and Cu clouds to climate and potentially improve our understanding of low-level cloud feedbacks.…”

“…The main goal of this study is to document spatial distributions and profiles of Sc and Cu clouds on a global scale, with the desire to further analyze long-term relationships between Sc-Cu clouds and environmental parameters in future studies. For this purpose, we need to i) distinguish the two cloud types based on observable cloud-properties and ii) use datasets that are available for a time-period sufficiently long (~ 10 years) to compute statistically significant relationships (e.g., using 4 years of GOCCP rather than 10 may decrease the amplitude of the relationship between low clouds and SST anomalies by more than 15 %, Cesana et al, 2019a). Although both the Sc and Cu clouds form within the planetary boundary layer (PBL), they have relatively different shapes as they are controlled by different physical mechanisms.…”