The Change in Low Cloud Cover in a Warmed Climate Inferred from AIRS, MODIS, and ERA-Interim

• DOI: 10.1175/jcli-d-15-0734.1
• Publication Date: 2017-02-01T19:51:40Z
• Authors: Daniel T. McCoy; Ryan Eastman; Dennis L. Hartmann; Robert Wood
• Keywords: 13. Climate action; 01 natural sciences; 0105 earth and related environmental sciences

Abstract Decreases in subtropical low cloud cover (LCC) occur in climate model simulations of global warming. In this study 8-day-averaged observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) spanning 2002–14 are combined with European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis to compute the dependence of the observed variability of LCC on various predictor variables. Large-scale thermodynamic and dynamic predictors of LCC are selected based on insight from large-eddy simulations (LESs) and observational analysis. It is found that increased estimated inversion strength (EIS) is associated with increased LCC. Drying of the free troposphere is associated with decreased LCC. Decreased LCC accompanies subsidence in regions of relatively low EIS; the opposite is found in regions of high EIS. Finally, it is found that increasing sea surface temperature (SST) leads to a decrease in LCC. These results are in keeping with previous studies of monthly and annual data. Based upon the observed response of LCC to natural variability of the control parameters, the change in LCC is estimated for an idealized warming scenario where SST increases by 1 K and EIS increases by 0.2 K. For this change in EIS and SST the LCC is inferred to decrease by 0.5%–2.7% when the regression models are trained on data observed between 40°S and 40°N and by 1.1%–1.4% when trained on data from trade cumulus–dominated regions. When the data used to train the regression model are restricted to stratocumulus-dominated regions the change in LCC is highly uncertain and varies between −1.6% and +1.4%, depending on the stratocumulus-dominated region used to train the regression model.