Tony Slingo's Indelible Art: The Legacy of Cloud Radiation Parameterization and its Application in Global Climate Models

In this presentation, we focus on the invaluable insight and outcomes that form one major legacy of Tony Slingo in providing simplified cloud spectral radiation representations for global climate modeling purposes, such that these models can be used productively for evaluating the role of clouds in climate. We discuss the adaptation of the shortwave cloud optics formulation initiated and developed by Tony (Journal of the Atmospheric Sciences, 1989) into a global climate model, and using it to understand the model's representation of cloud-climate interactions. Tony's parameterization of the shortwave spectral properties of water clouds remains to-date the most significant yet elegant representation. Simulations of downward shortwave surface fluxes by the coupled Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 general circulation model using the Slingo shortwave cloud parameterization are compared against climatology derived from the Baseline Surface Radiation Network (BSRN), Global Energy Balance Archive, and International Satellite Cloud Climatology Project ISCCP datasets. An investigation is made of how these relate to accompanying biases in total cloud amount and aerosol optical depth and how they affect the surface temperature simulation. Comparing CM2.1's clear-sky fluxes against BSRN site values, for European, Asian, and North American locations, there are underestimates in the direct and overestimates in the diffuse, resulting in underestimates in the total flux. These are related to overestimates of sulfate aerosol optical depth, arising owing to the behavior of the parameterization function for hygroscopic growth of these aerosols at very high relative humidity. All-sky flux biases consist of underestimates for North America, Eurasia, southern Africa, and northern oceanic regions and overestimates for the Amazon region, equatorial Africa, off the west coast of the Americas, and southern oceanic regions. These biases show strong correlations with cloud amount biases. There are modest correlations of the flux biases with cool surface temperature biases over North America and Eurasia, warm biases over the Amazon region, and cool (warm) biases over the northern (southern) oceanic regions. Thus, the biases in aerosol and cloud amounts lead to a direct impact on land surface temperatures. The model-observation comparisons are providing a critical appraisal of the strengths and limitations of the model's climate simulations as related to aerosols and clouds.