A31A-0015
Impact of Shallow Clouds on Cloud-Permitting WRF Simulations of the Diurnal Cycle of Convection over the Amazon
Wednesday, 16 December 2015
Poster Hall (Moscone South)
Casey D. Burleyson1, Zhe Feng1, Samson M Hagos2, Jennifer M Comstock1, Larry K Berg1, Courtney Schumacher3, Scott E Giangrande4 and Charles N. Long5, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Joint Global Change Research Institute, College Park, MD, United States, (3)Texas A & M University College Station, College Station, TX, United States, (4)Brookhaven National Laboratory, Upton, NY, United States, (5)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States
Abstract:
Understanding and representing the diurnal cycle of convection in climate models remains one of the most significant challenges in climate science. In this study, we examine the radiative impact of shallow cumulus clouds on a month-long cloud-permitting (Δx = 1 km) WRF simulation of convection over the Amazon region during the GoAmazon2014/5 field campaign. Compared to observations collected at the DOE Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF), the model underestimates the frequency of shallow cumulus clouds during daytime, resulting in large positive biases in downward shortwave radiation reaching the surface. This overestimation of shortwave heating in turn drives excessive surface latent and sensible heat fluxes in the model. As a result, simulated deep convection peaks too early during the day and total precipitation is excessive compared to observations. We use the AMF observations to develop a shallow cumulus cloud parameterization scheme that mitigates the bias in shallow cumulus cloud radiative effects. Impacts of the new parameterization of shallow cumulus on the simulation of deep convection and precipitation are examined. Our results highlight the importance of cloud-radiative interactions and land-atmosphere feedbacks on the diurnal cycle of convection in this region.
