Radiative forcing of the tropical thick anvil evaluated by combining TRMM with atmospheric radiative transfer model

Abstract

AbstractThe presences of anvil clouds significantly affect the tropical mean radiation budget and increase the uncertainty of climate model simulations. In this study, the climatological mean distributions of thick anvil parameters, such as top, bottom, occurrence, cloud effective radius (CER) and cloud optical depth (COD) in the tropics (20°S–20°N) are investigated by Tropical Rainfall Measuring Mission's (TRMM) precipitation radar (PR) and visible and infrared scanner (VIRS) from 1998 to 2007. The thick anvil radiative forcing at shortwave (0.2 ∼ 4 µm) and longwave (4 ∼ 50 µm) length, i.e. Shortwave radiative forcing (SRF) and Longwave radiative forcing (LRF) and their net effects at different altitudes are simulated with Santa Barbara DISORT Atmospheric Radiative Transfer Model (SBDART). The results show that thick anvils present higher top/bottom, smaller CER, and thicker COD over land than those over ocean. At the top of atmosphere (TOA), net radiative effects of thick anvils are positive warming, which means the earth‐atmosphere system obtains energy forced by thick anvils. At earth surface, net radiative effects of thick anvils are positive warming at land surface and negative cooling at ocean surface, respectively. In general, anvil SRF, LRF and net effects vary with different geographical locations and also present large land–ocean differences in the tropics, due to different anvil properties forced by the surface heating and topography. All spatial patterns of stronger anvil SRF, LRF and net effects are well matched with the places where exist higher fractions of anvils, such as Asian monsoon zone, the Intertropical Convergence Zone (ITCZ), the South Pacific Convergence Zone (SPCZ), tropical Africa, Mid‐America and South America. In addition, the present work provides an evidence that it is an effective approach to calculate quantitatively the grid‐cell SRF and LRF of cloud at a large scale by using the SBDART model with inputs from satellite observations.