The Tropical Oceanic Energy Budget from the TRMM Perspective. Part I: Algorithm and Uncertainties

Abstract

The earth's weather and climate is driven by the meridional transport of energy required to establish a global balance between incoming energy from the sun and outgoing thermal energy emitted by the atmosphere and surface. Clouds and precipitation play an integral role in the exchange of these sources of energy between the surface, atmosphere, and space?enhancing reflection of solar radiation to space, trapping thermal emission from the surface, and providing a mechanism for the direct transfer of energy to the atmosphere through the release of latent heat in precipitation. This paper introduces a new multisensor algorithm for extracting longwave, shortwave, and latent heat fluxes over oceans from the sensors aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The technique synthesizes complementary information from distinct retrievals of high and low clouds and precipitation from the TRMM Microwave Imager (TMI) and Visible and Infrared Scanner (VIRS) instruments to initialize broadband radiative transfer calculations for deriving the structure of radiative heating in oceanic regions from 40°S to 40°N and its evolution on daily and monthly timescales. Sensitivity studies using rigorous estimates of the uncertainties in all input parameters and detailed comparisons with flux observations from the Clouds and Earth's Radiant Energy System (CERES) are used to study the dominant influences on the algorithm's performance and to assess the accuracy of its products. The results demonstrate that the technique provides monthly mean estimates of oceanic longwave fluxes at 1° resolution to an accuracy of ?10 W m?2. Uncertainties in these estimates are found to arise primarily from a lack of explicit vertical cloud boundary information and errors in prescribed temperature and humidity profiles. Corresponding shortwave flux estimates are shown to be accurate to ?25 W m?2, with uncertainties due to errors in cloud detection, poorly constrained cloud particle sizes, and uncertainties in the prescribed surface albedo. When viewed as a whole, the components of the method provide a tool to diagnose relationships between the climate, hydrologic cycle, and the earth's energy budget.