The Tropical Atmospheric El Niño Signal in Satellite Precipitation Data and a Global Climate Model

Abstract

Aspects of the tropical atmospheric response to El Niño related to the global energy and water cycle are examined using satellite retrievals from the Tropical Rainfall Measuring Mission and the Advanced Microwave Scanning Radiometer-E and simulations from the Goddard Institute for Space Studies (GISS) general circulation model (GCM). The El Niño signal is extracted from climate fields using a linear cross-correlation technique that captures local and remote in-phase and lagged responses. Passive microwave and radar precipitation anomalies for the 1997/98 and 2002/03 El Niños and the intervening La Niña are highly correlated, but anomalies in stratiform?convective rainfall partitioning in the two datasets are not. The GISS GCM produces too much rainfall in general over ocean and too little over land. Its atmospheric response to El Niño is weaker and decays a season too early. Underestimated stratiform rainfall fraction (SRF) and convective downdraft mass flux in the GISS GCM and excessive shallow convective and low stratiform cloud result in latent heating that peaks at lower altitudes than inferred from the data. The GISS GCM also underestimates the column water vapor content throughout the Tropics, which causes it to overestimate outgoing longwave radiation. The response of both quantities to interannual Hadley circulation anomalies is too weak. The GISS GCM?s Walker circulation also exhibits a weak remote response to El Niño, especially over the Maritime Continent and western Indian Ocean. This appears to be a consequence of weak static stability due to the model?s lack of upper-level stratiform anvil heating, excessive low-level heating, and excessive dissipation due to cumulus momentum mixing. Our results suggest that parameterizations of mesoscale updrafts, convective downdrafts, and cumulus-scale pressure gradient effects on momentum transport are keys to a reasonable GISS GCM simulation of tropical interannual variability.