Tropospheric temperature and the tropical ENSO teleconnection

Abstract

The warming of the entire tropical free troposphere in response to an El Niño is well established, and suggests a tropical mechanism for the El Niño - Southern Oscillation (ENSO) teleconnection. We examine the potential impact of this warming on remote tropical climate through investigating the adjustment of a single column model to imposed tropospheric temperature variations, assuming that ENSO controls interannual tropospheric temperature variations at all tropical locations. The column model predicts the impact of these variations in three typical (precipitation > evaporation; precipitation < evaporation; no convection) tropical climate states over a slab mixed layer ocean. Model precipitation and sea surface temperature (SST) respond significantly to the imposed tropospheric forcing in the first two climate states. Their amplitude and phase are sensitive to the imposed mixed layer depth, the nature of the response depending on how fast the ocean adjusts to imposed tropospheric temperature forcing. For larger mixed layer depth, the SST lags the tropospheric temperature by a longer time, allowing greater disequilibrium between atmosphere and ocean. This causes larger surface flux variations, which drive larger precipitation variations. Moist convective processes are responsible for communicating the tropospheric temperature signal to the surface in this model.

Preliminary observational analysis suggests that our mechanism may be applicable to interpreting interannual climate variability in the remote tropics. In particular, it offers a simple explanation for the gross spatial structure of the observed surface temperature response to ENSO, including the response over land and the lack thereof over the southeast tropical Atlantic and southeast tropical Indian oceans. The mechanism predicts that the air-sea humidity difference variation is a driver of ENSO-related remote tropical surface temperature variability, an addition to wind speed and cloudiness variations that previous studies have shown to be important.