ENSO Surface Winds in CCM3 Simulation: Diagnosis of Errors

Abstract

The structure of surface-wind anomalies associated with ENSO variability is extracted from ComprehensiveOcean?Atmosphere Dataset observations and European Centre for Medium-Range Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) reanalyses, along with estimates of uncertainty. The targets are used to evaluate ENSO surface winds produced by the National Center for Atmospheric Research?s atmospheric GCM known as the Community Climate Model, version 3 (CCM3), when integrated in the climate-simulation mode. Simulated anomalies have stronger easterlies in the off-equatorial Tropics and stronger equatorward flow in the Pacific than any of the observational estimates do. CCM3?s wind departures are found to be large when compared with the difference of the reanalysis anomalies and should thus be considered to be errors. In a companion paper, the authors make a compelling case for the presence of robust errors in CCM3?s ENSO heating distribution, based on comparisons with the residually diagnosed heating anomalies from ECMWF and NCEP reanalyses. The linkage between specific features of CCM3?s surface-wind and heating errors is investigated using a steady, linear, global, primitive equation model (18 vertical σ levels, 30 zonal waves, and latitude spacing of 2.5°). Diagnostic modeling indicates that stronger equatorward flow in the Pacific results largely from excessive diabatic cooling in the off-equatorial Tropics, a key heating error linked to a more meridional redistribution of ENSO heating in CCM3. The ?bottom-heavy? structure of CCM3?s equatorial heating anomalies, on the other hand, is implicated in the generation of zonal-wind errors in the central and eastern tropical Pacific. In the diagnostic simulation of CCM3?s ENSO variability, the longwave heating anomalies, with peak values near 850 mb, contribute as much to surface zonal winds as do all other heating components together?a novel finding, needing corroboration. This study, along with the companion paper, illustrates the dynamical diagnosis strategy?of circulation and forcing intercomparisons with observed counterparts, followed by diagnostic modeling?for analyzing errors in the GCM?s simulation of climate variability.