Understanding the bias in surface latent and sensible heat fluxes in contemporary AGCMs over tropical oceans

Abstract

The performance of 20 models participating in the atmospheric model intercomparison project (AMIP) is evaluated concerning surface latent (QLH) and sensible (QSH) heat flux over the tropical oceans (30°S–30°N). Biases were calculated by comparing model fluxes to observations from moored buoys and the objectively analyzed air–sea fluxes (OAFlux) database. All 20 AMIP models overestimate QLH with an ensemble mean bias of 20 W m−2, and 18 of the 20 models overestimate QSH with an ensemble mean bias of 5 W m−2 when compared to OAFlux, implying a systematic positive bias over the tropical oceans. A comparison with buoy observations also showed similar biases. To obtain insights into the causes behind model bias, we quantified the contribution from near-surface winds, specific humidity, and temperatures. It is found that near-surface humidity contributes more to the bias in QLH than wind speed, while air temperature contributes more to bias in QSH than wind speed. On the other hand, the root mean squared error (RMSE) in QLH has contributions from both near-surface humidity and wind. The contribution from humidity to the mean bias in QLH is 13 W m−2, with RMSE of 15 W m−2, suggesting a systematic overestimation of sea-air humidity difference in models. The model ensemble, in general, simulates QLH and QSH better than individual models. Models with higher horizontal and vertical resolutions perform better than coarse resolution models.