EAPSI: Diagnosing the heat engines of massive tropical cloud systems

Bowers Matthew C, West Lafayette IN

Investigators

Abstract

Accurately simulating tropical cloud systems has long been identified as a critical component of global weather and climate predictions. Indeed, prediction bias in the tropics is known to corrupt forecasts in higher latitudes, impeding the mitigation of weather and climate hazards posed to the global society. The dominant 20--90 day feature of the tropical atmosphere is a massive, eastward-propagating cloud system known as the Madden-Julian Oscillation (MJO). Due to the elusive nature of its physical origins and its influence on global weather and climate, accurately predicting the MJO is a demanding and critical scientific challenge. This research aims to provide a novel procedure for evaluating the ability of weather and climate models to realistically simulate the MJO and for diagnosing the sources of bias in model simulations. The methodology is based on analyzing and contrasting the heat engines, or energetics, of both observed and simulated MJO systems. The research will be conducted in collaboration with Dr.Wei-Ting Chen, a noted expert on tropical meteorology and model simulation of the MJO at the National Taiwan University. This research will involve contrasting features of observed MJO convection signals with those produced by model simulations. In particular, for both observational and model data, statistical machine learning techniques will be utilized to identify stages (or phases) of both predominantly equatorially-symmetric and antisymmetric MJO signals. Various energetics terms are to be computed within the cyclic framework, enabling an examination of energy transformation processes throughout the MJO life cycle. Contrasting features of the energy transformation processes in observations versus model simulations provides a powerful basis for assessing model representation of the MJO and diagnosing the sources of model biases, tasks which are fundamental for improving weather and climate simulations. This NSF EAPSI award is funded in collaboration with the National Science Council of Taiwan.