2 Improve the Representation of Convection - PBL Interactions in Two Global
نویسنده
چکیده
Reducing systematic errors of state-of-the-art General Circulation Models (GCMs) will increase our confidence on the projection of future climate change and improve our forecast skills of present-day weather and climate variability. One systematic error of current GCMs is the failure to simulate a dominant tropical atmospheric variability: Madden-Julian Oscillation (MJO). Through downscale and upscale influences, MJO may modulate the occurrence of extreme hydo-meteorological events (e.g., hurricane, flood and drought etc.) and the onset and development of ENSO. This common model error not only hinders the achievement of seamless forecasts, but also undermines our confidence on the projection of future climate change. In this proposed study, we aim to improve the representation of MJO in CAM and ECHAM GCMs. Both models suffer a too red spectrum instead of an outstanding peak around 30-60-day owing to the overestimated persistence of daily precipitation. Through analyzing 3-D moisture profiles obtained from Aqua/AIRS, we discovered significant boundary-layer dryness underneath the MJO convection, which may play a critical role in disrupting the persistent rainfall. This new observational feature is missing in both CAM and ECHAM simulations and the ECMWF and NCEP reanalysis. This systematic bias is likely originated from the inappropriate representation of the convection-PBL interactions in the models. Another new observational feature is that MJO rainfall includes a large stratiform component. However, the effects of stratiform clouds, in particularly, mesoscale stratiform clouds, are not well represented in the current state-of-the-art GCMs. In this proposed study, we plan to systematically address the aforementioned issues/problems. First, high-resolution dynamically consistent regional datasets will be developed for two field campaign (TWP_ICE and THORPEX_PARC) periods using advanced data assimilation techniques. Then, these datasets will be used to diagnose convection-PBL interactions. Finally, improved parameterizations of convective and mesoscale stratiform clouds will be tested and implemented in CAM and ECHAM. The accomplishment of this project will provide improved regional datasets and reduce the systematic errors of CAM and ECHAM in simulating and predicting present-day weather and climate variability. We will also make a parameterization package available to global modeling community, which may lead toward the improved projection of future climate change, particularly those extreme events modulated by MJO.
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