Land-Surface Modeling and Climate Simulations: Results over the Autstralian Region from Sixteen AMIP2 Models

Zhang, H ; Henderson-Sellers, A ; Irannejad, P ; Sharmeen, S ; Phillips, T ; McGuffie, K

Lawrence Livermore National Laboratory

关键词: Probability;    Feedback;    Forecasting;    Seas;    General Circulation Models;   

DOI  :  10.2172/15013451
RP-ID  :  UCRL-ID-148208
RP-ID  :  W-7405-ENG-48
RP-ID  :  15013451

美国|英语

来源: UNT Digital Library

【 摘 要 】

This report presents analyses of sixteen models from the Atmospheric Model Intercomparison Project II (AMIP2) over the Australian region. It is focused on assessing how well surface climate and fluxes over this region are simulated in current Atmospheric General Circulation Models (AGCMs) forced by observed sea surface temperatures (SSTs). The importance of land-surface modeling on model predictability is also investigated. In this preliminary analysis, the Bureau of Meteorology (BoM) observational rainfall, temperature and surface evapotranspiration datasets are used in validating surface climatologies simulated by the 16 models. Specifically, the Linear Error in Probability Space (LEPS) score is calculated in assessing the skill of the models in simulating surface Climate anomalies for the 17-year period (1979 to 1995). Numerous model differences are seen with some aspects of the model performance being linked to the complexity of land-surface schemes used. The connection between model skill in simulating surface climate anomalies and surface flux anomalies is explored. Lag-correlation analysis is conducted. Results reveal that ''climatic memory'' derived from land-surface processes (e.g: soil moisture) has different features in the sixteen models: some models show rapid feedback processes between land-surface and the overlying atmosphere, while others show slowly varying processes in which anomalous surface conditions have impacts on the model integrations on longer time-scales. It is found that models with simple bucket-type scheme tend to have a more rapid decay rate in the retention of soil moisture anomalies, and therefore, soil moisture conditions have a much weaker influence on forecasting surface climate anomalies. This study suggests that land-surface modeling has the potential to influence AGCM predictability on seasonal and even longer time scales.