海-陆-气全球耦合模式能量收支的误差

通过分析GOALS模式两个版本GOALS 1.1和GOALS 2的能量收支 ,并与观测对比 ,结果表明 :模式模拟的地表净短波辐射通量在高纬地区偏低 ,而净长波辐射通量又偏高 ,导致极地表面温度偏低 ,感热通量在高纬地区为很高的负值。而在陆地上感热加热作用显著偏强 ,使地表有较大的向上净能量给大气 ,引起陆地上有些暖中心也偏强 ,这也解释了模式模拟地表面空气温度场的误差原因。海洋上潜热通量偏低 ,特别是在副热带洋面上偏少更明显。陆地上的欧亚和北美大陆大部分地区潜热通量仍偏低。这也是模式降水在大部分地区偏少的重要原因。两模式大气顶OLR偏低的模拟主要是在中低纬度 ,大气顶净短波辐射通量的模拟在中低纬度虽然与NCEP结果接近 ,但与地球辐射收支试验ERBE资料比较仍偏小较多 ,说明改进中低纬度云 辐射参数化方案对改进全球能量收支的模拟有重要意义。GOALS 2模式中诊断云方案模拟的云量除赤道地区外普遍偏小 ,尤以中纬度为甚 ,造成那里能量收支出现大的误差 ,这表明了更好的云参数化方案的引入是今后模式发展的重要任务之一

ENERGY BUDGET BIAS IN GLOBAL COUPLED OCEAN-ATMOSPHERE-LAND MODEL

The energy budget of two versions of GOALS model (GOALS 1.1 and GOALS 2) is described, and compared to the observational estimates. The results illustrate that the simulated net surface shortwave radiation flux is underestimated in the high latitude regions while the net longwave radiation flux is substantially overestimated in that region, which results in the lower surface air temperature (SAT) in the polar region and the strongly negative sensible heat flux in the high latitudes. The overestimated sensible heat flux from surface to atmosphere in continents causes the much warmer SAT centers, which are the reasons for the bias of model SAT. The reasons that the simulated precipitation in most of the regions is less than observation are closely related to the underestimated latent heat flux over most of Eurasian continent and the oceans, especially over the subtropical ocean. It can be seen that the bias in the OLR of the two models lies in mid latitudes and low latitudes, where the simulated solar absorbed shortwave radiation flux at top of atmosphere is comparable to NCEP reanalysis, but much less than ERBE data. This indicates that the improvement of cloud radiation parameterized scheme in mid latitudes and low latitudes is of critical importance to the simulation of global energy budget. The simulated cloud cover from GOALS 2 model with diagnosed cloud scheme is generally less except equator areas, especially in the mid latitude areas, which causes the large bias of energy budget there. This suggests that the refinement of cloud parameterization is one of the most important tasks in the model's future development.