一种新的模式倾向误差估计算法及其在ENSO模拟中的应用

A NEW ALGORITHM OF ESTIMATION FOR MODEL TENDENCY ERRORS AND THE APPLICATION IN ENSO SIMULATION

气候模式是我们理解、模拟和预报气候演变的重要工具。然而即使是目前最先进的耦合模式,其模拟和预报与大气/海洋的真实状态相比,仍存在较大偏差,这是由于在模式的倾向方程中不可避免地存在系统性的误差(倾向误差)。因此,减小模式倾向误差对改进模式的模拟和预报效果具有重要意义。该研究首先发展了一种新的计算模式倾向误差的估计算法——基于局地集合变换卡尔曼滤波器(local ensemble transform kalman filter,LETKF)同化技术的倾向误差估计算法。在此基础上,将新发展的算法应用到Zebiak-Cane(ZC)模式,通过同化海表面温度异常(sea surface temperature anomaly,SSTA)数据,估计随时空变化的倾向误差,并使用计算得到的倾向误差订正模式,进行积分模拟。结果表明:(1)倾向误差和ZC模式的模拟偏差具有高度相关性;(2)订正后的模式改善了对厄尔尼诺-南方涛动(El Ni?o-Southern Oscillation,ENSO)的一些重要特征的模拟。这说明新发展的模式倾向误差估计算法十分有效且在ENSO模拟中具有较好的应用价值,此外,这种新的模式倾向误差估计算法,计算高效简便,可便捷地应用于各模式中,利于推广。

Climate models are important tools for us to understand,simulate and forecast the evolution of the climate.However,even with the current state-of-the-art coupled models,due to the inevitable systematic errors in the tendency equation of model,the model tendency error,the simulations and forecasts are still far from the true state of the atmosphere/ocean.Therefore,reducing the model tendency error is of great significance to improve the simulation and forecasting effect of the model.A novel algorithm was developed for estimating the tendency error of a model using assimilation technique with local ensemble transform Kalman filter(LETKF).The new algorithm was applied to the Zebiak-Cane(ZC)model to estimate the space-time dependent tendency error by assimilating the observed data of sea surface temperature anomaly(SSTA),and the calculated tendency error was used to correct the model,and then an integral simulation was carried out.Results reveal a high correlation between the tendency error and the simulation error of the ZC model.The corrected model improved some important characteristics of the simulation of El Ni?o-Southern Oscillation(ENSO).Overall,the new algorithm is very effective and simple computationally,shows good application value in ENSO simulation,and can be easily applied to various models,and thus shall be promoted.