中文摘要：

物理过程参数化方案的不确定性是目前气候系统模式不确定性的重要来源之一.随着模式内在复杂度攀升,模拟场景多样化,参数化方案中基于先验的和人工的物理参数选取方法已经逐步成为限制模式模拟能力的瓶颈之一.为此,本文设计并提出了初选与寻优相结合的两步法参数优化方案.初选阶段用全因子采样方法对不确定参数空间进行初始敏感性分析,估计最优解所在区域;寻优步采用单纯型下山法,基于初选阶段确定的参数组合快速寻优.将两步法应用于中国科学院大气物理研究所（英文缩写：IAP）大气科学和地球流体力学数值模拟国家重点实验室（英文缩写：LASG）格点大气模式第2版：GAMIL2,选取其深对流方案和云量方案中的3个重要参数开展寻优,优化以综合减小模式降水、风场、温度、湿度、位势高度以及辐射通量的误差为目标.这些变量用GAMIL2标准版本标准化后形成单一的目标.结果显示,优化后的目标函数值比GAMIL2标准版本改进了7.5%.机理分析表明,调优后的参数优化了大气中的水汽凝结作用,进而减少模式的湿度偏差,改进云量的模拟效果;同时水汽凝结作用的变化通过大气内部动力和热力相互作用及响应影响温度、位势高度和风场的模拟.

英文摘要：

Physical parameterization is one of the most important sources of uncertainties in the current climate system models. With the increasing complexity of models and the diverse requirements for climate studies,the priori and manual model tuning method for physical parameterization has become a bottleneck to further improve the climate system model. In this study,we propose a ＂two-step＂parameter optimization approach. In the firststep,an improved full factor sampling scheme is presented to determine the area where the optimal solutions are likely to be found. In the second step,the simplex downhill algorithm is used to perform the search with low computational costs. When applying this ＂two-step＂method to GAMIL2,the grid-point atmospheric model of LASG（ State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics）,IAP（ Institute of Atmospheric Physics）,three important parameters from deep convection scheme and cloud fraction scheme are tuned to improve the model performance measured by a comprehensive metrics based on precipitation,wind,temperature,humidity,potential height as well as radiation flux fields. Results show that the proposed metrics is improved by 7. 5% compared with the standard GAMIL2 version using our proposed optimization method. The optimal parameters improve the condensation efficiency,leading to reducing the simulated bias of moisture and cloud fraction. Meanwhile,the adjustment of condensation further affects the simulation of temperature,geopotential height,and wind.