Earth system models (ESMs) consist of parameterization schemes based on one’s perception of how the Earth system functions. A typical ESM contains a large number of parameters (i.e., the constants and exponents in the parameterization schemes) whose specification can have a significant impact on an ESM’s simulation capabilities. Sensitivity analyses (SA) is an important tool for assessing how parameter specification influences model simulations. In this study, we used an Earth system model of intermediate complexity (EMIC)—LOVECLIM as an example to illustrate how SA methods can be used to identify the most sensitive parameters that control the simulations of several key global water and energy cycle variables, including global annual mean absolute surface air temperature (T_G), precipitation and evaporation over the land and over the oceans (P_L, P_O, E_L, E_O), and land runoff (R_L). We also demonstrate how judiciously specifying model parameters can improve the simulations of those variables. Three SA methods MARS, RF, and sparse PCE-based Sobol’ method were used to evaluate a pool of 25 adjustable parameters chosen from land, atmosphere, and ocean components of LOVECLIM and their results were intercompared to ensure robustness of the results. It is found that with different parameter specification, T_G can vary from 10 to 20 °C, and the values of P_L, P_O, E_L, and E_O can change by more than 100%. An interesting observation is that the value of R_L vary from 13,000 to 35,000 km³, far below the observed climatological value of 40,000 km³, indicating a model structural deficiency in representing land runoff by LOVECLIM which must be corrected to obtain more reasonable global water budgets. We also note that parameter sensitivities are significantly different at different latitudes. Finally, we showed that global water and energy cycle simulations can be significantly improved by even a crude automatic parameter tuning, indicating that parameter optimization can be a viable way to improve ESM climate simulations. The results from this study should help us to understand the parameter uncertainty of a full-scale ESM.

中文翻译：

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中等复杂程度的地球系统模型的参数说明如何影响其气候模拟

地球系统模型（ESM）由参数化方案组成，这些参数化方案基于人们对地球系统如何运行的认识。典型的ESM包含大量参数（即，参数化方案中的常数和指数），其规格可能会对ESM的仿真功能产生重大影响。灵敏度分析（SA）是评估参数规格如何影响模型仿真的重要工具。在本研究中，我们以中等复杂度（EMIC）的地球系统模型（LOVECLIM）为例，说明如何使用SA方法识别最敏感的参数，这些参数控制着几个关键的全球水和能源循环变量的模拟，包括全球年平均绝对地面气温（T _G），降水和蒸发，在陆地和海洋上空（P_大号，P _ø，ê_大号，ë _Ò）和陆地径流（ř_大号）。我们还演示了如何明智地指定模型参数可以改善这些变量的仿真。基于MARS，RF和基于PCE的稀疏PCE的三种SA方法用于评估从LOVECLIM的陆地，大气和海洋成分中选择的25个可调参数的集合，并将它们的结果进行比较以确保结果的鲁棒性。结果发现，在不同的参数规格下，T _G的变化范围为10至20°C，P _L的值，P _O，E _L和E _O的变化可以超过100％。一个有趣的发现是R _L的值在13,000至35,000 km ^3之间，远低于观测到的40,000 km ^3的气候值。，表明LOVECLIM代表土地径流存在模型结构性缺陷，必须进行修正以获取更合理的全球水资源预算。我们还注意到，在不同的纬度下，参数敏感性明显不同。最后，我们表明，甚至可以通过粗略的自动参数调整来显着改善全球水和能源循环模拟，这表明参数优化可以是改善ESM气候模拟的可行方法。这项研究的结果应有助于我们理解全面ESM的参数不确定性。