How parameter specification of an Earth system model of intermediate complexity influences its climate simulations

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 km 3 , far below the observed climatological value of 40,000 km 3 , 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.