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

Yuhan Shi,Wei Gong,Cunde Xiao,Heng Wang,Qingyun Duan,Jackson Charles

doi:10.1186/s40645-019-0294-x

Abstract

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 (TG), precipitation and evaporation over the land and over the oceans (PL, PO, EL, EO), and land runoff (RL). 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, TG can vary from 10 to 20 °C, and the values of PL, PO, EL, and EO can change by more than 100%. An interesting observation is that the value of RL 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.

Highlights

Earth system models (ESMs) are an indispensable tool for gaining an understanding on how the climate system works and how its various components such as land, atmosphere, and oceans interact with each other
Parameter optimization of climate models is an enormous computational task for the following reasons: (1) optimization of climate model parameters is a high-dimensional problem because climate models usually contain a large number of adjustable parameters, and the number of model experiments to optimize those parameters is exponentially proportional to the dimensionality; (2) climate models simulate many climatic variables, and the optimization problem must be framed as a multiobjective optimization problem, which further increases the number of model experiments needed to identify optimal parameter solutions; and (3) climate models are expensive to run because they must be run globally and cover time span over many years
The climatological means of observations of TG used is the twentiethcentury average provided by NOAA, and the global water budget components runoff over land surface (RL), PL, PO, EL, and EO is obtained from the work by Trenberth et al (2007) based on ERA40 reanalysis data

Summary

Introduction

Earth system models (ESMs) are an indispensable tool for gaining an understanding on how the climate system works and how its various components such as land, atmosphere, and oceans interact with each other They have been used extensively to simulate the past and future. To explore the various feedbacks among different components of the climate system, EMICs simulate long-term climate changes with parameterization schemes to simplify the various processes and details of the climate system These models are usually applied to certain scientific questions, such as understanding climate feedbacks on millennial time scales or exploring sensitivities in which long model integrations or large ensembles are required (Claussen et al.2002; Petoukhov et al 2005). This study uses an EMIC model as an example to study how parameter specification affects climate model simulations and to explore potentials for improving climate model simulation through parameter perturbations

Objectives

Methods

Results

Conclusion