Surrogate-based uncertainty quantification and parameter optimization in simulations of the West African monsoon

Abstract

The West African monsoon (WAM) is a complex climatic system with major impacts. This study uses a surrogate-based framework to quantify uncertainties in model parameterizations and to improve simulations with the ICON model. By applying Gaussian process and principal component regression, the approach enables efficient sensitivity analysis and optimization. Results highlight key parameter influences on the WAM and demonstrate the framework’s effectiveness.