Investigation of Uncertainties in Multi-variable Bias Adjustment in Multi-model Ensemble

Abstract

Abstract. Post-processing methods such as univariate bias adjustment have been widely used to reduce the bias in the individual variable. These methods are applied to variables independently without considering the inter-variable dependence. However, in compound events, multiple atmospheric factors occur simultaneously or in succession, leading to more severe and complex impacts. Therefore, a multi-variable bias adjustment is necessary to retain the inter-variable dependence between the atmospheric drivers. The present study focuses on a multi-variable bias adjustment of surface air temperature and relative humidity in a multi-model ensemble. We investigated added values and biases before and after adjusting the variables. There are gains and losses throughout the process of adjustment. The bias adjustment effectively reduces bias in surface air temperature; however, it shows bias amplification for relative humidity at higher altitudes. Added values were improved at lower altitudes but showed reductions in surface air temperature at higher altitudes. Overall, the bias adjustment shows improvement in reducing bias over low-altitude urban areas, encouraging its application to assess compound events. These findings highlight a potential bias adjustment approach for the regions with a constraint on observational data.