Abstract
This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March–May (MAM) and October–December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.
Highlights
The changes in the frequency and intensity of extreme events have led to severe climate-related disasters across many parts of the world
From a policy formulation perspective, global climate models (GCMs) and regional climate models (RCMs) are examples of datasets used in forecasting and projection studies
There were a few adjustments in mean bias, root-mean-square difference (RMSD), and mean absolute error (MAE) in most models, with notable performance depicted by the Commonwealth Scientific and Industrial Research Organization (CSIRO) model during this season (Table 2)
Summary
The changes in the frequency and intensity of extreme events have led to severe climate-related disasters across many parts of the world. These extreme events (i.e., floods, droughts, and heat waves) have gained considerable attention from climate scientists and the general public due to their devastating impact on the ecosystem and different sectors of the economy. Model outputs from GCMs and RCMs are sometimes used as an input data sources in the prediction and projection of the extreme events. These model outputs are saddled with uncertainties that arise due to systematic and random biases relative to in-situ datasets [3,4]. Allen et al [6] linked systematic errors (model biases) to model coarser resolutions or parameterizations schemes
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