Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics

Abstract

Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties. climate is measured or estimated, GCM outputs are provided with past, present, and future climate variables. This feature thus allows a variety of applications that were infeasible with past and present climate data repositories. Although useful for general climate information purposes, GCMs suffer from uncertainty in process representation, error propagation, uncertainty in observational data, and sensitivity in resolution [1]. This final drawback is particularly common when GCMs are used to resolve regional-scale features due to being originally designed to serve global needs in terms of providing likely accurate climate information. A common and widely adopted solution within the climate research community is the use of high-resolution regional climate models (RCMs) driven by GCMs or reanalysis data as boundary conditions of the domain area being modeled [2].Like GCMs, RCMs result from the representation of climate processes, but with a particular emphasis on resolving regional-scale climate features. As improved and regionally resolved versions of GCMs, the availability of RCMs for past, present, and future climate data has provided new possibilities not only for understanding regional-scale historical climate phenomena, but also for inferring their likelihood in the future. RCMs are thus important tools for local governments, institutions, researchers, and local communities [1,3].The improvements in resolution provided by RCMs are not error-free. Being derived from GCM data means RCMs might be subjected to GCM-inherited errors [4]. The various uses of regional climate data suggest that different assumptions and considerations are required for different features and metrics with respect to the domain of application and highlight the...

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