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.
Highlights
Global climate models (GCMs) represent the scientific basis of what is known about the overall climate system, including its components, processes, and their different interactions
The fundamental reason for such improvement is related to the fact that GCMs are tuned based on the agreement between the global improvement is related to the fact that GCMs are tuned based on the agreement between the global mean top-of-atmosphere energy balance and observations [45], while regional climate models (RCMs) are calibrated with respect mean top-of-atmosphere energy balance and observations [45], while RCMs are calibrated with to the current climate simulation driven by reanalysis [46]
We comprehensively evaluated the Rossby Centre Regional Climate Model (RCA4)
Summary
Global climate models (GCMs) represent the scientific basis of what is known about the overall climate system, including its components, processes, and their different interactions. Atmosphere 2019, 10, 802 climate is measured or estimated, GCM outputs are provided with past, present, and future climate variables. This feature allows a variety of applications that were infeasible with past and present climate data repositories. 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 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]
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