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Regional climate models: 30 years of dynamical downscaling

Regional Climate Models (RCMs) emerged 30 years ago as a transient tool to provide detailed estimates of meteorological parameters (temperature, precipitation, humidity, wind, and others) for regional applications. Their dynamic downscaling approach was intended to fill the gap between the global bu...

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Published in:Atmospheric research 2020-05, Vol.235, p.104785, Article 104785
Main Authors: Tapiador, Francisco J., Navarro, Andrés, Moreno, Raúl, Sánchez, José Luis, García-Ortega, Eduardo
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Language:English
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description Regional Climate Models (RCMs) emerged 30 years ago as a transient tool to provide detailed estimates of meteorological parameters (temperature, precipitation, humidity, wind, and others) for regional applications. Their dynamic downscaling approach was intended to fill the gap between the global but coarse estimates of Global Climate/Circulation Models (GCMs), which typically had a 2.5° resolution, and practical requirements such as estimating precipitation for hydrologic operations in small basins under conditions of increased greenhouse gas emissions. Over the three decades, RCMs provided data to inform policies and helped to increase knowledge of the present climate and the impacts of global warming at regional level. This paper describes the major achievements of RCMs, critically reviewing the main issues and limitations that have been featured in the literature. It puts forward a controversial claim aimed at starting a debate in the climate community, namely, that the cycle of RCM research has reached an end for informing policies. This is because these models have recently been superseded for that purpose by high-resolution GCMs and Earth System Models (ESM). •The paper reviews the main issues and achievements of RCMs since its inception 30 years ago.•Current high-resolution GCMs may supersede RCMs for informing policies.•RCMs may still be useful for benchmarking new parameterizations and rapid prototyping.
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title Regional climate models: 30 years of dynamical downscaling
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