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

Gnitou, Gnim Tchalim; Ma, Tinghuai; Tan, Guirong; Ayugi, Brian; Nooni, Isaac Kwesi; Alabdulkarim, Alia; Tian, Yuan

doi:10.3390/atmos10120802

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

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Yuan Tian

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School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China

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School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

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School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

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Information Technology Department, King Saud University, Riyadh 11451, Saudi Arabia

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Nanjing Institute of Technology, Jiangsu, Nanjing 211167, China

Author to whom correspondence should be addressed.

Atmosphere 2019, 10(12), 802; https://doi.org/10.3390/atmos10120802

Received: 27 October 2019 / Revised: 22 November 2019 / Accepted: 10 December 2019 / Published: 12 December 2019

(This article belongs to the Section Meteorology)

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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. View Full-Text

Keywords: regional climate models; global climate models; statistical assessment; precipitation; West Africa

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Gnitou, G.T.; Ma, T.; Tan, G.; Ayugi, B.; Nooni, I.K.; Alabdulkarim, A.; Tian, Y. Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics. Atmosphere 2019, 10, 802. https://doi.org/10.3390/atmos10120802

AMA Style

Gnitou GT, Ma T, Tan G, Ayugi B, Nooni IK, Alabdulkarim A, Tian Y. Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics. Atmosphere. 2019; 10(12):802. https://doi.org/10.3390/atmos10120802

Chicago/Turabian Style

Gnitou, Gnim Tchalim, Tinghuai Ma, Guirong Tan, Brian Ayugi, Isaac Kwesi Nooni, Alia Alabdulkarim, and Yuan Tian. 2019. "Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics" Atmosphere 10, no. 12: 802. https://doi.org/10.3390/atmos10120802

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

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