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Exploring the Influence of Improved Horizontal Resolution on Extreme Precipitation in Southern Africa Major River Basins: Insights from CMIP6 HighResMIP Simulations

2024Open accessUniversity of Botswana

Abstract

<title>Abstract</title> This study examines the impact of enhanced horizontal resolution on simulating mean and precipitation extremes in the major river basins of southern Africa. Seven global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP) within the Coupled Model Intercomparison Project Phase 6 (CMIP6) are employed. The models are available at both high-resolution (HR) and low-resolution (LR) resolutions. Three datasets are used to assess the models for the period 1983-2014 during December-January-February. The distributions of daily precipitation from the HR models are nearly identical to those of their LR counterparts. However, the bias of intense daily precipitation is not uniform across the three observations. Most HR and LR models reasonably simulate mean precipitation, maximum consecutive dry days (CDD), and the number of rainy days (RR1), albeit with some biases. Improvements due to enhanced horizontal resolution are realised for mean precipitation, CDD, and RR1 as noted from high spatial correlation coefficients (SCCs), low root mean square errors, and biases. The CMIP6 HighResMIP models tend to overestimate very and extreme wet days (R95p and R99p), maximum one-day precipitation (Rx1day), and simple daily intensity (SDII) with a pronounced wet bias in HR models for R95p and R99p. Most HR models outperform their LR counterparts in simulating R95p, R99p, and SDII. Our results indicate that enhanced horizontal resolution under CMIP6 HighResMIP results in either improvements (e.g., increased SCC) or deterioration (e.g., decreased SCC), depending on precipitation extremes, river basin, and model. The findings of this study are important for both climate scientists and policymakers.

Research topics

  • Climate variability and models
  • Precipitation Measurement and Analysis
  • Meteorological Phenomena and Simulations

Sustainable Development Goals

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DOI: 10.21203/rs.3.rs-3963936/v1

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