A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa

Author:

Kalognomou Evangelia-Anna1,Lennard Christopher1,Shongwe Mxolisi2,Pinto Izidine1,Favre Alice3,Kent Michael1,Hewitson Bruce1,Dosio Alessandro4,Nikulin Grigory5,Panitz Hans-Jürgen6,Büchner Matthias7

Affiliation:

1. * University of Cape Town, Cape Town, South Africa, and Laboratory of Heat Transfer and Environmental Engineering, Aristotle University, Thessaloniki, Greece

2. South African Weather Service, Pretoria, South Africa

3. University of Cape Town, Cape Town, South Africa, and Centre de Recherches de Climatologie, Biogéosciences CNRS, Université de Bourgogne, Dijon, France

4. European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy

5. Rossby Centre, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

6. ** Institut für Meteorologie und Klimaforschung, Karlsruher Institut für Technologie, Karlsruhe, Germany

7. Potsdam Institute for Climate Impact Research, Potsdam, Germany

Abstract

The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of ~50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim; 1989–2008). Results are compared against a number of observational datasets.In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain. Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Niño (La Niña) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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