A Diagnostic Evaluation of Precipitation
in CORDEX Models over Southern Africa
Evangelia-Anna Kalognomou*, Christopher Lennard*, Mxolisi Shongwe+, Izidine Pinto*, Alice Favre#, Michael Kent*, Bruce Hewitson*, Alessandro Dosio@, Grigory Nikulin&, Hans-Jürgen Panitz**, and Matthias Büchner++
* University of Cape Town, Cape Town, South Africa, and Laboratory of Heat Transfer and Environmental Engineering, Aristotle University, Thessaloniki, Greece
+ South African Weather Service, Pretoria, South Africa
# University of Cape Town, Cape Town, South Africa, and Centre de Recherches de Climatologie, Biogéosciences CNRS, Université de Bourgogne, Dijon, France
@ European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
& Rossby Centre, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
** Institut für Meteorologie und Klimaforschung, Karlsruher Institut für Technologie, Karlsruhe, Germany
++ Potsdam Institute for Climate Impact Research, Potsdam, Germany
American Meteorological Society - JOURNAL OF CLIMATE - JCLI -VOLUME 26 - 1 December 2013 - P P 9477 - 9506
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.
Keywords: Climate prediction; Climate variability; Climatology; Regional models