Alice Favre

Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors

Monthly-mean precipitation, mean (TAVG), maximum (TMAX) and minimum (TMIN) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for TAVG and TMAX than for TMIN, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.

Spatial distribution of precipitation annual cycles over South Africa in 10 CORDEX regional climate model present-day simulations.

This study presents an evaluation of the ability of 10 regional climate models (RCMs) participating in the COordinated Regional climate Downscaling Experiment-Africa to reproduce the present-day spatial distribution of annual cycles of precipitation over the South African region and its borders. As found in previous studies, annual mean precipitation is quasi-systematically overestimated by the RCMs over a large part of southern Africa south of about 20°S and more strongly over South Africa. The spatial analysis of precipitation over the studied region shows that in most models the distribution of biases appears to be linked to orography. Wet biases are quasi-systematic in regions with higher elevation with inversely neutral to dry biases particularly in the coastal fringes. This spatial pattern of biases is particularly obvious during summer and specifically at the beginning of the rainy season (November and December) when the wet biases are found to be the strongest across all models. Applying a k-means algorithm, a classification of annual cycles is performed using observed precipitation data, and is compared with those derived from modeled data. It is found that the in-homogeneity of the spatial and temporal distribution of biases tends to impact the modeled seasonality of precipitation. Generally, the pattern of rainfall seasonality in the ensemble mean of the 10 RCMs tends to be shifted to the southwest. This spatial shift is mainly linked to a strong overestimation of convective precipitation at the beginning of the rainy season over the plateau inducing an early annual peak and to an underestimation of stratiform rainfall in winter and spring over southwestern South Africa.

Evaluation of rainfall simulations over Uganda in CORDEX regional climate models

The study evaluates the ability of ten regional climate models (RCMs) to simulate the present-day rainfall over Uganda within the Coordinated Regional Downscaling Experiment (CORDEX) for the period 1990–2008. The models’ ability to reproduce the space-time variability of annual, seasonal, and interannual rainfall has been diagnosed. A series of metrics have been employed to quantify the RCM-simulated rainfall pattern discrepancies and biases compared to three gridded observational datasets. It is found that most models underestimate the annual rainfall over the country; however, the seasonality of rainfall is properly reproduced by the RCMs with a bimodal component over the major part of the country and a unimodal component over the north. Models reproduce the interannual variability of the dry season (December–February) but fail with the long and short rains seasons even if the ENSO and IOD signal is correctly simulated by most models. In many aspects, the UQAM-CRCM5 RCM is found to perform best over the region. Overall, the ensemble mean of the ten RCMs reproduces the rainfall climatology over Uganda with reasonable skill.