Analysis of precipitation based on ensembles of regional climate model simulations and observational databases over Ethiopia for the period 1989–2008

Abstract

This study examines the performance of multimodel numerical simulations and multiobservational databases focusing on seasonal cycles and spatial variations of precipitation over Ethiopia. Seven regional climate models (RCMs) driven by the European Center for Medium Range Weather Forecasting (ECMWF) Interim reanalysis (ERA-Interim) and generated in the framework of COordinated Regional climate Downscaling EXperiment (CORDEX) project, and four observational databases computed using different interpolation techniques and blending strategies were evaluated against typical observational database produced by Climate Research Unit (CRU) over Ethiopia on monthly basis. All were produced at 48.8 km grid resolution for the period 1989-2008. The preliminary results showed that ensembles [multimodel ensemble (MME) + multiobservational ensemble (MOE)] were as good as CRU in reproducing the temporal variability and the geographical distribution of precipitation. Comparison of seasonal means and temporal correlation results revealed that there were good agreements between ensembles and CRU at each grid point and in close proximity to each other. Results of rotated principal components (RPCs), rotated empirical orthogonal functions (REOFs), and the associated power spectra showed that every ensemble's element was able to simulate the seasonal cycles and homogeneous precipitation zones of CRU reasonably well. Excessive and deficient rainfall periods, which were seen in every ensemble's RPCs, matched CRU historical records. BackgroundClimate models are perpetually ameliorated and upgraded to higher and higher grid resolution with the intention of reproducing climate variables at finer scale. However, as climate model resolution tends to be finer, the noise on grid cells is likely to happen. Uncertainty in climate model is becoming much of the present interest as poor performance in present climate conditions is linked with outliers in the future projection (Knutti et al., 2010;Brands et al., 2011). Despite the fact that RCMs are downscaling tools aspired to upgrade the modelling of local physical processes, they are highly sensitive to model formulation, grid resolution, numerical schemes, and other physical parameterizations and result, therefore, in differences in downscaling skills (Fowler and Ekström, 2009;Maraun et al., 2010). There is growing evidence that regions and seasons showing the greatest model biases in the simulation of climate variables are often those with the greatest intermodel differences (Frei et al., 2006;Fowler et al., 2007;Maraun et al., 2010). Before using RCMs for future studies, it is believed that * Correspondence to: D. T. Reda, Department of Physics, College of Natural Science, Addis Ababa University, Addis Ababa, Ethiopia. E-mail: daniell.tsegay@gmail.com they should pass through some evaluation mechanisms. To address this issue, several climate modelling groups from around the world have been downscaling global climate models (GCMs) to regional scale using their own model setup. The aim ...

Evaluation of CORDEX rainfall in northwest Ethiopia: Sensitivity to the model representation of the orography

“…A similar overestimation was found by Reda et al . () and Endris et al . () when comparing the CORDEX‐Africa runs with CRU, GPCC and GPCP for the northwestern highlands in Ethiopia.…”

“…However, the range of observational sets lies mostly within the range of the CORDEX ensemble, except for March, April and September when the lowest CORDEX values supersede the smallest observed values. A similar overestimation was found by Reda et al (2015) and Endris et al (2013) when comparing the CORDEX-Africa runs with CRU, GPCC and GPCP for the northwestern highlands in Ethiopia. Such overestimation is quite common, for example, for the regional climate model RegCM (Segele et al, 2009c;Diro et al, 2012;Zeleke et al, 2013).…”

“…When comparing the corresponding CORDEX models of this study with the one of Endris et al (2013) and Reda et al (2015) the spatial patterns roughly agree. However, the connection with the orography is more clear here because our study area features a larger elevational variability and non-smoothened fields.…”

“…Previous work about RCM downscaling in Ethiopia includes the studies of Diro et al (2012), Endris et al (2013) and Reda et al (2015). In general, for our study area in northwest Ethiopia there is an overestimation of precipitation (e.g., Segele et al, 2009c;Diro et al, 2012;Endris et al, 2013;Zeleke et al, 2013;Reda et al, 2015). RCMs do not always provide added value.…”

Effects of Irrigation Systems on Physicochemical Properties of Soil at Different Depths: A Case Study at a Farm Near Ziway Lake, Ethiopia

“…The dependency on rainfall has exposed the country to poverty and food insecurity that has led over 30% of the total population to live below the poverty line (Gebrehiwot and van der Veen, 2014). In particular, the eastern lowlands and the rift valley regions of Ethiopia are highly impacted by inadequate amounts of rainfall, of erratic distribution, short duration and variable occurrence (Lyon, 2014;Reda et al, 2015). In particular, the eastern lowlands and the rift valley regions of Ethiopia are highly impacted by inadequate amounts of rainfall, of erratic distribution, short duration and variable occurrence (Lyon, 2014;Reda et al, 2015).…”