Evaluation of CORDEX‐CORE regional climate models in simulating rainfall variability in Rwanda

Abstract

In this study, three regional climate models (RCMs), CCLM5-0-15, RegCM4-7 and REMO2015, from CORDEX-CORE are evaluated in their ability to reproduce rainfall variability in Rwanda for the period 1981-2005. They are driven by three different global climate models (GCMs), namely MPI-M-MPI-ESM-LR, NCC-NorESM1-M and MOHC-HadGEM2-ES, and the European Centre for Medium-Range Weather Forecasts Reanalysis (ECMWF-ERAINT). Simulated rainfall is evaluated against observations from Rwanda Meteorology Agency to assess models' performance. A set of metrics are used to quantify discrepancies of models' simulations from observations. A possible association of El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) to rainfall over Rwanda is investigated. It is found that in general, all RCMs, their ensemble and multimodel ensemble means reproduce satisfactorily the spatial distribution of the mean seasonal rainfall (MSR), the mean rainfall annual cycle, and the interannual variability of the MSR for both March-April-May (MAM) and October-November-December (OND). However, significant biases in individual RCMs are observed with varying magnitude of bias in space. Observed MSR indicates a positive trend of 0.045 and 0.058 mmÁdayÁyear −1 , respectively, for MAM and OND at 0.05 significance level, but almost all models indicate no significant trend (at 0.05 significance level). The seasonal correlations between observed rainfall anomalies and sea surface temperature (SST) anomalies indices across the tropical Pacific (Niño1+2 and Niño3.4) and Indian Oceans associated, respectively, with ENSO and IOD, although relatively weak, are reproduced by the three RCMs driven by ECMWF-ERAINT and the multimodel ensemble means of ECMWF-ERAINT and MPI-M-MPI-ESM-LR. Analysis of the Taylor diagram indicates that CCLM5-0-15_MPI-M-MPI-ESM-LR and the multimodel ensemble mean of MPI-M-MPI-ESM-LR outperform individual models. Overall, the evaluation finds reasonable model skill in representing seasonal rainfall climatology and variability, suggesting the potential use of CORDEX-CORE (AFR-22) RCMs for the assessment of future climate projections in Rwanda.

Spatial and Temporal Analysis of Rainfall Variability and Trends for Improved Climate Risk Management in Kayonza District, Eastern Rwanda

“…Mann-Kendall (M.K), a nonparametric rank-based test, was employed under this study to test the statistical signifcance of the observed trends where positive values show an upward slope, while a negative value indicates a downward slope [57][58][59][60]. Tis method has been widely used in previous research over Rwanda [29,45,46,61].…”

“…Tis dataset was generated by merging quality-controlled observed data with satellite rainfall estimates data from Tropical Applications of Meteorology using SATellite (TAMSAT). Te used rainfall dataset was used in the previous studies [27,34,[45][46][47][48]. More information on the generation of the dataset is found in [27].…”

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

“…This observation is significant because it suggests that while the Indian Ocean is a critical driver of climate variability in many parts of East Africa, its direct influence on rainfall during the MAM season in Rwanda is relatively subdued [67,68]. The positive phase of the IOD, commonly referred to as the positive dipole, correlates with increased precipitation over EA in general [42].…”

“…Conversely, the 850 hPa level is closer to the Earth's surface and plays a pivotal role in the low-level transport of moisture [71,75]. GHs at this atmospheric level are indicative of the presence and characteristics of air masses; low heights often signal the presence of moist air masses, while high heights are indicative of drier conditions [66,67,73]. The distribution and movement of these air masses are critical in determining the moisture availability for precipitation over Rwanda.…”

Sensitivity of Australian rainfall to driving SST datasets in a variable-resolution global atmospheric model

“…Dynamical downscaling has emerged as a widely employed approach for investigating the teleconnections between ENSO/IOD and localized rainfall patterns (e.g., Boulard et al 2013, Ratna et al 2017, Whan and Zwiers 2017, Worku et al 2018, Verma and Bhatla 2021, Safari et al 2023. Regional Climate Models (RCMs) with typical resolutions higher than 30km are employed to simulate the atmosphere driven by reanalysis or general circulation model (GCM) outputs with coarser resolutions exceeding 100km.…”