This study aimed at assessing the evolution, distribution and the socioeconomic impacts of extreme rainfall over East Africa during the March, April and May (MAM) rainfall season focusing on assessing the trends and contribution of MAM rainfall in mean annual rainfall across the region. It employed Principal Component Analysis (PCA) methods to capture the patterns and variability of MAM rainfall. The PCA results indicated that the first Principal Component (PC) describe 17% of the total variance, while the first six PCs account only 53.5% of the total variance in MAM rainfall, underscoring the complexity of rainfall forcing factors in the region. It has been observed that MAM rainfall accounts about 30%-60% of the mean annual rainfall in most parts of the region, signifying its importance in agriculture, water, energy and other socioeconomic sectors. MAM has been characterized by increasing variability with varying trend patterns across the region. The MAM rainfall trend is not homogeneous across the region; some areas are experiencing a slight decreasing rainfall trend, while other areas are experiencing a slight increasing rainfall trend. The observed trend dynamics is consistent with the global trend patterns in precipitation as depicted in recent Intergovernmental Panel on Climate Change (IPCC) reports. Over the last five years MAM rainfall season have been characterized by record-breaking extremes. On 8 th May 2017, Tanga and Mombasa meteorological stations recorded 316 mm

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.

No abstract

<p>The aim of the Flagship Pilot Study (FPS) “Modelling the Southeast African regional Climate” is to study processes and phenomena relevant to regional climate change in south-eastern Africa. The region is vulnerable to climate change due to socio-economic factors as well as its exposure to weather and climate extremes such as floods, droughts and heat waves. The FPS will foster regional collaboration on modelling and the analysis of precipitation and temperature that will be beneficial for the society in general. The FPS South-eastern Africa includes various scientists from the National Meteorological and Hydrological Services (NMHSs) and academia of South Africa, Mozambique, Zimbabwe, Malawi, Tanzania, Kenya, Rwanda, Burundi and Norway. The research will involve analysis of local observations, reanalysis, simulations from regional climate models (RCMs) and empirical-statistical downscaling (ESD) to study dependencies between large-scale conditions and local variability in the rain and temperature statistics. The expected impacts of the FPS are skills development in data analysis and modelling, and a better understanding of regional climate that is fundamental to climate services and provides guidance to decision-makers and planners. The involvement of NMHS in the project provides access to their observational networks, whose use will assist with verification of model simulations, and also increase the value of NMHSs’ work with observations and data management. Actionable information will be extracted for decision-makers, based on a synthesis of multiple sources of information which take into account the local climate, past and future trends, models’ skill, known weather/climatological phenomena, and other geographical information. Biases between the model climate and observations will be adjusted through appropriate adjustment methods such as the Quantile Mapping approach. The work will also involve capacity building on R programming language as well as other tools (e.g. CDO, python) and use R-based shiny web applications in distillation efforts and to provide a gateway to the information embedded in complex data structures.</p>