Evaluation of rainfall simulations over Uganda in CORDEX regional climate models

Kisembe, Jesse; Favre, Alice; Dosio, Alessandro; Lennard, Christopher; Sabiiti, Geoffrey; Nimusiima, Alex

doi:10.1007/s00704-018-2643-x

Cited by 60 publications

(63 citation statements)

References 85 publications

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“…Efforts have also been made to understand the models performance in reproducing the current climate (e.g. Otieno and Anyah 2013a, Endris et al 2013, Ongoma et al 2019, Kisembe et al 2019 and assess future projected changes in rainfall and temperature over the GHA (e.g. Shongwe et al 2011, Anyah and Qiu 2012, Otieno and Anyah 2013b, Omondi et al 2014, Endris et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Projected effects of 1.5 °C and 2 °C global warming levels on the intra-seasonal rainfall characteristics over the Greater Horn of Africa

Gudoshava

Misiani

Segele

et al. 2020

Environ. Res. Lett.

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This study examines the effects of 1.5°C and 2°C global warming levels (GWLs) on intra-seasonal rainfall characteristics over the Greater Horn of Africa. The impacts are analysed based on the outputs of a 25-member regional multi-model ensemble from the Coordinated Regional Climate Downscaling Experiment project. The regional climate models were driven by Coupled Model Intercomparison Project Phase 5 Global Climate Models for historical and future (RCP8.5) periods. We analyse the three major seasons over the region, namely March-May, June-September, and October-December. Results indicate widespread robust changes in the mean intra-seasonal rainfall characteristics at 1.5°C and 2°C GWLs especially for the June-September and October-December seasons. The March-May season is projected to shift for both GWL scenarios with the season starting and ending early. During the June-September season, there is a robust indication of delayed onset, reduction in consecutive wet days and shortening of the length of rainy season over parts of the northern sector under 2°C GWL. During the October-December season, the region is projected to have late-onset, delayed cessation, reduced consecutive wet days and a longer season over most of the equatorial region under the 2°C GWL. These results indicate that it is crucial to limit the GWL to below 1.5°C as the differences between the 1.5°C and 2°C GWLs in some cases exacerbates changes in the intra-seasonal rainfall characteristics over the Greater Horn of Africa.

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Section: Introductionmentioning

confidence: 99%

Projected effects of 1.5 °C and 2 °C global warming levels on the intra-seasonal rainfall characteristics over the Greater Horn of Africa

Gudoshava

Misiani

Segele

et al. 2020

Environ. Res. Lett.

Self Cite

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“…Over the East Africa region, recent studies have reported existence of biases in RCMs and GCMs datasets (Endris et al, 2013;Kisembe et al, 2018;Ongoma et al, 2019). To illustrate, Ayugi et al (2020) demonstrated the manifestation of systematic dry (wet) biases over regions of low (high) altitude characterized by arid and semi-arid lands (ASALs) or complex topography.…”

Section: Examples Of the Bias Correction Methods Include The Delta Comentioning

confidence: 98%

Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

Ayugi¹,

Tan²,

Niu³

et al. 2020

Preprint

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Accurate assessment and projections of extreme climate events requires the use of climate datasets with no or minimal error. This study uses quantile mapping bias correction (QMBC) method to correct the bias of five Regional Climate Models (RCMs) from the latest output of Rossby Climate Model Center (RCA4) over Kenya, East Africa. The outputs were validated using various scalar metrics such as Root Mean Square Difference (RMSD), Mean Absolute Error (MAE) and mean Bias. The study found that the QMBC algorithm demonstrate varying performance among the models in the study domain. The results show that most of the models exhibit significant improvement after corrections at seasonal and annual timescales. Specifically, the European community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict exemplary improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model show little improvement across various timescales (i.e. March-April-May (MAM) and October-November-December (OND)). The projections forced with bias corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data prior to performing any regional impact analysis. The corrected models can be used for projections of drought and flood extreme events over the study area. This study analysis is crucial from the sustainable planning for adaptation and mitigation of climate change and disaster risk reduction perspective.

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“…The correlation facilitates a better evaluation of the capacity of a climate model to reproduce the observed inter-annual variability [24]. The inter-annual correlation/variability between climate model-simulated and observed rainfalls was also investigated in earlier studies [2,10,[24][25][26][27]. The statistical significance of the correlation was determined by applying the standard Student's t-test.…”

Section: Study Area Methodology and Datamentioning

confidence: 99%

Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model

Mondal

Nowreen

Sakib

2020

Climate

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The regional climate model, Providing REgional Climates for Impact Studies (PRECIS), has been widely used throughout the world to generate climate change projections for impact studies and adaptations. Its recent application in South Asia also includes the projection of rainfall extremes. In spite of its wide application, a stringent validation of the model is yet to be reported. In this study, we assessed the performance of the model in simulating annual, monthly and extreme rainfalls over Bangladesh by using a number of statistical techniques, e.g., pattern (both spatial and temporal) correlation, root mean square difference (RMSD), mean absolute difference (MAD), Student’s t-test for significance, probability density functions, etc. The results indicated that the PRECIS model could capture the overall spatial pattern of mean annual and monthly rainfalls very well. However, the inter-annual variability was poorly simulated by the model. In addition, the model could not capture the rainfall extremes. A spatial aggregation of rainfall data did not improve the reliability of the model as far as variability and extremes are concerned. Therefore, further improvements of the model and/or its driving global climate model are warranted for its practical use in the generation of rainfall scenarios.

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Evaluation of rainfall simulations over Uganda in CORDEX regional climate models

Cited by 60 publications

References 85 publications

Projected effects of 1.5 °C and 2 °C global warming levels on the intra-seasonal rainfall characteristics over the Greater Horn of Africa

Projected effects of 1.5 °C and 2 °C global warming levels on the intra-seasonal rainfall characteristics over the Greater Horn of Africa

Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model

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