What Can We Know About Recent Past Precipitation Over Africa? Daily Characteristics of African Precipitation From a Large Ensemble of Observational Products for Model Evaluation

Dosio, Alessandro; Pinto, Izidine; Lennard, Christopher; Sylla, Mouhamadou Bamba; Jack, Christopher; Nikulin, Grigory

doi:10.1029/2020ea001466

Cited by 32 publications

(30 citation statements)

References 71 publications

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“…Notably, Rx1day shows the largest discrepancy between modelled and observed results, and the largest uncertainty amongst COR-DEX simulations. This is particularly true for the western Sahel, where Dosio et al (2021a) showed that half of the CORDEX results are outside the range of uncertainty of a large ensemble of observational products.…”

Section: Precipitation Indicesmentioning

confidence: 99%

“…As discussed by many studies (Ayugi et al 2019;Hua et al 2019;Dinku et al 2018;Gebrechorkos et al 2019;Maidment et al 2015;Nicholson et al 2018;Harrison et al 2019;Dosio et al 2021a), estimates of the observed recent climatology are very uncertain over Africa especially for regions where gauge networks are sparse (e.g. central Africa).…”

Section: Observational Datasetmentioning

confidence: 99%

“…Ethiopian highlands). Additionally, even between contiguous subregions, substantial differences in observed (Dosio et al 2021a) and projected (Dosio et al 2021b) precipitation indices exist.…”

Section: Definition Of Subregionsmentioning

confidence: 99%

“…Simulations are grouped by colour according to the RCM. For comparison, the WFDE5 data is shown (black thick line) as well as the range of a large ensemble of observational datasets including gauge, reanalysis and satellite-based products,(Dosio et al 2021a). The figure also shows the sub-regions used for the analysis.…”

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confidence: 99%

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Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations

2022

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We present a dataset of daily, bias-adjusted temperature and precipitation projections for continental Africa based on a large ensemble of regional climate model simulations, which can be useful for climate change impact studies in several sectors. We provide guidance on the benefits and caveats of using the dataset by investigating the effect of bias-adjustment on impact-relevant indices (both their future absolute value and change). Extreme threshold-based temperature indices show large differences between original and bias-adjusted values at the end of the century due to the general underestimation of temperature in the present climate. These results indicate that when biases are accounted for, projected risks of extreme temperature-related hazards are higher than previously found, with possible consequences for the planning of adaptation measures. Bias-adjusted results for precipitation indices are usually consistent with the original results, with the median change preserved for most regions and indices. The interquartile and full range of the original model ensemble is usually well preserved by bias-adjustment, with the exception of maximum daily precipitation, whose range is usually greatly reduced by the bias-adjustment. This is due to the poor simulation and extremely large model range for this index over the reference period; when the bias is reduced, most models converge in projecting a similar change. Finally, we provide a methodology to select a small subset of simulations that preserves the overall uncertainty in the future projections of the large model ensemble. This result can be useful in practical applications when process-based impact models are too expensive to be run with the full ensemble of model simulations.

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Section: Precipitation Indicesmentioning

confidence: 99%

Section: Observational Datasetmentioning

confidence: 99%

Section: Definition Of Subregionsmentioning

confidence: 99%

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confidence: 99%

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Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations

2022

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“…This illustrates that the agreement of the observations is less than that of the model simulations (see Fig. 5f and, e.g., Dosio et al, 2021b). Interestingly, CORE shows the largest overestimation of R20mm than CMIP5 and CMIP6 over the Orange basin (all basins) during DJF (MAM and SON, except for Zambezi during MAM).…”

Section: Spatial Distribution Of Biasesmentioning

confidence: 73%

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa. Part I: Assessment of the historical simulations

Samuel

Dosio

Mphale

et al. 2022

Preprint

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This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodal spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodal spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

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Evaluation of gridded precipitation datasets over Madagascar

Randriatsara

et al. 2022

Intl Journal of Climatology

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Madagascar is among the countries whose agriculture is heavily dependent on rainfall. However, the country lacks accurate and reliable early warning systems for droughts and floods, partly due to insufficient station rainfall data. The purpose of this study is to identify rainfall datasets that can complement observation data by appraising 15 datasets (gauge-based, reanalysis, and satellite estimates). The study compares the temporal and spatial performance of datasets at annual and seasonal scales during 1983-2015. In all the analyses, CHIRPS presents lower biases, so it is chosen as the reference data in the Taylor diagram for the final evaluation analysis. Even though ranking datasets is neither possible nor appropriate since each dataset performs differently throughout each analysis, some datasets show reasonable consistency. This is the case with MSWEP, ERA5, and UDEL. On the other hand, MERRA2, CMAP, and TAMSAT are least preferred for use due to their considerable biases (specifically TAMSAT during the dry season). CRU, PRECL, ERAINT, CFSR, and JRA55 also present some degrees of deficiencies at either annual or seasonal scales. These findings are crucial for any future rainfall analysis over the country in order to minimize inaccuracy in monitoring rainfall.

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What Can We Know About Recent Past Precipitation Over Africa? Daily Characteristics of African Precipitation From a Large Ensemble of Observational Products for Model Evaluation

Cited by 32 publications

References 71 publications

Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations

Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa. Part I: Assessment of the historical simulations

Evaluation of gridded precipitation datasets over Madagascar

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