2022
DOI: 10.1007/s00704-021-03884-7
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Application of PPIE method to assess the uncertainty and accuracy of multi-climate model-based temperature and precipitation downscaling

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Cited by 8 publications
(2 citation statements)
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“…Therefore, for raw GCMs or RCMs to be used for any impact assessment, bias correction must be employed. Generally, the performance of the CMIP6 data shows better performance in terms of simulating the observed data, especially for precipitation, compared to the previous CMIP5 models presented in (Ajayi and Ilori, 2020;Nourani et al, 2022;Okafor et al, 2019). This signifies the enhancement in modeling precipitation in CMIP6.…”
Section: Model Performance Evaluationmentioning
confidence: 88%
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“…Therefore, for raw GCMs or RCMs to be used for any impact assessment, bias correction must be employed. Generally, the performance of the CMIP6 data shows better performance in terms of simulating the observed data, especially for precipitation, compared to the previous CMIP5 models presented in (Ajayi and Ilori, 2020;Nourani et al, 2022;Okafor et al, 2019). This signifies the enhancement in modeling precipitation in CMIP6.…”
Section: Model Performance Evaluationmentioning
confidence: 88%
“…The study used the six Regional Climate Models (BCC-CSM2-MR, CanESM5, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and MRI-ESM2-0) for the statistical biascorrection. The CMIP6 data was used due to its improvement in terms of resolution and accuracy over the previous version (CMIP 5) (Ajayi and Ilori, 2020;Nourani et al, 2022;Okafor et al, 2019), and the new scenarios which embed socio-economic aspects of climate change. The new set of SSP scenarios developed by CMIP6 of the World Climate Research Program (WCRP) was considered for the study (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5).…”
Section: Gcm Data and Climate Scenariosmentioning
confidence: 99%