An improved deep learning procedure for statistical downscaling of climate data
Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is k...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/137604 |
| _version_ | 1855529059768860672 |
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| author | Ahmed M.S. Kheir Abdelrazek Elnashar Alaa Mosad Ajit Govind |
| author_browse | Abdelrazek Elnashar Ahmed M.S. Kheir Ajit Govind Alaa Mosad |
| author_facet | Ahmed M.S. Kheir Abdelrazek Elnashar Alaa Mosad Ajit Govind |
| author_sort | Ahmed M.S. Kheir |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015-2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security. |
| format | Journal Article |
| id | CGSpace137604 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1376042025-12-08T09:54:28Z An improved deep learning procedure for statistical downscaling of climate data Ahmed M.S. Kheir Abdelrazek Elnashar Alaa Mosad Ajit Govind climate change climate change adaptation Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015-2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security. 2023-07 2024-01-11T19:54:26Z 2024-01-11T19:54:26Z Journal Article https://hdl.handle.net/10568/137604 en Open Access Elsevier Ahmed M.S. Kheir, Abdelrazek Elnashar, Alaa Mosad, Ajit Govind, 2023. An improved deep learning procedure for statistical downscaling of climate data. Heliyon 9(7) |
| spellingShingle | climate change climate change adaptation Ahmed M.S. Kheir Abdelrazek Elnashar Alaa Mosad Ajit Govind An improved deep learning procedure for statistical downscaling of climate data |
| title | An improved deep learning procedure for statistical downscaling of climate data |
| title_full | An improved deep learning procedure for statistical downscaling of climate data |
| title_fullStr | An improved deep learning procedure for statistical downscaling of climate data |
| title_full_unstemmed | An improved deep learning procedure for statistical downscaling of climate data |
| title_short | An improved deep learning procedure for statistical downscaling of climate data |
| title_sort | improved deep learning procedure for statistical downscaling of climate data |
| topic | climate change climate change adaptation |
| url | https://hdl.handle.net/10568/137604 |
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