A high-resolution database of historical and future climate for Africa developed with deep neural networks
Abstract This study contributes an accessible, comprehensive database of interpolated climate data for Africa that includes monthly, annual, decadal, and 30-year normal climate data for the last 120 years (1901 to present) as well as multi-model CMIP6 climate change projections for the 21st century....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05575-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238121265233920 |
|---|---|
| author | Sarah A. Namiiro Andreas Hamann Tongli Wang Dante Castellanos-Acuña Colin R. Mahony |
| author_facet | Sarah A. Namiiro Andreas Hamann Tongli Wang Dante Castellanos-Acuña Colin R. Mahony |
| author_sort | Sarah A. Namiiro |
| collection | DOAJ |
| description | Abstract This study contributes an accessible, comprehensive database of interpolated climate data for Africa that includes monthly, annual, decadal, and 30-year normal climate data for the last 120 years (1901 to present) as well as multi-model CMIP6 climate change projections for the 21st century. The database includes variables relevant for ecological research and infrastructure planning, and it comprises more than 25,000 climate grids that can be queried with a provided ClimateAF software package. In addition, 30 arcsecond (~1 km) resolution gridded data are available for download. The climate grids were developed with a three-step approach, using thin-plate spline interpolations of weather station data as a first approximation. Subsequently, a novel deep learning approach is used to model orographic precipitation, rain shadows, lake and coastal effects at moderate resolution. Lastly, lapse-rate based downscaling is applied to generate high-resolution grids. The climate estimates were optimized and cross-validated with a checkerboard approach to ensure that training data was spatially distanced from validation data. We conclude with a discussion of applications and limitations of this database. |
| format | Article |
| id | doaj-art-0b3ba2a0bdbc4a188e4819c9bea46627 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-0b3ba2a0bdbc4a188e4819c9bea466272025-08-20T04:01:43ZengNature PortfolioScientific Data2052-44632025-07-0112111310.1038/s41597-025-05575-8A high-resolution database of historical and future climate for Africa developed with deep neural networksSarah A. Namiiro0Andreas Hamann1Tongli Wang2Dante Castellanos-Acuña3Colin R. Mahony4Department of Renewable Resources, University of Alberta, 751 General Services BuildingDepartment of Renewable Resources, University of Alberta, 751 General Services BuildingCentre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, University of British ColumbiaDepartment of Renewable Resources, University of Alberta, 751 General Services BuildingBritish Columbia Ministry of ForestsAbstract This study contributes an accessible, comprehensive database of interpolated climate data for Africa that includes monthly, annual, decadal, and 30-year normal climate data for the last 120 years (1901 to present) as well as multi-model CMIP6 climate change projections for the 21st century. The database includes variables relevant for ecological research and infrastructure planning, and it comprises more than 25,000 climate grids that can be queried with a provided ClimateAF software package. In addition, 30 arcsecond (~1 km) resolution gridded data are available for download. The climate grids were developed with a three-step approach, using thin-plate spline interpolations of weather station data as a first approximation. Subsequently, a novel deep learning approach is used to model orographic precipitation, rain shadows, lake and coastal effects at moderate resolution. Lastly, lapse-rate based downscaling is applied to generate high-resolution grids. The climate estimates were optimized and cross-validated with a checkerboard approach to ensure that training data was spatially distanced from validation data. We conclude with a discussion of applications and limitations of this database.https://doi.org/10.1038/s41597-025-05575-8 |
| spellingShingle | Sarah A. Namiiro Andreas Hamann Tongli Wang Dante Castellanos-Acuña Colin R. Mahony A high-resolution database of historical and future climate for Africa developed with deep neural networks Scientific Data |
| title | A high-resolution database of historical and future climate for Africa developed with deep neural networks |
| title_full | A high-resolution database of historical and future climate for Africa developed with deep neural networks |
| title_fullStr | A high-resolution database of historical and future climate for Africa developed with deep neural networks |
| title_full_unstemmed | A high-resolution database of historical and future climate for Africa developed with deep neural networks |
| title_short | A high-resolution database of historical and future climate for Africa developed with deep neural networks |
| title_sort | high resolution database of historical and future climate for africa developed with deep neural networks |
| url | https://doi.org/10.1038/s41597-025-05575-8 |
| work_keys_str_mv | AT sarahanamiiro ahighresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT andreashamann ahighresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT tongliwang ahighresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT dantecastellanosacuna ahighresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT colinrmahony ahighresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT sarahanamiiro highresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT andreashamann highresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT tongliwang highresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT dantecastellanosacuna highresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks AT colinrmahony highresolutiondatabaseofhistoricalandfutureclimateforafricadevelopedwithdeepneuralnetworks |