Attributing drivers of the 2016 Kenyan drought
In 2016 and continuing into 2017, Kenya experienced drought conditions, with over three million people in need of food aid due to low rainfall during 2016. Whenever extreme events like this happen, questions are raised about the role of climate change and how natural variability such as the El Nino...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Journal article |
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Royal Meteorological Society
2017
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| _version_ | 1797074430225022976 |
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| author | Uhe, P Philip, S Kew, S Shah, K Kimutai, J Mwangi, E van Oldenborgh, G Singh, R Arrighi, J Jjemba, E Cullen, H Otto, F |
| author_facet | Uhe, P Philip, S Kew, S Shah, K Kimutai, J Mwangi, E van Oldenborgh, G Singh, R Arrighi, J Jjemba, E Cullen, H Otto, F |
| author_sort | Uhe, P |
| collection | OXFORD |
| description | In 2016 and continuing into 2017, Kenya experienced drought conditions, with over three million people in need of food aid due to low rainfall during 2016. Whenever extreme events like this happen, questions are raised about the role of climate change and how natural variability such as the El Nino Southern Oscillation influenced ˜ the likelihood and intensity of the event. Here we aim to quantify the relative contributions of different climate drivers to this drought by applying three independent methodologies of extreme event attribution. Analysing precipitation data for the southeast and northwest of Kenya we found no consistent signal from human-induced climate change and thus conclude that it has not greatly affected the likelihood of low rainfall such as in 2016. However 2016 was a La Nina year and we show that this event was indeed more likely because of the specific ˜ sea-surface temperatures. There is a trend in temperatures in the region due to climate change that may have exacerbated the effects of this drought. By analysing precipitation minus evaporation and soil moisture, simulated by one climate model only, we did not see a reduction in moisture in simulations in the current climate compared with simulations without climate change. However, there are expected effects of higher temperatures that our simulations do not cover, such as increased demand on water resources and stress on livestock. Although we find no significant influence of climate change on precipitation, we cannot rule out that temperature-related impacts of drought are linked to human-induced climate change |
| first_indexed | 2024-03-06T23:36:02Z |
| format | Journal article |
| id | oxford-uuid:6db0e4b7-df58-4f06-b686-94620409fffd |
| institution | University of Oxford |
| last_indexed | 2024-03-06T23:36:02Z |
| publishDate | 2017 |
| publisher | Royal Meteorological Society |
| record_format | dspace |
| spelling | oxford-uuid:6db0e4b7-df58-4f06-b686-94620409fffd2022-03-26T19:19:25ZAttributing drivers of the 2016 Kenyan droughtJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6db0e4b7-df58-4f06-b686-94620409fffdSymplectic Elements at OxfordRoyal Meteorological Society2017Uhe, PPhilip, SKew, SShah, KKimutai, JMwangi, Evan Oldenborgh, GSingh, RArrighi, JJjemba, ECullen, HOtto, FIn 2016 and continuing into 2017, Kenya experienced drought conditions, with over three million people in need of food aid due to low rainfall during 2016. Whenever extreme events like this happen, questions are raised about the role of climate change and how natural variability such as the El Nino Southern Oscillation influenced ˜ the likelihood and intensity of the event. Here we aim to quantify the relative contributions of different climate drivers to this drought by applying three independent methodologies of extreme event attribution. Analysing precipitation data for the southeast and northwest of Kenya we found no consistent signal from human-induced climate change and thus conclude that it has not greatly affected the likelihood of low rainfall such as in 2016. However 2016 was a La Nina year and we show that this event was indeed more likely because of the specific ˜ sea-surface temperatures. There is a trend in temperatures in the region due to climate change that may have exacerbated the effects of this drought. By analysing precipitation minus evaporation and soil moisture, simulated by one climate model only, we did not see a reduction in moisture in simulations in the current climate compared with simulations without climate change. However, there are expected effects of higher temperatures that our simulations do not cover, such as increased demand on water resources and stress on livestock. Although we find no significant influence of climate change on precipitation, we cannot rule out that temperature-related impacts of drought are linked to human-induced climate change |
| spellingShingle | Uhe, P Philip, S Kew, S Shah, K Kimutai, J Mwangi, E van Oldenborgh, G Singh, R Arrighi, J Jjemba, E Cullen, H Otto, F Attributing drivers of the 2016 Kenyan drought |
| title | Attributing drivers of the 2016 Kenyan drought |
| title_full | Attributing drivers of the 2016 Kenyan drought |
| title_fullStr | Attributing drivers of the 2016 Kenyan drought |
| title_full_unstemmed | Attributing drivers of the 2016 Kenyan drought |
| title_short | Attributing drivers of the 2016 Kenyan drought |
| title_sort | attributing drivers of the 2016 kenyan drought |
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