Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change
Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global war...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2021-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac26f1 |
| _version_ | 1827869991420035072 |
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| author | Cornelia Klein Lawrence S Jackson Douglas J Parker John H Marsham Christopher M Taylor David P Rowell Françoise Guichard Théo Vischel Adjoua Moïse Famien Arona Diedhiou |
| author_facet | Cornelia Klein Lawrence S Jackson Douglas J Parker John H Marsham Christopher M Taylor David P Rowell Françoise Guichard Théo Vischel Adjoua Moïse Famien Arona Diedhiou |
| author_sort | Cornelia Klein |
| collection | DOAJ |
| description | Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37%–75% for 2070–2100 (mean 55%, and 18%–30% for 2030–2060). This is considerably higher than the +0–60% (mean +30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone. |
| first_indexed | 2024-03-12T15:51:27Z |
| format | Article |
| id | doaj.art-07b7d68aea42455eab015895a99631fc |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:51:27Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-07b7d68aea42455eab015895a99631fc2023-08-09T15:06:47ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-01161010402310.1088/1748-9326/ac26f1Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate changeCornelia Klein0https://orcid.org/0000-0001-6686-0458Lawrence S Jackson1https://orcid.org/0000-0001-8143-2777Douglas J Parker2https://orcid.org/0000-0003-2335-8198John H Marsham3Christopher M Taylor4https://orcid.org/0000-0002-0120-3198David P Rowell5Françoise Guichard6Théo Vischel7Adjoua Moïse Famien8https://orcid.org/0000-0002-4551-7033Arona Diedhiou9https://orcid.org/0000-0003-3841-1027U.K. Centre for Ecology and Hydrology , Wallingford, United Kingdom; Department of Atmospheric and Cryospheric Sciences, University of Innsbruck , Innsbruck, AustriaInstitute for Climate and Atmospheric Science, University of Leeds , Leeds, United KingdomInstitute for Climate and Atmospheric Science, University of Leeds , Leeds, United KingdomInstitute for Climate and Atmospheric Science, University of Leeds , Leeds, United KingdomU.K. Centre for Ecology and Hydrology , Wallingford, United Kingdom; National Centre for Earth Observation ,Wallingford, United KingdomMet Office Hadley Centre , Exeter, United KingdomCNRM, Université de Toulouse, Météo-France, CNRS , Toulouse, FranceUniversité Grenoble Alpes, IRD, CNRS, Grenoble-INP, IGE , Grenoble, FranceLOCEAN, Sorbonne Universités UPMC-CNRS-IRD-MNHN, IPSL , Paris, France; Université Félix Houphouët Boigny, LAPAMF-UFR SSMT , Abidjan, Côte d’IvoireUniversité Grenoble Alpes, IRD, CNRS, Grenoble-INP, IGE , Grenoble, France; Centre d’Excellence Africain en Changement Climatique, Biodiversité et Agriculture Durable (CCBAD), Université Félix Houphouët Boigny , Abidjan, Côte d’IvoireDue to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37%–75% for 2070–2100 (mean 55%, and 18%–30% for 2030–2060). This is considerably higher than the +0–60% (mean +30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone.https://doi.org/10.1088/1748-9326/ac26f1CMIPmesoscale convective systemWest Africaclimate projectionatmospheric moisture scalingconvection-permitting |
| spellingShingle | Cornelia Klein Lawrence S Jackson Douglas J Parker John H Marsham Christopher M Taylor David P Rowell Françoise Guichard Théo Vischel Adjoua Moïse Famien Arona Diedhiou Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change Environmental Research Letters CMIP mesoscale convective system West Africa climate projection atmospheric moisture scaling convection-permitting |
| title | Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change |
| title_full | Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change |
| title_fullStr | Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change |
| title_full_unstemmed | Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change |
| title_short | Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change |
| title_sort | combining cmip data with a regional convection permitting model and observations to project extreme rainfall under climate change |
| topic | CMIP mesoscale convective system West Africa climate projection atmospheric moisture scaling convection-permitting |
| url | https://doi.org/10.1088/1748-9326/ac26f1 |
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