{"doi":"10.1002/joc.5069","title":"Uncertainty assessment for climate change impact on intense precipitation: how many model runs do we need?","abstract":"<jats:title>ABSTRACT</jats:title><jats:p>Precipitation projections are typically obtained from general circulation model (<jats:styled-content style=\"fixed-case\">GCM</jats:styled-content>) outputs under different future scenarios, then downscaled for hydrological applications to a watershed or site‐specific scale. However, uncertainties in projections are known to be present and need to be quantified. Although <jats:styled-content style=\"fixed-case\">GCMs</jats:styled-content> are commonly considered the major contributor of uncertainty for hydrological impact assessment of climate change, other uncertainty sources must be taken into account for a thorough understanding of the hydrological impact. This study investigates uncertainties related to <jats:styled-content style=\"fixed-case\">GCMs</jats:styled-content>, <jats:styled-content style=\"fixed-case\">GCM</jats:styled-content> initial conditions and representative concentration pathways (<jats:styled-content style=\"fixed-case\">RCPs</jats:styled-content>) and their sensitivity to the selection of <jats:styled-content style=\"fixed-case\">GCM</jats:styled-content> runs in order to quantify the impact of climate change on extreme precipitation and intensity/duration/frequency statistics. The results from a large ensemble of 140 <jats:styled-content style=\"fixed-case\">CMIP5 GCM</jats:styled-content> runs including 15 <jats:styled-content style=\"fixed-case\">GCMs</jats:styled-content>, 3–10 <jats:styled-content style=\"fixed-case\">GCM</jats:styled-content> initial conditions and 4 <jats:styled-content style=\"fixed-case\">RCPs</jats:styled-content> are analysed. Albeit the choice of <jats:styled-content style=\"fixed-case\">GCM</jats:styled-content> is the major contributor (up to 65% for some cases) to intense precipitation change uncertainty for all return periods (1 year, 10 years) and aggregation levels (1‐, 5‐, 10‐, 15‐ and 30‐day), uncertainties related to the <jats:styled-content style=\"fixed-case\">GCM</jats:styled-content> initial conditions and <jats:styled-content style=\"fixed-case\">RCPs</jats:styled-content> of up to 38 and 23%, respectively, are found in some cases. The sensitivity analysis reveals that the <jats:styled-content style=\"fixed-case\">GCM</jats:styled-content>, <jats:styled-content style=\"fixed-case\">RCP</jats:styled-content> and <jats:styled-content style=\"fixed-case\">GCM</jats:styled-content> initial condition uncertainties are greatly influenced by the set of climate model runs considered, especially for more extreme precipitation at finer time scales.</jats:p>","journal":"International Journal of Climatology","year":2017,"id":25886,"datarank":2.3272854400944505,"base_score":4.330733340286331,"endowment":4.330733340286331,"self_citation_contribution":0.6496100010429497,"citation_network_contribution":1.6776754390515005,"self_endowment_contribution":0.6496100010429497,"citer_contribution":1.6776754390515005,"corpus_percentile":null,"corpus_rank":null,"citation_count":75,"citer_count":52,"citers_with_citation_signal":45,"citers_with_endowment":45,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":151146,"name":"Hossein Tabari","orcid":null,"position":1,"is_corresponding":false},{"id":151147,"name":"Patrick Willems","orcid":null,"position":2,"is_corresponding":false},{"id":151145,"name":"Parisa Hosseinzadehtalaei","orcid":"0000-0003-1208-569X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":4.330733340286331,"endowment":4.330733340286331,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"18998881","pmcid":null,"openalex_id":"https://openalex.org/W2607243597","authors":[],"funders":[{"funder_name":"European Commission","grant_id":"730004","title":"Pan-European Urban Climate Services"},{"funder_name":"European Commission","grant_id":"700699","title":"BRIdges the GAp for Innovations in Disaster resilience"}],"total_grants":2,"fwci":6.8741,"citation_percentile":0.97495036,"influential_citations":1,"citation_trend":[{"year":2017,"count":1},{"year":2018,"count":11},{"year":2019,"count":13},{"year":2020,"count":15},{"year":2021,"count":8},{"year":2022,"count":8},{"year":2023,"count":5},{"year":2024,"count":7},{"year":2025,"count":5},{"year":2026,"count":2}],"oa_status":"green","license":"other-oa","oa_locations":[{"url":"https://lirias.kuleuven.be/handle/123456789/574648","host_type":"repository"},{"url":"https://lirias.kuleuven.be/handle/123456789/574648","host_type":"repository"},{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjoc.5069","host_type":"publisher"},{"url":"https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5069","host_type":"publisher"},{"url":"https://doi.org/10.1002/joc.5069","host_type":"journal"},{"url":"https://biblio.ugent.be/publication/01HV696PSZT0M79Q45VQ9MDVFR","host_type":"repository"},{"url":"https://zenodo.org/record/3492578","host_type":"repository"},{"url":"https://dx.doi.org/10.1002/joc.5069","host_type":""},{"url":"https://biblio.vub.ac.be/vubir/uncertainty-assessment-for-climate-change-impact-on-intense-precipitation-how-many-model-runs-do-we-need(5752cba1-0ce5-4f1c-a2b6-65d55024a35f).html","host_type":""},{"url":"http://dx.doi.org/10.1002/joc.5069","host_type":""}],"fields_of_study":["Climate variability and models","Hydrology and Watershed Management Studies","Hydrology and Drought Analysis","Environmental Science","0207 environmental engineering","02 engineering and technology","01 natural sciences","0105 earth and related environmental sciences"],"mesh_terms":[],"keywords":["GCM transcription factors","Environmental science","Precipitation","Climatology","Representative Concentration Pathways","General Circulation Model","Climate change","Climate model","Downscaling","Impact assessment","Meteorology","Geology","Geography","BIAS CORRECTION","INDEXES","ensemble size","3702 Climate change science","DURATION-FREQUENCY CURVES","0905 Civil Engineering","CMIP5 GCM","sensitivity analysis","Meteorology & Atmospheric Sciences","MULTIMODEL ENSEMBLE","CMIP5","EXTREME PRECIPITATION","RAINFALL","uncertainty analysis","TEMPERATURE","Statistical downscaling","Science & Technology","3707 Hydrology","RIVER-BASIN","SIMULATIONS","0907 Environmental Engineering","Rainfall extremes","Physical Sciences","0401 Atmospheric Sciences","3701 Atmospheric sciences"],"sdg_mappings":[{"sdg_number":13,"sdg_label":"13. 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