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dc.contributor.authorKim, Yeon-Hee
dc.contributor.authorMin, Seung-Ki
dc.contributor.authorZhang, Xuebin
dc.contributor.authorSillmann, Jana
dc.contributor.authorSandstad, Marit
dc.date.accessioned2021-08-13T10:24:36Z
dc.date.available2021-08-13T10:24:36Z
dc.date.created2020-07-21T09:39:47Z
dc.date.issued2020
dc.identifier.citationWeather and Climate Extremes. 2020, 29 .en_US
dc.identifier.issn2212-0947
dc.identifier.urihttps://hdl.handle.net/11250/2767750
dc.description.abstractThis study evaluates global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) for their performance in simulating the climate extreme indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We compare global climatology patterns of the indices simulated by the CMIP6 models with those from HadEX3 and four reanalysis datasets and the CMIP5 multi-model ensemble using root-mean-square errors for the 1981–2000 period. Regional evaluations are conducted for 41 sub-regions, defined for the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, regional mean biases are analyzed for the 20-year return values (20RV) of the warmest day and coldest night temperatures (TXx and TNn) and annual maximum of daily precipitation (RX1day) using a Generalized Extreme Value (GEV) analysis. Results show that the CMIP6 models generally capture the observed global and regional patterns of temperature extremes with limited improvements compared to the CMIP5 models. Systematic biases like a cold bias in cold extremes over high-latitude regions remain even in stronger amplitudes. The CMIP6 model skills for the precipitation intensity and frequency indices are also largely comparable to those of CMIP5 models, but precipitation intensity simulations are found to be improved with reduced dry biases. The GEV analysis results indicate that the regional biases in 20RV of temperature extremes are dominated by GEV location parameter (related to mean intensity) with relatively small contribution from GEV scale/shape parameters (related to interannual variability). CMIP6-simulated 20RV of RX1day is characterized by dry biases over the tropics and subtropical rain band areas, as in the CMIP5 models, for which biases in both GEV location and scale/shape parameters are important.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectClimate extremesen_US
dc.subjectETCCDI indicesen_US
dc.subjectCMIP6en_US
dc.subjectModel evaluationen_US
dc.subjectGEV analysisen_US
dc.titleEvaluation of the CMIP6 multi-model ensemble for climate extreme indicesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber0en_US
dc.source.volume29en_US
dc.source.journalWeather and Climate Extremesen_US
dc.identifier.doi10.1016/j.wace.2020.100269
dc.identifier.cristin1819975
dc.relation.projectNotur/NorStore: NS9188Ken_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
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