Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia

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cg.authorship.typesCGIAR and developing country instituteen
cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationNational Meteorological Agency, Ethiopiaen
cg.contributor.affiliationNanjing University of Information Science & Technologyen
cg.contributor.affiliationInternational Joint Research Laboratory of Climate and Environment Changeen
cg.contributor.affiliationColumbia Universityen
cg.contributor.affiliationInternational Research Institute for Climate and Societyen
cg.contributor.affiliationNORCE Norwegian Research Centeren
cg.contributor.affiliationBjerknes Centre for Climate Researchen
cg.contributor.affiliationCGIAR Research Program on Climate Change, Agriculture and Food Securityen
cg.contributor.affiliationEthiopian Institute of Agricultural Researchen
cg.contributor.affiliationScuola Superiore Sant'Annaen
cg.contributor.affiliationPennsylvania State Universityen
cg.contributor.crpClimate Change, Agriculture and Food Security
cg.coverage.countryEthiopia
cg.coverage.iso3166-alpha2ET
cg.coverage.regionAfrica
cg.coverage.regionSub-Saharan Africa
cg.coverage.regionEastern Africa
cg.creator.identifierAsaminew Teshome: 0000-0002-6457-1385en
cg.creator.identifierJie Zhang: 0000-0002-2190-5883en
cg.creator.identifierQianrong Ma: 0000-0001-7789-5293en
cg.creator.identifierTeferi Demissie: 0000-0002-0228-1972en
cg.creator.identifierTufa Dinku: 0000-0003-1720-2816en
cg.creator.identifierAsher Siebert: 0000-0002-7111-8722en
cg.creator.identifierJemal Seid: 0000-0001-8754-3574en
cg.creator.identifierNACHIKETA ACHARYA: 0000-0002-0794-689Xen
cg.identifier.doihttps://doi.org/10.4236/acs.2022.121005en
cg.issn2160-0422en
cg.issue1en
cg.journalAtmospheric and Climate Sciencesen
cg.subject.impactAreaClimate adaptation and mitigation
cg.subject.impactAreaNutrition, health and food security
cg.subject.sdgSDG 2 - Zero hungeren
cg.subject.sdgSDG 13 - Climate actionen
cg.volume12en
dc.contributor.authorTeshome, Asaminewen
dc.contributor.authorZhang, Jieen
dc.contributor.authorMa, Qianrongen
dc.contributor.authorZebiak, Stephen E.en
dc.contributor.authorDemissie, Teferi Dejeneen
dc.contributor.authorDinku, Tufaen
dc.contributor.authorSiebert, Asheren
dc.contributor.authorAhmed, Jemal Seiden
dc.contributor.authorAcharya, Nachiketaen
dc.date.accessioned2022-01-06T13:56:13Zen
dc.date.available2022-01-06T13:56:13Zen
dc.identifier.urihttps://hdl.handle.net/10568/117369
dc.titleSkill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopiaen
dcterms.abstractIn recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 - 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted seasonal rainfall for the better performing models—GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012—is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1- CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.en
dcterms.accessRightsOpen Access
dcterms.audienceAcademicsen
dcterms.audienceCGIARen
dcterms.audienceDevelopment Practitionersen
dcterms.audiencePolicy Makersen
dcterms.audienceScientistsen
dcterms.bibliographicCitationTeshome A, Zhang J, Ma Q, Zebiak SE, Demissie TD, Dinku T, Siebert A, Seid J, Acharya N. 2022. Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia. Atmospheric and Climate Sciences 12(1):54-73.en
dcterms.extent54-73en
dcterms.issued2022en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherScientific Research Publishing, Inc.en
dcterms.subjectethiopiaen
dcterms.subjectensembleen
dcterms.subjectskillen
dcterms.subjectagricultureen
dcterms.subjectfood securityen
dcterms.subjectclimate changeen
dcterms.typeJournal Article

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