Regionally aggregated, stitched and de‐drifted CMIP‐climate data, processed with netCDF‐SCM v2.0.0

dc.bibliographicCitation.firstPage154eng
dc.bibliographicCitation.issue2eng
dc.bibliographicCitation.journalTitleGeoscience Data Journaleng
dc.bibliographicCitation.lastPage198eng
dc.bibliographicCitation.volume8eng
dc.contributor.authorNicholls, Zebedee
dc.contributor.authorLewis, Jared
dc.contributor.authorMakin, Melissa
dc.contributor.authorNattala, Usha
dc.contributor.authorZhang, Geordie Z.
dc.contributor.authorMutch, Simon J.
dc.contributor.authorTescari, Edoardo
dc.contributor.authorMeinshausen, Malte
dc.date.accessioned2022-02-23T10:50:29Z
dc.date.available2022-02-23T10:50:29Z
dc.date.issued2021
dc.description.abstractThe world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/8062
dc.identifier.urihttps://doi.org/10.34657/7103
dc.language.isoengeng
dc.publisherChichester [u.a.] : Wileyeng
dc.relation.doihttps://doi.org/10.1002/gdj3.113
dc.relation.essn2049-6060
dc.rights.licenseCC BY 4.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subject.ddc550eng
dc.subject.otheraggregateeng
dc.subject.otherclimateeng
dc.subject.otherCMIPeng
dc.subject.othermodeleng
dc.subject.otherprojectionseng
dc.titleRegionally aggregated, stitched and de‐drifted CMIP‐climate data, processed with netCDF‐SCM v2.0.0eng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccesseng
wgl.contributorPIKeng
wgl.subjectGeowissenschafteneng
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