Show simple item record

dc.contributor.authorForster, Piers M.
dc.contributor.authorRichardson, T.
dc.contributor.authorMaycock, A. C.
dc.contributor.authorSmith, C. J.
dc.contributor.authorSamset, Bjørn Hallvard
dc.contributor.authorMyhre, Gunnar
dc.contributor.authorAndrews, T.
dc.contributor.authorPincus, R.
dc.contributor.authorSchulz, Michael
dc.date.accessioned2017-11-14T09:50:32Z
dc.date.available2017-11-14T09:50:32Z
dc.date.created2017-01-09T15:56:56Z
dc.date.issued2016
dc.identifier.citationJournal of Geophysical Research - Atmospheres. 2016, 121 (20), 12460-12475.nb_NO
dc.identifier.issn2169-897X
dc.identifier.urihttp://hdl.handle.net/11250/2466100
dc.description.abstractThe usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m−2. For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons.nb_NO
dc.language.isoengnb_NO
dc.titleRecommendations for diagnosing effective radiative forcing from climate models for CMIP6nb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2016 American Geophysical Union. All Rights Reservednb_NO
dc.source.pagenumber12460-12475nb_NO
dc.source.volume121nb_NO
dc.source.journalJournal of Geophysical Research - Atmospheresnb_NO
dc.source.issue20nb_NO
dc.identifier.doi10.1002/2016jd025320
dc.identifier.cristin1423670
dc.relation.projectNorges forskningsråd: 229796nb_NO
dc.relation.projectNorges forskningsråd: 235548nb_NO
dc.relation.projectNotur/NorStore: nn9188knb_NO
dc.relation.projectNotur/NorStore: NS9042Knb_NO
cristin.unitcode7475,0,0,0
cristin.unitnameCICERO Senter for klimaforskning
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode2


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record