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The aridity index under global warming

Greve, Peter; Roderick, Michael; Ukkola, Anna; Wada, Yoshihide

Description

Aridity is a complex concept that ideally requires a comprehensive assessment of hydroclimatological and hydroecological variables to fully understand anticipated changes. A widely used (offline) impact model to assess projected changes in aridity is the aridity index (AI) (defined as the ratio of potential evaporation to precipitation), summarizing the aridity concept into a single number. Based on the AI, it was shown that aridity will generally increase under conditions of increased CO2 and...[Show more]

dc.contributor.authorGreve, Peter
dc.contributor.authorRoderick, Michael
dc.contributor.authorUkkola, Anna
dc.contributor.authorWada, Yoshihide
dc.date.accessioned2020-11-06T04:12:11Z
dc.date.available2020-11-06T04:12:11Z
dc.identifier.citationP Greve et al 2019 Environ. Res. Lett. 14 124006
dc.identifier.issn1748-9326
dc.identifier.urihttp://hdl.handle.net/1885/214107
dc.description.abstractAridity is a complex concept that ideally requires a comprehensive assessment of hydroclimatological and hydroecological variables to fully understand anticipated changes. A widely used (offline) impact model to assess projected changes in aridity is the aridity index (AI) (defined as the ratio of potential evaporation to precipitation), summarizing the aridity concept into a single number. Based on the AI, it was shown that aridity will generally increase under conditions of increased CO2 and associated global warming. However, assessing the same climate model output directly suggests a more nuanced response of aridity to global warming, raising the question if the AI provides a good representation of the complex nature of anticipated aridity changes. By systematically comparing projections of the AI against projections for various hydroclimatological and ecohydrological variables, we show that the AI generally provides a rather poor proxy for projected aridity conditions. Direct climate model output is shown to contradict signals of increasing aridity obtained from the AI in at least half of the global land area with robust change. We further show that part of this discrepancy can be related to the parameterization of potential evaporation. Especially the most commonly used potential evaporation model likely leads to an overestimation of future aridity due to incorrect assumptions under increasing atmospheric CO2. Our results show that AI-based approaches do not correctly communicate changes projected by the fully coupled climate models. The solution is to directly analyse the model outputs rather than use a separate offline impact model. We thus urge for a direct and joint assessment of climate model output when assessing future aridity changes rather than using simple index-based impact models that use climate model output as input and are potentially subject to significant biases.
dc.description.sponsorshipThis study is financially supported by from EUCP (European Climate Prediction System) project funded by the European Union under Horizon2020 (Grant Agreement: 776613).
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherIOP Publishing
dc.rights© 2019 The Author(s)
dc.sourceEnvironmental Research Letters
dc.titleThe aridity index under global warming
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume14
dcterms.dateAccepted2019-10-22
dc.date.issued2019-11-22
local.identifier.absfor040608 - Surfacewater Hydrology
local.identifier.ariespublicationu5786633xPUB1332
local.publisher.urlhttps://iopscience.iop.org/
local.type.statusPublished Version
local.contributor.affiliationGreve, Peter, Institute for Applied Systems Analysis
local.contributor.affiliationRoderick, Michael, College of Science, ANU
local.contributor.affiliationUkkola, Anna, College of Science, ANU
local.contributor.affiliationWada, Yoshihide, International Institute for Applied Systems Analysis
local.bibliographicCitation.issue12
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage11
local.identifier.doi10.1088/1748-9326/ab5046
local.identifier.absseo960301 - Climate Change Adaptation Measures
dc.date.updated2020-07-06T08:26:38Z
local.identifier.thomsonIDWOS:000499334000001
dcterms.accessRightsOpen Access
dc.provenanceOriginal content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
dc.rights.licenseCreative Commons Attribution 3.0 licence
CollectionsANU Research Publications

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