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The aridity Index under global warming

Environmental research letters, 2019-12, Vol.14 (12), p.124006 [Peer Reviewed Journal]

2019 The Author(s). Published by IOP Publishing Ltd ;2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1748-9326 ;EISSN: 1748-9326 ;DOI: 10.1088/1748-9326/ab5046 ;CODEN: ERLNAL

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  • Title:
    The aridity Index under global warming
  • Author: Greve, P ; Roderick, M L ; Ukkola, A M ; Wada, Y
  • Subjects: Aridity ; Atmospheric models ; Carbon dioxide ; Climate change ; Climate models ; Evaporation ; Evaporation rate ; Global warming ; Parameterization ; vegetation ; water availability
  • Is Part Of: Environmental research letters, 2019-12, Vol.14 (12), p.124006
  • 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 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.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1748-9326
    EISSN: 1748-9326
    DOI: 10.1088/1748-9326/ab5046
    CODEN: ERLNAL
  • Source: Open Access: IOP Publishing Free Content
    Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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