The aridity Index under global warming

Bibliographic Data

ID15549181
AuthorsPeter Greve (0000-0002-9454-0125, International Institute for Applied Systems Analysis, corresponding author), M L Roderick (Australian National University), Anna Ukkola (0000-0003-1207-3146, Australian National University), Yoshihide Wada (0000-0003-4770-2539, International Institute for Applied Systems Analysis)
Year2019
Volume14
Issue12
Pages124006-124006
Publication date2019-10-22
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ab5046
OpenAlexW2982268723
LanguageEN
Citations received14
References cited47

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 CO 2 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 CO 2 . 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

Arid · Aridity index · Climate change · Climatology · Geography · Global warming · Index (typography · Machine learning · Meteorology · Precipitation · Proxy (statistics · Atmospheric and Environmental Gas Dynamics · Climate variability and models · Computer Science · Environmental Science · Plant Water Relations and Carbon Dynamics · Ecology · Geology

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