Climate Services (Apr 2024)

Assessment of plausible changes in Climatic Impact-Drivers relevant for the viticulture sector: A storyline approach with a climate service perspective

  • J. Mindlin,
  • C.S. Vera,
  • T.G. Shepherd,
  • F.J. Doblas-Reyes,
  • N. Gonzalez-Reviriego,
  • M. Osman,
  • M. Terrado

Journal volume & issue
Vol. 34
p. 100480

Abstract

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Under the pressing warming of climate, interpretable and useful-for-adaptation information has become a need in society and has promoted rapid methodological advances in climate science. One such advance is the development of the dynamical-storyline approach, with which the spread in multi-model scenario projections can be represented as a set of physically plausible scenarios (storylines) defined by (a) a global warming level and (b) changes in large-scale dynamical conditions that arise from climate forcing. Moreover, if changes in regional climate are assessed in such a way that they can clearly inform societal systems or management of natural ecosystems, they can potentially aid decision-making in a practical manner. Such is the aim of the climatic impact-driver (CID) framework, proposed in the Sixth Assessment Report (AR6) from the Intergovernmental Panel on Climate Change. Here, we combine the dynamical-storyline approach with the CID framework and apply them to climate services. We focus on CIDs associated with the viticulture sector and the region of the South American Andes, where currently both Argentina and Chile produce wine. We explain the benefits of this approach from a communication and adaptation perspective. In particular, we found that the CIDs related to seasonally aggregated temperatures are mainly dependent on the global warming level although in some regions, but they can also be sensitive to changes in dynamical conditions. Meanwhile, CIDs related to extreme temperature values and precipitation depend strongly on the dynamical response. We show how adaptation to climate-related compound risks can be informed by a storyline approach, given that they can address compound uncertainty in multiple locations, variables and seasons.

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