Communicating future climate projections of precipitation change

Joe Daron*, Susanne Lorenz, Andrea Taylor, Suraje Dessai

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)
48 Downloads (Pure)

Abstract

Understanding how precipitation may change in the future is important for guiding climate change adaptation. Climate models are the primary tools for providing information on future precipitation change, though communicating and interpreting results of different model simulations is challenging. Using an online survey, completed by producers and users of climate model information, we compare and evaluate interpretations of different approaches used to summarise and visualise future climate projections. Results reveal large differences in interpretations of precipitation change arising from choices made in summarising and visualising the data. Respondents interpret significantly smaller ranges of future precipitation change when provided with the multi-model ensemble mean or percentile information, which are commonly used to summarise climate model projections, compared to information about the full ensemble. The ensemble mean is found to be particularly misleading, even when used with information to show model agreement in the sign of change. We conclude that these approaches can lead to distorted interpretations which may impact on adaptation policy and decision-making. To help improve the interpretation and use of climate projections in decision-making, regular testing of visualisations and sustained engagement with target audiences is required to determine the most effective and appropriate visualisation approaches.
Original languageEnglish
Article number23
JournalClimatic Change
Volume166
Issue number23
DOIs
Publication statusPublished - 27 May 2021

Bibliographical note

Funding Information:
We acknowledge the Met Office Academic Partnership (MOAP) Collaboration Fund for supporting collaboration with the University of Leeds. We thank the anonymous reviewers for exceptionally useful comments that have substantially improved the manuscript. We’re also grateful to Yasmina Okan, Astrid Kause, Carol McSweeney, Doug McNeall, Jason Lowe, Erika Palin, Bernd Becker and Neil Kaye for their helpful input to the research, and Chris Hewitt for valuable feedback on the draft manuscript.

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • interpretation
  • visualisation
  • uncertainties
  • ensembles

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