Interpreting climate data visualisations to inform adaptation decisions
Open accessAbstract
The appropriate development of graphical visualisations to communicate climate data is fundamental to the provision of climate services to guide climate change adaptation decisions. However, at present there is a lack of empirical evidence, particularly in Africa, to help climate information providers determine how best to communicate and display climate data. To help address this issue, an online survey, primarily targeted at the African vulnerability, impacts and adaptation community, was designed and disseminated widely. The survey examines the interpretation of climate data as a function of the style and information content of graphical visualisations. It is shown that choices made when constructing the visualisations, such as presenting percentile information versus showing the range, significantly impact on interpretation. Results also show that respondents who interpret a higher likelihood of future changes to climate, based on the visualisation of climate model projections, express greater confidence in their interpretations. The findings have relevance to the climate risk community in Africa and elsewhere across the world, and imply that a naïve approach to visualising climate data risks misinterpretation and unjustified levels of trust, with the potential to misinform adaptation and policy decisions.
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- Authors
- Daron, J. D., Lorenz, S., Wolski, P., Blamey, R. C., & Jack, C.
- Affiliations
- Met Office (GB)
- University of Leeds (GB)
- University of Cape Town (ZA)
- Corresponding author
- Daron, Joseph D.
- Venue
- Climate Risk Management
- Publisher
- Elsevier BV
- Published
- 2015
- Volume
- 10
- Pages
- 17-26
- ISSN
- 2212-0963 (ISSN-L)
- OpenAlex
- W802769563
- Cited by
- 50
- References
- 45
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 7 · 2020: 9 · 2019: 4 · 2018: 5 · 2017: 3 · 2016: 2 · 2015: 3
- FWCI
- 14.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Climate Risk Management (publishedVersion, cc-by-nc-nd)
- Explore Bristol Research (publishedVersion, other-oa)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Funders
- World Universities Network
- Topics
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Environmental Science › Global and Planetary Change › Sustainability and Climate Change Governance
- Keywords
- climate change adaptation, climate services, climate model projections, adaptation decision-making, Africa
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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