Visualizations of Projected Rainfall Change in the United Kingdom: An Interview Study about User Perceptions

Stakeholders from public, private, and third sectors need to adapt to a changing climate. Communications about climate may be challenging, especially for audiences with limited climate expertise. Here, we study how such audience members perceive visualizations about projected future rainfall. In semi-structured interviews, we presented 24 participants from climate-conscious organizations across the UK with three prototypical visualizations about projected future rainfall, adopted from the probabilistic United Kingdom Climate Projections: (1) Maps displaying a central estimate and confidence intervals, (2) a line graph and boxplots displaying change over time and associated confidence intervals, and (3) a probability density function for distributions of rainfall change. We analyzed participants’ responses using “Thematic Analysis”. In our analysis, we identified features that facilitated understanding—such as colors, simple captions, and comparisons between different emission scenarios—and barriers that hindered understanding, such as unfamiliar acronyms and terminology, confusing usage of probabilistic estimates, and expressions of relative change in percentages. We integrate these findings with the interdisciplinary risk communication literature and suggest content-related and editorial strategies for effectively designing visualizations about uncertain climate projections for audiences with limited climate expertise. These strategies will help organizations such as National Met Services to effectively communicate about a changing climate.

[1]  P. Ubel,et al.  Reducing the Influence of Anecdotal Reasoning on People’s Health Care Decisions: Is a Picture Worth a Thousand Statistics? , 2005, Medical decision making : an international journal of the Society for Medical Decision Making.

[2]  Milind Kandlikar,et al.  Representing and communicating deep uncertainty in climate-change assessments , 2005 .

[3]  Gerd Gigerenzer,et al.  “A 30% Chance of Rain Tomorrow”: How Does the Public Understand Probabilistic Weather Forecasts? , 2005, Risk analysis : an official publication of the Society for Risk Analysis.

[4]  Jessica S. Ancker,et al.  The Practice of Informatics: Design Features of Graphs in Health Risk Communication: A Systematic Review , 2006, J. Am. Medical Informatics Assoc..

[5]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[6]  Lisa M. Schwartz,et al.  PSYCHOLOGICAL SCIENCE IN THE PUBLIC INTEREST Helping Doctors and Patients Make Sense of Health Statistics , 2022 .

[7]  E. Hawkins,et al.  The Potential to Narrow Uncertainty in Regional Climate Predictions , 2009 .

[8]  Martin L. Weitzman,et al.  Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change , 2011, Review of Environmental Economics and Policy.

[9]  B. Fischhoff,et al.  The role of social and decision sciences in communicating uncertain climate risks , 2011 .

[10]  P. Ubel,et al.  Helping patients decide: ten steps to better risk communication. , 2011, Journal of the National Cancer Institute.

[11]  Edward T. Cokely,et al.  Effective communication of risks to young adults: using message framing and visual aids to increase condom use and STD screening. , 2011, Journal of experimental psychology. Applied.

[12]  Deborah Hemming,et al.  Mapping the climate: guidance on appropriate techniques to map climate variables and their uncertainty , 2011 .

[13]  S. Broomell,et al.  Effective communication of uncertainty in the IPCC reports , 2012, Climatic Change.

[14]  Mike Pearson,et al.  Visualizing Uncertainty About the Future , 2022 .

[15]  David Coley,et al.  On the creation of future probabilistic design weather years from UKCP09 , 2011 .

[16]  David J Spiegelhalter,et al.  Don't know, can't know: embracing deeper uncertainties when analysing risks , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[17]  David Kent,et al.  Use of Representative Climate Futures in impact and adaptation assessment , 2012, Climatic Change.

[18]  Elisabeth Stephens,et al.  Communicating probabilistic information from climate model ensembles—lessons from numerical weather prediction , 2012 .

[19]  Edward T. Cokely,et al.  Measuring Risk Literacy: The Berlin Numeracy Test , 2012, Judgment and Decision Making.

[20]  Cleotilde González,et al.  Why Do We Want to Delay Actions on Climate Change? Effects of Probability and Timing of Climate Consequences , 2012 .

[21]  Hans R. Künsch,et al.  Climate change projections for Switzerland based on a Bayesian multi‐model approach , 2012 .

[22]  W. Bruin,et al.  Effects of simplifying outreach materials for energy conservation programs that target low-income consumers , 2013 .

[23]  A. Bostrom,et al.  Assessing what to address in science communication , 2013, Proceedings of the National Academy of Sciences.

[24]  S. Lorenz,et al.  The communication of physical science uncertainty in European National Adaptation Strategies , 2013, Climatic Change.

[25]  Scientific uncertainty and climate change: Part II. Uncertainty and mitigation , 2014, Climatic Change.

[26]  Suraje Dessai,et al.  Communicating uncertainty in seasonal and interannual climate forecasts in Europe , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[27]  Reto Knutti,et al.  The unseen uncertainties in climate change: reviewing comprehension of an IPCC scenario graph , 2015, Climatic Change.

[28]  When, not if: the inescapability of an uncertain climate future , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[29]  C. von Wagner,et al.  How do people interpret information about colorectal cancer screening: observations from a think‐aloud study , 2013, Health expectations : an international journal of public participation in health care and health policy.

[30]  P. Wolski,et al.  Interpreting climate data visualisations to inform adaptation decisions , 2015 .

[31]  Susanne Lorenz,et al.  Tailoring the visual communication of climate projections for local adaptation practitioners in Germany and the UK , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[32]  Nathan F Dieckmann,et al.  At Home on the Range? Lay Interpretations of Numerical Uncertainty Ranges , 2015, Risk analysis : an official publication of the Society for Risk Analysis.

[33]  T. Shipley,et al.  Cognitive and psychological science insights to improve climate change data visualization , 2016 .

[34]  Felix G. Rebitschek,et al.  A Simple Tool for Communicating the Benefits and Harms of Health Interventions , 2016, MDM policy & practice.

[35]  Mirta Galesic,et al.  A Sampling Framework for Uncertainty in Individual Environmental Decisions , 2016, Top. Cogn. Sci..

[36]  M. Covi,et al.  Sea-Level Rise Risk Communication: Public Understanding, Risk Perception, and Attitudes about Information , 2016 .

[37]  Cynthia A. Brewer,et al.  Guidance for representing uncertainty on global temperature change maps , 2016 .

[38]  Valentina Bosetti,et al.  COP21 climate negotiators’ responses to climate model forecasts , 2017 .

[39]  Edward T. Cokely,et al.  Designing Visual Aids That Promote Risk Literacy: A Systematic Review of Health Research and Evidence-Based Design Heuristics , 2017, Hum. Factors.

[40]  Michelle McDowell,et al.  Meta-Analysis of the Effect of Natural Frequencies on Bayesian Reasoning , 2017, Psychological bulletin.

[41]  L. Steg,et al.  Meta-analyses of factors motivating climate change adaptation behaviour , 2019, Nature Climate Change.

[42]  Glen P. Peters,et al.  Emissions – the ‘business as usual’ story is misleading , 2020, Nature.