TY - JOUR
T1 - Improving the visual communication of environmental model projections
AU - Bannister, Hayley J.
AU - Blackwell, Paul G.
AU - Hyder, Kieran
AU - Webb, Thomas J.
N1 - Funding Information: This research was conducted as part of a postgraduate studentship to HJB, funded by the Department of Animal and Plant Sciences and the School of Mathematics and Statistics at the University of Sheffield and the Centre for Environment, Fisheries and Aquaculture Science, with additional support from the Natural Environment Research Council and the Department for Environment, Food and Rural Affairs [Grant Number NE/L003279/1, Marine Ecosystems Research Programme] to TJW and PGB. The climate model outputs used in the survey were sourced from the World Data Center for Climate (WDCC) at DKRZ. The survey was developed using the Qual-trics online survey software (qualtrics.com) and some of the visualisations were based on the work of Professor Edward Hawkins and Dr. Rowan Sutton from the National Centre for Atmospheric Science.
PY - 2021/9/27
Y1 - 2021/9/27
N2 - Environmental and ecosystem models can help to guide management of changing natural systems by projecting alternative future states under a common set of scenarios. Combining contrasting models into multi-model ensembles (MMEs) can improve the skill and reliability of projections, but associated uncertainty complicates communication of outputs, affecting both the effectiveness of management decisions and, sometimes, public trust in scientific evidence itself. Effective data visualisation can play a key role in accurately communicating such complex outcomes, but we lack an evidence base to enable us to design them to be visually appealing whilst also effectively communicating accurate information. To address this, we conducted a survey to identify the most effective methods for visually communicating the outputs of an ensemble of global climate models. We measured the accuracy, confidence, and ease with which the survey participants were able to interpret 10 visualisations depicting the same set of model outputs in different ways, as well as their preferences. Dot and box plots outperformed all other visualisations, heat maps and radar plots were comparatively ineffective, while our infographic scored highly for visual appeal but lacked information necessary for accurate interpretation. We provide a set of guidelines for visually communicating the outputs of MMEs across a wide range of research areas, aimed at maximising the impact of the visualisations, whilst minimizing the potential for misinterpretations, increasing the societal impact of the models and ensuring they are well-placed to support management in the future.
AB - Environmental and ecosystem models can help to guide management of changing natural systems by projecting alternative future states under a common set of scenarios. Combining contrasting models into multi-model ensembles (MMEs) can improve the skill and reliability of projections, but associated uncertainty complicates communication of outputs, affecting both the effectiveness of management decisions and, sometimes, public trust in scientific evidence itself. Effective data visualisation can play a key role in accurately communicating such complex outcomes, but we lack an evidence base to enable us to design them to be visually appealing whilst also effectively communicating accurate information. To address this, we conducted a survey to identify the most effective methods for visually communicating the outputs of an ensemble of global climate models. We measured the accuracy, confidence, and ease with which the survey participants were able to interpret 10 visualisations depicting the same set of model outputs in different ways, as well as their preferences. Dot and box plots outperformed all other visualisations, heat maps and radar plots were comparatively ineffective, while our infographic scored highly for visual appeal but lacked information necessary for accurate interpretation. We provide a set of guidelines for visually communicating the outputs of MMEs across a wide range of research areas, aimed at maximising the impact of the visualisations, whilst minimizing the potential for misinterpretations, increasing the societal impact of the models and ensuring they are well-placed to support management in the future.
UR - http://www.scopus.com/inward/record.url?scp=85115761098&partnerID=8YFLogxK
U2 - 10.1038/s41598-021-98290-4
DO - 10.1038/s41598-021-98290-4
M3 - Article
C2 - 34580337
AN - SCOPUS:85115761098
VL - 11
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
M1 - 19157
ER -