Decisions are made for the future given the best available information that we have in the present. Thus, the improvement of ecological forecasts have tremendous potential to improve short-to-medium term decisions. Though ecological forecasting is a more nascent field, seasonal climate outlooks (2-week to 1-year) are well-established and distributed widely to decision-making entities. The National Oceanic and Atmospheric Administration Climate Prediction Center provides future-oriented information on various climate outcomes, such as temperature and precipitation. This information can be viewed as a leading indicator, as it provides predictive, albeit uncertain probabilistic information tracked over time that is intended to be useful for decision-making. Structuring the delivery of this information is challenging for many reasons, foremost being that visualizing uncertainty for geospatial data has been an open area of research for many years. However, careful attention to delivery of uncertain information is of high importance because incorporating uncertainty into decisions can be challenging, even when uncertainty is small. Larger uncertainties compound this problem because users often conflate larger uncertainty with less utility of the information to decision-making. This study, as an exemplar for similar challenges faced by ecological forecasts, assesses and diagnoses interpretation and use challenges with climate forecasts visualization.
To identify the target users and understand potential challenges with the interpretation of scientific forecasts, we conducted in-depth semi-structured interviews. Our interviewees were high-level scientific translators whose role is to understand the forecasts and contextualize them for their clients. The interviews were transcribed and coded for qualitative data analysis using NVivo. In general, the interviewees identified the climate outlooks target audience as a user with some technical expertise. However, the current outlook products are not well understood. For example, the expert-level interviewees expressed uncertainty in their interpretation of outlooks that they do not frequently use. Thus, they strongly recommended correcting visual and probability representations because the inconsistencies amongst outlook products further compounds confusion. Building on these preliminary outcomes, we will diagnose specific design problems and rigorously test modifications to minimize interpretation problems. More promising than the generalizability of this work to improve the design and interpretation of scientific forecasts, is the potential to fully realize adaptive environmental management. By developing iterative ecological forecasts and improving the processes to facilitate uptake and use of new predictions, decision rules could be constructed that would enable refinements that would improve the likelihood of environmental outcomes.