Abstract
AbstractClimate predictions, from three weeks to a decade into the future, can provide invaluable information for climate-sensitive socioeconomic sectors, such as renewable energy, agriculture, or insurance. However, communicating and interpreting these predictions is not straightforward. Barriers hindering user uptake include a terminology gap between climate scientists and users, the difficulties of dealing with probabilistic outcomes for decision-making, and the lower skill of climate predictions compared to the skill of weather forecasts. This paper presents a gaming approach to break communication and understanding barriers through the application of the Weather Roulette conceptual framework. In the game, the player can choose between two forecast options, one that uses ECMWF seasonal predictions against one using climatology-derived probabilities. For each forecast option, the bet is spread proportionally to the predicted probabilities, either in a single year game or a game for the whole period of 33 past years. This paper provides skill maps of forecast quality metrics commonly used by the climate prediction community (e.g., ignorance skill score and ranked probability skill score), which in the game are linked to metrics easily understood by the business sector (e.g., interest rate and return on investment). In a simplified context, we illustrate how in skillful regions the economic benefits of using ECMWF predictions arise in the long term and are higher than using climatology. This paper provides an example of how to convey the usefulness of climate predictions and transfer the knowledge from climate science to potential users. If applied, this approach could provide the basis for a better integration of knowledge about climate anomalies into operational and managerial processes.
Highlights
Seasonal-to-decadal climate predictions try to anticipate the most likely climate conditions for the few months up to a decade into the future (Doblas-Reyes et al 2013; Meehl et al 2014)
Main barriers hindering users’ uptake of climate predictions include (i) the lack of a common and widely accepted terminology between climate scientists and user communities, (ii) the difficulty to deal with probabilistic rather than deterministic outcomes, (iii) their lower skill compared to the skill of weather forecasts, and (iv) the need to move from a short- to a long-term approach for the assessment of benefits in the business sector, since the benefits from adopting climate predictions can only be perceived in the long term
The case of the locations in Denmark and eastern United States (X24592 and X36231; middle row) illustrates situations where, the skill is nonsignificant (RPSS < 0.15), using climate predictions is still better than using climatology
Summary
Seasonal-to-decadal climate predictions try to anticipate the most likely climate conditions for the few months up to a decade into the future (Doblas-Reyes et al 2013; Meehl et al 2014). Main barriers hindering users’ uptake of climate predictions include (i) the lack of a common and widely accepted terminology between climate scientists and user communities, (ii) the difficulty to deal with probabilistic rather than deterministic outcomes, (iii) their lower skill (i.e., the quality of the prediction based on its performance in the past) compared to the skill of weather forecasts, and (iv) the need to move from a short- to a long-term approach for the assessment of benefits in the business sector, since the benefits from adopting climate predictions can only be perceived in the long term. There is a need to improve the way in which actionable climate information is made salient and relevant to different users
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