Integrating human dimensions in decadal-scale prediction
for marine social–ecological systems: lighting the grey
The dynamics of marine systems at decadal scales are notoriously hard to predict—hence references to this timescale as the “grey zone” for ocean prediction. Nevertheless, decadal-scale prediction is a rapidly developing field with an increasing number of applications to help guide ocean stewardship and sustainable use of marine environments. Such predictions can provide industry and managers with information more suited to support planning and management over strategic timeframes, as compared to seasonal forecasts or long-term (century-scale) predictions. The most significant advances in capability for decadal-scale prediction over recent years have been for ocean physics and biogeochemistry, with some notable advances in ecological prediction skill. In this paper, we argue that the process of “lighting the grey zone” by providing improved predictions at decadal scales should also focus on including human dimensions in prediction systems to better meet the needs and priorities of end users. Our paper reviews information needs for decision-making at decadal scales and assesses current capabilities for meeting these needs. We identify key gaps in current capabilities, including the particular challenge of integrating human elements into decadal prediction systems. We then suggest approaches for overcoming these challenges and gaps, highlighting the important role of co-production of tools and scenarios, to build trust and ensure uptake with end users of decadal prediction systems. We also highlight opportunities for combining narratives and quantitative predictions to better incorporate the human dimension in future efforts to light the grey zone of decadal-scale prediction.
Authors: Melbourne-Thomas, Jess, Tommasi, Desiree, Gehlen, Marion, Murphy, Eugene J. ORCID record for Eugene J. Murphy, Beckensteiner, Jennifer, Bravo, Francisco, Eddy, Tyler D., Fischer, Mibu, Fulton, Elizabeth, Gogina, Mayya, Hofmann, Eileen, Ito, Maysa, Mynott, Sara, Ortega-Cisneros, Kelly, Osiecka, Anna N., Payne, Mark R., Saldívar-Lucio, Romeo, Scherrer, Kim J.N.