Author(s): Drenkard, E., C. Stock, A. Adcroft, M. Alexander, V. Balaji, S. J. Bograd, M. Butenschön, W. Cheng, E. Curchitser, E. Di Lorenzo, K. W. Dixon, R. Dussin, A. Haynie, M. Harrison, A. Hermann, A. Hollowed, K. Holsman, J. Holt, M. G. Jacox, C. Joo Jang, K. A. Kearney, B. A. Muhling, M. Pozo Buil, A. C. Ross, V. Saba, A. Britt Sandø, D. Tommasi, M. Wang.
Project PI: Hollowed
Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.