Along the west coast of the U.S., the dynamic and very biodiverse California Current System (CCS) supports a variety of fisheries and marine services. Researchers use Earth System Models (ESMs) to predict how the CCS may change over time, but these models have limited ability to represent these processes at a high resolution. The Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) Program supported a new study that improves bias and accuracy in predicting future CCS conditions. MAPP-funded scientist Mercedes Pozo Buil and collaborators Jerome Fiechter, Michael Jacox, and Steven Bograd of the University of California, Santa Cruz, along with Michael A. Alexander of NOAA’s Physical Sciences Laboratory, explored three methods of reducing bias in ESMs.
Large ESMs are often used as a basis to create a smaller-scale model to capture finer details, but they are known to cause bias. The methods used to reduce this bias have not been adequately compared, so this study, published in Earth and Space Science, investigates three bias-correction methods before the downscaling. The results show that it is crucial to perform this step before downscaling for the most accurate representation of coastal conditions that are relevant to ecosystem health. All three methods produced similar results of seasonal changes, but the experiment showed that without the bias correction, the simulations were not representative of the CCS. This work builds on a MAPP initiative to improve the modeling of how climate impacts the predictability of fisheries and other living marine resources across timescales.
For more information, contact Clara Deck.
Image credit: Greater Farallones National Marine Sanctuary