Skillful seasonal predictions of the California Current sea surface temperature possible, study says
A seasonal multi-model forecast system can produce skillful predictions of sea surface temperature in the CaliforniaCurrent System up to 4 months in advance, according to a new study published in Climate Dynamics. These findings suggest considerable promise for producing forecasts at a finer scale in the region and for improving short-term marine resources management.
Fisheries management strategies currently don’t incorporate seasonal forecasts of changes in the Current's physical characteristics, which can significantly impact marine resources. CPO-supported researchers found that forecasts from the North American Multi-Model Ensemble (NMME) produce skillful sea surface temperature forecasts, particularly for the late winter and spring.
The California Current System, a southward moving current of cool water along the western coast of North America, is one of the world’s most productive marine ecosystems. It is home to many different marine animals and supports a large fishing community. Changes in physical characteristics such as sea surface temperature can significantly impact its marine resources.
This study was supported by the CPO Modeling, Analysis, Predictions, and Projections (MAPP) Program.
Read the paper
The California Current System (CCS) is a biologically productive Eastern Boundary Upwelling System that experiences considerable environmental variability on seasonal and interannual timescales. Given that this variability drives changes in ecologically and economically important living marine resources, predictive skill for regional oceanographic conditions is highly desirable. Here, we assess the skill of seasonal sea surface temperature (SST) forecasts in the CCS using output from Global Climate Forecast Systems in the North American Multi-Model Ensemble (NMME), and describe mechanisms that underlie SST predictability. A simple persistence forecast provides considerable skill for lead times up to ~4 months, while skill above persistence is mostly confined to forecasts of late winter/spring and derives primarily from predictable evolution of ENSO-related variability. Specifically, anomalously weak (strong) equatorward winds are skillfully forecast during El Niño (La Niña) events, and drive negative (positive) upwelling anomalies and consequently warm (cold) temperature anomalies. This mechanism prevails during moderate to strong ENSO events, while years of ENSO-neutral conditions are not associated with significant forecast skill in the wind or significant skill above persistence in SST. We find also a strong latitudinal gradient in predictability within the CCS; SST forecast skill is highest off the Washington/Oregon coast and lowest off southern California, consistent with variable wind forcing being the dominant driver of SST predictability. These findings have direct implications for regional downscaling of seasonal forecasts and for short-term management of living marine resources.