Subseasonal‐to‐Seasonal Forecast Skill in the California Current System and Its Connection to Coastal Kelvin Waves. Issue 1 (23rd January 2022)

• Record Type: Journal Article
• Authors: Amaya, Dillon J.; Jacox, Michael G.; Dias, Juliana; Alexander, Michael A.; Karnauskas, Kristopher B.; Scott, James D.; Gehne, Maria
• Is Part Of: Journal of geophysical research. Volume 127:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-23
• Subjects: S2S forecast -- California current system -- sea level -- ocean Kelvin wave -- ocean reanalysis -- coastal inundation; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2021JC017892
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: Accurate dynamical forecasts of ocean variables in the California Current System (CCS) are essential decision support tools for advancing ecosystem‐based marine resource management. However, model and dynamical uncertainties present a significant challenge when attempting to incorporate these forecasts into a formal decision‐making process. To provide guidance on the reliability of dynamical forecasts, previous studies have suggested that deterministic climate processes associated with atmospheric or oceanic teleconnections may provide opportunities for enhanced forecast skill. Recent computational advances have led to the availability of subseasonal‐to‐seasonal (S2S) forecasts of key oceanic variables such as sea surface height (SSH), which may be leveraged to identify such "forecast opportunities." In this study, we conduct a S2S forecast skill assessment of SSH anomalies in the CCS using an ensemble of 46‐day reforecasts made by the European Center for Medium‐range Weather Forecasting (ECMWF) model for the period 2000–2018. We find that the ECMWF model consistently produces skillful dynamical forecasts of SSH, particularly in both the southern and northern CCS at leads of 4–7 weeks. Using a high‐resolution ocean reanalysis, we develop a new index designed to characterize the location and intensity of coastally trapped waves propagating through the CCS. We then show that the S2S dynamical forecasts have enhanced skill in forecasts of SSH in weeks 4–7 when initialized with strong or extreme coastally trapped wave conditions, explaining 30–40% more SSH variance than the corresponding persistence forecast. Plain Language Summary: Accurate ocean forecasts along the U.S. west coast are important tools to enable proactive decision making by marine resource managers. However, due to the chaotic nature of the climate system, these forecasts can often be unreliable on timescales of weeks to months. To provide guidance on when such forecasts can be trusted, we analyze the skill of a new ocean forecasting model with a focus on forecasts of sea surface height in the California Current System (CCS) up to 46 days out. We find that the ocean forecasts are skillful, particularly in both the southern and northern CCS at lead times of 4–7 weeks. In addition, we use a high‐resolution ocean reanalysis to show that this forecast skill is further enhanced when a particular type of coastal ocean wave is present. Our results provide context for marine resource managers using operational ocean forecasts along the U.S. west coast. We also provide new physical insights into a long‐studied ocean process, which opens up innovative and exciting areas for additional analysis. Key Points: We have created a new index to describe the time variability of coastally trapped waves over the last 25 years The ECMWF S2S forecast model skillfully predicts SSH along the U.S. west coast at leads of up to 4–7 weeks Forecast skill is enhanced when the forecast is initialized with strong coastal wave activity