Exploiting subsurface ocean dynamics for decadal predictability in the upwelling systems of the Eastern North Pacific

Given the strong recent interest in the decadal timescale variability and the potential for its predictability, it is critical to identify dynamics that carry inherent decadal-scale predictability. This work enhances our understanding and prediction capability of the subsurface signature of the decadal variability in the eastern North Pacific upwelling systems using reanalysis products and a set of eddy-resolving ocean model simulations. We show that subsurface temperature anomalies propagated by mean advection along the North Pacific Current significantly contribute through mean upwelling to decadal changes of surface temperature in the Gulf of Alaska. We also show that this influence is comparable to the contribution associated with variations in atmospheric winds. We find that subsurface anomalies in the core of the North Pacific Current propagate temperature, salinity, and oxygen signals downstream into the coastal California Current upwelling system, following the path of the mean gyre circulation with a time scale of 10 years. We suggest these propagation dynamics lead to potential predictability of ocean tracers, specifically oxygen and nutrients. Using reanalysis products and a set of eddy-resolving ocean model simulations, we provide evidence that supports the proposed inherent decadal predictability associated with the propagation of subsurface anomalies. We quantify the predictability of impacts associated with the arrival of the subsurface anomalies in the California Current upwelling system. We find a region of strong deterministic, predictable variance in the core of the North Pacific Current and in the sub-polar gyre region. Finally, we propose a dynamical subsurface connection between the western and eastern boundary, with subsurface anomalies generating and propagating eastward from the Kuroshio-Oyashio Extension region in the Western Pacific all the way to the California Current region in the Eastern Pacific.Ph.D

Observational Needs Supporting Marine Ecosystems Modeling and Forecasting: From the Global Ocean to Regional and Coastal Systems

International audienceMany coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation. Due to the socioeconomic and ecological importance of these areas, predicting relevant indicators of the ecosystem state on sub-seasonal to interannual timescales is gaining increasing attention. Depending on the application, forecasts may be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents), chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators). Many components of the marine ecosystem are known to be influenced by leading modes of climate variability, which provide a physical basis for predictability. However, prediction capabilities remain limited by the lack of a clear understanding of the physical and biological processes involved, as well as by insufficient observations for forecast initialization and verification. The situation is further complicated by the influence of climate change on ocean conditions along coastal areas, including sea level rise, increased stratification, and shoaling of oxygen minimum zones. Observations are thus vital to all aspects of marine forecasting: statistical and/or dynamical model development, forecast initialization, and forecast validation, each of which has different observational requirements, which may be also specific to the study region. Here, we use examples from United States (U.S.) coastal applications to identify and describe the key requirements for an observational network that is needed to facilitate improved process understanding, as well as for sustaining operational ecosystem forecasting. We also describe new holistic observational approaches, e.g., approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the potential to support and expand ecosystem modeling and forecasting activities by bridging global and local observations