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Abstract Low frequency variability of the California Current System (CCS) is investigated using circulation estimates based on a 31-year (1980–2010) sequence of historical analyses of the CCS calculated using the Regional Ocean Modeling System (ROMS) 4-dimensional variational (4D-Var) data assimilation system. The leading 3-dimensional multivariate empirical orthogonal functions (3D EOFs) of the CCS circulation were computed and provide a detailed view of low-frequency circulation variability within the CCS. The 3D EOFs are used as basis functions for a linear inverse model of the circulation, and several Principal Oscillation Patterns (POPs) of the circulation are identified. The leading POPs have periods in the range ∼ 4 − 10 years, and shed light on the 3-dimensional time evolving structure associated with low-frequency variability in the circulation. A particular focus here is coastal upwelling. In particular, a POP with a period close to 10 years appears to be preferentially excited as a resonant response to forcing associated with the regional expression of the Pacific Decadal Oscillation, the North Pacific Gyre Oscillation and the El Niño Southern Oscillation. [ABSTRACT FROM AUTHOR]

Data assimilation is now used routinely in oceanography on both regional and global scales for computing ocean circulation estimates and for making ocean forecasts. Regional ocean observing systems are also expanding rapidly, and observations from a wide array of different platforms and sensor types are now available. Evaluation of the impact of the observing system on ocean circulation estimates (and forecasts) is therefore of considerable interest to the oceanographic community. In this paper, we quantify the impact of different observing platforms on estimates of the California Current System (CCS) spanning a three decade period (1980–2010). Specifically, we focus attention on several dynamically related aspects of the circulation (coastal upwelling, the transport of the California Current and the California Undercurrent, thermocline depth and eddy kinetic energy) which in many ways describe defining characteristics of the CCS. The circulation estimates were computed using a 4-dimensional variational (4D-Var) data assimilation system, and our analyses also focus on the impact of the different elements of the control vector ( i.e. the initial conditions, surface forcing, and open boundary conditions) on the circulation. While the influence of each component of the control vector varies between different metrics of the circulation, the impact of each observing system across metrics is very robust. In addition, the mean amplitude of the circulation increments ( i.e. the difference between the analysis and background) remains relatively stable throughout the three decade period despite the addition of new observing platforms whose impact is redistributed according to the relative uncertainty of observations from each platform. We also consider the impact of each observing platform on CCS circulation variability associated with low-frequency climate variability. The low-frequency nature of the dominant climate modes in this region allows us to track through time the impact of each observation on the circulation, and illustrates how observations from some platforms can influence the circulation up to a decade into the future. [ABSTRACT FROM AUTHOR]

Coupled physical and biological data assimilation is performed within the California Current System using model twin experiments. The initial condition of physical and biological variables is estimated using the four-dimensional variational (4DVar) method under the Gaussian and lognormal error distributions assumption, respectively. Errors are assumed to be independent, yet variables are coupled by assimilation through model dynamics. Using a nutrient-phytoplankton-zooplankton-detritus (NPZD) model coupled to an ocean circulation model (the Regional Ocean Modeling System, ROMS), the coupled data assimilation procedure is evaluated by comparing results to experiments with no assimilation and with assimilation of physical data and biological data separately. Independent assimilation of physical (biological) data reduces the root-mean-squared error (RMSE) of physical (biological) state variables by more than 56% (43%). However, the improvement in biological (physical) state variables is less than 7% (13%). In contrast, coupled data assimilation improves both physical and biological components by 57% and 49%, respectively. Coupled data assimilation shows robust performance with varied observational errors, resulting in significantly smaller RMSEs compared to the free run. It still produces the estimation of observed variables better than that from the free run even with the physical and biological model error, but leads to higher RMSEs for unobserved variables. A series of twin experiments illustrates that coupled physical and biological 4DVar assimilation is computationally efficient and practical, capable of providing the reliable estimation of the coupled system with the same and ready to be examined in a realistic configuration. [ABSTRACT FROM AUTHOR]

Biogeochemical numerical models coupled to physical ocean circulation models are commonly combined with data assimilation in order to improve the models’ state or parameter estimates. Yet much still needs to be learned about important aspects of biogeochemical data assimilation, such as the effect of model complexity and the importance of more realistic model formulations on assimilation results. In this study, 4D-Var-based state estimation is applied to two biogeochemical ocean models: a simple NPZD model with 4 biogeochemical variables (including 1 phytoplankton, 1 zooplankton) and the more complex NEMURO model, containing 11 biogeochemical variables (including 2 phytoplankton, 3 zooplankton). Both models are coupled to a 3-dimensional physical ocean circulation model of the U.S. west coast based on the Regional Ocean Modelling System (ROMS). Chlorophyll satellite observations and physical observations are assimilated into the model, yielding substantial improvements in state estimates for the observed physical and biogeochemical variables in both model formulations. In comparison to the simpler NPZD model, NEMURO shows a better overall fit to the observations. The assimilation also results in small improvements for simulated nitrate concentrations in both models and no apparent degradation of the output for other unobserved variables. The forecasting skill of the biogeochemical models is strongly linked to model performance without data assimilation: for both models, the improved fit obtained through assimilation degrades at similar relative rates, but drops to different absolute levels. Despite the better performance of NEMURO in our experiments, the choice of model and desired level of complexity should depend on the model application and the data available for assimilation. [ABSTRACT FROM AUTHOR]