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SUMMARYThis study considers the problem of marine ecological prediction in the context of online estimation and forecasting. Process oriented dynamic ecosystem models are combined with marine observations. The nonlinear, nonGaussian state space model provides the statistical framework. The associated filtering (nowcasting) and prediction (forecasting) problems are addressed via sequential Monte Carlo methods, in this instance a sequential importance resampler combined with Metropolis-Hastings MCMC. The specific focus is on a prototypical marine ecosystem model comprised of four interacting populations (phytoplankton, zooplankton, nutrients and detritus; PZND) whose co-evolution is described by system of coupled nonlinear differential equations. Stochastic environmental variation is introduced through a stochastic growth parameter, as well as through dynamical noise in the state evolution equations. The dynamic behaviour of this stochastic ecosystem model is complex: it regularly transitions through a Hopf bifurcation and exhibits episodic blooms of variable magnitude and duration. The model is applied to a case with weak seasonality, that is the oceanic mixed layer in the eastern equatorial Pacific. A partially observed state is considered comprised of a five year satellite (SeaWiFS) derived time series of ocean phytoplankton concentration at 12 N 95 W. Filtering estimates for the ecosystem state and a dynamic parameter were obtained using the sequential Monte Carlo approach. These showed predictor-corrector behaviour at observation times, including abrupt shifts in the median level after forecasts over measurement void. A corresponding variance (also skewness and kurtosis) growth and subsequent collapse was also seen. Forecasting experiments indicate some negative bias, and suggest there is predictive skill for forecasts out to 10-15 days.

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Key points Eddy-rich ocean model simulations feature large variability of spatial deep convection patterns in subpolar North Atlantic  In 2015-2018, deep convection showed exceptional large and small relative contributions of eastern and western subpolar gyre, respectively  Small western contribution is potentially associated with enhanced Greenland melting and recent eastern North Atlantic fresh anomaly AbstractDeep convection and associated deep water formation are key processes for climate variability, since they impact the oceanic uptake of heat and trace gases and alter the structure and strength of the global overturning circulation. For long, deep convection in the subpolar North Atlantic was thought to be confined to the central Labrador Sea in the western subpolar gyre (SPG). However, there is increasing observational evidence that deep convection also has occured in the eastern SPG south of Cape Farewell and in the Irminger Sea, in particular, in 2015-2018.Here we assess this recent event in the context of the temporal evolution of spatial deep convection patterns in the SPG since the mid-twentieth century, using realistic eddy-rich ocean model simulations. These reveal a large interannual variability with changing contributions of the eastern SPG to the total deep convection volume. Notably, in the late 1980s to early 1990s, the period with highest deep convection intensity in the Labrador Sea related to a persistent positive phase of the North Atlantic Oscillation, the relative contribution of the eastern SPG was small. In contrast, in 2015-2018, deep convection occurred with an unprecedented large relative contribution of the eastern SPG. This is partly linked to a smaller north-westward extent of deep convection in the Labrador Sea compared to previous periods of intensified deep convection, and may be a first fingerprint of freshening trends in the Labrador Sea potentially associated with enhanced Greenland melting and the oceanic advection of the 2012-2016 eastern North Atlantic fresh anomaly.

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A simple ecosystem model is coupled to a 3-dimensional general circulation model for the North Atlantic. The physical model is based on the Los Alamos Parallel Ocean Program (POP) and forced by climatological monthly mean data. Four biological components (phytoplankton, zooplankton, nutrients and detritus) are incorporated into POP as additional tracers with biological sources and sinks. The model solutions, obtained with different physical and biological parameterizations are compared against monthly mean SeaWiFS colour data averaged over the period 1997-2003 and Levitus's climatological nitrate data. A reference model solution, with constant biological model parameters over the whole basin, underestimates both the average chlorophyll level and its regional variability at mid-to high-latitudes. Experiments with a different parameterization of heat and freshwater fluxes, which affects upper ocean mixing, indicate a strong impact of such parameterizations on nutrient supply to the surface layer at high latitudes, but with little impact on simulated chlorophyll. Other experiments where advection of the biological tracers is turned off show basically the same result: strong impact on regional nutrient patterns but a negligible impact on phytoplankton patterns. Only model runs with spatially variable biological parameters, obtained from a previous zero-dimensional ecosystem model calibration on CZCS ocean colour data, could reproduce regional scale patterns in the SeaWiFS imagery. We hypothesize that some of these patterns can be linked to coccolithophore blooms in areas influenced by the N. Atlantic Drift during summer and to effects of temperature on plankton loss rates during spring. Future work should focus on identifying the main factors responsible for these spatial patterns and developing the ecosystem models that can capture them.

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