Abstract
Abstract. Satellite-derived surface chlorophyll data are assimilated daily into a three-dimensional 24-member ensemble configuration of an online-coupled NEMO (Nucleus for European Modeling of the Ocean)–PISCES (Pelagic Interaction Scheme of Carbon and Ecosystem Studies) model for the North Atlantic Ocean. A 1-year multivariate assimilation experiment is performed to evaluate the impacts on analyses and forecast ensembles. Our results demonstrate that the integration of data improves surface analysis and forecast chlorophyll representation in a major part of the model domain, where the assimilated simulation outperforms the probabilistic skills of a non-assimilated analogous simulation. However, improvements are dependent on the reliability of the prior free ensemble. A regional diagnosis shows that surface chlorophyll is overestimated in the northern limit of the subtropical North Atlantic, where the prior ensemble spread does not cover the observation's variability. There, the system cannot deal with corrections that alter the equilibrium between the observed and unobserved state variables producing instabilities that propagate into the forecast. To alleviate these inconsistencies, a 1-month sensitivity experiment in which the assimilation process is only applied to model fluctuations is performed. Results suggest the use of this methodology may decrease the effect of corrections on the correlations between state vectors. Overall, the experiments presented here evidence the need of refining the description of model's uncertainties according to the biogeochemical characteristics of each oceanic region.
Highlights
Estimating the biogeochemical state of the ocean has become fundamental under the current climate change context due to its key role in mediating global carbon stocks (e.g., Houghton et al, 2001)
3.1 Skill in reproducing surface Chl a. Both the assimilated and the free-run simulations are compared to daily surface Chl a fields obtained from the Global Ocean Satellite Observations
By employing data assimilation (DA), we aim to reduce the impacts of model errors on the representation of ocean biogeochemistry by combining model information with available observations (Gregg et al, 2009; Ciavatta et al, 2011; Ford and Barciela, 2017)
Summary
Estimating the biogeochemical state of the ocean has become fundamental under the current climate change context due to its key role in mediating global carbon stocks (e.g., Houghton et al, 2001). In order to achieve model–data integration it is of utmost importance to explicitly identify the structure of the uncertainties that affect the model and the observations (Lahoz et al, 2010). In this sense, ensemble methods (e.g., Bessières et al, 2017) are designed to provide a statistical description of the inaccuracies associated with a complex model system by describing the evolution of the probability density function (PDF).
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