Physical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizations and potential impact on data assimilation

Abstract

Abstract. We generate ocean biogeochemical model ensembles including several kinds of stochastic parameterizations. The NEMO stochastic modules are complemented by integrating a subroutine to calculate variable anisotropic spatial scales, which are of particular importance in high-resolution coastal configurations. The domain covers the Bay of Biscay at 1/36° resolution, as a case study for open-ocean and coastal shelf dynamics. At first, we identify uncertainties from assumptions subject to erroneous atmospheric forcing, ocean model improper parameterizations and ecosystem state uncertainties. The error regimes are found to be mainly driven by the wind forcing, with the rest of the perturbed tendencies locally augmenting the ensemble spread. Biogeochemical uncertainties arise from inborn ecosystem model errors and from errors in the physical state. Model errors in physics are found to have larger impact on chlorophyll spread than those of the ecosystem. In a second step, the ensembles undergo verification with respect to observations, focusing on upper-ocean properties. We investigate the statistical consistency of prior model errors and observation estimates, in view of joint uncertainty vicinities, associated with both sources of information. OSTIA-SST L4 distribution appears to be compatible with ensembles perturbing physics, since vicinities overlap, enabling data assimilation. The most consistent configuration for SLA along-track L3 data is in the Abyssal plain, where the spread is increased due to mesoscale eddy decorrelation. The largest statistical SLA biases are observed in coastal regions, sometimes to the point that vicinities become disjoint. Missing error processes in relation to SLA hint at the presence of high-frequency error sources currently unaccounted for, potentially leading to ill-posed assimilation problems. Ecosystem model-data samples with respect to Ocean Colour L4 appear to be compatible with each other only at times, with data assimilation being marginally well-posed. In a third step, we illustrate the potential influence of those uncertainties on data assimilation impact exercise, by means of multivariate representers and EnKF-type incremental analysis for a few members. Corrections on physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. The increments are often characteristic of the underlying mesoscale features, chlorophyll included due to the vertical velocity field. Small scale local corrections are visible over the shelves. Chlorophyll information seems to have a very measurable potential impact on physical variables.

Interactions between barotropic tides and mesoscale processes in deep ocean and shelf regions

“…These regional results are expected to be valid for similar ocean cases in which the effects of eddies influence both ocean dynamics and ecosystems (Chelton et al 2011;Gaube et al 2014;Stanev et al 2014;McGillicuddy Jr. 2016;Vervatis et al 2019). Furthermore, adding barotropic tides to the forcing of eddy-resolving models is advisable.…”

“…They demonstrated that internal tides in the region were characterized by complex pycnocline displacement patterns. Recently, another fine-resolution model was developed by Vervatis et al (2019) for the Bay of Biscay at 1/36°resolution, and it was used to analyze the interaction between open ocean and coastal shelf dynamics. They found that the differences between the along-track altimeter data and simulations in the open ocean were due to mesoscale eddy decorrelation (Vervatis et al 2019), and this result is also relevant to the major issue addressed here.…”

A new stochastic ocean physics package and its application to hybrid‐covariance data assimilation

“…(2015) have shown the positive impact of stochastic physics in high‐resolution atmospheric ensemble data assimilation, in terms of improved skill scores of the mean state and ensemble diagnostics. Within ocean applications, preliminary studies show that coastal predictions benefit from improved ensemble spread generated by stochastic elements (Quattrocchi et al ., 2014; Vervatis et al ., 2016; 2019). In downstream applications such as biogeochemical models and oil spill, the adoption of ensemble forecasting is shown to have even larger impacts (Khade et al ., 2017).…”