Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

Abstract

Abstract. A set of observing system simulation experiments has been performed to explore the impact on global ocean biogeochemical reanalyses of assimilating chlorophyll from remotely sensed ocean colour, and assess the potential impact of assimilating in situ observations of chlorophyll, nitrate, oxygen, and pH from a proposed array of Biogeochemical-Argo (BGC-Argo) floats. Two different potential BGC-Argo array distributions were tested: one where biogeochemical sensors are placed on all current Argo floats, and one where biogeochemical sensors are placed on a quarter of current Argo floats. This latter approximately corresponds to the proposed BGC-Argo array of 1000 floats. Different strategies for updating model variables when assimilating ocean colour were assessed. All similarly improved the assimilated variable surface chlorophyll, but had a mixed impact on the wider ecosystem and carbon cycle, degrading some key variables of interest. Assimilating BGC-Argo data gave no added benefit over ocean colour in terms of simulating surface chlorophyll, but for most other variables, including sub-surface chlorophyll, adding BGC-Argo greatly improved results throughout the water column. This included surface partial pressure of carbon dioxide (pCO2), which was not assimilated but is an important output of reanalyses. Both BGC-Argo array distributions gave benefits, with greater improvements seen with more observations. From the point of view of ocean reanalysis, it is recommended to proceed with development of BGC-Argo as a priority. The proposed array of 1000 floats will lead to clear improvements in reanalyses, with a larger array likely to bring further benefits. The ocean colour satellite observing system should also be maintained, as ocean colour and BGC-Argo will provide complementary benefits. There is also much potential to improve the use of existing observations, particularly in terms of multivariate balancing, through improving assimilation methodologies.

Comment on bg-2021-2

“…The comparison between BGC-Argo data and model simulations is not only beneficial for the modelling community but also for the BGC-Argo community. Observation System Simulation Experiments (OSSEs) are generally used to inform, a priori, observing network design (Ford, 2020). Here, we showed that model-observations comparison is, also data to account that sChl covers several orders of magnitude and is lognormally distributed 5 (Campbell, 1995).…”

Towards a multi-platform assimilative system for North Sea biogeochemistry

“…[2015] extended to biogeochemical variables by Ford [2020], i.e. the combined assimilation of satellite OC and glider chlorophyll data is performed by following a scheme previously applied to temperature by Waters et al [2015].…”

“…the combined assimilation of satellite OC and glider chlorophyll data is performed by following a scheme previously applied to temperature by Waters et al [2015]. The satellite and in situ glider data are combined to calculate a single set of 3D increments, while allowing for different observation errors to be specified for the different data sources (for the details see Waters et al [2015]; Ford [2020]). Since each of the physical data, chlorophyll and oxygen assimilation provides increments for different variables, the multi-platform assimilation simply aggregates the increments from the physical, chlorophyll and oxygen assimilative components.…”

“…The vertical length-scales use the scheme from Waters et al [2015]; King et al [2018]; Ford [2020], where NEMOVAR calculates directly the set of 3D increments (we call this scheme a "3D variant") using flow-dependent vertical length-scales ( ), which are the following function of depth (d):…”

Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment

“…The first examples of the assimilation of float observations into biogeochemical models have improved the estimates of the vertical variability in biogeochemical variables (Cossarini et al, 2019;Verdy and Mazloff, 2017). Moreover, the potential benefits of integrating BGC-Argo observations with satellite data and biogeochemical modelling have been demonstrated by recent observing system simulation experiments (OSSEs) and parameter optimization studies (Ford, 2020b;Germineaud et al, 2019;Wang et al, 2020).…”