Modeling Global Ocean Biogeochemistry With Physical Data Assimilation: A Pragmatic Solution to the Equatorial Instability

Abstract

Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled‐climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model‐data weights that removed spurious biogeochemical signals while benefitting from off‐equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.

Assessing the role and consistency of satellite observation products in global physical-biogeochemical ocean reanalysis

“…This was found to be due to issues with the model and data assimilation system, rather than any lack of consistency between the observations being assimilated. Much of this was down to known issues with physical data assimilation causing spurious vertical mixing, which is not unique to the system used in this study (While et al, 2010;Raghukumar et al, 2015;Park et al, 2018). But it also revealed complex interactions between the model and assimilation, with the assimilation of individual variables improving some non-assimilated variables while degrading others, and correcting some compensating errors while introducing others.…”

“…2f-g). The reason for this is the issue mentioned in the introduction, and explored by While et al (2010) and Park et al (2018), that assimilating SLA and T&S results in spurious vertical mixing, especially in equatorial regions, bringing excess nutrients to the surface and fuelling primary production. Counterintuitively, despite the 9 https://doi.org/10.5194/os-2019-118 Preprint.…”

“…It is not yet routine though to combine the assimilation of physics and biogeochemistry in a single ocean reanalysis. A major reason for this is that, especially in global models, assimilating physical observations has been widely found to degrade biogeochemical fields (While et al, 2010;Raghukumar et al, 2015;Park et al, 2018) through the creation of spurious vertical mixing. This is an outstanding issue for the ocean data assimilation community, and has not been solved in this study.…”

Observational Needs Supporting Marine Ecosystems Modeling and Forecasting: From the Global Ocean to Regional and Coastal Systems

“…Ocean data assimilation also considers the different uncertainties inherent in different measurements and sensors when adjusting the ocean state estimates, and enables observations of an observed variable (e.g., sea surface temperature, SST) to adjust, through the model dynamics, estimates of other less easily observed variables (e.g., subsurface currents; Moore et al, 2017). An improved physical ocean state estimate can also translate into improved biogeochemical estimates in coupled physical-biogeochemical models, although assimilation of physical variables in the presence of a coupled biogeochemical component is a very challenging process due to the high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation (Park et al, 2018). The importance of data assimilation for ocean analyses and forecasting has motivated the establishment of the GODAE OceanView program 2 , whose main goal is the consolidation and improvement of global and regional reanalysis and forecasting activities using both physical and biogeochemical models.…”

Importance of El Niño reproducibility for reconstructing historical CO<sub>2</sub> flux variations in the equatorial Pacific

“…Variety of data assimilation techniques has been adopted, ranging from simple nudging technique (e.g., Behringer et al, 1998;Ji et al, 1998;Smith et al, 2007;Keenlyside et al, 2008;Pohlmann et al, 2009;Sugiura et al, 2009;Mochizuki et al, 2010;Tatebe et al, 2012) to more complex and computationally demanding techniques such as four-dimensional variational method or ensemble Kalman filter (e.g., Kalman, 1960;Sasaki, 1969Sasaki, , 1970Evensen, 1994;Hunt et al, 2004;Kalnay et al, 2007;Yang et al, 2013). Furthermore, through incorporation into ESMs, the application of data assimilation systems has been expanded to include biogeochemical properties, e.g., CO2F monitoring, phytoplankton biomass monitoring, and marine resource management (Brasseur et al, 2009;Tommasi et al, 2017aTommasi et al, , 2017bPark et al, 2018). Li et al (2016Li et al ( , 2019) studied the predictability of CO2F fluctuations of the global ocean by initializing ESMs with a data assimilation system.…”