Modeling Global Ocean Biogeochemistry with Physical Data Assimilation: a Pragmatic Solution to the Equatorial Instability

Journal of Advances in Modeling Earth Systems RESEARCH ARTICLE Modeling Global Ocean Biogeochemistry With Physical Data 10.1002/2017MS001223 Assimilation: A Pragmatic Solution to the Equatorial Instability Key Points: Jong-Yeon Park1,2 , Charles A. Stock2 , Xiaosong Yang2, John P. Dunne2 , Anthony Rosati2, Physical data assimilation causes Jasmin John2 , and Shaoqing Zhang3 marked degradation of simulated equatorial marine biogeochemistry 1 2 due to spurious vertical velocities Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ, USA, National Oceanic and 3 This issue can be addressed by Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA, Physical Oceanography weighting model over data Laboratory/CIMST, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, constraints in a narrow band around Qingdao, China the equator Off-equatorial data constraints still improve physical and biogeochemical simulations relative Abstract Reliable estimates of historical and current biogeochemistry are essential for understanding to nonassimilative control simulation past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state despite down weighting of equatorial estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensi- data tivity 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 Supporting Information: upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to Supporting Information S1 understand and predict the biogeochemical consequences of El Nino~ and La Nina.~ We develop a strategy to Correspondence to: robustly integrate an ocean biogeochemical model with an ensemble coupled-climate data assimilation sys- J.-Y. Park, tem used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires [email protected] 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 Citation: remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter Park, J.-Y., Stock, C. A., Yang, X., Dunne, J. P., Rosati, A., John, J., et al. fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of (2018). Modeling global ocean model-data weights that removed spurious biogeochemical signals while benefitting from off-equatorial biogeochemistry with physical data constraints that still substantially improve equatorial physical ocean simulations. Compared to the uncon- assimilation: A pragmatic solution to the equatorial instability. Journal of strained control run, the optimally constrained model reduces equatorial biogeochemical biases and mark- Advances in Modeling Earth Systems, 10, edly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach 891–906. https://doi.org/10.1002/ described herein offers a means of advancing earth system prediction in parallel with continued data assim- 2017MS001223 ilation advances aimed at fully considering equatorial data constraints. Received 30 OCT 2017 Accepted 7 MAR 2018 Accepted article online 12 MAR 2018 Published online 30 MAR 2018 1. Introduction Current state-of-the-art global ocean models can successfully simulate many aspects of large-scale ocean circulation and its variability. Biases due to imperfect representation of ocean processes and forcing, however, persist across regional and ocean-basin scales (Danabasoglu et al., 2014; Griffies et al., 2014; Large & Yeager, 2009). In addition, ocean prediction efforts require initializations that, to the extent possible, contain dynamics (i.e., waves and instabilities) that are in phase with those observed. Data assimilation aims to improve estimates of ocean conditions by combining diverse observations with the dynamical equations embedded in ocean models. In recent decades, ocean data assimilation systems have advanced consider- ably from a largely experimental endeavor to a key element of operational ocean retrospective state estimates, nowcasts, and seasonal to multiannual forecasts (Balmaseda et al., 2013; Behringer et al., 1998; VC 2018. The Authors. Carton & Giese, 2008; Chang et al., 2013; Hoteit et al., 2010; Kohl & Stammer, 2008; Saha et al., 2014; Storto This is an open access article under the et al., 2016; Yang et al., 2013; Zhang et al., 2007). terms of the Creative Commons Attribution-NonCommercial-NoDerivs Improved ocean data assimilation and prediction has spurred expanding applications beyond inference of License, which permits use and physical properties, such as phytoplankton biomass monitoring, ocean carbon cycle monitoring/assess- distribution in any medium, provided ment, and marine resource management (Brasseur et al., 2009; Tommasi et al., 2017a, 2017b). Physical prop- the original work is properly cited, the erties (e.g., temperature and salinity anomalies) often provide useful proxies for marine resource responses use is non-commercial and no modifications or adaptations are for limited periods, but such relationships eventually break down (Myers, 1998). More holistic estimates and made. predictions of ecosystem state are needed for robust prediction. Such efforts are further motivated by PARK ET AL. 891 Journal of Advances in Modeling Earth Systems 10.1002/2017MS001223 potentially abrupt changes in ecosystem states (Mollmann et al., 2015) and the potential for ecosystem processes to amplify subtle changes in physical or lower trophic level perturbations (Chust et al., 2014; Kearney et al., 2013; Stock et al., 2014a, 2017). While there is a clear need for biogeochemical assimilation and prediction, the field is less mature than its physical counterpart. This reflects an array of added challenges including uncertainties originated from the representation of physical and biogeochemical processes in earth system models, relatively sparse global- scale observations of subsurface biogeochemical variables, and the large number of biogeochemical tracers that increase the complexity of relationships between observed variables and those that need to be inferred. In addition, the large positive skewness of biological variables often violates the Gaussian distribution assumption used in some data assimilation approaches (Edwards et al., 2015; Hawkins & Sutton, 2009; Song et al., 2016). Another serious impediment to progress toward fully coupled physical-biogeochemical data assimilation is the extremely high sensitivity of ocean biogeochemistry to spurious vertical velocities that can arise due to dynamical imbalances induced during data assimilation (Anderson et al., 2000; Raghukumar et al., 2015). Across most of the global ocean, the exchange of nutrients between the nutrient-rich ocean interior and well-lit but nutrient-poor surface ocean is tightly regulated by a sharp pycnocline and associated sharp nutricline. Even small spurious vertical transports across this surface can have disastrous impacts on biogeochemical states. This issue has proven to be most acute along the equator, where the dominant geostrophic balance shaping large-scale ocean circulation breaks down. Without this dynamical constraint, perturbations to the ocean density field introduce a shock that can induce waves and instabilities similar to those generated by westerly wind bursts (Balmaseda et al., 2007; Bell et al., 2004; Burgers et al., 2002; Vidard et al., 2007; Waters et al., 2017; While et al., 2010). Previous studies have proposed several possible approaches to improve the performance of assimilative models at the equator. For example, Burgers et al. (2002) hypothesized that the poor performance of data assimilation for velocity fields is due to the lack of balance in the data assimilation updates near the equator. They used an extended ‘‘geostrophic’’ relationship and applied east-west velocity increments together with height increments. Bell et al. (2004) suggested a pressure correction scheme to update slowly evolving bias fields, which aims to dampen spurious circulations during assimilation. They calculated a bias-corrected pressure field from temperature and salinity biases, and then applied it in the horizontal momentum equations. Waters et al. (2017) proposed a new approach, named as incremental pressure correction scheme, to balance equatorial increments and to reduce initialization shock, which is based on the pressure correction scheme suggested by Bell et al. (2004), but applied over shorter time scales. Although these studies show positive results, the spurious velocity problem has not been satisfactorily resolved enough to prevent the bias in sensitive biogeochemistry variables. The range of challenges described in the preceding paragraphs has led many biogeochemical assimilation efforts to adopt idealized model frameworks or to focus on regional scales where ocean biogeochemical observing systems are well established (Ciavatta et al., 2014; Fontana et al., 2009; Hemmings et al., 2008; Ish- izaka, 1990; Natvik & Evensen, 2003; Pelc et al., 2012; Shulman et al., 2013). A few pioneering studies, however, have tested global-scale biogeochemical assimilation

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