The ECCO‐Darwin Data‐Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean pCO₂ and Air‐Sea CO₂ Flux

Abstract

Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO 2. To address this challenge, we have updated and improved ECCO-Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint-based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data-constrained ECCO physics, a Green's function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995-2017), ECCO-Darwin exhibits broad-scale consistency with observed surface ocean pCO 2 and air-sea CO 2 flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO 2 uptake occur in subpolar seasonally stratified biomes, where ECCO-Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO-Darwin has a time-mean global ocean CO 2 sink (2.47 ± 0.50 Pg C year −1) and interannual variability that are more consistent with interpolation-based products. Compared to interpolation-based methods, ECCO-Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO-Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate-related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property-conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies. Plain Language Summary Data-driven estimates of how much carbon dioxide the ocean is absorbing (the so-called "ocean carbon sink") have improved substantially in recent years. However, computational ocean models that include biogeochemistry continue to play a critical role as they allow us to isolate and understand the individual processes that control ocean carbon sequestration. The ideal scenario is a combination of the above two methods, where data are ingested and then used to improve a model's fit to the observed ocean, also known as, data assimilation. While the physical oceanographic community has made great progress in developing data assimilation systems, for example, the Estimating the Circulation and Climate of the Ocean (ECCO) consortium, the biogeochemical community has generally lagged behind. The ECCO-Darwin model presented in this paper represents an important technological step forward as it is the first global ocean biogeochemistry model that (1) ingests both physical and biogeochemical observations into the model in a realistic manner and (2) considers how the nature of the ocean carbon sink has changed over multiple decades. As the ECCO ocean circulation

Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models

“…To our knowledge, this is the first time satellite-derived SSS has been used to detect the freshening in the Bering Sea associated with the Yukon River. The results are consistent, appearing on day 150 of 2019, with the known seasonal discharge of the Yukon River peaking in boreal summer [39] with 5663 m 3 s -1 . Thus, these results are encouraging for application of satellite-derived salinity to monitor changes in the Yukon River discharge plume associated with changes in seaice melt, shelf dynamics, and freshwater discharge.…”

“…The spectra and coherence of the LLC4320 model compared to Saildrone indicates that significant variability exists at scales <100 km, providing justification for future studies. Additionally, work is already being conducted comparing biogeochemical Saildrone observations with the ECCO-Darwin global-ocean biogeochemistry model [39]. The integration of Saildrone and satellite observations is critical for advancing research in both physical and biological oceanography.…”

Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020)

“…We provide the regional mean NBE seasonal cycle, its variability, and uncertainty based on the three regional masks (Table 5). Here we briefly describe the characteristics of the NBE seasonal cycle over the 11 TransCom regions and its comparison to three independent top-down inversion results based on surface CO 2 , which are CT-Europe (e.g., van der Laan-Luijkx et al, 2017), CAMS (Chevallier et al, 2005), and Jena CarbonScope (Rödenbeck et al, 2003). CMS-Flux NBE differs the most from surface-CO 2 -based inversions over the South American tropical, northern Africa, tropical Asia, and NH boreal regions.…”

“…Global top-down atmospheric CO 2 flux inversions have been historically used to estimate regional terrestrial NBE. They make uses of the spatiotemporal variability of atmospheric CO 2 , which is dominated by NBE, to infer net carbon exchange at the surface (Chevallier et al, 2005;Baker et al, 2006a;Liu et al, 2014). The accuracy of the NBE from top-down flux inversions is determined by the density and accuracy of the CO 2 observations, the accuracy of modeled atmospheric transport, and knowledge of the prior uncertainties of the flux inventories.…”

Counteracting Contributions of the Upper and Lower Meridional Overturning Limbs to the North Atlantic Nutrient Budgets: Enhanced Imbalance in 2010

“…However, the mechanistic understanding of the regional drivers at seasonal to multidecadal timescales, as well as the temporal and spatial coherences, is still work in progress. Even if promising results are now being published on global ocean biogeochemistry models that assimilate both physical and biogeochemical observations (e.g., ECCO-Darwin; Carroll et al, 2020), adding improvement to previous nonassimilation-based models (e.g. Galbraith et al, 2010;Yool et al, 2013;Stock et al, 2014;Aumont et al, 2015), these are still in the evaluation phase.…”