Recent trends and variability in the oceanic storage of dissolved inorganic carbon

Abstract

\justify Several methods have been developed to quantify the oceanic accumulation of anthropogenic carbon dioxide (CO$_2$) in response to rising atmospheric CO$_2$. Yet, we still lack a corresponding estimate of the changes in the total oceanic dissolved inorganic carbon (DIC). In addition to the increase in anthropogenic CO$_2$, changes in DIC also include alterations of natural CO$_2$. Once integrated globally, changes in DIC reflect the net oceanic sink for atmospheric CO$_2$, complementary to estimates of the air-sea CO$_2$ exchange based on surface measurements. Here, we extend the MOBO-DIC machine learning approach by \citeA{keppler_mapped_2020} to estimate global monthly fields of DIC at 1$^{\circ}$ resolution over the top 1500 m from 2004 through 2019. We find that over these 16 years and extrapolated to cover the whole global ocean down to 4000 m, the oceanic DIC pool increased close to linearly at an average rate of 3.2$\pm$0.7 Pg C yr$^{-1}$. This trend is statistically indistinguishable from current estimates of the oceanic uptake of anthropogenic CO$_2$ over the same period. Thus, our study implies no detectable net loss or gain of natural CO$_2$ by the ocean, albeit the large uncertainties could be masking it. Our reconstructions suggest substantial internal redistributions of natural oceanic CO$_2$, with a shift from the mid-latitudes to the tropics and from the surface to below $\sim$200 m. Such redistributions correspond with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. The interannual variability of DIC is strongest in the tropical Western Pacific, consistent with the El Ni$\tilde{n}$o Southern Oscillation.

Four Decades of Trends and Drivers of Global Surface Ocean Acidification

“…A similar reduction of C nat in the surface ocean has recently been identified by Keppler et al. (2023) on the basis of machine‐learning‐based reconstructions of the spatiotemporal evolution of DIC. They suggest that this reduction at the surface is compensated by an increase at depths below 200 m, such that the total ocean inventory of C nat did not change.…”

Decadal Trends in the Oceanic Storage of Anthropogenic Carbon From 1994 to 2014

“…Furthermore, progress in the development of statistical methods to separate storage changes of natural and anthropogenic carbon based on a consistent interpretation of ocean interior observations alone could provide new valuable insight. The application of neural networks to reconstruct ocean interior dynamics of DIC are a meaningful first step in this direction (Broullón et al., 2019, 2020; Keppler et al., 2020, 2023), albeit being based on even fewer data than surface pCO 2 products.…”

Skillful Multi‐Month Predictions of Ecosystem Stressors in the Surface and Subsurface Ocean

“…These novel products allow for the first sub‐surface model skill assessments at global and regional scales for biogeochemistry. The Mapped Observation‐Based Oceanic DIC (MOBO‐DIC2004‐2019) machine learning approach uses GLODAP cruise data for DIC, along with a series of physical and biogeochemical predictor data to derive a relationship between the tracers and applies this relationship to obtain mapped monthly fields of DIC in the upper 1,500 m of the global ocean ((Keppler et al., 2023b); Figure 1). Similarly, Sharp et al.…”

“… Temporal evolution of monthly surface ocean dissolved inorganic carbon anomalies (mmol C m −3 ) averaged over the California Current Large Marine Ecosystem from 2000 to 2020 in (red line) four randomly selected CESM SMYLE ensemble forecasts, (black line) SMYLE FOSI, (gray line) CESM2‐LE, and (blue line) the observation‐based product of Keppler et al. (2023b). DIC anomalies (mean between 2000 and 2020 removed) are plotted (a) with seasonal cycle and long‐term trend present, and (b) with seasonal cycle and long‐term trend removed.…”

“…Among those studies that assess true model skill using observations, nearly all focus on forecast validation in the surface ocean, where observations tend to be more plentiful (Brady et al., 2020; Li et al., 2019; Park et al., 2019). Recent advances in ocean biogeochemical observing systems (e.g., Biogeochemical Argo (2023)), together with the widespread use of machine‐learning techniques in oceanography, have led to the development of novel global mapped, observation‐based products that provide estimates of dissolved oxygen (DO) and dissolved inorganic carbon (DIC) in four dimensions (latitude, longitude, depth, and time) (Keppler et al., 2023b; Sharp et al., 2022b). These new observation‐based products facilitate, for the first time, observation‐based skill assessment in the interior ocean for short‐term forecasts of ocean biogeochemistry.…”