OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification

Abstract

Abstract. Ocean acidification has profoundly altered the ocean's carbonate chemistry since preindustrial times, with potentially serious consequences for marine life. Yet, no long-term, global observation-based data set exists that allows us to study changes in ocean acidification for all carbonate system parameters over the last few decades. Here, we fill this gap and present a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2), pH, and the saturation state with respect to mineral CaCO3 (Ω) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1∘×1∘. This data set, named OceanSODA-ETHZ, was created by extrapolating in time and space the surface ocean observations of pCO2 (from the Surface Ocean CO2 Atlas, SOCAT) and total alkalinity (TA; from the Global Ocean Data Analysis Project, GLODAP) using the newly developed Geospatial Random Cluster Ensemble Regression (GRaCER) method (code available at https://doi.org/10.5281/zenodo.4455354, Gregor, 2021). This method is based on a two-step (cluster-regression) approach but extends it by considering an ensemble of such cluster regressions, leading to improved robustness. Surface ocean DIC, pH, and Ω were then computed from the globally mapped pCO2 and TA using the thermodynamic equations of the carbonate system. For the open ocean, the cluster-regression method estimates pCO2 and TA with global near-zero biases and root mean squared errors of 12 µatm and 13 µmol kg−1, respectively. Taking into account also the measurement and representation errors, the total uncertainty increases to 14 µatm and 21 µmol kg−1, respectively. We assess the fidelity of the computed parameters by comparing them to direct observations from GLODAP, finding surface ocean pH and DIC global biases of near zero, as well as root mean squared errors of 0.023 and 16 µmol kg−1, respectively. These uncertainties are very comparable to those expected by propagating the total uncertainty from pCO2 and TA through the thermodynamic computations, indicating a robust and conservative assessment of the uncertainties. We illustrate the potential of this new data set by analyzing the climatological mean seasonal cycles of the different parameters of the surface ocean carbonate system, highlighting their commonalities and differences. Further, this data set provides a novel constraint on the global- and basin-scale trends in ocean acidification for all parameters. Concretely, we find for the period 1990 through 2018 global mean trends of 8.6 ± 0.1 µmol kg−1 per decade for DIC, −0.016 ± 0.000 per decade for pH, 16.5 ± 0.1 µatm per decade for pCO2, and −0.07 ± 0.00 per decade for Ω. The OceanSODA-ETHZ data can be downloaded from https://doi.org/10.25921/m5wx-ja34 (Gregor and Gruber, 2020).

Surface pH Record (1990–2013) of the Arabian Sea From Boron Isotopes of Lakshadweep Corals—Trend, Variability, and Control

“…Further, numerical modeling study focusing on the coastal upwelling region of California Current System has also reported a similar pH range (7.85–8.20) (Hauri et al., 2013). Model derived pH records available for our study region (Gregor & Gruber, 2021) (Figure 5b) and for the north‐western Arabian Sea (Sreeush et al., 2019) are characterized by long‐term declining trend with much smaller pH variability. Whereas our δ 11 B coral derived pH record shows much larger pH variability with no discernable decreasing trend (Figure 5a) highlighting divergence between the proxy based reconstruction and numerical model simulations of the past pH.…”

“…(a) The coral δ 11 B derived pH record from the Lakshadweep. (b) OceanSODA‐ETHZ model derived pH data extracted for 2° latitude × 2° longitude box centered at 8°N and 73°E (Gregor & Gruber, 2021). (c) Theoretical pH curves calculated using CO2sys program to understand how pCO 2 forcing and sea surface temperature contributed individually to pH changes near the study site during the studied time interval.…”

“…This short and discrete record of BOBOA is insufficient to quantify OA rates and to decipher any long-term OA trend in the Indian Ocean. The lack of instrumental and proxy based pH data leads to a dependency on calculation or model-based estimation of pH change for this region (Gregor & Gruber, 2021;Jiang et al, 2019;Joshi et al, 2020;Sreeush et al, 2019;Valsala et al, 2021;Xue et al, 2014). Thus, the regional and global scale forcing factors that can potentially influence the amplitude and frequency of pH changes in the Indian Ocean need to be recognized and to quantify their relative roles.…”

Climate mitigation averts corrosive acidification in the upper ocean

“…We also benchmark the performance of ESM2M's saturation state of aragonite (Ω arag ) during the contemporary period against data-based estimates. Speci cally we assess ESM2M mean-state, anthropogenic trend and seasonal cycle relative to Ω arag derived from ETHZ-OceanSoda 43 , SOM-FFN 44 , JMA-MLR 45 and for the Southern Ocean, the Biogeochemical Southern Ocean State Estimate 46 (B-SOSE). Model validation is discussed more fully in Supplementary Discussion 1.…”

Decadal Dynamics of the CO2 System and Associated Ocean Acidification in Coastal Ecosystems of the North East Atlantic Ocean

“…The SOOP-CO 2 provided pCO 2 data acquired from ships of opportunity 1 , while the growing BGC-Argo array (Claustre et al, 2020) will provide open ocean pH observations, in addition to salinity-derived TA. SOCAT (Bakker et al, 2016) and LDEO Global Surface Database (Takahashi et al, 2020) contribute to these international efforts by compiling quality CO 2 observations from the scientific community, while GLODAP (Lauvset et al, 2016) and OceanSODA-ETHZ (Gregor and Gruber, 2021) databases provide global gridded data of the open ocean carbonate system. The limited CO 2 system variability in the oceanic environment, compared to coastal ecosystems, favors observations of the alterations associated to climate change such as carbon accumulation or OA.…”