Seasonal patterns of surface inorganic carbon system variables in the Gulf of Mexico inferred from a regional high-resolution ocean biogeochemical model

Abstract

<jats:p>Abstract. Uncertainties in carbon chemistry variability still remain large in the Gulf of Mexico (GoM), as data gaps limit our ability to infer basin-wide patterns. Here we configure and validate a regional high-resolution ocean biogeochemical model for the GoM to describe seasonal patterns in surface pressure of CO2 (pCO2), aragonite saturation state (ΩAr), and sea–air CO2 flux. Model results indicate that seasonal changes in surface pCO2 are strongly controlled by temperature across most of the GoM basin, except in the vicinity of the Mississippi–Atchafalaya river system delta, where runoff largely controls dissolved inorganic carbon (DIC) and total alkalinity (TA) changes. Our model results also show that seasonal patterns of surface ΩAr are driven by seasonal changes in DIC and TA, and reinforced by the seasonal changes in temperature. Simulated sea–air CO2 fluxes are consistent with previous observation-based estimates that show CO2 uptake during winter–spring, and CO2 outgassing during summer–fall. Annually, our model indicates a basin-wide mean CO2 uptake of 0.35 molm-2yr-1, and a northern GoM shelf (&lt; 200 m) uptake of 0.93 molm-2yr-1. The observation and model-derived patterns of surface pCO2 and CO2 fluxes show good correspondence; thus this study contributes to improved constraints of the carbon budget in the region. </jats:p>

Coastal Ocean Data Analysis Product in North America (CODAP-NA) – an internally consistent data product for discrete inorganic carbon, oxygen, and nutrients on the North American ocean margins

“…The Global Ocean Data Analysis Project (GLODAPv2) offers an internally consistent data product for discretesampling-based open-ocean carbonate chemistry, nutrient chemistry, isotope, and transient tracer data (Olsen et al, 2016(Olsen et al, , 2020, allowing for a slew of new research products related to OA and its temporal trends in the global ocean (e.g., Jiang et al, 2015a;Gruber et al, 2019;. While there are several data products and climatologies for coastal surface water partial pres-sure of CO 2 (pCO 2 ) (e.g., Bakker et al, 2016;Laruelle et al, 2017;Roobaert et al, 2019;Takahashi et al, 2020), internally consistent data products for water column carbonate and nutrient chemistry data in the coastal ocean currently do not exist.…”

A 17-year dataset of surface water fugacity of CO&lt;sub&gt;2&lt;/sub&gt; along with calculated pH, aragonite saturation state and air–sea CO&lt;sub&gt;2&lt;/sub&gt; fluxes in the northern Caribbean Sea

“…This uncertainty is small based on the low uncertainty of variables in each cell as shown from their standard deviations provided in the gridded products (for column headers, see Table 3). This is confirmed by comparing this method with the approach of calculating the pH and then gridding and mapping as done in Lauvset et al (2016) and Jiang et al (2015). To examine the differences derived from using one or the other approach, both were compared for 2017 data.…”

The impact of oyster aquaculture on the estuarine carbonate system

“…As shellfish aquaculture continues to expand worldwide, the need to consider not only how local and regional ocean acidification may affect growth of the species but also how shellfish growth itself, when at high densities, may impact the local carbonate budget is clear. An increasing number of biogeochemical models include some variables of the carbonate system, including calcification and dissolution (Shen et al, 2019;Gomez et al, 2020); however, depending on the resolution of the model, they may not capture localized impacts of shellfish aquaculture, and these local conditions are often important when predicting future growth rates and determining an ecological carrying capacity. We suggest that biogeochemical models of areas with emerging or sustained shellfish aquaculture industries include local growth rates and number of shellfish as part of the carbonate cycling variables to increase processlevel understanding and, ultimately, predictive capacity.…”