Long‐Term Changes of Carbonate Chemistry Variables Along the North American East Coast

Abstract

Decadal variability of carbonate chemistry variables has been studied for the open ocean using observations and models, but less is known about the variations in the coastal ocean due to observational gaps and the more complex environments. In this work, we use a Bayesian‐neural‐network approach to reconstruct surface carbonate chemistry variables for the Mid‐Atlantic Bight (MAB) and the South Atlantic Bight (SAB) along the North American East Coast from 1982 to 2015. The reconstructed monthly time series data suggest that the rate of fCO2 increase in the MAB (18 ± 1 μatm per decade) is faster than those in the SAB (14 ± 1 μatm per decade) and the open ocean (14 ± 1 μatm per decade). Correspondingly, pH decreases faster in the MAB. The observed stagnation in the aragonite saturation state, Ωarag decrease during 2005–2015 in the MAB, is attributed to the intrusion of water from southern and offshore regions with high Ωarag, which offsets the decrease expected from anthropogenic CO2 uptake. Furthermore, seasonal asymmetry in the evolution of long‐term change leads to the faster change in the amplitudes of the seasonal cycle in carbonate chemistry variables in coastal waters than those in the open ocean. In particular, the increase in the seasonal‐cycle amplitude of dissolved inorganic carbon in the MAB is 2.9 times larger than that of the open ocean. This leads to the faster increase in the season‐cycle amplitude of Ωarag and earlier occurrence of undersaturation in coastal waters as acidification continues.

Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data

“…Although the R 2 obtained by our model is lower than Alin et al's (2012), our RMSE (0.024) is equal. Furthermore, the lower perfor- mance of the pH T model compared to other carbonate parameters is consistent with an empirical model developed for predicting surface carbonate system conditions the MAB, which resulted in R 2 values of 0.96 for DIC, 0.94 for Ω AR , and 0.83 for pH T (Xu et al, 2020). The alternative method applied here to estimate pH T , which uses empirical model estimates for TA and DIC as inputs in CO2SYS to calculate pH T (TA E , DIC E ), did not improve evaluation, which makes sense because error in the pH T calculation also affects the evaluation observations (Figure 3f).…”

“…model, as well as the updated relationship reported in Xu et al. (2020), represent a larger fraction of variability in the MAB (Figure 7). This comparison indicates that including O 2 as a predictor enables our MLR to represent TA in the entire region across the full range of salinities, as opposed to requiring a stepwise function.…”

“…The single equation developed here (Regression V), which includes O 2 in addition to S, applied across a wide range of salinity environments (27-37) performs similarly to the empirical relationships of Cai et al (2010). While the MLR developed here represents a larger fraction of the variability in TA than Cai et al (2010) models for the Woods Hole Transect and GoME, the Cai et al model, as well as the updated relationship reported in Xu et al (2020), represent a larger fraction of variability in the MAB (Figure 7). This comparison indicates that including O 2 as a predictor enables our MLR to represent TA in the entire region across the full range of salinities, as opposed to requiring a stepwise function.…”

“…An updated relationship reported in Xu et al. (2020) for the MAB is also compared. ECOA2, East Coast Ocean Acidification 2; TA, total alkalinity.…”

Autonomous Observation of Seasonal Carbonate Chemistry Dynamics in the Mid‐Atlantic Bight

“…While there may be no net change in pH or Ω arag over a full annual cycle, seasonal changes operate under a time scale that could affect biological processes in the nearshore (Gledhill et al, 2015; Waldbusser & Salisbury, 2014). Monitoring seasonal changes also provide a basis for identifying long‐term changes in carbonate chemistry due to shifts in salinity, temperature, atmospheric CO 2 , and coastal inputs (Gledhill et al, 2015; Goldsmith et al, 2019; Xu et al, 2020).…”