Reconstructing Aragonite Saturation State Based on an Empirical Relationship for Northern California

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

“…Most carbonate system observations are recorded during summer months. Thus, there is strong motivation to apply the empirical models to year‐round basic hydrographic data to reconstruct the seasonal cycle of carbonate chemistry, as has been done in other regions (Davis et al., 2018). However, the models themselves were calibrated using only summer data.…”

Ocean change within shoreline communities: from biomechanics to behaviour and beyond

“…These correlations suggest a relationship between climate-driven shifts in ocean dynamics and larval survival, potentially due to NPGO-related changes in phytoplankton food availability. Connections between mussel performance, food and global change factors (including temperature and seawater pH; Juranek et al., 2009; Alin et al., 2012; Davis et al., 2018) are further reiterated by correlations between geographic variation in the strength of coastal upwelling and growth of juvenile mussels. Throughout California and other portions of the US west coast, decreased pH and low food are associated with reduced mussel growth, as are peak aerial temperatures during low-tide emergence (Kroeker et al., 2016).…”

Predicting Carbonate Chemistry on the Northwest Atlantic Shelf Using Neural Networks

“…A number of other studies have successfully developed empirical predictions of carbonate chemistry variables using methods that vary from relatively simple to significantly more complex and range from regionally specific algorithms to global assessments. The most common strategy for predicting carbonate chemistry variables is a multi‐linear regression approach, either predicting dissolved inorganic carbon (DIC) and/or total alkalinity (TA) and calculating the carbonate chemistry variable of interest (e.g., pH, saturation state, pCO 2 ), or directly predicting variable of interest (e.g., Alin et al., 2012; Bostock et al., 2013; Carter et al., 2018, 2021; Davis et al., 2018; Evans et al., 2013; Hales et al., 2012; Juranek et al., 2009; Kim et al., 2015; Lee et al., 2006; McGarry et al., 2021; Millero et al., 1998; Turk et al., 2017; Vance et al., 2022; Velo et al., 2013; Williams et al., 2016). More recently, some studies have incorporated machine learning techniques such as neural networks or random forest regression (Bittig et al., 2018; Broullon et al., 2019; Chen et al., 2019; Fourrier et al., 2020; Li, Bellerby, Ge, et al., 2020; Li, Bellerby, Wallhead, et al., 2020; Lohrenz et al., 2018; McNeil & Sasse, 2016; Sasse et al., 2013; Sauzède et al., 2017; Velo et al., 2013; Xu et al., 2020).…”