Carbon dioxide released to the atmosphere by humans can adversely impact aquatic ecosystems, so it is crucial that we understand the current state of carbon variables and anticipate future conditions. Carbon cycling in the coastal ocean is the result of the interaction of physical and biological processes that occur on multiple time and space scales. Sparse sampling of carbon variables presents challenges to our understanding of carbon cycling in the coastal ocean. Other seawater properties measured more frequently with better spatial coverage, including temperature, salinity, oxygen concentration, and nitrate concentration, can be used in combination to estimate carbon variables. In this study, we rely on measurements from cruises along the northeast US shelf to develop equations to predict carbon variables from other seawater properties. These equations can be used to fill gaps in observations and help incorporate observations into ocean models. The statistical relationships between carbon variables and other seawater properties identified here vary depending on the region, because a balance of different processes is important in each region. On the northeast US shelf, salinity emerges as an important predictor for all explored carbon variables.
Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data
- Author(s): K. McGarry, S. A. Siedlecki, J. Salisbury, S. R. Alin
- Journal of Geophysical Research: Oceans
- December 14, 2020
Citation: McGarry, K.*, S.A. Siedlecki, J. Salisbury, and S. R. Alin (2021) Multiple linear regression models for reconstructing and exploring processes controlling the carbonate system of the northeast US from basic hydrographic data. JGR-Oceans, 126, e2020JC016480. https://doi.org/10.1029/2020JC016480
Supported by NOAA OAP Grant# NA19OAR0170351