气候变化领域集成服务门户(CCPortal): Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data

CCPortal

DOI	10.1029/2020JC016480
	Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data
	McGarry K.; Siedlecki S.A.; Salisbury J.; Alin S.R.
发表日期	2021
ISSN	21699275
卷号	126 期号:2
英文摘要	In the coastal ocean, local carbonate system variability is determined by the interaction between ocean acidification and local processes. Sporadic observations indicate that biological metabolism, river input, and water mass mixing are dominant local processes driving carbonate system variability in northeast US shelf waters. These processes are also reflected in the variability of observed temperature (T), salinity (S), oxygen concentration (O2), and nitrate concentration (NO3−). Therefore, regionally specific empirical models can be developed, which relate carbonate system parameters to a combination of basic hydrographic parameters. Here, we develop multiple linear regression models that represent the processes that drive carbonate system variability in the Mid-Atlantic Bight and Gulf of Maine using observations obtained on three hydrographic surveys in summers between 2007 and 2015. The empirical model equations reveal the observation-based relationships between carbonate parameters and basic hydrographic variables. Unlike other regions where empirical models have been developed, salinity appears in all models. T is the most important parameter for predicting aragonite saturation state (ΩAR), while S and O2 are most important for predicting pH on total scale (pHT). The basic hydrographic variables explain over 98% of the variability in total alkalinity (TA), dissolved inorganic carbon (DIC), and ΩAR and 89% of the variability in pHT in the calibration data. We recommend applying models that depend on T, S, O2, and NO3− as predictors, which reproduce TA and DIC with R2 > 0.97, ΩAR with R2 > 0.93, and pHT with R2 > 0.77, to reconstruct carbonate system parameters in the region. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	biogeochemistry; carbon; empirical model; Gulf of Maine; Mid-Atlantic Bight; shelf
语种	英语
来源期刊	Journal of Geophysical Research: Oceans
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186458
作者单位	Department of Marine Sciences, University of Connecticut, Groton, CT, United States; Ocean Process Analysis Laboratory, University of New Hampshire, Durham, NH, United States; NOAA Pacific Marine Environmental Laboratory (PMEL), Seattle, WA, United States
推荐引用方式 GB/T 7714	McGarry K.,Siedlecki S.A.,Salisbury J.,et al. Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data[J],2021,126(2).
APA	McGarry K.,Siedlecki S.A.,Salisbury J.,&Alin S.R..(2021).Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data.Journal of Geophysical Research: Oceans,126(2).
MLA	McGarry K.,et al."Multiple Linear Regression Models for Reconstructing and Exploring Processes Controlling the Carbonate System of the Northeast US From Basic Hydrographic Data".Journal of Geophysical Research: Oceans 126.2(2021).