Updated Methods for Global Locally Interpolated Estimation of Alkalinity, PH, and Nitrate

Authors

B. R. Carter, Joint Institute for the Study of the Atmosphere and Ocean
R. A. Feely, National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory, Seattle, Washington
N. L. Williams, Oregon State UniversityFollow
A. G. Dickson, University of California San Diego
M. B. Fong, University of California San Diego
Y. Takeshita, Monterey Bay Aquarium Research Institute

Document Type

Article

Publication Date

2018

Digital Object Identifier (DOI)

https://doi.org/10.1002/lom3.10232

Abstract

We have taken advantage of the release of version 2 of the Global Data Analysis Project data product (Olsen et al. 2016) to refine the locally interpolated alkalinity regression (LIAR) code for global estimation of total titration alkalinity of seawater (AT), and to extend the method to also produce estimates of nitrate (N) and in situ pH (total scale). The updated MATLAB software and methods are distributed as Supporting Information for this article and referred to as LIAR version 2 (LIARv2), locally interpolated nitrate regression (LINR), and locally interpolated pH regression (LIPHR). Collectively they are referred to as locally interpolated regressions (LIRs). Relative to LIARv1, LIARv2 has an 18% lower average AT estimate root mean squared error (RMSE), improved uncertainty estimates, and fewer regions in which the method has little or no available training data. LIARv2, LINR, and LIPHR produce estimates globally with skill that is comparable to or better than regional alternatives used in their respective regions. LIPHR pH estimates have an optional adjustment to account for ongoing ocean acidification. We have used the improved uncertainty estimates to develop LIR functionality that selects the lowest-uncertainty estimate from among possible estimates. Current and future versions of LIR software will be available on GitHub at https://github.com/BRCScienceProducts/LIRs.

Rights Information

This work is licensed under a Creative Commons Attribution 4.0 License.

Was this content written or created while at USF?

No

Citation / Publisher Attribution

Limnology and Oceanography: Methods, v. 16, issue 2, p. 119-131

Scholar Commons Citation

Carter, B. R.; Feely, R. A.; Williams, N. L.; Dickson, A. G.; Fong, M. B.; and Takeshita, Y., "Updated Methods for Global Locally Interpolated Estimation of Alkalinity, PH, and Nitrate" (2018). Marine Science Faculty Publications. 1350.
https://digitalcommons.usf.edu/msc_facpub/1350