CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
research
A diel method of estimating gross primary production: 1. Validation with a realistic numerical model of Chesapeake Bay
Authors
Malcolm E. Scully
Publication date
25 November 2018
Publisher
'American Geophysical Union (AGU)'
Doi
Cite
Abstract
Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 123(11), (2018): 8411-8429, doi: 10.1029/2018JC014178.A method for estimating gross primary production (GPP) is presented and validated against a numerical model of Chesapeake Bay that includes realistic physical and biological forcing. The method statistically fits a photosynthesis‐irradiance response curve using the observed near‐surface time rate of change of dissolved oxygen and the incoming solar radiation, yielding estimates of the light‐saturated photosynthetic rate and the initial slope of the photosynthesis‐irradiance response curve. This allows estimation of GPP with 15‐day temporal resolution. The method is applied to the output from a numerical model that has high skill at reproducing both surface and near‐bottom dissolved oxygen variations observed in Chesapeake Bay in 2013. The rate of GPP predicted by the numerical model is known, as are the contributions from physical processes, allowing the proposed diel method to be rigorously assessed. At locations throughout the main stem of the Bay, the method accurately extracts the underlying rate of GPP, including pronounced seasonal variability and spatial variability. Errors associated with the method are primarily the result of contributions by the divergence in turbulent oxygen flux, which changes sign over the surface mixed layer. As a result, there is an optimal vertical location with minimal bias where application of the method is most accurate.This paper is the result of research funded in part by NOAA's U.S. Integrated Ocean Observing System (IOOS) Program Office as a subcontract to the Woods Hole Oceanographic Institution under award NA13NOS120139 to the Southeastern University Research Association. All of the model output, as well as both the CBIBS data (2010–2016) and the bottom oxygen data of Scully (2016b), are publicly available through the THREDDS server associated with the IOOS Coastal Modeling Testbed site: https://comt.ioos.us/projects/cb_hypoxia.2019-05-2
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Woods Hole Open Access Server
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:darchive.mblwhoilibrary.or...
Last time updated on 07/08/2019