Variability of the Global Ocean Carbon Sink (1998 through 2011)
type: CreativeWork
description: In this thesis a newly developed 2–step neural network approach is used to reconstruct basin–wide monthly maps of the sea surface partial pressure of CO2 (pCO2) at a resolution of 1��1� for both the Atlantic Ocean from 1998 through 2007 and the global ocean from 1998 through 2011. From those, air–sea CO2 flux maps are computed using a standard gas exchange parameterization and high–resolution wind speeds. Observations form the basis of the studies conducted in this thesis. The neural network estimates benefit from a continuous improvement of the observations, i.e., the Surface Ocean CO2 Atlas (SOCAT) database. Additionally, bottle samples were collected along the UK–Caribbean line to investigate the variability of the sea surface pCO2 and its drivers. The neural network derived pCO2 estimates fit the observed pCO2 data with a root mean square error (RMSE) of about 10 �atm in the Atlantic Ocean from 1998 through 2007 and about 12 �atm in the global ocean from 1998 through 2011, with almost no bias in both studies. A check against independent pCO2 data reveals a larger RMSE, in particular in regions with strong pCO2 variability and gradients. Temporal mean contemporary flux estimates for the Atlantic Ocean (–0.45�0.15 Pg C � yr
abstract: In this thesis a newly developed 2–step neural network approach is used to reconstruct basin–wide monthly maps of the sea surface partial pressure of CO2 (pCO2) at a resolution of 1��1� for both the Atlantic Ocean from 1998 through 2007 and the global ocean from 1998 through 2011. From those, air–sea CO2 flux maps are computed using a standard gas exchange parameterization and high–resolution wind speeds. Observations form the basis of the studies conducted in this thesis. The neural network estimates benefit from a continuous improvement of the observations, i.e., the Surface Ocean CO2 Atlas (SOCAT) database. Additionally, bottle samples were collected along the UK–Caribbean line to investigate the variability of the sea surface pCO2 and its drivers. The neural network derived pCO2 estimates fit the observed pCO2 data with a root mean square error (RMSE) of about 10 �atm in the Atlantic Ocean from 1998 through 2007 and about 12 �atm in the global ocean from 1998 through 2011, with almost no bias in both studies. A check against independent pCO2 data reveals a larger RMSE, in particular in regions with strong pCO2 variability and gradients. Temporal mean contemporary flux estimates for the Atlantic Ocean (–0.45�0.15 Pg C � yr
author: Landschutzer, Peter
language: en
date published: 2014-04
date modified: 2026-07-24T02:12:01Z
provider: University of East Anglia
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of East Anglia