A new 2-D Model to analyze uncertainty sources of sparse sea surface CO2 partial pressure

TL;DR

This paper introduces a novel 2-D model that separately quantifies analytical, spatial, and sampling uncertainties in sea surface pCO2 measurements to improve the accuracy of sea-air CO2 flux estimates.

Contribution

It presents an innovative method to distinguish and quantify three different sources of uncertainty in sparse sea surface pCO2 data, enhancing the reliability of carbon cycle assessments.

Findings

Successfully separates uncertainty sources in pCO2 data

Provides a comprehensive error assessment framework

Improves accuracy of sea-air CO2 flux estimation

Abstract

In order to better comprehend the global carbon cycle and predict the prognosis for the response to climate change, accurate assessment of sea-air CO2 flux is necessary. Comparing to the relative homogeneously distribution of atmospheric CO2 , the pCO2 in the sea surface water is exposed to huge spatio-temporal variability, which leaves a prominent uncertainty resource. Many regional studies typically divided the observational pCO2 data into grid boxes so as to obtain enough data points statistically for their calculatio. However, using the data inside the grid box areas to represent its holistic property (such as standard deviation to represent spatial variance) will mix up three different uncertainty sources. First, the analytical error in the pCO2 determination and the associated environmental parameters used in deriving pCO2 . Second, the spatial variance because of inhomogenous spatial pattern of sea surface pCO2 , especially the region with a dramatically dynamic circumstance like: coastal areas, boundaries or fronts and etc. Third, the estimation process in undersampling condition, specifically, this kind of uncertainty origins from the process that using a sparse data to represent its holistic property of the box area. Common uncertainty quantification by Standard Deviation will mix up the different sources of uncertainty. In this paper, it introduces an optimized procedure to determine three sources of uncertainty (1st analytical error, 2nd spatial variance, 3rd bias from undersampling.) using the combined remote sensing-derived and field-measured pCO2 data. In order to provide a comprehensive error assessment report.