What question did this study set out to answer?

Develop and test a framework to decouple and quantify three primary sources of uncertainty—analytical error, spatial variance, and undersampling bias—in gridded sea surface pCO2 datasets.

July 24, 2014Journal of Geophysical Research OceansOpen Access

Quantifying uncertainty sources in the gridded data of sea surface CO2 partial pressure

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Develop and test a framework to decouple and quantify three primary sources of uncertainty—analytical error, spatial variance, and undersampling bias—in gridded sea surface pCO2 datasets.
Combined field measurements with satellite remote sensing-derived pCO2 data to separate analytical error, natural spatial variance, and geometric sampling bias.
Evaluated the uncertainty framework across 1° × 1° gridded ocean data collected in the East China Sea.
Spatial distributions of analytical error, spatial variance, and sampling bias did not match standard deviation trends across grid cells.
Spatial variance represented the largest uncertainty component in grid cells with dense sampling coverage, whereas undersampling bias dominated grid cells with sparse coverage.

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Implication

Observational modeling analysis demonstrates that distinct analytical, spatial, and sampling biases drive gridded sea surface pCO2 uncertainty, indicating standard deviations misrepresent data...

Cite This Study

Wang et al. (2014) studied this question.

synapsesocial.com/papers/6a929d1beb36ee4226182b84 https://doi.org/10.1002/2013jc009577

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Implication

Observational modeling analysis demonstrates that distinct analytical, spatial, and sampling biases drive gridded sea surface pCO2 uncertainty, indicating standard deviations misrepresent data...

Quantifying uncertainty sources in the gridded data of sea surface CO2 partial pressure

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Discussion

Implication

Cite This Study

Quantifying uncertainty sources in the gridded data of sea surface CO2 partial pressure

Key Points

Discussion

Implication

Cite This Study

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