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Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability

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Title
Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability
Author(s)
Gloege, Lucas; McKinley, Galen A.; Landschuetzer, Peter; Fay, Amanda R.; Froelicher, Thomas L.; Fyfe, John C.; Ilyina, Tatiana; Jones, Steve; Lovenduski, Nicole S.; Keith B. Rodgers; Schlunegger, Sarah; Takano, Yohei
Publication Date
2021-04
Journal
GLOBAL BIOGEOCHEMICAL CYCLES, v.35, no.4
Publisher
AMER GEOPHYSICAL UNION
Abstract
Reducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from pCO(2) observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions' ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO(2) fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real-world observations. The power of a testbed is that the perfect reconstruction is known for each of the original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a neural-network approach can skillfully reconstruct air-sea CO2 fluxes when it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 31% (15%:58%, interquartile range) overestimation of amplitude, and phasing is only moderately correlated with known truth (r = 0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% (3%:34%). Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.
URI
https://pr.ibs.re.kr/handle/8788114/10350
DOI
10.1029/2020GB006788
ISSN
0886-6236
Appears in Collections:
Center for Climate Physics(기후물리 연구단) > 1. Journal Papers (저널논문)
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