4.7 Article

Increase in CO2 Uptake Capacity in the Arctic Chukchi Sea During Summer Revealed by Satellite-Based Estimation

Journal

GEOPHYSICAL RESEARCH LETTERS
Volume 48, Issue 15, Pages -

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2021GL093844

Keywords

sea surface pCO(2); satellite observations; Chukchi Sea; machine learning; remote sensing; carbon cycle

Funding

  1. National Natural Science Foundation of China [41630969, 41976164, 41941013]
  2. Natural Science Foundation of Zhejiang Province for Distinguished Young Scholars [LR20D060002]
  3. National Key Research and Development Program of China [2019YFE0114800]
  4. Key Deployment Project of Centre for Ocean Mega-Research of Science, CAS [COMS2020Q12]
  5. Chinese Projects for Investigations and Assessments of the Arctic and Antarctic [CHINARE2017-2020]
  6. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography [QNHX1805]

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The study constructed a 17-year time series of summer sea surface partial pressure of CO2 in the Chukchi Sea using machine learning, attributing the long-term increase in CO2 uptake capacity to enhanced biological uptake. The intraseasonal variability of surface CO2 in early summer also highlights the importance of sea ice melt and enhanced photosynthesis.
The capacity of CO2 uptake in the Chukchi Sea is particularly sensitive to rapid physical and biological changes. However, scarce field observations pose a challenge in understanding the long-term trend of CO2 uptake capacity on this continental shelf. We adopted a machine-learning-based approach to construct a 17-years (2003-2019) long-term time series of summer sea surface partial pressure of CO2 (pCO(2)) from remote sensing products. We show that the long-term increase in CO2 uptake capacity can be attributed to strong and enhanced biological uptake. In addition, the intraseasonal variability of surface pCO(2) in early summer confirms the crucial role of sea ice melt and the subsequently enhanced photosynthesis as soon as the surface ocean converts into an open system. Our results thus highlight the use of remote sensing data in interpolating/extrapolating the highly dynamic carbonate system in the continental shelf sea and shed light into future studies involving machine learning or algorithms.

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