Impact of sea ice on air-sea CO2 exchange – A critical review of polar eddy covariance studies

被引:0
|
作者
Watts, Jennifer [1 ]
Bell, Thomas G. [2 ]
Anderson, Karen [3 ]
Butterworth, Brian J. [4 ]
Miller, Scott [5 ]
Else, Brent [6 ]
Shutler, Jamie [1 ]
机构
[1] Centre for Geography and Environmental Science, University of Exeter, Penryn,Cornwall, United Kingdom
[2] Plymouth Marine Laboratory, Plymouth,Devon, United Kingdom
[3] Environment and Sustainability Institute, University of Exeter, Penryn,Cornwall, United Kingdom
[4] University of Wisconsin-Madison, Madison,WI, United States
[5] Atmospheric Sciences Research Centre, University of Albany (State University of New York), United States
[6] Department of Geography, University of Calgary, Canada
基金
英国自然环境研究理事会;
关键词
Remote sensing - Gases - Uncertainty analysis - Air - Carbon dioxide;
D O I
10.1016/j.pocean.2022.102741
中图分类号
学科分类号
摘要
Sparse in situ measurements and poor understanding of the impact of sea ice on air-sea gas exchange introduce large uncertainties to models of polar oceanic carbon uptake. The eddy covariance technique can be used to produce insightful air-sea gas exchange datasets in the presence of sea ice, but results differ between studies. We present a critical review of historical polar eddy covariance studies and can identify only five that present comparable flux datasets. Assessment of ancillary datasets, including sea-ice coverage and type and air-sea concentration gradient of carbon dioxide, used to interpret flux datasets (with a specific focus on their role in estimating and interpreting sea ice zone gas transfer velocities) identifies that standardised methodologies to characterise the flux footprint would be beneficial. In heterogeneous ice environments both ancillary data uncertainties and controls on gas exchange are notably complex. To address the poor understanding, we highlight how future efforts should focus on the collection of robust gas flux datasets within heterogeneous sea ice regions during key seasonal processes alongside consistent ancillary data with a full characterisation of their associated uncertainties. © 2022
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