Reconstruction of monthly CO distribution in the Ross Sea, Antarctica during 1998 -2018 using machine learning technique and observational data sets

• Published in: GEO DATA
• Main Authors: Ahra Mo, Jung-ok Choi, Keyhong Park
• Format: Article in Journal/Newspaper
• Language: English; Korean
• Published: GeoAI Data Society 2022
• Subjects: ross sea; machine learning; carbon dioxide; random forest; Environmental sciences; GE1-350; Geology; QE1-996.5; Ross Sea; Southern Ocean; Antarc*; Antarctica; Sea ice
• Online Access: https://doi.org/10.22761/DJ2022.4.3.003; https://doaj.org/article/936cc96eee86481d90df8773d09aa292

The ocean is a major reservoir of anthropogenic carbon dioxide, especially the Southern Ocean has been known to absorb 40% of the carbon dioxide emitted by human activity. The Ross Sea is one of the most productive regions in the Southern Ocean; however, its carbon dioxide absorption capacity has not been clearly evaluated yet. Because the Southern Ocean is geographically isolated from civilization and thus, its remoteness prevents making sufficient observations from proving reliable carbon dioxide sink strength estimates. Thus, in order to overcome the current spatial and temporal limitations of direct observations, the fugacity of carbon dioxide (fCO2) data was reproduced using a machine learning technique (i.e., random forest technique). The technique is a type of machine learning frequently used to reproduce marine environmental variations through training satellite data and modeled data as well as existing observational data. Furthermore, to reproduce more reliable fCO2 estimates, in addition to marine environmental variables (i.e., sea surface temperature, sea ice concentration, and chlorophyll-a concentration), cloud cover, wind speed, and El Niño index were included in the machine learning procedure. In this study, we provide the past 21 years (1998 – 2018) of monthly spatial and temporal variation information of dissolved carbon dioxide in the Ross Sea, Antarctica.