FN Archimer Export Format
PT J
TI Surface ocean CO2 concentration and air-sea flux estimate by machine learning with modelled variable trends
BT 
AF Zeng, Jiye
   Iida, Yosuke
   Matsunaga, Tsuneo
   Shirai, Tomoko
AS 1:1;2:2;3:1;4:1;
FF 1:;2:;3:;4:;
C1 Earth Systems Division, National Institute for Environmental Studies, Tsukuba, Japan
   Atmosphere and Ocean Department, Japan Meteorological Agency, Tokyo, Japan
C2 NIES, JAPAN
   JAPAN METEOROL AGCY, JAPAN
IN DOAJ
IF 3.7
TC 4
UR https://archimer.ifremer.fr/doc/00795/90662/96252.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
DE ;global ocean CO2 flux;machine learning;random forest;gradient boost machine;neural network
AB The global ocean is a major sink of anthropogenic carbon dioxide (CO2) emitted into the atmosphere. Machine learning has been actively used in the past decades to estimate the oceanic sink, but it is still a challenge to obtain an accurate estimate due to scarcely available CO2 measurements. One of the methods to deal with data scarcity was normalizing multiple years’ CO2 values to a reference year to increase the spatial coverage. The practice assumed a constant CO2 trend for the normalization. Here, we used three machine learning models to extract variable ocean CO2 trends on a decadal scale and proposed a method to use the extracted ocean CO2 trends to correct the decadal atmospheric CO2 trends for data normalization. The method minimizes assumptions of using the extracted ocean CO2 trends directly. Comparisons of our CO2 flux estimate with machine learning products included in Global Carbon Budget 2021 indicates that using the variable trends improved the bias resulted from using a constant trend and that the trends are a critical factor for machine learning methods. Our dataset includes monthly distributions of surface ocean CO2 concentration and air-sea flux in 1980-2020 with a spatial resolution of 1×1 degree. 
PY 2022
PD SEP
SO Frontiers In Marine Science
SN 2296-7745
PU Frontiers Media SA
VL 9
UT 000861846100001
DI 10.3389/fmars.2022.989233
ID 90662
ER

EF