FN Archimer Export Format
PT J
TI A framework to evaluate and elucidate the driving mechanisms of coastal sea surface pCO(2) seasonality using an ocean general circulation model (MOM6-COBALT)
BT 
AF ROOBAERT, Alizee
   RESPLANDY, Laure
   LARUELLE, Goulven G.
   LIAO, Enhui
   REGNIER, Pierre
AS 1:1;2:2,3;3:1;4:2;5:1;
FF 1:;2:;3:;4:;5:;
C1 Univ Libre Bruxelles, Dept Geosci Environm & Soc BGEOSYS, Brussels, Belgium.
   Princeton Univ, Dept Geosci, Princeton, NJ 08544, USA.
   Princeton Univ, High Meadows Environm Inst, Princeton, NJ 08544, USA.
C2 UNIV LIBRE BRUXELLES, BELGIUM
   UNIV PRINCETON, USA
   UNIV PRINCETON, USA
IN DOAJ
IF 3.2
TC 7
UR https://archimer.ifremer.fr/doc/00755/86714/92207.pdf
   https://archimer.ifremer.fr/doc/00755/86714/92208.zip
   https://archimer.ifremer.fr/doc/00755/86714/92209.pdf
   https://archimer.ifremer.fr/doc/00755/86714/92210.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
AB The temporal variability of the sea surface partial pressure of CO2 (pCO(2)) and the underlying processes driving this variability are poorly understood in the coastal ocean. In this study, we tailor an existing method that quantifies the effects of thermal changes, biological activity, ocean circulation and freshwater fluxes to examine seasonal pCO(2) changes in highly variable coastal environments. We first use the Modular Ocean Model version 6 (MOM6) and bio-geochemical module Carbon Ocean Biogeochemistry And Lower Trophics version 2 (COBALTv2) at a half-degree resolution to simulate coastal CO2 dynamics and evaluate them against pCO(2) from the Surface Ocean CO2 Atlas database (SOCAT) and from the continuous coastal pCO(2) product generated from SOCAT by a two-step neuronal network interpolation method (coastal Self-Organizing Map Feed-Forward neural Network SOM-FFN, Laruelle et al., 2017). The MOM6-COBALT model reproduces the observed spatiotemporal variability not only in pCO(2) but also in sea surface temperature, salinity and nutrients in most coastal environments, except in a few specific regions such as marginal seas. Based on this evaluation, we identify coastal regions of "high" and "medium" agreement between model and coastal SOM-FFN where the drivers of coastal pCO(2) seasonal changes can be examined with reasonable confidence. Second, we apply our decomposition method in three contrasted coastal regions: an eastern (US East Coast) and a western (the Californian Current) boundary current and a polar coastal region (the Norwegian Basin). Results show that differences in pCO(2) seasonality in the three regions are controlled by the balance between ocean circulation and bio-logical and thermal changes. Circulation controls the pCO(2) seasonality in the Californian Current; biological activity controls pCO(2) in the Norwegian Basin; and the interplay between biological processes and thermal and circulation changes is key on the US East Coast. The refined approach presented here allows the attribution of pCO(2) changes with small residual biases in the coastal ocean, allowing for future work on the mechanisms controlling coastal air-sea CO2 exchanges and how they are likely to be affected by future changes in sea surface temperature, hydrodynamics and biological dynamics.
PY 2022
PD JAN
SO Ocean Science
SN 1812-0784
PU Copernicus Gesellschaft Mbh
VL 18
IS 1
UT 000740962900001
BP 67
EP 88
DI 10.5194/os-18-67-2022
ID 86714
ER

EF