FN Archimer Export Format
PT J
TI Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean
BT 
AF GREGOR, Luke
   KOK, Schalk
   MONTEIRO, Pedro M. S.
AS 1:1,2;2:3;3:1;
FF 1:;2:;3:;
C1 CSIR, SOCCO, Cape Town, South Africa.
   Univ Cape Town, Dept Oceanog, Cape Town, South Africa.
   Univ Pretoria, Dept Mech & Aeronaut Engn, Pretoria, South Africa.
C2 CSIR, SOUTH AFRICA
   UNIV CAPE TOWN, SOUTH AFRICA
   UNIV PRETORIA, SOUTH AFRICA
IN DOAJ
IF 3.951
TC 37
UR https://archimer.ifremer.fr/doc/00673/78492/80822.pdf
   https://archimer.ifremer.fr/doc/00673/78492/80823.pdf
   https://archimer.ifremer.fr/doc/00673/78492/80825.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
AB Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO(2) to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO(2). In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Land-schutzer et al. (2016). The interpolated estimates of Delta pCO(2) are separated into nine regions in the Southern Ocean defined by basin (Indian, Pacific, and Atlantic) and biomes (as defined by Fay and McKinley, 2014a). The regional approach shows that, while there is good agreement in the overall trend of the products, there are periods and regions where the confidence in estimated Delta pCO(2) is low due to disagreement between the products. The regional breakdown of the data highlighted the seasonal decoupling of the modes for summer and winter interannual variability. Winter interannual variability had a longer mode of variability compared to summer, which varied on a 4-6-year timescale. We separate the analysis of the Delta pCO(2) and its drivers into summer and winter. We find that understanding the variability of Delta pCO(2) and its drivers on shorter timescales is critical to resolving the long-term variability of Delta pCO(2). Results show that Delta pCO(2) is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of Delta pCO(2) variability with mixed layer depth Summer pCO(2) variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower pCO(2) concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of Delta pCO(2). In summary we propose that sub-decadal variability is explained by summer drivers, while winter variability contributes to the long-term changes associated with the SAM. This approach is a useful framework to assess the drivers of Delta pCO(2) but would greatly benefit from improved estimates of Delta pCO(2) and a longer time series. 
PY 2018
PD APR
SO Biogeosciences
SN 1726-4170
PU Copernicus Gesellschaft Mbh
VL 15
IS 7
UT 000430485900005
BP 2361
EP 2378
DI 10.5194/bg-15-2361-2018
ID 78492
ER

EF