Abstract:
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 pCO2 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 locationrestricted
ship measurements of pCO2. 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 feedforward
neural network (SOM-FFN) method from Landschützer
et al. (2016). The interpolated estimates of 1pCO2
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 1pCO2 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 1pCO2 and its drivers into summer and winter.
We find that understanding the variability of 1pCO2 and its drivers on shorter timescales is critical to resolving the
long-term variability of 1pCO2. Results show that 1pCO2
is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of
1pCO2 variability with mixed layer depth. Summer pCO2
variability is consistent with chlorophyll a variability, where
higher concentrations of chlorophyll a correspond with lower
pCO2 concentrations. In regions of low chlorophyll a concentrations,
wind stress and sea surface temperature emerged
as stronger drivers of 1pCO2. 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 1pCO2 but would greatly benefit from
improved estimates of 1pCO2 and a longer time series.
