Interannual drivers of the seasonal cycle of CO<sub>2</sub> in the Southern Ocean

Abstract

Abstract. Resolving and understanding the drivers of variability of CO 2 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 CO 2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time-and locationrestricted 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 , and the self-organising-map feedforward neural network (SOM-FFN) method from Landschützer et al. (2016). The interpolated estimates of 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 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 pCO 2 and its drivers into summer and winter. We find that understanding the variability of pCO 2 and its drivers on shorter timescales is critical to resolving the long-term variability of pCO 2 . Results show that pCO 2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of 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 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 pCO 2 but would greatly benefit from improved estimates of pCO 2 and a longer time series.

Detecting Regional Modes of Variability in Observation‐Based Surface Ocean pCO2

“…Both Southern Ocean pCO 2 and SAM spectra are however the only two that illustrate a maximum between 10 and 15 years. This is in agreement with observed variations from recent study investigating the CO 2 variability south of Tasmania based on in situ measurements (Xue et al, 2018), whereas a study by Gregor et al (2018) suggests that modes of summer variability are substantially smaller in the Southern Ocean. While the anomaly time series between the pCO 2 and the SAM index exhibit low correlations (R = 0.01), the power spectrum suggests a moderate link (R = 0.35) between the two.…”

The Observed Seasonal Cycle of Macronutrients in Drake Passage: Relationship to Fronts and Utility as a Model Metric

“…The majority of models have pCO 2 biases, and reducing these biases is an active area of research (see Jiang et al, 2014;Mongwe et al, 2018). Recent studies demonstrate that models that accurately reproduce pCO 2 on seasonal time scales are more skillful at capturing pCO 2 variability on longer time scales and are therefore important for climate change simulations (Gregor et al, 2018;Gruber et al, 2019). In order to diagnose these model biases, validation studies have focused on examining the modeled versus observed pCO 2 decomposition, comparing the seasonal cycles of total pCO 2 , pCO 2(T) , and pCO 2(nonT) (demonstrated in section 3.4).…”

“…Our current understanding of the drivers of CO 2 flux in the Southern Ocean is limited to a few observation-based studies which are heavily weighted toward the Drake Passage Time-series (DPT; Gregor et al, 2018;Fay et al, 2018;Landschützer et al, 2015;. The variability in the strength of the Southern Ocean carbon sink on seasonal time scales is largely driven by compensatory variations in temperature, biology, and vertical exchange with the enriched deep waters, which in turn, drive variability on interannual time scales (Gregor et al, 2018;Takahashi et al, 1993). The DPT is the only multiyear, year-round ship-based biogeochemical sampling program in the Southern Ocean and, in particular, the ACC.…”

Utilizing the Drake Passage Time-series to understand variability and change in subpolar Southern Ocean <i>p</i>CO<sub>2</sub>

“…The surface ocean pCO 2 observations from the DPT have provided the foundation for larger datasets, which have been extensively used to examine variability and trends in CO 2 uptake in the broader Southern Ocean (Fay and McKinley, 2013;Majkut et al, 2014;Landschützer et al, 2014bLandschützer et al, , 2015bRödenbeck et al, 2015, Gregor et al, 2018. In many of these studies, interpolated estimates of Southern Ocean pCO 2 are used in conjunction with measurements of atmospheric pCO 2 to estimate variability and trends in the air-sea pCO 2 gradient and, when combined with wind speed, air-sea CO 2 fluxes.…”