Observed small spatial scale and seasonal variability of the CO<sub>2</sub> system in the Southern Ocean

Abstract

Abstract. The considerable uncertainties in the carbon budget of the Southern Ocean are largely attributed to unresolved variability, in particular at a seasonal timescale and small spatial scale (∼ 100 km). In this study, the variability of surface pCO 2 and dissolved inorganic carbon (DIC) at seasonal and small spatial scales is examined using a data set of surface drifters including ∼ 80 000 measurements at high spatiotemporal resolution. On spatial scales of 100 km, we find gradients ranging from 5 to 50 µatm for pCO 2 and 2 to 30 µmol kg −1 for DIC, with highest values in energetic and frontal regions. This result is supported by a second estimate obtained with sea surface temperature (SST) satellite images and local DIC-SST relationships derived from drifter observations. We find that dynamical processes drive the variability of DIC at small spatial scale in most regions of the Southern Ocean and the cascade of large-scale gradients down to small spatial scales, leading to gradients up to 15 µmol kg −1 over 100 km. Although the role of biological activity is more localized, it enhances the variability up to 30 µmol kg −1 over 100 km. The seasonal cycle of surface DIC is reconstructed following Mahadevan et al. (2011), using an annual climatology of DIC and a monthly climatology of mixed layer depth. This method is evaluated using drifter observations and proves to be a reasonable first-order estimate of the seasonality in the Southern Ocean that could be used to validate model simulations. We find that small spatial-scale structures are a non-negligible source of variability for DIC, with amplitudes of about a third of the variations associated with the seasonality and up to 10 times the magnitude of largescale gradients. The amplitude of small-scale variability reported here should be kept in mind when inferring temporal changes (seasonality, interannual variability, decadal trends) of the carbon budget from low-resolution observations and models.

Calculating surface ocean pCO2 from biogeochemical Argo floats equipped with pH: An uncertainty analysis

“…Also, the float performs its Global Biogeochemical Cycles 10.1002/2016GB005541 first profile after drifting at 1000 m depth for approximately 18 h after deployment, and this time and space lag, along with the uncertainty in underway pCO 2sw measurements, could account for the scatter in the difference between the biascorrected float and the underway pCO 2sw values. In the Southern Ocean, drifters and shipboard underway measurements have observed gradients in pCO 2sw that can range from 5 to 50 μatm over scales of 100 km that cannot be fully explained by gradients in sea surface temperature (SST) [Lo Monaco et al, 2014;Resplandy et al, 2014].…”

The Southern Ocean as a constraint to reduce uncertainty in future ocean carbon sinks

“…The SO region also has the largest differences in the net CO 2 fluxes as estimated from different methods involving observations and models (Landschützer et al, 2014). Recently, Resplandy et al (2014) found that DIC-induced small spatial scale pCO 2 structures existing in the SO are non-negligible. Such small-scale processes are generally missing in the coarseresolution CMIP5 models and sparse observations.…”

The Biological Pump and Seasonal Variability of pCO2 in the Southern Ocean: Exploring the Role of Diatom Adaptation to Low Iron

“…Nevertheless, large uncertainties remain regarding the drivers and the magnitude of this uptake partly due to the lack of observations at the seasonal scale (Gruber et al, 2009;Lenton et al, 2006Lenton et al, , 2013Metzl et al, 2006), a direct consequence of the remote situation and the stormy nature of this region. Observations indicate that seasonality is the main mode of variability of pCO 2 in the SO (Lenton et al, 2006;Thomalla et al, 2011), which is driven by a combination of biological, physical, and thermodynamic processes that act at different time scales from seasonal to subseasonal and are highly variable in space (Lenton et al, 2006;Monteiro et al, 2015;Resplandy et al, 2014;Shadwick et al, 2015;Takahashi et al, 2012). Modeling and disentangling the balance between these different processes have proved challenging as illustrated by the large spread in the phasing and amplitude of the seasonal cycle of pCO 2 simulated by CMIP5 models in different parts of the SO (Anav et al, 2013;Lenton et al, 2013;Majkut et al, 2014).…”