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We assess the Southern Ocean CO2 uptake (1985–2018) using data sets gathered in the REgional Carbon Cycle Assessment and Processes Project Phase 2. The Southern Ocean acted as a sink for CO2 with close agreement between simulation results from global ocean biogeochemistry models (GOBMs, 0.75 ± 0.28 PgC yr−1) and pCO2‐observation‐based products (0.73 ± 0.07 PgC yr−1). This sink is only half that reported by RECCAP1 for the same region and timeframe. The present‐day net uptake is to first order a response to rising atmospheric CO2, driving large amounts of anthropogenic CO2 (Cant) into the ocean, thereby overcompensating the loss of natural CO2 to the atmosphere. An apparent knowledge gap is the increase of the sink since 2000, with pCO2‐products suggesting a growth that is more than twice as strong and uncertain as that of GOBMs (0.26 ± 0.06 and 0.11 ± 0.03 Pg C yr−1 decade−1, respectively). This is despite nearly identical pCO2 trends in GOBMs and pCO2‐products when both products are compared only at the locations where pCO2 was measured. Seasonal analyses revealed agreement in driving processes in winter with uncertainty in the magnitude of outgassing, whereas discrepancies are more fundamental in summer, when GOBMs exhibit difficulties in simulating the effects of the non‐thermal processes of biology and mixing/circulation. Ocean interior accumulation of Cant points to an underestimate of Cant uptake and storage in GOBMs. Future work needs to link surface fluxes and interior ocean transport, build long overdue systematic observation networks and push toward better process understanding of drivers of the carbon cycle. Plain Language Summary: The ocean takes up CO2 from the atmosphere and thus slows climate change. The Southern Ocean has long known to be an important region for ocean CO2 uptake. Here, we bring together all available data sets that estimate the Southern Ocean CO2 uptake, from models that simulate ocean circulation and physical and biological processes that affect the ocean carbon cycle, from surface ocean observation‐based estimates, from atmospheric transport models that ingest atmospheric CO2 observations, and from interior ocean biogeochemical observations. With these data sets, we find good agreement on the mean Southern Ocean CO2 uptake 1985–2018, which is 50% smaller than previous estimates when recalculated for the time period and spatial extent used in the previous estimate. However, the estimates of the temporal change of the Southern Ocean CO2 uptake differ by a factor of two and thus are not in agreement. We further highlight that knowledge gaps exist not only in winter when observations are typically rare, but equally in summer when biology plays a larger role, which is typically represented too simplistically in the dynamic models. Key Points: Ocean models and machine learning estimates agree on the mean Southern Ocean CO2 sink, but the trend since 2000 differs by a factor of twoREgional Carbon Cycle Assessment and Processes Project Phase 2 estimates a 50% smaller Southern Ocean CO2 sink for the same region and timeframe as RECCAP1Large model spread in summer and winter indicates that sustained efforts are required to understand driving processes in all seasons [ABSTRACT FROM AUTHOR]

Quantifying the strength and efficiency of the Southern Ocean biological carbon pump (BCP) and its response to predicted changes in the Earth's climate is fundamental to our ability to predict long-term changes in the global carbon cycle and, by extension, the impact of continued anthropogenic perturbation of atmospheric CO2. There is little agreement, however, in climate model projections of the sensitivity of the Southern Ocean BCP to climate change, with a lack of consensus in even the direction of predicted change, highlighting a gap in our understanding of a major planetary carbon flux. In this review, we summarize relevant research that highlights the important role of fine-scale dynamics (both temporal and spatial) that link physical forcing mechanisms to biogeochemical responses that impact the characteristics of the seasonal cycle of phytoplankton and by extension the BCP. This approach highlights the potential for integrating autonomous and remote sensing observations of fine scale dynamics to derive regionally optimized biogeochemical parameterizations for Southern Ocean models. Ongoing development in both the observational and modelling fields will generate new insights into Southern Ocean ecosystem function for improved predictions of the sensitivity of the Southern Ocean BCP to climate change. This article is part of a discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'. [ABSTRACT FROM AUTHOR]

[[12]] examine the role of the Southern Ocean in setting global ocean heat uptake in coupled climate models, as well as its impacts on climate metrics such as the transient climate response where it exerts an impact greater than its proportional area. [[7]] document how a new type of dataset coming from sailboats, particularly racing craft, navigating the Southern Ocean can help fill the observational gap and be useful in our understanding of Southern Ocean carbon budget. Crucially, the response of future Southern Ocean heat and carbon uptake to projected global warming scenarios, and the nature of the feedbacks that these may generate, are poorly understood and even more poorly constrained by observations. Keywords: oceanography; Southern Ocean; ocean carbon EN oceanography Southern Ocean ocean carbon 1 5 5 05/09/23 20230626 NES 230626 The Southern Ocean is an extreme environment. [Extracted from the article]

The Southern Ocean is a complex system yet is sparsely sampled in both space and time. These factors raise questions about the confidence in present sampling strategies and associated machine learning (ML) reconstructions. Previous studies have not yielded a clear understanding of the origin of uncertainties and biases for the reconstructions of the partial pressure of carbon dioxide (p CO 2) at the surface ocean (pCO2ocean). We examine these questions through a series of semi-idealized observing system simulation experiments (OSSEs) using a high-resolution (± 10 km) coupled physical and biogeochemical model (NEMO-PISCES, Nucleus for European Modelling of the Ocean, Pelagic Interactions Scheme for Carbon and Ecosystem Studies). Here we choose 1 year of the model sub-domain of 10 ∘ of latitude (40–50 ∘ S) by 20 ∘ of longitude (10 ∘ W–10 ∘ E). This domain is crossed by the sub-Antarctic front and thus includes both the sub-Antarctic zone and the polar frontal zone in the south-east Atlantic Ocean, which are the two most sampled sub-regions of the Southern Ocean. We show that while this sub-domain is small relative to the Southern Ocean scales, it is representative of the scales of variability we aim to examine. The OSSEs simulated the observational scales of pCO2ocean in ways that are comparable to existing ocean CO 2 observing platforms (ships, Wave Gliders, carbon floats, Saildrones) in terms of their temporal sampling scales and not necessarily their spatial ones. The p CO 2 reconstructions were carried out using a two-member ensemble approach that consisted of two machine learning (ML) methods, (1) the feed-forward neural network and (2) the gradient boosting machines. The baseline data were from the ship-based simulations mimicking ship-based observations from the Surface Ocean CO 2 Atlas (SOCAT). For each of the sampling-scale scenarios, we applied the two-member ensemble method to reconstruct the full sub-domain pCO2ocean. The reconstruction skill was then assessed through a statistical comparison of reconstructed pCO2ocean and the model domain mean. The analysis shows that uncertainties and biases for pCO2ocean reconstructions are very sensitive to both the spatial and the temporal scales of p CO 2 sampling in the model domain. The four key findings from our investigation are as follows: (1) improving ML-based p CO 2 reconstructions in the Southern Ocean requires simultaneous high-resolution observations (<3 d) of the seasonal cycle of the meridional gradients of pCO2ocean ; (2) Saildrones stand out as the optimal platforms to simultaneously address these requirements; (3) Wave Gliders with hourly/daily resolution in pseudo-mooring mode improve on carbon floats (10 d period), which suggests that sampling aliases from the 10 d sampling period might have a greater negative impact on their uncertainties, biases, and reconstruction means; and (4) the present seasonal sampling biases (towards summer) in SOCAT data in the Southern Ocean may be behind a significant winter bias in the reconstructed seasonal cycle of pCO2ocean. [ABSTRACT FROM AUTHOR]

The article focuses on the Intergovernmental Panel on Climate Change (IPCC) Assessment Report Six (AR6) Working Group I (WG1) report focus on the assessment of the global climate-carbon system with implications for adaptation and mitigation action in southern Africa. It mentions climate change attribution science is capable of quantifying the role of human influence in the occurrence of individual weather events.

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 ΔpCO2 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 ΔpCO2 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 ΔpCO2 and its drivers into summer and winter. We find that understanding the variability of ΔpCO2 and its drivers on shorter timescales is critical to resolving the long-term variability of ΔpCO2. Results show that ΔpCO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of ΔpCO2 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 ΔpCO2. 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 ΔpCO2 but would greatly benefit from improved estimates of ΔpCO2 and a longer time series. [ABSTRACT FROM AUTHOR]

Machine learning methods (support vector regression and random forest regression) were used to map gridded estimates of ΔpCO2 in the Southern Ocean from SOCAT v3 data. A low (1°  × monthly) and high (0.25° × 16-day) resolution implementation of each of these methods as well as the SOM-FFN method of Landschützer et al. (2014) were added to a five member ensemble. The ensemble mean ΔpCO2 was used to calculate FCO2 (air-sea CO2 flux). Data was separated into nine domains defined by basin (Indian, Pacific and Atlantic) and biomes defined by Fay and McKinley (2014). The regional approach showed large zonal asymmetry in ΔpCO2 and FCO2 estimates. Importantly, there was a seasonal decoupling of the modes summer and winter interannual variability. Winter trends had a larger 10 year mode of variability compared to summer trends, which had a shorter 4-6 year mode. To understand this variability of FCO2, we separately assessed changes in summer and winter ΔpCO2 and the drivers thereof. The dominant winter changes were driven by wind stress variability. Summer variability correlated well with chlorophyll-a variability where the latter had high concentrations. In regions of low chlorophyll-a concentrations, wind stress and sea surface temperature were lower order drivers of ΔpCO2. [ABSTRACT FROM AUTHOR]

The Southern Ocean forms a key component of the global carbon cycle. Recent studies, however, show that CMIP5 Earth System Models (ESM) disagree on the representation of the seasonal cycle of the CO2 flux (FCO2) and compare poorly to observations in the Southern Ocean. This model-observations bias has important implications on the ability of ESMs to predict century scale CO2 sink and related climate feedbacks. In this study, we used a specialized diagnostic analysis on 10 CMIP5 models in the Southern Ocean to discriminate the role of the major drivers, namely the temperature control and the concentration of dissolved inorganic carbon (DIC). Our analysis shows that the FCO2 biases in CMIP5 models cluster in two major groups. Group A models (MPI-ESM-MR, NorESM2 and HadGEM-ES) are characterized by exaggerated primary production such that biologically driven DIC changes mainly regulate the seasonal cycle of FCO2. Group-B (CMCC-CESM, GFDL-ESM2M, IPSL-CM5A-MR, MRI-ESM, CanESM2, CNRS-CERFACS) overestimates the role of temperature and thus the change in CO2 solubility becomes a dominant driver of FCO2 variability. While CMIP5 models mostly show a singular dominant influence of these two extremes, observations show a modest influence of both, with a dominance of DIC regulation. We found that CMIP5 models overestimate cooling and warming rates during autumn and spring with respect to observations. Because of this, the role of solubility is overestimated, particularly during these seasons (autumn and spring) in group B models, to the extent of contradicting the biological CO2 uptake during spring. Group A does not show this solubility driven bias due to the overestimation of DIC draw down. This finding strongly implies that the inability of the CMIP5 ESMs to resolve CO2 biological uptake during spring might be crucially related to the sensitivity of the pCO2 to temperature in addition to underestimated biological CO2 uptake. [ABSTRACT FROM AUTHOR]