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We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12°. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of establishing a regionally focused and coordinated carbon dioxide removal (CDR) science-policy platform in southern Africa. The topics include the significance of CDR in steering the planet towards a safe climate, the challenges and opportunities associated with CDR interventions, and the need for governance mechanisms, technologies, and scientific capabilities to support CDR development.

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]

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]

The Southern Ocean accounts for 40% of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap-filling empirical methods have been used to good effect to approximate pCO2 from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: support vector regression (SVR) and random forest regression (RFR). The methods are used to estimate ΔpCO2 in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by Landschützer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude (14.84 and 16.45 µatm, where 1 atm=101 325 Pa) where the SVR method has a larger RMSE (24.40 µatm). However, the larger errors for SVR and RFR are, in part, due to an increase in coastal observations from SOCAT v2 to v3, where the SOM-FFN method used v2 data. The success of both SOM-FFN and RFR depends on the ability to adapt to different modes of variability. The SOM-FFN achieves this by having independent regression models for each cluster, while this flexibility is intrinsic to the RFR method. Analyses of the estimates shows that the SVR and RFR's respective sensitivity and robustness to outliers define the outcome significantly. Further analyses on the methods were performed by using a synthetic dataset to assess the following: which method (RFR or SVR) has the best performance? What is the effect of using time, latitude and longitude as proxy variables on ΔpCO2? What is the impact of the sampling bias in the SOCAT v3 dataset on the estimates? We find that while RFR is indeed better than SVR, the ensemble of the two methods outperforms either one, due to complementary strengths and weaknesses of the methods. Results also show that for the RFR and SVR implementations, it is better to include coordinates as proxy variables as RMSE scores are lowered and the phasing of the seasonal cycle is more accurate. Lastly, we show that there is only a weak bias due to undersampling. The synthetic data provide a useful framework to test methods in regions of sparse data coverage and show potential as a useful tool to evaluate methods in future studies. [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]

This study was undertaken to characterise the seasonal cycle of air-sea fluxes of carbon dioxide (CO2) in the southern Benguela upwelling system off the South African west coast. Samples were collected from six monthly cross-shelf cruises in the St. Helena Bay region during 2010. CO2 fluxes were calculated from pCO2 derived from total alkalinity and dissolved inorganic carbon and scatterometer-based winds. Notwithstanding that it is one of the most biologically productive eastern boundary upwelling systems in the global ocean, the southern Benguela was found to be a very small net annual CO2 sink of -1.4 ± 0.6 mol C/m2 per year (1.7 Mt C/year). Regional primary productivity was offset by nearly equal rates of sediment and sub-thermocline remineralisation flux of CO2, which is recirculated to surface waters by upwelling. The juxtaposition of the strong, narrow near-shore out-gassing region and the larger, weaker offshore sink resulted in the shelf area being a weak CO2 sink in all seasons but autumn (-5.8, 1.4 and -3.4 mmol C/m2 per day for summer, autumn and winter, respectively). [ABSTRACT FROM AUTHOR]

Carbon dioxide plays a central role in earth's atmospheric, ocean and terrestrial systems.1,2 About 40% of the total anthropogenic emissions since 1750 have remained in the atmosphere, with the balance being removed by the ocean and vegetation sinks.3 Increasing atmospheric CO2 concentrations have been well documented,3 as have widespread impacts on human and natural systems, such as warmer surface temperatures, ocean warming and decreasing pH, loss of ice mass over the cryosphere, increasing global mean sea level, and alterations in the global hydrological cycle.3,4 The impact of increased atmospheric concentrations of CO2 on the biosphere includes shifting species extent, seasonal activities, migration patterns and abundances, as well as changes in species interactions. Monitoring of atmospheric CO2 and other greenhouse gases (GHGs) has been identified as a priority by international agencies, such as the United Nations Framework Convention on Climate Change and government departments that are interested in mitigating the effects of climate change. South Africa has made a commitment to a low carbon future as part of its role in global climate policy instruments through a national low carbon development strategy.5,6 At the Conference of the Parties in November 2015 (COP21), high level of agreement by developed and developing countries encouraged stakeholders to urgent action to address climate change. The agreement emphasises the urgent mitigation pledges with respect to GHG emissions by 2020. As South Africa implements its White Paper on Climate Change, to stimulate a shift towards a low carbon economy, it faces a monitoring and evaluation challenge. Currently, the South African GHG emission inventory is based on fossil fuel emissions, as part of the National Atmospheric Emissions Inventory System, under the National Air Quality Act, 2004 (Act No. 39 of 2004). Briefly, emissions are rarely measured directly, but rather based on proxy estimates of activity, extrapolated by an emission factor for the specific activity. There is therefore a need to independently assess the effectiveness of emissions reductions within the context of natural CO2 fluxes. Understanding the changing driving forces of climate change and evaluation of the carbon emission reduction activities requires long-term and high-precision measurements of CO2 gas emissions and sinks as well as their evolution. Land can act as both a source and a sink for GHGs.7 Currently the baseline GHG emissions from land and agriculture are thought to amount to 3.03x1010 kg CO2 eq per year in South Africa. The land sector is responsible for an uptake of 2.1x1010 kg CO2 eq per year while agriculture is responsible for a release of 5.06x1010 kg CO2 eq per year.7 The GHG emissions for South African industry amounted to ~5.45x1011 kg CO2 eq in 20108,9, with approximately 79% from the energy sector -- an order of magnitude larger than the emissions from agriculture7. Under the proposed White Paper policy, South Africa's GHG peak, plateau and decline trajectory anticipates emissions to peak at 6.1x1011 kg CO2 eq between 2020 and 2025, plateau at this range for about 10 years and decline to ~4.3x1011 kg CO2 eq by 2050.6 Determining these fluxes accurately will facilitate assessment of the proposed commitments to mitigation and adaptation strategies adopted by South Africa. At present there is infrastructure deployed in South Africa for the measurement of the concentrations and fluxes of CO2, which include observations in the atmosphere, on land and in the ocean. [ABSTRACT FROM AUTHOR]