Search

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 sub‐seasonal CO2 flux (FCO2) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned surface and profiling gliders experiments were used to investigate how storms influence FCO2 through the air‐sea gradient in partial pressure of CO2 (ΔpCO2) in the sub‐Antarctic zone. Winter‐spring storms caused ΔpCO2 to weaken (by 22–37 μatm) due to mixing/entrainment and weaker stratification. This weakening in ΔpCO2 was in phase with the increase in wind stress resulting in a reduction of the storm‐driven CO2 uptake by 6%–27%. During summer, stronger stratification explained the weaker sensitivity of ΔpCO2 to storms, instead temperature changes dominated the ΔpCO2 variability. These results highlight the importance of observing synoptic‐scale variability in ΔpCO2, the absence of which may propagate significant biases to the mean annual FCO2 estimates from large‐scale observing programmes and reconstructions. Plain Language Summary: The sub‐Antarctic zone of the Southern Ocean is a region that mostly experiences carbon dioxide (CO2) uptake because of its low temperature, strong winds and lower CO2 content. The wind can influence the CO2 uptake through two pathways: the speed of CO2 transfer between the air‐sea interface (kw) and the difference in CO2 concentration in the surface ocean and overlying atmosphere (ΔpCO2). Using autonomous robots that can measure hourly air and water conditions simultaneously, we show that not resolving ΔpCO2 during a storm event can lead to overestimating the CO2 uptake. This is particularly important during winter and spring when the ocean's surface layers are less stratified. The warmer temperatures during summer meant a more stratified surface layer resulting in a weaker and delayed impact of storms on the ΔpCO2. This study shows that the various annual CO2 uptake estimation methods used by the research community should not neglect ΔpCO2 responses during storms. Key Points: Hourly glider observations show that the impact of storms on both kw and ΔpCO2 simultaneously modulates the ocean CO2 uptake variabilityWinter‐spring storms weaken ΔpCO2 through enhanced entrainment and mixing, partially counteracting the increase in CO2 uptake due to kw aloneBy not accounting for the storm‐linked positive feedback in ΔpCO2, the cumulative seasonal CO2 uptake was found to be overestimated by ∼6% [ABSTRACT FROM AUTHOR]

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]

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.

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]

Interactions between the upper ocean and air-ice-ocean fluxes in the Southern Ocean play a critical role in global climate by impacting the overturning circulation and oceanic heat and carbon uptake. Remote and challenging conditions have led to sparse observational coverage, while ongoing field programmes often fail to collect sufficient information in the right place or at the time-space scales required to constrain the variability occurring in the coupled ocean-atmosphere system. Only within the last 10 years have we been able to directly observe and assess the role of the fine-scale ocean and rapidly evolving atmospheric marine boundary layer on the upper limb of the Southern Ocean's overturning circulation. This review summarizes advances in mechanistic understanding, arising in part from observational programmes using autonomous platforms, of the fine-scale processes (1–100 km, hours-seasons) influencing the Southern Ocean mixed layer and its variability. We also review progress in observing the ocean interior connections and the coupled interactions between the ocean, atmosphere and cryosphere that moderate air-sea fluxes of heat and carbon. Most examples provided are for the ice-free Southern Ocean, while major challenges remain for observing the ice-covered ocean. We attempt to elucidate contemporary research gaps and ongoing/future efforts needed to address them. 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]

The subpolar Southern Ocean is a critical region where CO2 outgassing influences the global mean air-sea CO2 flux (FCO2). However, the processes controlling the outgassing remain elusive. We show, using a multi-glider dataset combining FCO2 and ocean turbulence, that the air-sea gradient of CO2 (∆pCO2) is modulated by synoptic storm-driven ocean variability (20 µatm, 1–10 days) through two processes. Ekman transport explains 60% of the variability, and entrainment drives strong episodic CO2 outgassing events of 2–4 mol m−2 yr−1. Extrapolation across the subpolar Southern Ocean using a process model shows how ocean fronts spatially modulate synoptic variability in ∆pCO2 (6 µatm2 average) and how spatial variations in stratification influence synoptic entrainment of deeper carbon into the mixed layer (3.5 mol m−2 yr−1 average). These results not only constrain aliased-driven uncertainties in FCO2 but also the effects of synoptic variability on slower seasonal or longer ocean physics-carbon dynamics. Storms dominate the subpolar Southern Ocean, where upwelling CO2 drives outgassing that impacts global CO2 budget, yet how storms modify this outgassing is unknown. Here, the authors present coupled atmosphere-ocean observations to show how storm-driven ocean mixing and circulation cause substantial CO2 variability and outgassing. [ABSTRACT FROM AUTHOR]

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.

The Southern Ocean forms an important component of the Earth system as a major sink of CO2 and heat. Recent studies based on the Coupled Model Intercomparison Project version 5 (CMIP5) Earth system models (ESMs) show that CMIP5 models disagree on the phasing of the seasonal cycle of the CO2 flux (FCO2/ and compare poorly with available observation products for the Southern Ocean. Because the seasonal cycle is the dominant mode of CO2 variability in the Southern Ocean, its simulation is a rigorous test for models and their long-term projections. Here we examine the competing roles of temperature and dissolved inorganic carbon (DIC) as drivers of the seasonal cycle of pCO2 in the Southern Ocean to explain the mechanistic basis for the seasonal biases in CMIP5 models. We find that despite significant differences in the spatial characteristics of the mean annual fluxes, the intra-model homogeneity in the seasonal cycle of FCO2 is greater than observational products. FCO2 biases in CMIP5 models can be grouped into two main categories, i.e., group-SST and group-DIC. Group-SST models show an exaggeration of the seasonal rates of change of sea surface temperature (SST) in autumn and spring during the cooling and warming peaks. These higher-than-observed rates of change of SST tip the control of the seasonal cycle of pCO2 and FCO2 towards SST and result in a divergence between the observed and modeled seasonal cycles, particularly in the Sub-Antarctic Zone. While almost all analyzed models (9 out of 10) show these SST-driven biases, 3 out of 10 (namely NorESM1-ME, HadGEM-ES and MPI-ESM, collectively the group-DIC models) compensate for the solubility bias because of their overly exaggerated primary production, such that biologically driven DIC changes mainly regulate the seasonal cycle of FCO2. [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]

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]

We have used low-energy implanted muons as a volume sensitive probe of the magnetic properties of EuO1-x thin films. We find that static and homogeneous magnetic order persists up to the elevated TC in the doped samples, and the muon signal displays the double dome feature also observed in the sample magnetization. Our results appear incompatible with either the magnetic phase separation or bound magnetic polaron descriptions previously suggested to explain the elevated TC, but are compatible with an RKKY-like interaction mediating magnetic interactions above 69 K. [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]

Here we study the effect of La-doping in EuO thin films using superconducting quantum interference device magnetometry, muon spin rotation (µSR), polarized neutron reflectivity (PNR), and density functional theory (DFT). The µSR data shows that the La0.15Eu0.85O is homogeneously magnetically ordered up to its elevated TC. It is concluded that bound magnetic polaron behavior does not explain the increase in TC and an Ruderman-Kittel-Kasuya-Yosida-like (RKKY-like) interaction is consistent with the µSR data. The estimation of the magnetic moment by DFT simulations concurs with the results obtained by PNR, showing a reduction of the magnetic moment per LaxEu1-xO for increasing lanthanum doping. This reduction of the magnetic moment is explained by the reduction of the number of Eu-4f electrons present in all the magnetic interactions in EuO films. Finally, we show that an upwards shift of the Fermi energy with La or Gd doping gives rise to half-metallicity for doping levels as high as 3.2%. [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]