Improved estimates and understanding of interannual trends of CO₂ fluxes in the Southern Ocean

The Southern Ocean plays an important role in mitigating the effects of anthropogenically driven climate change. The region accounts for 43% of oceanic uptake of anthropogenic carbon dioxide (CO₂). This is foreseen to change with increasing greenhouse gas emissions due to ocean chemistry and climate feedbacks that regulate the carbon cycle in the Southern Ocean. Studies have already shown that Southern Ocean CO₂ is subject to interannual variability. Measuring and understanding this change has been difficult due to sparse observational data that is biased toward summer. This leaves a crucial gap in our understanding of the Southern Ocean CO₂ seasonal cycle, which needs to be resolved to adequately monitor change and gain insight into the drivers of interannual variability. Machine learning has been successful in estimating CO₂ in may parts of the ocean by extrapolating existing data with satellite measurements of proxy variables of CO₂. However, in the Southern Ocean machine learning has proven less successful. Large differences between machine learning estimates stem from the paucity of data and complexity of the mechanisms that drive CO₂. In this study the aim is to reduce the uncertainty of estimates, advance our understanding of the interannual drivers, and optimise sampling of CO₂ in the Southern Ocean. Improving the estimates of CO₂ was achieved by investigating: the impact of increasing the gridding resolution of input data and proxy variables, and Support vector regression (SVR) and Random Forest Regression (RFR) as alternate machine learning methods. It was found that the improvement gained by increasing gridding resolution was minimal and only RFR was able to improve on existing error estimates. Yet, there was good agreement of the seasonal cycle and interannual trends between RFR, SVR and estimates from the literature. The ensemble mean of these methods was used to investigate the variability and interannual trends of CO₂ in the Southern Ocean. The interannual trends of the ensemble confirmed trends reported in the literature. A weakening of the sink in the early 2000's, followed by a strengthening a strengthening of the sink into the early 2010's. Wind was the overall driver of dominant decadal interannual trends, being more important during winter due to the increased efficacy of entrainment processes. Summer interannual variability of CO₂ was driven primarily by chlorophyll, which responded to basin scale changes in drivers by the complex interaction with underlying physics and possibly sub-mesoscale processes. Lastly CO₂ sampling platforms, namely ships, profiling floats and moorings, were tested in an idealised simulated model environment using a machine learning approach. Ships, simulated from existing cruise tracks, failed to adequately resolve CO₂ below the uncertainty threshold that is required to resolve the seasonal cycle of Southern Ocean CO₂. Eight high frequency sampling moorings narrowly outperformed 200 profiling floats, which were both able to adequately resolve the seasonal cycle. Though, a combination of ships and profiling floats achieved the smallest error

Seasonality of the marine carbonate system in the southern Benguela nutrient stoichiometry, alkalinity production, and CO flux

Includes bibliographical references.An observational study was undertaken to determine the seasonality of the marine carbonate system of the southern Benguela focusing on three key points: the processes driving bulk stoichiometry, alkalinity production on the continental shelf, and the air-sea flux of CO2. Monthly samples were taken along the St. Helena Bay Monitoring Line in the southern Benguela for ten of the months in 2010. Samples were analysed for dissolved inorganic carbon (DIC) and total alkalinity (TA). Temperature, salinity, oxygen and nutrients were also measured

Is the southern Benguela a significantregional sink of CO2?

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)

Is the southern Benguela a significant regional sink of CO2?

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)

Empirical methods for the estimation of Southern Ocean CO2 : support vector and random forest regression

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.This work is part of a PhD funded by the ACCESS program.https://www.biogeosciences.netam2018Mechanical and Aeronautical Engineerin

Publisher Correction: Projected poleward migration of the Southern Ocean CO2 sink region under high emissions

Correction to: Communications Earth & Environmenthttps://doi.org/10.1038/s43247-024-01382-y, published online 02 May 2024 The original version of this article omitted one of the affiliations of the corresponding author “Precious Mongwe”. The missing affiliation “National Institute for Theoretical and Computational Sciences (Nitec), Cape Town, South Africa” has been added. In the original version of this article, several reference numbers were incorrect. Specifically: In the section “Results”, subsection “Mechanisms of air-sea CO2 fluxes in the present climate”, third paragraph, references in the sentence starting “Some studies have linked this temperature bias to discrepancies”, were incorrectly given as “40,41”, whereas “40” is correct. In the section “Discussion”, second paragraph, references in the sentence starting “Relatively low model skill” were incorrectly given as “35, 47–49” following “biases in sea ice” whereas “36, 47–49” is correct; as “38,40” following “impact on heat fluxes”, whereas “38,40” is correct; and as “37,50” following “the AMOC”, whereas “37” is correct. In the section “Discussion”, last paragraph, the reference in the sentence starting “On the other hand, anthropogenic ice sheet melt in Antarctica” was incorrectly given as “41”, whereas “58” is correct. In the following sentence, starting “Moreover, ice sheet melt”, references were incorrectly given as “38,41”, whereas “58,59” is correct. In the section “Methods”, subsection “Earth System Models”, first sentence, the reference following “climate scenario” was incorrectly given as “59”, whereas “60” is correct. In the section “Methods”, subsection “Observation-based pCO2 -products”, first sentence, the reference following “Sea-Flux dataset” was incorrectly given as “60”, whereas “61” is correct; in the following sentence, references were incorrectly given as “61” following “CMEMS-LSCE-FFNN”, whereas “62” is correct; as “63” following “CSIR-ML6”, whereas “63” is correct; as “63” following “Jena-MLS”, whereas “64” is correct; as “64” following “JMA-MLR”, whereas “59” is correct; as “65” following “MPI-SOMFFN” whereas “65” is correct; and as “66” following “NIES-FNN” whereas “67” is correct. In the sentence starting “All methods use”, the reference following “SOCAT version 2020 or later” was incorrectly given as “66” whereas 68 is correct. In the sentence starting “Further, we use”, the reference following “World Ocean Atlas” was incorrectly given as “67”, whereas “69” is correct. In the next sentence starting “We use a monthly”, the references following “mixed layer depth by” were incorrectly given as “68,69”, whereas just “70” is correct. In the next sentence starting “Lastly, we use DIC”, the reference following “dissolved inorganic carbon dataset” was incorrectly given as “41”, whereas “71” is correct. In the section “Methods”, subsection “DIC decomposition”, first sentence, references following “(i.e., primary production and respiration)” were incorrectly given as “70,71”, whereas “72,73” is correct; in the next sentence starting “DIC is consumed”, references following “as regenerated DIC” were incorrectly given as “70,72”, whereas “72,74” is correct; in the sentence starting “In this study, we decompose”, references following “regenerated following” were incorrectly given as “70,72”, whereas “72,74” is correct; in the following sentence starting “Regenerated DIC”, the reference following “(Eq. 8)” was incorrectly given as “72”, whereas “73” is correct; in the following sentence starting “Since our analysis is focussed”, references following “air-sea exchange is complete (Cdis)” were incorrectly given as “70,73–75”, whereas “72,74–75” is correct. These reference errors have been corrected in the HTML and PDF versions of the article. In addition, the original version of this Article omitted a reference ‘Olsen, A. et al. GLODAPv2.2019—an update of GLODAPv2. Earth Syst. Sci. Data11, 1437–1461, https://doi.org/10.5194/essd-11-1437-2019 (2019). This has been added as reference 77

Projected poleward migration of the Southern Ocean CO2 sink region under high emissions

The Southern Ocean is a major region of ocean carbon uptake, but its future changes remain uncertain under climate change. Here we show the projected shift in the Southern Ocean CO2 sink using a suite of Earth System Models, revealing changes in the mechanism, position and seasonality of the carbon uptake. The region of dominant CO2 uptake shifts from the Subtropical to the Antarctic region under the high-emission scenario. The warming-driven sea-ice melt, increased ocean stratification, mixed layer shoaling, and a weaker vertical carbon gradient is projected to together reduce the winter de-gassing in the future, which will trigger the switch from mixing-driven outgassing to solubility-driven uptake in the Antarctic region during the winter season. The future Southern Ocean carbon sink will be poleward-shifted, operating in a hybrid mode between biologically-driven summertime and solubility-driven wintertime uptake with further amplification of biologically-driven uptake due to the increasing Revelle Factor

An oomycete NLP cytolysin forms transient small pores in lipid membranes

Microbial plant pathogens secrete a range of effector proteins that damage host plants and consequently constrain global food production. Necrosis and ethylene-inducing peptide 1-like proteins (NLPs) are produced by numerous phytopathogenic microbes that cause important crop diseases. Many NLPs are cytolytic, causing cell death and tissue necrosis by disrupting the plant plasma membrane. Here, we reveal the unique molecular mechanism underlying the membrane damage induced by the cytotoxic model NLP. This membrane disruption is a multistep process that includes electrostatic-driven, plant-specific lipid recognition, shallow membrane binding, protein aggregation, and transient pore formation. The NLP-induced damage is not caused by membrane reorganization or large-scale defects but by small membrane ruptures. This distinct mechanism of lipid membrane disruption is highly adapted to effectively damage plant cells.Peer reviewe

Validation of the Hidradenitis Suppurativa Investigator Global Assessment

Importance Few simplified instruments exist for use in hidradenitis suppurativa (HS) trials. Objective To assess psychometric properties of the Hidradenitis Suppurativa Investigator Global Assessment (HS-IGA) score using a clinical trial data set. Design, Setting, and Participants This retrospective analysis of a phase 2 randomized double-blind, placebo-controlled, active-reference arm trial (UCB HS0001) included adults with moderate-to-severe HS. Exposures Trial participants were randomized at baseline to receive bimekizumab, adalimumab, or placebo. Main Outcomes and Measures The HS-IGA score at prespecified time points up to 12 weeks after randomization. Results The HS-IGA score showed strong convergent validity with IHS4 and HS-PhGA scores at baseline (Spearman correlation, 0.86 [P < .001] and 0.74 [P < .001], respectively) and at week 12 (Spearman correlation, 0.73 [P < .001] and 0.64 [P < .001], respectively). The HS-IGA scores assessed during predosing visits at screening and baseline showed good test-retest reliability (intraclass correlation coefficient [ICC] = 0.92). At week 12, HS-IGA responders were significantly associated with HiSCR-(50/75/90) responders (χ2 = 18.45; P < .001; χ2 = 18.11; P < .001; and χ2 = 20.83; P < .001, respectively). The HS-IGA score was predictive of HiSCR-50/75/90 and HS-PhGA response at week 12 (AUC, 0.69, 0.73, 0.85, and 0.71, respectively). However, the HS-IGA as a measure of disease activity showed low predictive validity with patient-reported outcomes at week 12. Conclusions and Relevance The HS-IGA score demonstrated good psychometric properties compared with existing measures and may be considered for use as an end point in clinical trials for HS

A multi-decade record of high quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID