Consistency and Challenges in the Ocean Carbon Sink Estimate for the Global Carbon Budget

Based on the 2019 assessment of the Global Carbon Project, the ocean took up on average, 2.5 ± 0.6 PgC yr−1 or 23 ± 5% of the total anthropogenic CO2 emissions over the decade 2009–2018. This sink estimate is based on simulation results from global ocean biogeochemical models (GOBMs) and is compared to data-products based on observations of surface ocean pCO2 (partial pressure of CO2) accounting for the outgassing of river-derived CO2. Here we evaluate the GOBM simulations by comparing the simulated surface ocean pCO2 to observations. Based on this comparison, the simulations are well-suited for quantifying the global ocean carbon sink on the time-scale of the annual mean and its multi-decadal trend (RMSE <20 μatm), as well as on the time-scale of multi-year variability (RMSE <10 μatm), despite the large model-data mismatch on the seasonal time-scale (RMSE of 20–80 μatm). Biases in GOBMs have a small effect on the global mean ocean sink (0.05 PgC yr−1), but need to be addressed to improve the regional budgets and model-data comparison. Accounting for non-mapped areas in the data-products reduces their spread as measured by the standard deviation by a third. There is growing evidence and consistency among methods with regard to the patterns of the multi-year variability of the ocean carbon sink, with a global stagnation in the 1990s and an extra-tropical strengthening in the 2000s. GOBMs and data-products point consistently to a shift from a tropical CO2 source to a CO2 sink in recent years. On average, the GOBMs reveal less variations in the sink than the data-based products. Despite the reasonable simulation of surface ocean pCO2 by the GOBMs, there are discrepancies between the resulting sink estimate from GOBMs and data-products. These discrepancies are within the uncertainty of the river flux adjustment, increase over time, and largely stem from the Southern Ocean. Progress in our understanding of the global ocean carbon sink necessitates significant advancement in modeling and observing the Southern Ocean carbon sink including (i) a game-changing increase in high-quality pCO2 observations, and (ii) a critical re-evaluation of the regional river flux adjustment

Multiple Wolbachia strains provide comparative levels of protection against dengue virus infection in Aedes aegypti.

The insect bacterium Wolbachia pipientis is being introgressed into Aedes aegypti populations as an intervention against the transmission of medically important arboviruses. Here we compare Ae. aegypti mosquitoes infected with wMelCS or wAlbB to the widely used wMel Wolbachia strain on an Australian nuclear genetic background for their susceptibility to infection by dengue virus (DENV) genotypes spanning all four serotypes. All Wolbachia-infected mosquitoes were more resistant to intrathoracic DENV challenge than their wildtype counterparts. Blocking of DENV replication was greatest by wMelCS. Conversely, wAlbB-infected mosquitoes were more susceptible to whole body infection than wMel and wMelCS. We extended these findings via mosquito oral feeding experiments, using viremic blood from 36 acute, hospitalised dengue cases in Vietnam, additionally including wMel and wildtype mosquitoes on a Vietnamese nuclear genetic background. As above, wAlbB was less effective at blocking DENV replication in the abdomen compared to wMel and wMelCS. The transmission potential of all Wolbachia-infected mosquito lines (measured by the presence/absence of infectious DENV in mosquito saliva) after 14 days, was significantly reduced compared to their wildtype counterparts, and lowest for wMelCS and wAlbB. These data support the use of wAlbB and wMelCS strains for introgression field trials and the biocontrol of DENV transmission. Furthermore, despite observing significant differences in transmission potential between wildtype mosquitoes from Australia and Vietnam, no difference was observed between wMel-infected mosquitoes from each background suggesting that Wolbachia may override any underlying variation in DENV transmission potential

Genome evolution of dengue virus serotype 1 under selection by <i>Wolbachia pipientis</i> in <i>Aedes aegypti</i> mosquitoes.

The introgression of antiviral strains of Wolbachia into Aedes aegypti mosquito populations is a public health intervention for the control of dengue. Plausibly, dengue virus (DENV) could evolve to bypass the antiviral effects of Wolbachia and undermine this approach. Here, we established a serial-passage system to investigate the evolution of DENV in Ae. aegypti mosquitoes infected with the wMel strain of Wolbachia. Using this system, we report on virus genetic outcomes after twenty passages of serotype 1 of DENV (DENV-1). An amino acid substitution, E203K, in the DENV-1 envelope protein was more frequently detected in the consensus sequence of virus populations passaged in wMel-infected Ae. aegypti than wild-type counterparts. Positive selection at residue 203 was reproducible; it occurred in passaged virus populations from independent DENV-1-infected patients and also in a second, independent experimental system. In wild-type mosquitoes and human cells, the 203K variant was rapidly replaced by the progenitor sequence. These findings provide proof of concept that wMel-associated selection of virus populations can occur in experimental conditions. Field-based studies are needed to explore whether wMel imparts selective pressure on DENV evolution in locations where wMel is established

The influence of human genetic variation on early transcriptional responses and protective immunity following immunization with Rotarix vaccine in infants in Ho Chi Minh City in Vietnam : a study protocol for an open single-arm interventional trial [awaiting peer review]

Background: Rotavirus (RoV) remains the leading cause of acute gastroenteritis in infants and children aged under five years in both high- and low-middle-income countries (LMICs). In LMICs, RoV infections are associated with substantial mortality. Two RoV vaccines (Rotarix and Rotateq) are widely available for use in infants, both of which have been shown to be highly efficacious in Europe and North America. However, for unknown reasons, these RoV vaccines have markedly lower efficacy in LMICs. We hypothesize that poor RoV vaccine efficacy across in certain regions may be associated with genetic heritability or gene expression in the human host. Methods/design: We designed an open-label single-arm interventional trial with the Rotarix RoV vaccine to identify genetic and transcriptomic markers associated with generating a protective immune response against RoV. Overall, 1,000 infants will be recruited prior to Expanded Program on Immunization (EPI) vaccinations at two months of age and vaccinated with oral Rotarix vaccine at two and three months, after which the infants will be followed-up for diarrheal disease until 18 months of age. Blood sampling for genetics, transcriptomics, and immunological analysis will be conducted before each Rotarix vaccination, 2-3 days post-vaccination, and at each follow-up visit (i.e. 6, 12 and 18 months of age). Stool samples will be collected during each diarrheal episode to identify RoV infection. The primary outcome will be Rotarix vaccine failure events (i.e. symptomatic RoV infection despite vaccination), secondary outcomes will be antibody responses and genotypic characterization of the infection virus in Rotarix failure events. Discussion: This study will be the largest and best powered study of its kind to be conducted to date in infants, and will be critical for our understanding of RoV immunity, human genetics in the Vietnam population, and mechanisms determining RoV vaccine-mediated protection. Registration: ClinicalTrials.gov, ID: NCT03587389. Registered on 16 July 2018

Global Carbon Budget 2023

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based f CO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9 ± 0.5 Gt C yr−1 (10.2 ± 0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2 ± 0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 ± 0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8 ± 0.4 Gt C yr−1, and SLAND was 3.8 ± 0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1 ± 0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023)

Méthodologies non-paramétriques pour la reconstruction et l'estimation dans les modèles d'états non linéaires

The amount of both observational and model-simulated data within the environmental, climate and ocean sciences has grown at an accelerating rate. Observational (e.g. satellite, in-situ...) data are generally accurate but still subject to observational errors and available with a complicated spatio-temporal sampling. Increasing computer power and understandings of physical processes have permitted to advance in models accuracy and resolution but purely model driven solutions may still not be accurate enough. Filtering and smoothing (or sequential data assimilation methods) have developed to tackle the issues. Their contexts are usually formalized under the form of a space-state model including the dynamical model which describes the evolution of the physical process (state), and the observation model which describes the link between the physical process and the available observations. In this thesis, we tackle three problems related to statistical inference for nonlinear state-space models: state reconstruction, parameter estimation and replacement of the dynamic model by an emulator constructed from data. For the first problem, we will introduce an original smoothing algorithm which combines the Conditional Particle Filter (CPF) and Backward Simulation (BS) algorithms. This CPF-BS algorithm allows for efficient exploration of the state of the physical variable, sequentially refining exploration around trajectories which best meet the constraints of the dynamic model and observations. We will show on several toy models that, at the same computation time, the CPF-BS algorithm gives better results than the other CPF algorithms and the stochastic EnKS algorithm which is commonly used in real applications. We will then discuss the problem of estimating unknown parameters in state-space models. The most common statistical algorithm for estimating the parameters of a space-state model is based on EM algorithm, which makes it possible to iteratively compute a numerical approximation of the maximum likelihood estimators. We will show that the EM and CPF-BS algorithms can be combined to effectively estimate the parameters in toy models. In some applications, the dynamical model is unknown or very expensive to solve numerically but observations or simulations are available. It is thence possible to reconstruct the state conditionally to the observations by using filtering/smoothing algorithms in which the dynamical model is replaced by a statistical emulator constructed from the observations. We will show that the EM and CPF-BS algorithms can be adapted in this framework and allow to provide non-parametric estimation of the dynamic model of the state from noisy observations. Finally the proposed algorithms are applied to impute wind data (produced by M\'et\'eo France).Le volume des données disponibles permettant de décrire l’environnement, en particulier l’atmosphère et les océans, s’est accru à un rythme exponentiel. Ces données regroupent des observations et des sorties de modèles numériques. Les observations (satellite, in situ, etc.) sont généralement précises mais sujettes à des erreurs de mesure et disponibles avec un échantillonnage spatio-temporel irrégulier qui rend leur exploitation directe difficile. L’amélioration de la compréhension des processus physiques associée à la plus grande capacité des ordinateurs ont permis des avancés importantes dans la qualité des modèles numériques. Les solutions obtenues ne sont cependant pas encore de qualité suffisante pour certaines applications et ces méthodes demeurent lourdes à mettre en oeuvre. Filtrage et lissage (les méthodes d’assimilation de données séquentielles en pratique) sont développés pour abonder ces problèmes. Ils sont généralement formalisées sous la forme d’un modèle espace-état, dans lequel on distingue le modèle dynamique qui décrit l’évolution du processus physique (état), et le modèle d’observation qui décrit le lien entre le processus physique et les observations disponibles. Dans cette thèse, nous abordons trois problèmes liés à l’inférence statistique pour les modèles espace-états: reconstruction de l’état, estimation des paramètres et remplacement du modèle dynamique par un émulateur construit à partir de données. Pour le premier problème, nous introduirons tout d’abord un algorithme de lissage original qui combine les algorithmes Conditional Particle Filter (CPF) et Backward Simulation (BS). Cet algorithme CPF-BS permet une exploration efficace de l’état de la variable physique, en raffinant séquentiellement l’exploration autour des trajectoires qui respectent le mieux les contraintes du modèle dynamique et des observations. Nous montrerons sur plusieurs modèles jouets que, à temps de calcul égal, l’algorithme CPF-BS donne de meilleurs résultats que les autres CPF et l’algorithme EnKS stochastique qui est couramment utilisé dans les applications opérationnelles. Nous aborderons ensuite le problème de l’estimation des paramètres inconnus dans les modèles espace-état. L’algorithme le plus usuel en statistique pour estimer les paramètres d’un modèle espace-état est l’algorithme EM qui permet de calculer itérativement une approximation numérique des estimateurs du maximum de vraisemblance. Nous montrerons que les algorithmes EM et CPF-BS peuvent être combinés efficacement pour estimer les paramètres d’un modèle jouet. Pour certaines applications, le modèle dynamique est inconnu ou très coûteux à résoudre numériquement mais des observations ou des simulations sont disponibles. Il est alors possible de reconstruire l’état conditionnellement aux observations en utilisant des algorithmes de filtrage/lissage dans lesquels le modèle dynamique est remplacé par un émulateur statistique construit à partir des observations. Nous montrerons que les algorithmes EM et CPF-BS peuvent être adaptés dans ce cadre et permettent d’estimer de manière non-paramétrique le modèle dynamique de l’état à partir d'observations bruitées. Pour certaines applications, le modèle dynamique est inconnu ou très coûteux à résoudre numériquement mais des observations ou des simulations sont disponibles. Il est alors possible de reconstruire l’état conditionnellement aux observations en utilisant des algorithmes de filtrage/lissage dans lesquels le modèle dynamique est remplacé par un émulateur statistique construit à partir des observations. Nous montrerons que les algorithmes EM et CPF-BS peuvent être adaptés dans ce cadre et permettent d’estimer de manière non-paramétrique le modèle dynamique de l’état à partir d'observations bruitées. Enfin, les algorithmes proposés sont appliqués pour imputer les données de vent (produit par Méteo France)

A seamless ensemble-based reconstruction of surface ocean <i>p</i>CO₂ and air–sea CO₂ fluxes over the global coastal and open oceans

International audienceWe have estimated global air-sea CO 2 fluxes (fgCO 2) from the open ocean to coastal seas. Fluxes and associated uncertainty are computed from an ensemble-based reconstruction of CO 2 sea surface partial pressure (pCO 2) maps trained with gridded data from the Surface Ocean CO 2 Atlas v2020 database. The ensemble mean (which is the best estimate provided by the approach) fits independent data well, and a broad agreement between the spatial distribution of model-data differences and the ensemble standard deviation (which is our model uncertainty estimate) is seen. Ensemble-based uncertainty estimates are denoted by ±1σ. The space-time-varying uncertainty fields identify oceanic regions where improvements in data reconstruction and extensions of the observational network are needed. Poor reconstructions of pCO 2 are primarily found over the coasts and/or in regions with sparse observations, while fgCO 2 estimates with the largest uncertainty are observed over the open Southern Ocean (44 • S southward), the subpolar regions, the Indian Ocean gyre, and upwelling systems. Our estimate of the global net sink for the period 1985-2019 is 1.643 ± 0.125 PgC yr −1 including 0.150 ± 0.010 PgC yr −1 for the coastal net sink. Among the ocean basins, the Subtropical Pacific (18-49 • N) and the Subpolar Atlantic (49-76 • N) appear to be the strongest CO 2 sinks for the open ocean and the coastal ocean, respectively. Based on mean flux density per unit area, the most intense CO 2 drawdown is, however, observed over the Arctic (76 • N poleward) followed by the Subpolar Atlantic and Subtropical Pacific for both open-ocean and coastal sectors. Reconstruction results also show significant changes in the global annual integral of all open-and coastal-ocean CO 2 fluxes with a growth rate of +0.062 ± 0.006 PgC yr −2 and a temporal standard deviation of 0.526 ± 0.022 PgC yr −1 over the 35-year period. The link between the large interannual to multi-year variations of the global net sink and the El Niño-Southern Oscillation climate variability is reconfirmed

Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing

A decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions. As projected in Gattuso et al (2015), “under our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050”. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; Pörtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2021b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and Pérez et al (2021) put attention on analyzing secular trends of pH and their drivers extensively from time-series stations to ocean basins. This new OMI consists of the global maps of long-term pH trends and associated uncertainty derived from the CMEMS data-based product of monthly surface water pH (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, Chau et al., 2021a) at 1°×1° grid cells over the global ocean

Ocean monitoring indicator (OMI) of the copernicus marine environment monitoring service global yearly pH time series GLOBAL_OMI_HEALTH_carbon_ph_area_averaged

Ocean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, *Obtain values for alkalinity based on the so-called “locally interpolated alkalinity regression (LIAR)” method after Carter et al., 2016; 2018. *Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2021) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/) *Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity. The global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on the total hydrogen ion scal