dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Janssens-Maenhout et al. (2020) ). As its observational component relies heavily on satellite observations of CO 2 in the atmosphere, which is affected by the ocean as well as terrestrial emissions and removals, better estimates of f CO 2 would result in efficient estimates of airsea fluxes and thence benefit air-land flux accuracy, in addition to being directly interesting to users. The CO 2 MVS fits within the Global Greenhouse Gas Watch, an even larger greenhouse gas monitoring infrastructure that the World Meteorological Organization (WMO) is setting up (https://public. wmo. int/en/media/press-release/world-meteorological -congress-approves-global-greenhouse-gas-watch, last access: 20/9/2023). Here, we demonstrate the capability to retrieve global monthly maps of f CO 2 from SOCAT data and then to generate the corresponding fields of air-sea fluxes with a lag reduced to one month.
To do that, we extend the work of  Chau et al. (2022b)  who have been gap-filling SOCAT gridded data within the framework of the Copernicus Marine Environment Monitoring Service (CMEMS) based on an ensemble of feed-forward neural network models (also referred to as CMEMS-LSCE-FFNN) and a set of biological, chemical, and physical predictors. While  Chau et al. (2022b)  made the dates of the predictors and the date of the gridded SOCAT data coincide, we turn to a prediction mode in which the relationship found between the predictors and the SOCAT data more than 6 months before is kept. Section 2 below describes the method. We test the approach in the years 2021-2022 by examining the retrospective prediction skill based on the available SOCAT data. Then we expand model prediction of f CO 2 and generate fgCO 2 up to present with a latency of 1 month: data access via the Institut Pierre-Simon Laplace (LSCE/IPSL) data center, https://dods.
Materials and Methods CMEMS-LSCE-FFNN  (Chau et al. , 2022b ) is built on machine-learning techniques. It consists of an ensemble of feed-forward neural network (FFNN) models. This ensemble approach was developed at LSCE in order to reconstruct surface ocean carbonate system variables and to support the operational distribution of such datasets by CMEMS since 2019 (Product identity: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, https://doi. org/10. 48670/moi-00047, last access: 22/9/2023). The CMEMS-LSCE-FFNN fields cover the global ocean at a resolution of 1 • × 1 • currently and for the period since the year 1985 at monthly resolution. Under the hood, these FFNN models represent nonlinear mappings of f CO 2 against a set of predictors. Monthly gridded observation-based products of f CO 2 from SOCAT  (Bakker et al.
, 2016)  are used as the target data in model fitting. f CO 2 predictors are environmental variables: sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), chlorophyll-a (Chl-a), mix-layer-depth (MLD), CO 2 surface mole fractions (xCO 2 ), climatological f CO 2 (f CO 2 clim ), and geographical coordinates (latitude and longitude). Product resources of input datasets are detailed in Table  S1. CMEMS-LSCE-FFNN comprises monthly adaptive FFNN models for which the f CO 2 and predictor datasets available within a time span of 3 months for all the years since 1985 (the reconstruction month excepted) are used in the fitting phase. SOCAT f CO 2 in the reconstruction month is only used in model evaluation. The ensemble of multi-FFNN models was designed by randomly splitting two-thirds of the 3-month sliding datasets for training and the rest for model test  (Chau et al.
, 2022b). From the ensemble reconstructions, the model best estimate (ensemble mean) and 1σ -model uncertainty (ensemble standard deviation) of f CO 2 are derived at the desired resolution. Here we revisit the two versions of CMEMS-LSCE-FFNN referred to as FFNNv2021 and FFNNv2022. These two models respectively used SOCATv2021 and SOCATv2022 datasets  (Bakker et al. , 2021 (Bakker et al. , , 2022) )  as the target input data of f CO 2. Note that SOCAT has been annually published mid-June. Due to the delay mode for data collection, reprocessing, and qualify control, SOCAT provides gridded data up to the year before the publication date (see  Bakker et al. (2016 Bakker et al. ( , 2023) )  for instance). For the period 1985-2021, SOCATv2022 offers an amount of roughly 311700 monthly 1-degree gridded data, 5000 more than SOCATv2021 (Table  S3a ).
The FFNNv2021 (respectively FFNNv2022) model relies on SOCATv2021 (respectively SOCATv2022) and predictor datasets in  1985-2020 (respectively 1985-2021). This allows deriving the ensemble global reconstructions of f CO 2 over the 36-year and 37-year periods, accordingly. The ensemble of FFNN models is then applied to predict f CO 2 given the set of predictors in the years 2021-2022 for version 2021 and in the year 2022 for the latter. The quality assessments are made for (1) the two global reconstructions in the period 1985-2020, (2) FFNNv2021 one-year prediction against FFNNv2022 one-year reconstruction in 2021, and (3) FFNNv2021 two-year prediction against FFNNv2022 one-year prediction in 2022. Model performances will be qualified with the latest SOCAT data, i. e. , SOCATv2023  (Bakker et al.
, 2023). The number of evaluation data for prediction in the years 2021 and 2022 over the global ocean is 10908 and 8602, respectively (Table  S3a ), which is statistically sufficient for significant validation. Model skills are examined from global to sub-basin scale. Here we consider the subbasins defined by the REgional Carbon Cycle Assessment and Processes2 project (https:// github. com/RECCAP2-ocean/RECCAP2-shared-resources/tree/master/data/regions, last access: 20/3/2023). Due to a lack of evaluation data in several RECCAP2 biomes, we aggregate some of them, yielding 14 provinces in total (see Table  S2  and Figure  S1 ). These ocean provinces, therefore, differ from the original biomes proposed by  Fay and McKinley (2014). Apart from the Northern Indian Ocean (11. NIO), the number of data for prediction evaluation ranges from 133 (12.
SIO, i. e. , Southern Indian Ocean) to 2350 (2. NA-SS, i. e. , North Atlantic seasonally stratified) in the year 2021 and from 73 to 2265 in the year 2022. For the actual prediction in 2022 and 2023, the latest model (FFNNv2022) has been run given monthly data of predictors (Table  S1 ) in the year 2022 to present. We choose to release the maps of f CO 2 and fgCO 2 for the previous month on the 15th of each month.
(2019) ;  Chau et al. (2022b)  for further analysis). However, the difference between the RMSD of the two reconstructions is negligible then, as it fluctuates within [-0. 1, 0. 1] µatm. During the last four years, a monotonous increase in RMSD (Figure  1b ) coexists with a decrease in the number of SOCAT data (Figure  1a ), and the FFNNv2021 reconstruction slightly, but increasingly, underperforms compared to FFNNv2022. In 2021 and 2022, the FFNNv2021 prediction RMSD is 24. 3 µatm and 23. 1 µatm, respectively, roughly 0. 5-1 µatm higher than that of the FFNNv2022 reconstruction and prediction (Table  S3 ). Likewise, the variation of SOCAT f CO 2 is reproduced with high r 2 values (0. 74 and 0. 75), close to the one-year reconstruction and prediction of FFNNv2022 (0. 76) for the years 2021-2022.
PEQU-W, 12. SIO) following the trend in atmospheric CO 2 concentration. Among the f CO 2 predictors, xCO 2 stands out with its large increasing trend that brings some xCO 2 data used in the prediction above the range of those used in the training. The growth of atmospheric CO 2 is the primary factor driving the increase in sea surface f CO 2  (Bates et al. , 2014; Gruber et al. , 2019; Landschützer et al. , 2019; Friedlingstein et al. , 2022). The prediction skill, however, does not degrade compared to the reconstruction as the annual increment of f CO 2 is typically smaller than its intra-annual variability (Figure  S6 ). The latter is dominantly driven by temperature-dependent CO 2 solubility and biological processes  (Takahashi et al. , 2002; Gallego et al. , 2018; Rustogi et al. , 2023).
The range of the pre-2021  [pre-2022]  training datasets of physical and biological predictors (e. g. , SST, Chl-a) remains similar to that including input data in the next year, seasonality to multi-month variations of f CO 2 in the years 2021-2022 can be, therefore, propagated with these covariates overall. The majority of SOCAT f CO 2 data for 2021 [2022] stays within the full range of training data which also supports FFNNs to achieve a skillful prediction (Figure  S3 ). Further analysis of FFNN prediction skills over ocean basins is presented in the Supporting Information document.
The latest model version, FFNNv2022, was ultimately used to predict f CO 2 from January 2022 to August 2023, i. e. , up to 20 months beyond the coverage of its training dataset. This study also exemplified the assessment of air-sea CO 2 fluxes (fgCO 2 ) generated from the predicted f CO 2 in the years 2022-2023 over the eastern tropical Pacific, where regional carbon, as the reconstruction quality of f CO 2 drives that of the other variables  (Chau et al. , 2022a (Chau et al. , , 2022b)). The new datasets for the year 2022 (January) to 2023 (August) are available via the LSCE/IPSL data center (see Section Data availability) and are updated each month. This demonstration of an operational service will be extended at an increased horizontal resolution, following the current development of the reference CMEMS-LSCE-FFNN reconstructions  (Chau et al.
Plain Language Summary There is a growing need to monitor carbon emissions and removals over the globe in near real time in order to correctly interpret changes in CO 2 concentrations as they unfold. For the oceans, the best information comes from measurements of the surface ocean CO 2 fugacity (f CO 2 ) by the international marine carbon research community. So far, this data is mostly available 6 to 18 months behind real time after collection, qualification, harmonization, and processing. Here, we show that a set of biological, chemical, and physical predictors available in near-real time, allows the information contained in the "old" f CO 2 measurements to be transferred over time. Based on a statistical technique, we combine all these data sources to estimate global monthly maps of f CO 2 and of CO 2 fluxes at the air-sea interface within one month behind real time and with good accuracy.
, 2023). Measurements of surface ocean CO 2 fugacity (f CO 2 ) from ships, drifters, moorings, and autonomous surface platforms are the main reference to document the actual variation of air-sea fluxes (fgCO 2 ) in space and time  (Friedlingstein et al. , 2022)  because the two are linearly related. Long-term efforts in maintaining and expanding international observing networks together with a coordinated data aggregation of the Surface Ocean CO 2 Atlas database -SOCAT  (Bakker et al. , 2016 (Bakker et al. , , 2023) )   Here we revisit the two versions of CMEMS-LSCE-FFNN referred to as FFNNv2021 and FFNNv2022. These two models respectively used SOCATv2021 and SOCATv2022 datasets  (Bakker et al. , 2021 (Bakker et al. , , 2022) )  as the target input data of f CO 2. Note that SOCAT has been annually published mid-June.
, 2022a). For all experiments in this study, the ensemble size (i. e. , number of FFNN model runs) is set to 50. FFNN with 50 ensemble members has less computational complexity than with the usual size of 100 but it shows similar reconstruction skill  (Chau et al. (2022b) ; Figure  S2 ). The same input data of predictors is fed to the two FFNN model runs (Table  S1 ). The FFNNv2021 (respectively FFNNv2022) model relies on SOCATv2021 (respectively SOCATv2022) and predictor datasets in  1985-2020 (respectively 1985-2021). This allows deriving the ensemble global reconstructions of f CO 2 over the 36-year and 37-year periods, accordingly. The ensemble of FFNN models is then applied to predict f CO 2 given the set of predictors in the years 2021-2022 for version 2021 and in the year 2022 for the latter.
The quality assessments are made for (1) the two global reconstructions in the period 1985-2020, (2) FFNNv2021 one-year prediction against FFNNv2022 one-year reconstruction in 2021, and (3) FFNNv2021 two-year prediction against FFNNv2022 one-year prediction in 2022. Model performances will be qualified with the latest SOCAT data, i. e. , SOCATv2023  (Bakker et al. , 2023). The number of evaluation data for prediction in the years 2021 and 2022 over the global ocean is 10908 and 8602, respectively (Table  S3a ), which is statistically sufficient for significant validation. Model skills are examined from global to sub-basin scale. Here we consider the subbasins defined by the REgional Carbon Cycle Assessment and Processes2 project (https:// github. com/RECCAP2-ocean/RECCAP2-shared-resources/tree/master/data/regions, last access: 20/3/2023).
Due to a lack of evaluation data in several RECCAP2 biomes, we aggregate some of them, yielding 14 provinces in total (see Table  S2  and Figure  S1 ). These ocean provinces, therefore, differ from the original biomes proposed by  Fay and McKinley (2014). Apart from the Northern Indian Ocean (11. NIO), the number of data for prediction evaluation ranges from 133 (12. SIO, i. e. , Southern Indian Ocean) to 2350 (2. NA-SS, i. e. , North Atlantic seasonally stratified) in the year 2021 and from 73 to 2265 in the year 2022. For the actual prediction in 2022 and 2023, the latest model (FFNNv2022) has been run given monthly data of predictors (Table  S1 ) in the year 2022 to present. We choose to release the maps of f CO 2 and fgCO 2 for the previous month on the 15th of each month.
The latest model version, FFNNv2022, was ultimately used to predict f CO 2 from January 2022 to August 2023, i. e. , up to 20 months beyond the coverage of its training dataset. This study also exemplified the assessment of air-sea CO 2 fluxes (fgCO 2 ) generated from the predicted f CO 2 in the years 2022-2023 over the eastern tropical Pacific, where regional CO 2 gas exchanges greatly vary with El Niño-Southern Oscillation (ENSO) conditions and thus affect substantially on interannual variability of the global net sink. The year 2022 has been predicted with persistently high f CO 2 (strong CO 2 outgassing to the atmosphere) in response to the maintenance of La Niña since summer 2020. A remarkable reduction in the tropical Pacific CO 2 source in August 2023 relative to the year before coincides with the weakening of the cooling phase.
, 2022a (Chau et al. , , 2022b)). The new datasets for the year 2022 (January) to 2023 (August) are available via the LSCE/IPSL data center (see Section Data availability) and are updated each month. This demonstration of an operational service will be extended at an increased horizontal resolution, following the current development of the reference CMEMS-LSCE-FFNN reconstructions  (Chau et al. , 2023). 1. Evaluation and analysis for surface ocean CO 2 fugacity and air-sea fluxes 1. 1. Quality assessment for regional reconstruction and prediction of CO 2 fugacity ( f CO 2 ).
Acknowledgments We acknowledge funding support by the  MOB TAC  project of the  European Copernicus Marine Environment Monitoring Service  (CMEMS, https://marine. copernicus. eu/about/ producers/mob-tac, last access: 20/9/2023). SOCAT is an international effort endorsed by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study   (SOLAS)  and the  Integrated Marine Biogeochemistry and Ecosystem Research program   (IMBER) , to deliver a uniformly quality-controlled surface ocean CO 2 database. The data center LSCE/IPSL has offered data repository and access. We thank  François-Marie Bréon  for the tool development of xCO 2 extrapolation and comments on the first manuscript version and we thank  Cédric Bacour  for his help in running the extrapolation tool.
have provided millions of individual f CO 2 observations since the 1950s and associated gridded products. However, f CO 2 data are poorly sampled leaving out most areas for some or all of the year. Statistical databased reconstructions of f CO 2 (Rödenbeck et al. , 2013; Landschützer et al. , 2016; Gregor & Gruber, 2021; Chau et al. , 2022b)  have emerged to gap-fill the SOCAT database using auxiliary data, resulting in reconstructions of f CO 2 global monthly maps. They are still the topic of active research to improve the reconstruction quality, but these maps lag behind real time by 0. 5 to 1. 5 years: the update of the SOCAT archive follows an annual pace with a public release usually in June after measurement collection, quality control, and processing. This lag is problematic for the documentation of the carbon cycle as it evolves, while the main variables of the carbon cycle are progressively integrated within operational programmes with much faster data releases.
wmo. int/en/media/press-release/world-meteorological -congress-approves-global-greenhouse-gas-watch, last access: 20/9/2023). Here, we demonstrate the capability to retrieve global monthly maps of f CO 2 from SOCAT data and then to generate the corresponding fields of air-sea fluxes with a lag reduced to one month. To do that, we extend the work of Chau et al. (2022b)  who have been gap-filling SOCAT gridded data within the framework of the Copernicus Marine Environment Monitoring Service (CMEMS) based on an ensemble of feed-forward neural network models (also referred to as CMEMS-LSCE-FFNN) and a set of biological, chemical, and physical predictors. While Chau et al. (2022b)  made the dates of the predictors and the date of the gridded SOCAT data coincide, we turn to a prediction mode in which the relationship found between the predictors and the SOCAT data more than 6 months before is kept.
Section 2 below describes the method. We test the approach in the years 2021-2022 by examining the retrospective prediction skill based on the available SOCAT data. Then we expand model prediction of f CO 2 and generate fgCO 2 up to present with a latency of 1 month: data access via the Institut Pierre-Simon Laplace (LSCE/IPSL) data center, https://dods. lsce. ipsl. fr/invsat/FFNN_low-latency/. The results include the finding of anomalous variations in regional CO 2 uptake and release by the ocean predicted in January to August 2023, as described in Section 3. Section 4 draws the main conclusions of the study. 2 Materials and MethodsCMEMS-LSCE-FFNN (Chau et al. , 2022b ) is built on machine-learning techniques. It consists of an ensemble of feed-forward neural network (FFNN) models. This ensemble approach was developed at LSCE in order to reconstruct surface ocean carbonate system variables and to support the operational distribution of such datasets by CMEMS since 2019 (Product identity: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, https://doi.
org/10. 48670/moi-00047, last access: 22/9/2023). The CMEMS-LSCE-FFNN fields cover the global ocean at a resolution of 1 • × 1 • currently and for the period since the year 1985 at monthly resolution. Under the hood, these FFNN models represent nonlinear mappings of f CO 2 against a set of predictors. Monthly gridded observation-based products of f CO 2 from SOCAT(Bakker et al. , 2016)  are used as the target data in model fitting. f CO 2 predictors are environmental variables: sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), chlorophyll-a (Chl-a), mix-layer-depth (MLD), CO 2 surface mole fractions (xCO 2 ), climatological f CO 2 (f CO 2 clim ), and geographical coordinates (latitude and longitude). Product resources of input datasets are detailed in TableS1.
CMEMS-LSCE-FFNN comprises monthly adaptive FFNN models for which the f CO 2 and predictor datasets available within a time span of 3 months for all the years since 1985 (the reconstruction month excepted) are used in the fitting phase. SOCAT f CO 2 in the reconstruction month is only used in model evaluation. The ensemble of multi-FFNN models was designed by randomly splitting two-thirds of the 3-month sliding datasets for training and the rest for model test (Chau et al. , 2022b). From the ensemble reconstructions, the model best estimate (ensemble mean) and 1σ -model uncertainty (ensemble standard deviation) of f CO 2 are derived at the desired resolution.
, (2020) Notes: • Preprocessing for missing data in the reconstruction mode (before the 2000s): Input datasets for prediction are set to the same data resources as for reconstruction, these data are available within a few weeks behind real time. This condition is not met for the x CO 2 and MLD datasets that we use in 2023. For xCO 2 , we extrapolated the original dataset (the Variables Notati atmospheric inversion of the Copernicus Atmosphere Monitoring Service Product name References Measureme nts of CO 2 on f CO 2 for years 1985-2022, Table S1), knowing the recent measurements of the Surface ocean CO2 ATlas (SOCAT): SOCATv2021 , SOCATv2022 (last access 17/06/2022), Bakker et al. (2021, 2022, 2023) atmospheric CO 2 mole fraction at the Mauna Loa Observatory, Hawaii fugacity and SOCATv2023 (last access 20/06/2023) ( https://gml.
Each population generated over 3 million reads after specific cleaning of the Tn-seq data: transposon trimming with Trimmomatic  (Bolger et al. , 2014)  and then barcode removal. The reads were mapped using Bowtie-1. 1. 2 (Langmead et al. , 2009)  to the D. solani RNS 08. 23. 3. 1A genome  (Khayi et al. , 2018; GenBank CP016928. 1). Genome annotation was generated at the LABGeM (CEA/Genoscope CNRS UMR8030, Evry, France). Correspondence between the Genoscope annotation and NCBI annotation (released in April 2022 https://www. ncbi. nlm. nih. gov/ data-hub/genome/GCA_000511285. 2/) is provided in Table  S1. The mapping results (. bam files) were analyzed by the ARTIST pipeline  (Pritchard et al. , 2014)  using MATLAB software (MathWorks, Natick, USA). All. bam files of the 42 compared conditions were deposited as sequence read archives (SRA) in the bioproject PRJNA939571 at the National Center for Biotechnology Information (Table  S4 ).
Statistics The correlation between the different replicates of the Tn-seq mapping results was analyzed by calculating the linear regression coefficient (r 2 ). The traits measured in plants inoculated by the different bacterial genotypes (the CI values, length of the stem symptoms in cm, abundance of pathogens in rotten tissue or pots in CFU/g, and the number of symptomatic or asymptomatic plants) were compared by Kruskal-Wallis tests (a = 0. 05). Then, a Dunn's test was performed to compare traits of each constructed mutant with the strain RNS 08. 23. 3. 1A Rif R -Gm R. For all statistical analyses and coefficient calculation, we used GraphPad Prism version 9. 3. 0, GraphPad Software (www. graphpad. com).
Hence, N 2 is modeled as a non-adsorbing component for all the sorbents studied here. Due to the lack of data in the literature, H 2 O is also assumed to be inert towards the sorbents TMCM-41 and TPMS. We assume that all sorbents are stable in the presence of water, oxygen, steam, etc. A table highlighting the assumptions about the affinities of various species used in our process simulations is shown in Table  1   [33]. For APDES, NbOFFIVE, and SIFSIX, in this work, we assume that the H 2 O loading is unaffected in the presence of CO 2. Since there are no available data in the literature regarding steam adsorption, in this work, we assume that steam adsorbs like humid H 2 O. At this point, we note that the lack of data at relevant conditions impedes objective process evaluation and emphasize the need to obtain data.