dataesr/scientific-paragraphs-categorization huggingface dataset:
With these classes, SWAT model identified 1320 subbasins with 12 834 HRUs. For the HRUs definition, we selected only classes which occupy at least 10% on the subbasin to avoid anecdotal classes. The SAFRAN atmospheric fields (see section 2. 3. 1) have been used to force SWAT. River discharge observations used for calibration and validation come from Banque Hydro database. Monthly stream flow data from 20 selected gauging stations, including the outlet Tonneins, along the river network were used to calibrate and validate the model. The selection was made to sample all the hydrology and catchment diversity in soil, land use and climate conditions as proposed by  Grusson et al. (2017a). For each step (validation and calibration), three years of initialization are performed. The SWAT model was calibrated over ten years from 2000 to 2010 and validated on the available previous period from 1980 to 1997.
SIM evaluation against daily measured discharges was performed over the Garonne basin for 34 gauging stations available in the Banque Hydro database and located along the Garonne River and its main tributaries. In addition to the NSE, and in order to investigate over/ underestimation, percent bias (Pbias) has been computed (one minus the ration between modeled discharge and in situ discharge). It measures the average tendency of the simulated data to be larger or smaller than their observed counterparts. The optimal value of Pbias is 0, with low-magnitude values indicating accurate model simulation. Positive values indicate model underestimation bias, and negative values indicate model overestimation bias  (Gupta et al. , 1999). On average over the entire basin, SIM gives satisfactory results with an averaged NSE of 0.
GRACE and hydrological models cross-validation methodology As presented in previous sections, on one side, GRACE data are quite coarse and its capability to observe TWSA over the Garonne basin is questionable. On the other side, SIM and SWAT hydrological modeling of the basin are the two only operational distributed and physically based hydrological models currently deployed over the Garonne watershed, and some compartments of their mass budget cannot be validated based on in situ data alone. Therefore, and as stated in the introduction, the purpose of this study is to compare GRACE solutions and SIM/SWAT models outputs, in order to cross-validate them and to compute a hydrology water balance The following sections present the methodology used to compare and cross-validate GRACE TWSA with SIM and SWAT TWSA (section 2.
Quantitative comparison between monthly GRACE solutions and monthly hydrological models outputs has been performed only from January 2003 to December 2010 time span, as before and after GRACE time series have important gaps. For all datasets, the mean annual cycle has also been computed: for each month (January to December), the mean TWSA is computed for all corresponding month between 2003 and 2010. Time series without the mean annual cycle have also been calculated: for each month, the corresponding mean month value has been removed. The comparison is done by computing the correlation coefficient and the NSE between GRACE solutions and models full time series, their mean annual cycle and time series without their mean annual cycle. TWSA time series and hydrology budget have been interpreted qualitatively for all datasets over their whole common time period, i.
Garonne basin water balance After the intercomparison of all datasets, the basin hydrology water balance has been estimated. It corresponds to a water mass balance or continuity equation for a closed system, translating the fact that variations of TWS during a certain period of time is equal to the quantity of water received and lost during that period of time. For a watershed, water is gained via precipitation (P) and lost via evapotranspiration (sum of the soil evaporation, evaporation of the precipitation intercepted by the canopy and the vegetation transpiration; this sum is noted ETR) and water discharge at the outlet of the basin (Q, often approximated to river outflows, as aquifer outflows is neglected and not known). Therefore, the generic water balance equation for a watershed is: = - - dTWS dt t P t ETR t Q t ( ) ( ) ( ) ( ) (1) For a hydrological model, which provides only discrete estimations of variables in time and space, Eq.
It was expected to get better results with mascon solutions, as they decrease leakage errors and are more adapted locally than solution based on spherical harmonics  (Scanlon et al. , 2016). It is consistent with the results obtained by  Long et al. (2017)  over sixty basins. Worse results were also expected with time series without mean annual cycle (Table  4 ), as noise is proportionally more important in these time series than with the ones with the annual cycle. These comparisons cross-validate GRACE solutions and model outputs. All datasets are quite coherent between themselves, even without their mean annual cycle, indicating similar interannual variability. The mean annual cycles are very close, with similar amplitudes, but with 1-month delay between GRACE and hydrological models. It shows that GRACE is actually capable to provide TWSA for a 50,000 km 2 temperate basin, like the Garonne basin.
Since then, GRACE data processing has been quite improved and new techniques have been developed to study smaller basins. For example, Longuevergne et al. (  2010 ) have developed a methodology to derive GRACE TWS for some basins with drainage area close to mission resolution limit. This method has shown improvements on TWS estimation for a 200,000 km 2 aquifer in the US High Plains Aquifer, compared to GRACE global solutions. Lorenz et al. (2014)  estimated that smaller catchments could be observed, as long as the TWS annual cycle has important amplitude variations. Vishwakarma et al. (2018)  investigated the issue of GRACE spatial resolution for hydrology studies. They demonstrated that if, theoretically, the spatial resolution is related to the band-limit of the spherical harmonic spectrum of the GRACE datasets, in practice, because of the noise and corrections applied to GRACE data, the complete band-limited signal cannot be retrieved and the spatial resolution of GRACE cannot be clearly set.
Basin scale water balance As described in section 2. 4. 2, the possibility to compute Garonne basin water balance using both GRACE data and SIM/SWAT inputs and outputs has been investigated. Correlation coefficient and NSE between GRACE solutions TWS Δ ¯and P-ETR-Q from models have been computed to assess their consistency (Table  5 ), as they should be similar (Eq. 2). Different P-ETR-Q have been tested: only from SIM inputs/outputs (first line of Table  5 ), only from SWAT inputs/outputs (second line of Table  5 ), P-ETR from SIM or SWAT and in situ Q at Tonneins from Banque Hydro database (third and fourth line of Table  5 , respectively) and a composite estimate of P-ETR-Q with P from SAFRAN, ETR the mean between SIM and SWAT ETR and Q from in situ gauge (fifth line of Table  5 ). Only TWS Δ from CSR and GSFC mascons solutions have been considered, as section 3.
88 and 0. 59, respectively) than between the mascon solutions and the models. This highlights the spread of the GRACE solutions because of differences in their processing, even if they are based on the same raw measurements. These differences are exacerbated by the small size of the basin compared to the GRACE products resolutions. It also shows that, at least for the Garonne basin, the GSFC mascon solution seems to be the best suitable solution for basin scale water balance study. Fig. 8. a shows SIM and SWAT P-ETR-Q time series along with CSR and GSFC mascon solutions TWS Δ ¯. It shows the overall good agreement between the datasets, and some high frequency variations in SWAT P-ETR-Q, which are not present in other datasets. This could explain why NSE between SWAT and GSFC mascon is lower than for SIM.
However, P-ETR-Q and ΔTWS are almost identical (Fig. 9. b), their correlation coefficient and NSE are almost 1 (0. 999 and 0. 998, respectively). Therefore, results from Table  5  and 6 are mainly due to GRACE coarse resolution both in space and time. The fact that ΔTWS ¯tends to be smoother than P-ETR-Q (Fig. 9. a) might not be an issue for big river basins analyzed in previous studies. However, for 50,000 km 2 basins like the Garonne, using monthly mean TWSA will prevent to properly close the water balance equation. Some mismatch between GRACE mascon TWS Δ ¯and models P-ETR-Q could also be due to model issue. Especially, SIM and SWAT models do not consider actual irrigation, reservoirs and lakes. Finally, if precipitation comes from the most accurate database available over France mainland (SAFRAN), it still has some errors that could impact the comparison.
Acknowledgements  GRACE Land (i. e. global GRACE Tellus Land and JPL mascons)   are available at http://grace. jpl. nasa. gov,supported by the  NASA   MEaSUREs Program. GSFC mascons solution is available at https://neptune. gsfc. nasa. gov/gngphys/index. php?section=470 and also supported by the  NASA   MEaSUREs Program. GRACE and LAGEOS data from CNES/GRGS can be downloaded at https://grace. obsmip. fr. CSR mascon solution can be found at http://www2. csr. utexas. edu/grace/RL05_mascons. html. All the institutions (i. e. CSR, CNES/GRGS, GFZ, GSFC and JPL) that provide freely these solutions are warmly thanked. The Banque Hydro database (http://www. hydro. eaufrance. fr/) is acknowledged for providing freely in situ discharge at  Tonneins. Meteo-France  is thanked for providing freely SAFRAN atmospheric forcing data over the Garonne basin.
K. OSMOSIS (Ocean Surface Mixing, Ocean Submesoscale Interaction Study) experiment. We show that the front, which is generated by mesoscale frontogenesis, undergoes SI as downfront wind-driven Ekman currents erode upper-ocean stratification. The instability's development results in elevated upper-ocean kinetic energy, restratification, and turbulent dissipation, as expected from theoretical descriptions of SI. The paper is organized as follows. The OSMOSIS data set and our methodology are introduced in section 2, and the transient submesoscale front is documented in section 3. Section 4 presents observational evidence of the occurrence of SI. Conclusions are given in section 5.
Data Mooring observations. As part of the OSMOSIS project, nine subsurface moorings were deployed over the Porcupine Abyssal ; Figure  1a ) in the Northeast Atlantic Ocean for the period September 2012 to September 2013  (Buckingham et al. , 2016; Yu et al. , 2019). The mooring array was arranged in two concentric quadrilaterals with side lengths of ∼13 km (outer cluster) and ∼2 km (inner cluster) around a centrally located single mooring (Figure  S1a  in the supporting information). This configuration enabled simultaneous measurements of mesoscale and submesoscale properties. Mooring sensors comprised a series of paired MicroCAT conductivity-temperature-depth sensors and acoustic current meters at different depths, spanning the approximate depth interval 30-530 m. Temperature, salinity, and horizontal velocity measured at current meter and conductivitytemperature-depth instruments from the nine moorings were interpolated onto a uniform depth grid (10-m intervals between depths of 50 and 520 m) and averaged onto hourly intervals.
Further, a fourthorder low-pass Butterworth filter with a cutoff of one inertial period (16 hr) was then applied to the hourly data to remove superinertial motions. In this study, we use the mooring data collected from 3 to 12 April 2013, focusing on a particular submesoscale frontal event. More details on the OSMOSIS moorings, including observational uncertainties, are provided in  Yu et al. (2019). Seaglider observations. In addition to the mooring observations, the OSMOSIS region (approximately 20 × 20 km) was continuously sampled by at least two (five in total) autonomous underwater gliders for the entire year  (Damerell et al. , 2016; Erickson & Thompson, 2018; Evans et al. , 2018; Thompson et al. , 2016). The gliders navigated in a bow tie pattern across the mooring array, measuring temperature and salinity profiles within the top 1,000 m of the ocean at approximately 1-m depth intervals.
During 3-12 April 2013, one informative VIIRS SST image could be obtained (Figure  1a ). Reanalysis data sets. Air-sea heat and freshwater fluxes and wind stress data are taken from the ECMWF (European Centre for Medium-Range Weather Forecasting) ERA-Interim reanalysis product with a time interval of 3 hr  (Dee et al. , 2011). Using fields with a horizontal resolution of 0. 75°, data are linearly interpolated onto the OSMOSIS central mooring site. The net heat flux is calculated as the sum of shortwave radiation, longwave radiation, latent heat flux, and sensible heat flux. The reanalysis winds were validated with observed wind data from a neighboring Met Office buoy (at 48. 701°N, 12. 401°W), which is available from 5 September to 28 December 2012  (Yu, 2018). The ECMWF reanalysis and observed winds exhibit a good agreement, with a correlation coefficient of 0.
Conclusions The OSMOSIS mooring and glider measurements provide a unique observational data set that enables a dynamical description of upper-ocean forced submesoscale instabilities with horizontal scales down to O (1 km). In this work, we conduct a case study of a SI event in this mid-ocean setting, based on four lines of direct observational evidence: (i) Upper-ocean kinetic energy is enhanced during the SI event (Figure  2b ), as expected from the active development of a submesoscale instability; (ii) the event is associated with downfront winds and mesoscale frontogenesis (Figures  1b  and 2d ), conditions that favor the onset of SI and that are regularly met in mid-ocean environments; (iii) a shoaling of the mixed layer is observed (Figure  2a ), consistent with theoretical predictions for SI; and (iv) dissipation is elevated in a manner quantitatively consistent with SI extracting kinetic energy from the background flow, which is broadly in geostrophic balance (Figure  4 ).
, 2001 Smith et al. , , 2003)). The mapping strip was divided into a grid of 15 x 150 squares, each approximately 20 km across (Figure  2 ). The Cassini projection (centered at 90°E longitude) was chosen to give minimal distortion along the central meridian, ideal for a long and narrow north-south strip. Using ArcGIS software, the grid system was set-up as a polygon shapefile, in which each grid square was represented by a single square polygon object (more details in  Ramsdale et al. , 2017). Grid mapping is an efficient method to map small landforms over large areas  (Ramsdale et al. , 2017). Across the region, different water-or ice-related landforms were identified to understand the distribution and role of ground ice in Utopia Planitia (Figure  2 ). More details are provided in  Ramsdale et al.
1 software using a differential plot, similar to the incremental presentation used by Hartmann  (Hartmann & Neukum, 2001). The difference between the two plotting types is that the differential plot is normalized according to the choice of bin-width such that the plot is independent of the binning (aside from statistical effects; Michael, 2013). We chose a 10/decade binning to have a compromise between accuracy and smoothing of the data. The crater retention ages were calculated with Craterstats by visually selecting the portion of the crater size-frequency distributions that is parallel to some isochrons between steps of the distribution. The "Mars, Hartmann 2004 iteration" chronology system was used with the "Mars, Michael (2013)" chronology function and the "Mars, Hartmann (2005)" production function.
Acknowledgement This work is a joint effort of an international team sponsored by the International Space Science Institute (ISSI). Image credits from NASA/JPL/University of Arizona. The authors are funded by the Programme National de Planétologie of the Institut National des Sciences de l'Univers, and the National Science Centre Poland (Narodowe Centrum Nauki) Grant UMO-2013/08/S/ST10/00586. The derived data products are available at https://doi. org/10. 5281/zenodo. 1404142 (Séjourné, 2018). We thank Rossman P. Irwin III and Michael Zanetti for detailed reviews that improved the manuscript.
Continuous atmospheric CO 2 monitoring data indicate an increase in seasonal-cycle amplitude (SCA) of CO 2 exchange in northern high latitudes. The major drivers of enhanced SCA remain unclear and intensely debated with land-use change, CO 2 fertilization and warming identified as likely contributors. We integrated CO 2 -flux data from two atmospheric inversions (consistent with atmospheric records) and from and 11 state-of-the-art land-surface models (LSMs) to evaluate the relative importance of individual contributors to trends and drivers of the SCA of CO 2 -fluxes for 1980-2015. The LSMs 5 generally reproduce the latitudinal increase in SCA trends within the inversions range. Inversions and LSMs attribute SCA 1.
Land-surface Models Land-surface models (LSMs) provide a bottom-up approach to evaluate terrestrial CO 2 fluxes (i. e. net biome productivity, NBP), and allow deeper insight into the mechanisms driving changes in C-stocks and fluxes. The TRENDY intercomparison project compiles simulations from state-of-the-art LSMs to evaluate terrestrial energy, water and CO 2 exchanges since the preindustrial period  (Sitch et al. , 2015; Le Quéré et al. , 2018). We use LSMs from the TRENDY v6 simulations for 1860-2015. To identify the contributions of CO 2 fertilisation, climate, and LULCC and management to the observed changes in SCA N BP , we use outputs from three factorial simulations. The models in simulation S3 were forced by (i) atmospheric CO 2 concentrations from ice core data and observations, (ii) historical climate reanalysis from the CRU-NCEP v8  (Viovy, 2016; Harris et al.
Satellite-based vegetation datasets We further evaluated trends in the activity and growth of vegetation for the different land-cover classes using three satellitebased data sets: leaf-area index (LAI), net primary production (NPP), and aboveground biomass (AGB) stocks. The LAI data set was calculated from satellite imagery from Global Inventory Modeling and Mapping Studies (GIMMS LAI3g) described by  (Zhu et al. , 2015)  for 1982-2015. LAI data were provided in two time-steps per month on a regular latitude/longitude grid of 1/12°(subsequently aggregated to 0. 5°). (Smith et al. , 2016)  used the MODIS NPP algorithm and data for LAI and the fraction of photosynthetically active radiation from GIMMS to produce a 30-year global NPP data set, provided at monthly timescales for 1982-2011 at a latitude/longitude resolution of 1×1°.
The data are available at the NTSG data portal (https://wkolby. org/data-code/). AGB stocks can be derived from estimates of vegetation optical depth derived from passive-microwave satellite measurements. (Liu et al. , 2015)  produced a 20-year data set of AGB stocks for 1993-2012 based on measurements from a series of passive-microwave sensors. The data set is provided at a latitude/longitude resolution of 0. 25×0. 25°i n annual time intervals and is available at http://www. wenfo. org/wald/global-biomass/ (last access 13/02/2018). We tracked changes in LAI, NPP, and AGB stocks for different land-cover types over time by selecting periods of at least 20 years common to ESA-CCI LC and the vegetation data sets  (1992 -2012 for LAI, 1992 -2011 for NPP, and 1993 -2012  for AGB stocks). Vegetation variables were then aggregated for the four land-cover types at each time interval to account for land-cover changes.
Driving processes Validating the attribution of SCA N BP trends to CO 2 , climate, and LULCC by the LSMs at large scale is challenging. However, insights into the consistency of SCA N BP drivers can be obtained by statistical analysis. We fitted multiple general linear regression models (GLM) to the SCA N BP from the inversions and the S3 MMEM over each latitudinal band using multiple predictors from the TRENDYv6 forcing datasets (CO 2 concentration, climate variables and changes in land-cover and management practices). For each dataset, we identified the statistical model that could best explain SCA N BP trends with least number of predictors (Table  S2 ). We then tested the results from this statistical attribution for the MMEM against the corresponding factorial estimates (see Methods).
growing-season precipitation, growing-season temperature, atmospheric CO 2 concentration, change in extent of cropland and 6 Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2019-252 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 29 March 2019 c Author(s) 2019. CC BY 4. 0 License. forest. These variables were taken from the corresponding datasets used to force TRENDYv6 models. All possible combinations of n predictors (n=1,2,. , 7) were tested, and for each value of n, the "best" model (according to Akaike's information criterion) was chosen separately for each dataset. Above n=4 no model showed improved fit compared to the models with less predictors. The coefficients from the GLM fit for each dataset are shown in Figure S. We further tested the robustness of the statistical relationships by fitting the GLM to the differences between each TRENDYv6 experiment.
This review lays out the motivation, development, and present status of the Argo Program, and addresses the five issues mentioned above. It is important to note that Core Argo has reached and maintained full implementation through innovation and broad community support. Only a small fraction of the funding needed to support the ambitious community requests for an expanded Argo Program can be identified at present. It is important for Argo to meet its future challenges as a single integrated program. The present elements of Argo -Core, Deep, and BGC -and of its data management system are not separable, and any other future enhancements will similarly be considered as contributions to the unified effort.
Ice-avoidance measures in float controllers  (Klatt et al. , 2007)  have extended the range of Argo through the seasonal ice zones  (Wong and Riser, 2011). Improved CTD sensors, as well as procedures for delayed-mode quality control  (Owens and Wong, 2009)  have increased the accuracy and consistency of the Argo dataset. Float lifetimes have increased, to 4-5 years for most Argo National Programs, reducing the cost per profile while extending reseeding intervals. All of these improvements are propagated across the Argo national programs through communication of Best Practices  (Pearlman et al. , 2019). Argo's systematic and regular observation of the global subsurface ocean has transformed ocean observing. Northern hemisphere, near-coastal, and seasonal sampling biases of earlier eras are removed.
Each of the floats would carry sensors for six core ocean variables measured with targeted accuracies 2 : chlorophyll fluorescence (Chla), particle backscatter, oxygen, nitrate, pH, and irradiance. The 1000-float array would provide observational data to transform ability to quantify: (i) air-sea carbon fluxes, (ii) ocean deoxygenation, oxygen minimum zones and related denitrification fluxes, (iii) ocean acidification, (iv) the biological carbon pump, and (v) phytoplankton communities. The observing system would improve management of living marine resources and carbon budget verification, both key societal goals. In 2017, a BGC-Argo Scientific Steering Committee was formed (under the Argo Steering Team), to guide the development of the network and the implementation of the program objectives, and to continue developing a vision for the future.