dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Collected & Used Total deployment time of the TEMPO-mini module amounted to ,9 months, from 2011-09-29 to 2012-06-20. All instruments, except for the CHEMINI Fe analyser (Fe measurements came to a complete stop on 2012-03-26), kept on recording until recovery. However, all time series contained gaps, due to black-outs of the NEPTUNE observatory (sometimes adding up to a couple of days for all instrument recordings) or localised failed recordings (instrument-dependent). From 2011-10-07 at 8 h UTC to 2011-10-30 at 14 h UTC, ,550 hours of non-stop video were recorded and analysed with an hourly frequency, featuring 23 gaps (Fig. 3 ). For this period, temperature was recorded continuously (Fig. 4a ), i. e. no gaps in the data. Fe measurements contained many gaps (Fig. 4b ). The recording frequency of Fe measurements was changed on 2011-10-20 from three measuring cycles every twelve hours (twice a day) to once a day (at 6 h UTC), because of the drastic decrease observed in the reagents.
piscesae) extension/ Table  2. Overview of the environmental data recorded in the vicinity of the TEMPO-mini ecological module at the Grotto hydrothermal vent. retraction to high currents, changes in currents or turbidity were found  [10]. In the present study, correlations with temperature were revealed, although these should be treated with some caution due to the sub-optimal probe deployment. Emergence/retraction movements of siboglinid tubeworms were also proposed to be a thermoregulatory behaviour or suggested to be governed by oxygen requirements  [10, 34]. Urcuyo et al. [18]  indicated that most of the time individuals in a long-skinny tubeworm assemblage were exposed to extremely low levels of vent fluid and sulphide while their posterior sections were consistently immersed in sulphide concentrations 1000 times higher, which led these authors to suggest that this long-skinny ecotype might have the capacity/ability to acquire sulphide through its posterior end (root-like structure).
Acknowledgments The authors would like to explicitly thank the TEMPO-mini engineers and technicians who developed and maintained the TEMPO-mini module:  Julien Legrand ,  Yves Auffret ,  Stephane Barbot ,  Jean-Yves Coail ,  Laurent Delauney ,  Anthony Ferrant  and  Gerard Guyader. We also would like to thank the  NEPTUNE  scientists and engineers, whose engagement and professionalism made this study possible. Extended thanks go to the captain and crews of the R/V Thomas G. Thompson and ROV ROPOS during the deployment and recovery cruises in 2011 and 2012. Thanks to IMAR/ DOP-UAcfor support to DC during the final writing phase of this manuscript and to InterRidge for the cruise bursary (attributed to DC) for participation in the 'Wiring the abyss cruise 2013'.
This work was supported by the '' Laboratoire d'Excellence'' LabexMER  ( ANR-10-LABX-19 ), co-funded by a grant from the  French government  under the  program ''Investissements d'Avenir '' and by a postdoctoral fellowship ( D. Cuvelier ) from  Institut Carnot Ifremer EDROME. NEPTUNE Canada  ( Ocean Networks Canada ) has provided us with access to their cabled network. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
4 mM at 243 K and an ambient CH 3 OOH concentration of 150 pptv. [28]  This concentration is derived from the temperature dependent Henry's Law equation, K H ¼ K y H Â exp À DH soln R 1 T À 1 T y ð19Þ where K H is the temperature dependent Henry's Law constant, K H y (310) is the Henry's Law constant at 298 K (T y ). 2DH soln /R (5200) is the temperature dependence of the Henry's Law constant and T is the temperature we are studying, i. e. 243 K. The parameters for this equation were derived using Rolf Sander's database (http://www. henrys-law. org/henry. pdf, accessed 21 July 2014). We assume that the CH 3 OOH will only dissolve into the ice surface layer according to Henry's Law rather than into the bulk as well. Thus, locally, within the surface layer, CH 3 OOH concentrations may exceed the instrumental limit of detection (7 parts per billion per weight, ppbw, or 0.
The molecular weights of acetophenone at 120 (96 for carbon only) and methoxybenzene at 108 (84 for carbon only) allow us to calculate the molar concentrations later in each scenario. The OM concentration of 0. 4 mg carbon L À1 is expressed as the melted snow OM concentration and we must therefore account for the change in volume between melted and frozen snowpack in order to estimate snow OM concentration and molarity. We assume that the frozen snow volume is 30 % of the melted snow volume, which is equivalent to a snow density of 0. 3 g cm À3. [33]  The aqueous phase rate coefficients for the reaction of OH with acetophenone (6. 5 Â 10 12 cm 3 mol À1 s À1 ) and methoxybenzene (5. 4 Â 10 12 cm 3 mol À1 s À1 ) were obtained from the Radiation Laboratory database (http://www. rcdc. nd. edu/ compilations/Hydroxyl/OH.
Acknowledgements P. D. Hamer  and  D. E. Shallcross  thank  NERC  under the  CHABLIS  project for a studentship and  British Antarctic Survey (BAS)  for funding and for contribution to the publication fees. We thank  Anna Jones  at  BAS  and the colleagues within  CHABLIS  for their continued support and advice. Special thanks to  Greg Huey  for sharing the ISCAT 2000 field campaign data,  M. Kawasaki  and  D. E. Shallcross  thank the  Daiwa-Adrian Foundation  for an award to support this collaboration. This work is supported by a grant-inaid from  JSPS  ( 20245005 ). The authors thank  Centre National de Recherche Meteorologique, Meteo France , for funding.
Estimated mean seasonal cycles are similar to Figures the seminal climatology by  Takahashi et al. (2009). In addition, the scheme also reproduces part of day-to-day variations through parametrizations in terms of sea surface temperature and a few other variables. Using phosphate observations, plausibility of the scheme was tentatively confirmed on the seasonal time scale. As a next step, we now also consider interannual variations (IAV). In particular, we consider the following questions: -What are the main modes of interannual variability contributing to the global seaair CO 2 flux? -Not all parts of the ocean have been sampled frequently enough to reliably estimate interannual flux variations from the SOCAT data. In which parts of the ocean, and to which extent, does the available data coverage suffice to estimate interannual variations of the sea-air CO 2 flux? -The ocean-internal processes (biology, transport) that add/remove dissolved inorganic carbon to/from the mixed-layer and thus cause anomalies in sea-air CO 2 fluxes, simultaneously also remove/add dissolved oxygen, leading to anomalies in the sea-air oxygen exchange and finally to tiny but detectable variations in the atmospheric oxygen abundance.
1 ) for each pixel. (2) A cost function was formed to measure the mismatch between the individual pCO 2 data points in SOCAT and the model's pCO 2 field at the corresponding location and time. (3) The ocean-internal carbon sources and sinks were adjusted as to minimize this model-data mismatch. In order to interpolate areas/periods without data, additional smoothness constraints were applied (similar to those in the atmospheric inversion of  Rödenbeck, 2005). The only difference of the present estimates with respect to  Rödenbeck et al. (2013)  is that we updated the data source to the ship-board observations 1  of SOCAT v2  (Pfeil et al. , 2013; Bakker et al. , 2013)   The calculation has been done on a global grid of ≈ 4 • × 5 • pixels and daily time steps over  1985-2012 (inclusive). The first and last year will be discarded from any plots to avoid edge effects.
Residuals and comparison to independent time series As a consistency check of the fit, residuals between the estimated pCO 2 field and the SOCAT data are assessed. According to the Supplement (Sect. S3. 1), the data are fit within 10 to 15 µatm (root-mean-squared residuals), consistent with assumed mismatch uncertainty. Any remaining interannual signals in the residuals are negligible, confirming that the data information is used completely. Short-term variations are further validated against high-frequency time series from various moorings not used in the fit (Supplement, Sect. S3. 2). For testing interannual variations, independent continuous long-term surface-ocean carbon time series are used (Supplement, Sect. S3. 3).
Robustness To exclude that the results are dominated by uncertain parameters or input data sets used in the calculation, Fig. 5  shows the envelope of a set of sensitivity results (Sect. 2. 3. 4), using the tropical Pacific as example region. Reflecting the fact that pCO 2 (middle panel) is the quantity directly constrained by the data, alterations in parameters only have a very small effect (sensitivity band much more narrow than the interannual variations). There are short periods of enhanced sensitivity in 2009 and before about 1992, when the tropical Pacific is less well constrained by the SOCAT data such that it stays closer to the prior and thus the effects of our sensitivity cases on the prior cannot be completely overwritten. The sensitivity of the sea-air CO 2 fluxes (Fig. 5 , top) is slightly enhanced compared to that of pCO 2 , due to the direct effect of alterations in the gas exchange velocity.
Consistency to atmospheric oxygen observations In order to relate the SOCAT-based IAV estimates to the information from atmospheric oxygen, the oceanic APO flux has been calculated from run SFC (Sect. 2. 5). The information flow is illustrated in Fig. 7. Sea-air CO 2 flux (f The APO flux inferred from SOCAT is compared to that inferred from atmospheric oxygen data by a transport inversion  (Rödenbeck et al. , 2008, updated)  (Fig. 7 top ). Both of these independent estimates show enhanced APO outgassing during El Niño phases (grey stripes). While the SOCAT-based anomalies consistently lead ENSO events (due to the lead of f DIC int , compare Sect. 3. 4), the APO-based anomalies coincide with the 1995 and 1997/98 ENSO events but also lead the 2009 event. Both estimates are of roughly similar amplitudes, though the relative heights of the anomalies in individual ENSO events are different.
Various conceivable error influences have the potential to distort the comparison: (1) Both the SOCAT and the APO constraints suffer from incomplete spatial coverage, where the regions of good coverage do not necessarily coincide. An APO inversion of "synthetic data" suggests that this may explain part of the differences in timing (Supplement, Sect. S4). (  2 ) The information flow from the SOCAT data (Fig. 2 ) involves various uncertain model steps: even if run SFC would lead to a perfectly constrained p CO 2 m field, any errors in the parameterizations of carbonate chemistry (CC) and carbon budget (CB) would cause compensating spurious contributions to the internal DIC flux f DIC int (compare Fig. 5 ) which then propagate to the calculated APO flux. A major simplification is the carbon-oxygen coupling (TT) assumed to be Redfieldian.
Any errors in the oxygen budget (OB) and in the oxygen gas exchange (GE) additionally contribute, though their effect will be small due to the fast oxygen equilibration leading to f O 2 ma ≈ f O 2 int on interannual time scales. (3) The APO inversion involves various uncertainties both from necessary assumptions in the estimation and from observational error  (Rödenbeck et al. , 2008). Also, interannual variations in the sea-air N 2 (influencing the APO inversion because the atmospheric O 2 abundance is measured relative to the N 2 abundance) are neglected. In the light of all these error influences, but also considering that the SOCAT and APO constraints are fully independent from each other in terms of both data and model, the partial agreement in the interannual APO flux variations is remarkable.
This is in line with a relatively large spread in the ocean fluxes from different atmospheric inversions (see the RECCAP ensemble,  Peylin et al. , 2013) , confirming a limited constraint of the atmospheric data on land/ocean flux partitioning. Similar discrepancies between SOCAT-based and atmospheric inversion estimates are also found in regional fluxes (Supplement, Fig. S3 ), though at least the relative amplitudes of flux IAV between the regions broadly agree. Surprisingly similar variations are found in the tropical Indian. As the SOCAT-based estimates show more plausible ocean flux IAV than the atmospheric inversion at least in the region contributing the largest variability (Equatorial Pacific), it would be beneficial to add pCO 2 as further constraint to the atmospheric inversion: due to the atmospheric mass conservation, land flux estimates profit from any improvement in the poorly constrained ocean fluxes.
Even though interannual variations outside the Equatorial Pacific are not well constrained from the pCO 2 data in many places and thus stay close to the prior of the diagnostic scheme (Sect. 3. 2), they still contain the temperature-related part as represented in the parametrizations. In practical terms, even in areas where this is a bad approximation, using the SOCATbased estimates will not worsen the land fluxes from the atmospheric inversion much (compared to an inversion with a freely adjustable ocean flux) due to their small amplitude. Only in the Southern Ocean where influence from adjacent land regions is smallest, the constraint from atmospheric CO 2 data may be powerful, and able to compensate the weakness of the pCO 2 constraint there. The inclusion of the pCO 2 constraint into the atmospheric inversion can be imple- fluxes almost identical to run SFC (test run not shown)  8.
g. , see the spread of results by various inversion studies participating in the RECCAP ensemble,  Peylin et al. , 2013). Future developments of carbon cycle quantification should use the good constraint of pCO 2 data on the tropical Pacific and much of the Northern extratropics, but also the constraint of atmospheric CO 2 data on the Southern Ocean. Further, a joint CO 2 and APO inversion, linking sea-air CO 2 and O 2 fluxes through the diagnostic mixed-layer scheme, would exploit the potential of atmospheric oxygen observations to constrain the ocean-internal processes driving the sea-air CO 2 flux variability as demonstrated in Sect. 3. 5. The APO constraint applies on the same large spatial scales that drive the air-sea CO 2 flux variability, and thus would be a valuable complement to the pCO 2 and atmospheric CO 2 data in the previously underconstrained areas.
Conclusions Based on the SOCAT v2 data set of pCO 2 observations, we estimated interannual variations of the sea-air CO 2 exchange, using a data-driven diagnostic scheme of mixed-layer carbon biogeochemistry as a space-time interpolator. The scheme links sea-air CO 2 exchange to ocean-internal DIC sources and sinks, thereby also allowing to relate carbon anomalies to signals in oxygen (or nutrient) observations. -SOCAT pCO 2 data constrain interannual variations in the sea-air CO 2 flux in parts of the ocean, notably in the tropical Pacific where the largest interannual variations are found. -The tropical Pacific shows a reduced CO 2 outgassing during El Niño phases. In the East, this anomaly occurs more than 6 months later than in the West, likely due to different relative contributions of temperature-related and biologically/physically caused responses.
Weller, R. A. : Objectively Analyzed air-sea heat Fluxes (OAFlux) for the global oceans, B. Am. Meteorol. Soc. , 88, 527-539, 2007. 3198FiguresDatasets used as driver fields in the parameterizations of sea-air gas exchange, solubility, and carbonate chemistry (seeRödenbeck et al. , 2013).
Acknowledgements. We would like to thank all contributors to the SOCAT data base, the operators of the HOT and BATS time series stations, as well as all providers of atmospheric CO 2 data, which are the basis of this work. We gratefully acknowledge helpful discussions with  Nick Bates ,  Andrew Dickson ,  Niki Gruber ,  Roberta Hamme ,  Steve Jones ,  Bob Key ,  Armin Köhl ,  Corinne Le Quéré  (who also provided the process model results),  Sara Mikaloff Fletcher ,  Bill Munger ,  Andreas Oschlies ,  Gilles Reverdin ,  Keith Rodgers ,  Taro Takahashi ,  Yasunori Tohjima , and  Lisan Yu. This study was also supported through  EU   FP7  project  CARBOCHANGE  (  264879 ) and is a contribution to the international IMBER/SOLAS projects. D. C. E. B. received support from the  UK Ocean Acidification Research Programme  ( NE/H017046/1 ).
Ocean regions other than the tropical Pacific either have much less IAV, or are not well constrained by the available pCO 2 observations (Fig. S1). Comparison with independent data at station HOT in the North Pacific broadly confirms the SOCAT-based anomalies, including the low IAV there (Sect. S3. 3). Comparison at BATS is not successful in terms of interannual features, but still confirms the low amplitude in the North Atlantic as well (Sect. S3. 3). Despite their lower IAV, various extratropical regions can be expected to play an important role in the long-term flux trend. A more detailed consideration warrants a separate study.
(2009)  analyze PdF data from 13 months following May 2006 that cover three eruptions. They confirm the observations of preeruptive velocity reductions and apply a regionalization technique and find that most intensive velocity variations are concentrated in the summit area of PdF. The observation of velocity increase during a period of deflation is interpreted as an effect of surface deformation by  Duputel et al. (2009). In this study we investigate data of a densified seismic network from September 2009 to December 2011. These 28 months of continuous data contain five eruptions, four seismic crises accompanied by deformation due to non-eruptive intrusions and four seismic crises without detectable deformation. Dates of these events are summarized in Table  1. This long period with diverse activity monitored by a dense seismic network provides new insight into the signature of velocity changes associated with different volcanic processes.
Data To investigate temporal changes of seismic velocities inside Piton de la Fournaise volcano we use seismic noise recorded by the permanent observatory network and the seismic stations that were installed during the UnderVolc experiment  (Brenguier et al. , 2012)  run by the Institut de Physique du Globe de Paris, Observatoire Volcanologique du Piton de la Fournaise (IPGP/OVPF), and the Institut des Sciences de la Terre (ISTerre). In total 21 stations distributed in an area of about 10 by 16 km provide data for this study. The network is illustrated in Fig. 1. The map also shows locations of UnderVolc and OVPF GPS stations used to estimate surface displacements. GPS signals are recorded at a rate of 2 samples/min. Daily solutions are calculated using the GAMIT/ GLOBK software. GPS trends of stations located on the terminal cone show edifice inflation/deflation directly linked to volcano activity, whereas GPS on the eastern flank show a continuous sliding of a few cm/yr to the east  (Peltier et al.
The resulting averaged similarity matrix is displayed in Fig. 3C. It shows small fluctuations both before and after the October eruption and constant correlation value. This approach is not limited to the use of two reference periods. In fact each of the N daily NCFs can be used as reference which would, however, result in a high computational effort as the stretching has to be performed N times. But it is possible to find compromise between a few measurements with well averaged and many measurements with weakly averaged reference correlation functions that is adopted to the dynamics of the investigated system. For the present dataset we perform the final measurements of velocity variations using monthly averaged references with an overlap of half a month resulting in 48 separate reference periods.
Stations in the meander Here we will discuss temporal changes for the stations TNS-6, E-1, E-2, E-3, E-4E and E-5, located in the Polar front Meander, and which constitute a time series over the one month duration of this study. Note that no PP or EP data exist for TNS-6. From Table  2  it appears that the near doubling of the PP between E-1 and E-5 is not paralleled by an increase of EP and MR, except for the 30 % EP increase from E-1 to E-3. In fact overall EP shows a decreasing trend with time, while MR (150-400 m) stays rather constant, except for the decrease between E-1 and E-3 (Table  2 ). As reported above such a mismatch may result from differences in time scales characterizing the different processes that are compared. The more likely explanation, however, is that in this early stage of the growth season, phytoplankton biomass is accumulating in the surface layer and export is lagging behind.
Acknowledgements. We thank the officers and crew of  R/V Marion Dufresne  for their assistance during work at sea. We are indebted to chief scientist  S. Blain  and voyage leader B. Quéguiner for skillful leadership during the cruise and to the  CTD team  for managing rosette operation and CTD data. This research was supported by the  French Agency of National Research  grant (project  KEOPS 2 , # ANR-10-BLAN-0614 ), the  Belgian Science Policy (BELSPO)  project " BIGSOUTH " ( SD/CA/05A ),  Flanders Research Foundation  ( FWO  Project  G071512N ), the  European Union Seventh Framework Programme (Marie Curie CIG "MuSiCC"  under grant agreement no. 294146  to D. C. ) and the  Strategic Research Programme at Vrije Universiteit Brussel.
As an additional measure to rule out a topographic cause for greyscale variations approaching vesicle edges and crack margins, the sample stage was rotated and the same area imaged in a different orientation with respect to the incident electron beam and detector. Greyscale variations caused by sample topography (analogous to shadows in a photograph) would be reversed when the sample was rotated by 180 • ; the consistency of the greyscale variations observed here, irrespective of sample orientation, demonstrates that our results are not affected by sample surface topography. In some images taken after SIMS analysis, residual gold coat remains in cracks and vesicle interiors and appears bright white in BSEM (e. g. Fig. 3a ). Images were acquired at Durham University (GJ Russell Microscopy Facility) with a Hitachi SU-70 Analytical High Resolution SEM with attached Gatan Mono CL and associated DigitalMicrograph software using a 15 keV electron beam in backscatter mode with a working distance of 15 mm.
SIMS-BSEM calibration and data processing Following the technique of  Humphreys et al. (2008) , SIMS H 2 O t data were used to calibrate BSEM images in order to extract quantitative H 2 O t data at high spatial resolution (<1 μm). Greyscale values were extracted along a 5 μm-wide profile immediately adjacent to the visible SIMS track, using Gatan DigitalMicrograph software. Mean greyscale values of 5 μm segments were plotted against the corresponding SIMS measurements (Fig. 3 ). A linear regression fit was applied and the resulting calibration equation used to extract quantitative data in the same image, each new image requiring a separate calibration. 'Edge effects' at vesicle walls are narrow and anomalously bright and thus easily removed from extracted profiles; presented profiles therefore begin a few microns from the vesicle wall.
Presented images are enhanced to make greyscale variations more apparent by varying brightness, contrast and gamma settings; these settings do not alter the raw greyscale values used for calibration and data extraction. For each sample, multiple radial profiles were extracted around multiple vesicles. Once all extracted greyscale data were converted to H 2 O t data using the relevant image calibration equation they were compiled to create a composite dataset of H 2 O t as a function of distance from vesicle wall for the sample. An example figure which shows this profile-averaging methodology is presented in Supplementary Information. The mean H 2 O t value was calculated for all data within 2 μm segments; these mean values form the H 2 O t profile for each sample. Errors shown are twice the standard error of this mean.