dataesr/scientific-paragraphs-categorization huggingface dataset:
Data from two boundary arrays deployed along 34. 5 S are combined to produce the first continuous in situ time series observations of the basin-wide meridional overturning circulation (MOC) in the South Atlantic. Daily estimates of the MOC between March 2009 and December 2010 range between 3 Sv and 39 Sv (1 Sv 5 10 6 m 3 s 21 ) after a 10 day lowpass filter is applied. Much of the variability in this $20 month record occurs at periods shorter than 100 days. Approximately two-thirds of the MOC variability is due to changes in the geostrophic (baroclinic plus barotropic) volume transport, with the remainder associated with the direct wind-forced Ekman transport. When low-pass filtered to match previously published analyses in the North Atlantic, the observed temporal standard deviation at 34.
The resulting records were subsampled to one value per day at noon UTC. The acoustic travel time measurements were also collected hourly. There is no drift or strong tidal signal in the travel time measurements, so they were filtered with the same Butterworth filter and subsampled to one value per day to focus on similar time scales. Analysis of the bottom pressure measurements is fairly straightforward, however the analysis of the travel time measurements requires some additional explanation. [8] The earliest uses of IES involved comparing the travel time measurements to expendable bathythermograph (XBT) data in order to ''calibrate'' the travel time records into vertically integrated heat content [e. g. ,  Rossby, 1969]  or main thermocline depth [e. g. ,  Watts et al. , 1995]. Later research derived relationships between travel time and integrated quantities such as dynamic height [e.
The Fofonoff Potential is essentially the vertical integral of the dynamic height anomaly. Differences in Fofonoff Potential between neighboring sites are directly proportional to the baroclinic component of the transport, relative to an assumed level of no motion [e. g. ,  Meinen and Watts, 2000]. The relationship between Fofonoff Potential and travel time is used here mainly for estimating some of the errors inherent in the MOC calculations (see Appendix A). To analyze the PIES/ CPIES data collected by the pilot arrays, Argo float profiles and CTD profiles spanning from the surface down to at least 1000 dbar in the regions illustrated in Figure  2  were collected from the Argo data centers, the World Ocean Database, and from recent cruises; a total of 565 profiles were available near the western array and 770 profiles were available near the eastern array.
These flows are roughly an order of magnitude smaller than what is observed by current meters and the submarine cable at 26. 5 N, but nevertheless they are not negligible. There is insufficient observational data available to determine the daily transports in these small regions at 34. 5 S, however, the time-mean and standard deviations of the transports in these areas have been determined from three sources. The two numerical models discussed previously (OFES and NEMO) were used to estimate the transports inshore of Sites A and Z (Figure  6 ). The western and eastern inshore transports were, respectively, 23. 0 6 1. 6 Sv and 12. 2 6 2. 2 Sv from the OFES run and 24. 66 3. 3 Sv and 14. 1 6 4. 7 Sv from the NEMO run (time-mean 6 one standard deviation). Additionally, repeated trans-basin XBT section data collected on a nearby transect [e.
(Note that these differences are very important to heat transport calculations, but that is beyond the scope of the present study. ) The overall structure of the profiles is very similar. The XBT MOC estimate is an average of only those sections that most closely followed the 34. 5 S track [see  Garzoli et al. , 2013  for a discussion of the different tracks taken by the XBT vessels]. The Argo MOC profile shown in Figure  8  and discussed briefly in the previous paragraph is from an estimate presently under development by some of the authors using as its principal data set a climatology of all Argo float data collected up through mid-2012 in a band along 34 S, together with wind estimates and historical hydrographic climatology data near the shallow boundaries and below 2000 dbar. As noted above, the OFES and NEMO mean profiles are also similar, although they exhibit consistently weaker northward flow in the pressure range from roughly 100 dbar to 600 dbar.
Planned further developments of the transbasin array will provide the ability to calculate these   [Willis, 2010]  2. 4 Sv 26 N  [Cunningham et al. , 2007]  4. 9 Sv 3. 7 Sv 3. 6 Sv 16 N  [Send et al. , 2011]  3. 8 Sv 34. 5 S [This study] 8. 7 Sv 7. 6 Sv 4. 5 Sv 3. 9 Sv a Bold standard deviation estimates were published in the listed papersthe other estimates at 26 N were determined using the 26 N MOC time series for 2004-2011 (available from www. noc. soton. ac. uk/rapidmoc/). The final row shows the standard deviation results for the present study at 34. 5 S for comparison. ''higher-order'' quantities like baroclinic salinity flux through better resolved horizontal velocity information and coupling with other existing data sets like the Argo profiling float array. The western boundary observations have already demonstrated significant variability in the Deep Western Boundary Current, carrying part of the lower limb of the MOC  [Meinen et al.
[39] The accuracy of the 34. 5 S MOC estimates made in this study was derived as follows : first, the accuracy of the geostrophic transports (both the relative to an assumed level of no motion and the reference velocity components) was determined; second, the Ekman transport accuracy was estimated by comparison to an independent wind product; and third, estimates of the unmeasured transport by flows on the shallow continental shelves and upper slopes were made using independent data sets and numerical models. In what follows, the accuracies of the various terms will be briefly explained, and the sources of error will be classified as either random sources of error or potential bias sources (see also Table  A1 ). [40] The random accuracy of the geostrophic velocity relative to an assumed level of no motion is perhaps the most complicated component as it is dependent on several sources of error: [41] (1) The accuracy of the PIES measured travel time [0.
The sole random source of error in the determination of the reference velocity is the accuracy of the pressure gauges themselves (0. 01 dbar after exponential-linear drift removal)  [Donohue et al. , 2010]. The Ekman transport random accuracy was estimated as the standard deviation of the difference between the Ekman transport integrated between Sites A and Z along 34. 5 S using either the CCMP wind product described in the text or the NCEP/NCAR reanalysis winds (downloaded from ftp://ftp. cdc. noaa. gov/pub/Datasets/ncep. reanalysis/surfa-ce_gauss/) using the same drag coefficient and air density discussed in the text. Note that the NCEP/NCAR winds and the CCMP winds are not completely independent, as both incorporate many of the same observations, however the assumption here is that the largest source of error in the wind products is based on the gridding and interpolation techniques applied, and those are different for these two products.
The various random sources of error were all assumed to be independent of one another and were combined in a square-root of the sum of squares manner to yield an overall random accuracy for the daily MOC values of 5. 9 Sv. [47] Not all potential sources of error in the MOC calculation can be considered random-several would apply to the resulting time-mean MOC (and would have no impact on the time variations). These sources are as follows: [48] (1) The calibration of the PIES/CPIES measured travel times into travel time at 1000 dbar using concurrent CTD profiles. The time-varying baroclinic structure between 1000 dbar and the actual depth of the PIES/CPIES has already been accounted for in one of the random error sources, however when applying the calibration CTD data to the PIES/CPIES measured travel times it is possible that the time-mean of the resulting calibrated travel time record could be off, and this would result in an artificial time-mean density gradient between Sites A and Z, and hence a transport error for the estimated MOC.
To validate some of the assumptions associated with these parameterizations, models have been compared with global datasets of precipitation isotopic composition and Greenland ice core records  (Sjolte et al. , 2011). Only very limited datasets of direct Greenland snowfall measurements are available  (Grootes and Stuiver, 1997; Steen-Larsen et al. , 2011) , and, so far, atmospheric models equipped with water stable isotopes have not been validated against these data. The isotopic composition of atmospheric water vapor represents an intermediate product in the hydrological cycle between the source evaporation and cloud condensation. Combined with precipitation data, measurements of water vapor isotopic composition have therefore the potential of enhancing our knowledge of the processes governing the hydrological cycle.
Indeed, large-scale advection, vertical mixing in the atmospheric boundary layer, cloud microphysics, and exchange between the atmosphere and the snow surface do affect water vapor and precipitation isotopic composition. Direct isotopic water vapor measurements in the atmosphere are therefore needed. As the use of satellite remote sensing of water vapor isotopic composition is currently not available for high latitudes  (Worden et al. , 2006; Frankenberg et al. , 2009) , in situ observations are essential. So far, only few studies have carried out long-term monitoring of the water vapor in the atmosphere  (Jacob and Sonntag, 1991; Angert et al. , 2008)  or analyzed the water vapor isotopic composition above the Greenland Ice Sheet  (Grootes and Stuiver, 1997; Steen-Larsen et al. , 2011). We briefly review here existing Arctic water vapor isotope datasets.
, 1999; Lee et al. , 2005). In this study, we take advantage of different types of commercial laser instruments recently developed  (Crosson et al. , 2002; Baer et al. , 2002). By deploying two different laser instruments and comparing the independently produced data records we are directly able to evaluate the accuracy and precision. We have implemented specific calibration protocols to correct for instrumental drifts and produce results against international standard waters. To enhance the interpretation of ice core records, we have conducted our surface water vapor monitoring program at the NEEM deep ice core drilling camp, NW Greenland, during the 2010 summer field season. In Sect. 2, we describe the methods applied (NEEM site characteristics, field setup, calibration protocol, back trajectory analyses and modeling tools).
H. C. Steen-Larsen et al. : Continuous monitoring of atmospheric water vapor isotopes in Greenland 18 0 ° W 170° W 160 ° W 150 ° W 14 0 ° W 13 0 ° W 1 2 0 ° W 1 1 0 ° W 1 0 0 ° W 90 ° W 80 ° W 70 ° W 60 ° W 50 ° W 40 ° W 3 0 ° W 2 0 ° W 1 0 ° W 0 ° 10 ° E 20 ° E 0 3 ° E 40 ° E 50 ° E 60 ° N 70 ° N The proportion of the moisture originating from the Arctic as diagnosed from water tagging appears to co-vary with the observed NEEM d-excess (correlation coefficient of 0. 30). Days with high d-excess around days 170, 198 and 203 correspond to maxima in the fraction of NEEM moisture coming from the Arctic. This confirms our result from backtrajectory analysis, and supports our hypothesis of the link between high d-excess and Arctic origin. The model-data comparison further informs on the causes of the observed δD and d-excess variability.
Furthermore, our results have implications for past temperature estimates from ice core data. We indeed suggest that past d-excess variations preserved in ice cores drilled at NEEM may inform on variations in Arctic moisture transported to NEEM. Our new dataset demonstrates the value of continuous water vapor isotopic measurements for enhancing our understanding of the hydrological cycle of the Arctic, and assessing the ability of atmospheric general-circulation models to capture moisture origins. In a warmer Arctic, with a reduced sea-ice cover, changes in moisture sources indeed appear to be strongly model-dependent  (Masson-Delmotte et al. , 2011; Sime et al. , 2013). Only detailed process studies, enabled by continuous monitoring, can help to reduce these uncertainties. Such monitoring efforts should be extended over longer timescales, in order to address seasonal variations as well as interannual variations and their relationship with weather patterns, on timescales relevant for an improved interpretation of ice core records.
Acknowledgements. NEEM is directed and organized by the  Center of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (GSC), China (CAS) ,  Denmark (FIST), France (IPEV, INSU/CNRS  and  ANR VMC NEEM) ,  Germany (AWI) ,  Iceland (RannIs), Japan (NIPR) ,  Korea (KOPRI) , The  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) ,  United Kingdom (NERC)  and the  USA (US NSF, Office of Polar Programs). The numerical simulations with LMDZiso were performed on the NEC-SX8 of the IDRIS/CNRS computing center. The work was supported by the  Danish Council for Independent Research -Natural Sciences  grant number  09-072689  and  10-092850 , by the  French Agence Nationale de la Recherche  (grant VMC NEEM and  CEPS GREEN-LAND).
Acknowledgements. We thank  H. Stibor ,  Oceanopolis Brest ,  P. Pondaven , ,  A. C. Baudoux ,  R. Corvaisier ,  A. Masson ,  C. Liourzou  (IUEM),  E. Achterberg ,  C. Dumousseau ,  Z. Chowdhury, (NOC), C. Labry (IFREMER)  for technical support, discussions, and/or advice and the anonymous reviewers for insightful com-.
Travertine Magnetic Susceptibility Measurements. The preliminary rockmagnetic data show that the magnetic signature associated with travertine is generally characterized by low susceptibility values due to the carbonate nature. In order to better understand the process of incrustation, mineralogical enrichment, and paleocurrent flow, magnetic susceptibility signature is used. Magnetic susceptibility (MS) profiles were carried out in several sites. To reduce the local alteration effect, grooves were opened before getting accurate measurement using MS2 sensors. The location and the altitude of the different profiles we measured are calculated using the same differential bifrequency HTECH Z12 GPS. In fact, the MS2 Bartington instrument enables us to use both sensors: MS2D for calibration and the MS2F which is adapted for data acquisition along opened grooves (Figure  10(a) ).
Acknowledgments This paper was supported by ( Centre de Recherche en Astronomie, Astrophysique et Géophysique ) CRAAG, Project code  G002/08  ( Contribution géophysique à l'étude de l'évolution géodynamique du bassin sédimentaire de Guelma (Algérie) ). The authors are grateful to the  Magnetic Laboratory of CEREGE (France)  for the preliminary paleomagnetic pilot measurements. They thank  Dr. A. Harbi  for the numerous and fruitful discussions on the seismicity of the region. They are grateful to the Editor  Dr. Gambino  and anonymous reviewers for their comments, remarks, and suggestions.
e. Italy, France, Spain) and at least to the mid-19th century onwards across much of the region. However, available digital climate data are mostly restricted to the last third of the 20th century onwards, but their accessibility is restricted due to economic or political issues that rule out much data sharing, despite World Meteorological Organization (WMO) Resolution 40  (WMO Cg-XII, 1995). The existing surface climate data heritage is largely underexploited. Enhancing availability, accessibility and traceability to historical climate time series over a climate change hot spot region (such as the GMR is considered), whose socioecosystems are very sensitive to climate change and where its potential impacts are large brings a number of scientific and societal benefits. High-quality (i. e. quality controlled and homogenised), high-resolution (both in time and space) and longterm (longer than centennial scale) climate time series are the basic and key input for undertaking more robust climate analysis or for generating any climate service, product and application.
To locate, organise, assess, and preserve data sources is the first step to be taken prior defining or undertaking any plan to digitise historical record and subject the digitised series to quality controls, looking to identify non-systematic errors, homogeneity testing to assess systematic biases or for homogenising if inhomogeneities are found and the accompanying metadata provide clues for validating and adjusting the identified breakpoints in homogeneity. This article is aimed at documenting relevant historical data owners/holders and data sources containing Mediterranean climate data and metadata in non-digital format especially for the poorest documented Mediterranean parts, the southern and Middle East regions. Its emphasis is the investigation of online repositories worldwide and the French archives holdings, comprising data over France and overseas.
An exemplar case study will look at the Algerian holdings one of the poorest documented world region: the northern African sub-region, and describes some of the past and ongoing data rescue (DARE) initiatives that are improving availability, accessibility and traceability of historical climate data over the GMR. The article is organised as follows: historical climate data sources and holders, both online repositories and physical archives, are explored first splitting the section into three parts focused on the following: (a) relevant online repositories worldwide containing historical climate data for southern and Middle East Mediterranean countries, (b) the French archives and holdings comprising not only historical climate data over France but also for its past colonies in North Africa, and (c) the ongoing DARE program set up by the Algerian NMS as a case study.
Exploring Mediterranean climate data sources and holders Despite the rich heritage of historical surface climate data and although some NMSs and academic institutions have recently undertaken DARE activities aimed at transferring historical climate records from fragile media (paper forms) to new media (imaging), few of the necessary long-term records are readily available in digital form. Furthermore, the requirement for highquality integrated climate products is impeding the development of optimum strategies to mitigate and/or adapt to the negative impacts of global climate change over the Mediterranean Basin. This stems from the lack of long-term digital records. However, recently the digitisation (scanning) of collections of documents with meteorological/climatic data provides the opportunity to extend climatic studies to earlier periods.
Nowadays For both CDMP and BADC data repositories, the most extensive southern Mediterranean sites are for major towns/cities, e. g. Oran, Algiers, Tunis, Cairo, Beirut, Nicosia (from 1870s up to 1970s). Meteorological records were taken at observatories, civil/military administration buildings, hospitals, hotels, sea ports, and airports. However, rural and remote places were also covered by stations set up by the various state meteorological services to cover the whole territory of their country/region (often sparsely, but particularly densely in agricultural regions, i. e. the Nile Delta), and also where companies, services and foreign missions were activated such as construction companies (e. g. Suez Canal), lighthouses (semaphores), educational institutions (schools/universities), etc.
omm. urv. cat/MEDARE/index. html) data are being digitised and long series reconstructed from long daily maximum and minimum temperatures and precipitation totals, along with sub-daily data for surface air pressure over North African and Middle East Mediterranean countries. Figure  1  gives details of the locations and variables being digitised, quality controlled and homogenised after the records are merged with recent parts of the data recorded by the relevant Mediterranean NMSs, while Table  3  gives a detailed account of the stations, variables and periods recovered, digitised and quality controlled at the daily scale for temperature (maxima and minima) and precipitation (daily amounts) and at the sub-daily scales for air surface pressure parameters. This DARE exercise, enabled by the European Commission funds, will extend further back in time some of the longest surface temperature, precipitation and air pressure time series at the daily and sub-daily scales, respectively, which will allow scientists and policy-makers analyses to assess climate variability and change impacts over one of the poorest documented regions of the world.
The EURO4M project intend to also share the data rescued under this effort with appropriate global and regional databanks [e. g. ECA&D (European Climate Assessment and Dataset), CRU (Climatic Research Unit -data sets), ISTI (International Surface Temperature Initiative)], as has been the case for a set of air pressure records already provided to the ISPD (International Surface Pressure Databank). However, note that this exercise for recovering and bringing long-term and high-quality climate variables into the future only encompasses three of the measured climate variables, while the other climatic parameters contained in the sources will remain undigitised until some other opportunity for digitising them emerge. Therefore, there is still much scope for undertaking further DARE activities for the southern Mediterranean sub-region.
Auffray et al. (2009)  also illustrates the difficulties in recovering and gathering data of climate extreme events due to the wide dispersion of sources in France. The following epigraphs illustrate the most important holdings of French instrumental climate data. First, a description is provided of a selection of holdings for the 17th and 18th centuries; followed by the M et eo-France data holdings extending back to 1800 and ends with the description exploring some of the departmental archives (italics are used for the French names of institutions).
Historical sources extending back to the early 18th century From the French Institute Library (Librairie de l'Institut de France). The manuscripts of the first French instrumental oldest climate series made by Morin 1665 -1709 in Paris is stored in this library (quotation Ms 1488). The manuscripts were imaged by the Institut de France. These pressure data have recently been analysed by  Cornes et al. (2012)  developing a long series (but with a few gaps) to the present. From the Academy of Medicine Library (Librairie de l'Acad emie de M edecine). The Royal Society of Medicine in Paris created a meteorological network in 1776 and it continued until 1792. More than 200 doctors sent their daily observations to L. Cotte. The collection of the records, 13 800 pages of weather reports on daily basis over France, is kept in the Library archives.
The meteorological collection has been imaged thanks to the French-funded project ANR-CHEDAR. The images are accessible via internet from the Academy of Medicine website: http://www. academie-medecine. fr/userfiles/file/SRM/index. html. The next step, managed by M et eo-France, will be the digitisation of the data for relevant climate series. From Marseille Observatory (Observatoire Astronomique de Marseille-Provence -OAMP -). The Marseille Observatory (OAMP) holds observations recorded in the Marseille observatories since 1761 and the data registered by Catelin le Cadet (1744-1787). A precise inventory of this asset is available at http://www. oamp. fr/patrimoine/archives-f. html. Microfilms of this collection are available at the departmental archives Table  3. List of stations, along with their identifiers (station name, WMO code, and country), geographical details (coordinates and elevation), climatic variables and periods recovered, digitised and quality controlled under the EURO4M project.