dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Daily sea surface temperature (SST) data for the period 1981 to November 2016 are obtained from the NOAA daily Optimum Interpolation Sea Surface Temperature dataset (OISST, https://www. ncdc. noaa. gov/oisst), an analysis combining observations from different platforms (satellites, ships, buoys) interpolated on a regular global grid  (Banzon et al. , 2016)  at 0. 25°resolution. The methodology includes bias adjustment of satellite and ship observations (referenced to buoys) to compensate for platform differences and sensor biases. The version of OISST we consider uses as relevant satellite SST sensor the Advanced Very High Resolution Radiometer (AVHRR), which, as an infrared instrument, can make observations at relatively high resolution but cannot see through clouds. Daily mean sea height anomalies over the Mediterranean with a spatial resolution of 0.
125°for the period 1993-2016 were obtained from the Copernicus Marine and Environment Monitoring Service (CMEMS, http://marine. copernicus. eu/). They are produced from the SL-TAC multi-mission altimeter data processing system. Optimal Interpolation is applied to all altimeter fields in order to obtain gridded sea level anomalies merging all the satellites data. Sea surface wind data are taken from the V2. 0 Cross-Calibrated Multi-Platform (CCMP, http://www. remss. com/measurements/ ccmp) gridded surface vector winds product  (Atlas et al. , 2011; Wentz et al. , 2015). The V2. 0 CCMP processing merges Remote Sensing System (RSS) radiometer wind speeds (SSM/I, SSMIS, AMSR, TMI, WindSat and GMI), QuikSCAT and ASCAT scatterometer wind vectors, moored buoy wind data, and ERA-Interim model wind fields using a Variational Analysis Method (VAM,  Hoffman et al.
2003)  to produce 6 hourly maps of 0. 25°degree gridded vector winds. VAM combines RSS instrument data with moored buoy measurements and a starting estimate of the wind field, provided by the ERA-Interim reanalysis winds. All wind observations (satellite and buoy) and model analysis fields are referenced to a height of 10 meters. We have remapped the sea surface height data on the coarser 0. 25°grid of the winds and sea surface temperature by bilinear interpolation. The wind data have a time resolution of 6 hours, while sea surface temperature and height are provided daily. The wind dataset extends from July 1987 to July 2015, the sea surface temperature dataset from 1981 to November 2016, the sea surface height dataset from January 1993 to December 2016. We have therefore limited the analysis to the overlapping period 1993-2014.
The average wind intensity over the Mediterranean region computed from the CCMP dataset shows a strong non-stationarity across the 1993-2014 period. The average kinetic energy of the winds increases by a 20% factor during this time period. This is likely to be a bias of the dataset, caused by inhomogeneities in the assimilation procedure used to merge the ERA-Interim first guess with observations (Ross Hoffman, personal communication). Due to the non stationary number of observational data, observations tend to weight more in the estimate towards the end of the time period, while in the first part the ERA-Interim background field dominates the average. Since ERA-Interim has a coarse spatial resolution, it tends to underestimate wind intensity extremes with respect to observations. More intense wind events are thus observed in the second half of the time period rather than in the first half.
Using the CCMP dataset to study climatic trends at global and regional scale  (Zheng et al. , 2016) , at least in the Mediterranean area, is therefore questionable. This issue is of relatively little concern for our analysis though, as we are not interested in the temporal changes of the statistics of wind intensity. Having artificially weaker extreme winds in the first period of the dataset leads to miss events happening at earlier times, hence to have a poorer statistics overall, but does not affect our results otherwise. See Appendix A for a more in depth discussion on this issue.
From the CCMP dataset we consider the instantaneous 6-hourly surface wind velocity vector -→ u (φ, λ, t 6 ) at latitude φ, longi-10 tude λ and time t 6. Figure  1  shows u 95 (φ, λ), the 95th percentile of the local winter wind intensity u(φ, λ, t 6 ) = | - → u (φ, λ, t 6 )|. Locations with larger values of u 95 feature more intense and more frequent extreme wind events. Maximum values are found in the area of the Gulf of Lions in the Western Mediterranean, with secondary maxima present in the Eastern Mediterranean. We limit the analysis to the area characterized by u 95 > 15 ms -1 (solid black line in figure  1  The Gulf of Lions has a scale size of less than 500 km, and it is thus expected that spatial correlations (both in the atmospheric forcing and in the ocean conditions) are present.
To avoid making statistics out of strongly correlated data, we define an intense wind event as the occurrence | -→ u (φ, λ, t 6 )| > a, with a a threshold varied between 16 m s -1 and 20 m s -1 , for a temporal coordinate (t 6 ) and for at least one couple of spatial coordinates (φ, λ) within the region inside the closed solid line in fig. 1. Furthermore, the wind dataset has a time resolution of 6 hours, while sea surface temperature and sea surface height have a time resolution of 1 day. For this reason, we define an intense wind day a day in which there is at least one intense wind event, as previously defined. Each intense wind day is assigned to the winter or summer dataset depending on its date of occurrence, t i , with i = 1,. , N , and with N=N w , N s the total number of intense wind days in the winter and summer datasets, respectively.
Composite analysis of SSTA The composite average of the time evolution of winter SSTA before and after the occurrence of intense wind events, computed for the different values of the threshold is shown in figure  2a. The area averaged sea surface temperature anomaly drops by O(10 -1 ) K, reaching a minimum a few days after the occurrence of the intense wind. Note that the drop actually starts slightly before the occurrence of the intense wind event. This signal might be due to the fact that SST are provided daily in the OISST dataset, but they have been obtained by performing a bias adjustment using a spatially smoothed 7-day in situ SST average, which might anticipate the temporal signal  (Reynolds et al. , 2007). Additionally, weather conditions favoring surface cooling are probably present in the days preceding the individual days with intense winds as those are usually embedded in a synoptic scale perturbation, and during wintertime winds have a temporal correlation of about a week (not shown).
This is likely due to the fact that SSHA is a vertically integrated measure which depends on the thermal properties of the entire water column, and it is thus less sensitive to the fast fluctuations of the surface fluxes due to weather activity. Beside that, the general behavior of the SSHA signal qualitatively resembles that of the SSTA signal, with some notable differences. Composite SSHA starts decreasing well before, 10 to 15 days, the occurrence of the strong wind event, much earlier than the SSTA signal. This is likely not related to the passage of the atmospheric dynamical structure itself, and it is unlikely that it is due to some interpolation procedure in the creation of the dataset. The same feature has been observed in a similar analysis of the effect on SSHA of the passage of hurricanes in the Tropics (Supplementary Information in  Mei et al.
Winter SSHA shows a similar initial recovery period as SSTA: it increases for the first 60 days, and is significantly different from zero, according to the uncertainty estimate, at least for the first 40 days. Thanks to the fact that the SSHA is less noisy than SSTA, it is more clear the appearance of an oscillation on a period of about 20 days, whose nature remains unclear, but whose presence appears ubiquitous in our composite analysis. During the summer season, no significant SSHA is detected, due to the large fluctuations associated to the low number of events occurring during this season (see fig. 3b ). It might also be that the signal is smaller during summer than winter because events are not associated to synoptic scale cyclones and the Ekman divergence is smaller. We have performed a composite analysis of the impact of intense wind events on the state of the upper ocean in the Western Mediterranean area from 1993 to 2014, using publicly available high resolution observational datasets for wind, sea surface temperature and sea surface height.
, 2013)  that a strong wind event can influence weather on a time scale of a week through the signature on the SST and its consequent impact on the air-sea fluxes. Similarly, air-sea coupling on sub-monthly time scale has The presence of this spurious trend makes questionable to use the CCMP dataset for studying global and regional trends, at least in the Mediterranean region, despite having be used sometimes for this purpose in the recent past (e. g. Zheng et al. , 2016). For our study however this is not a serious issue, as we are not interested in the statistics of wind speed by itself. The underestimate of strong winds at earlier times results in our case simply in missing intense wind events in the first part of the dataset (indeed we observe that about two thirds of intense wind events are found the second half of the considered period).
| Metabolomic fingerprint For each experimental treatment, six plants were randomly collected and pooled to constitute one sample. To standardize the samples, ten centimeters from the top of the stem (including stem, leaves, and buds) and roots were sampled and immediately snap-frozen in liquid nitrogen. Then, samples were stored at -80°C until being ground into liquid nitrogen. Samples were lyophilized over 48 hr using a Cosmos 20K (Cryotec, Saint-Gély-du-Fesc, France). Gas chromatography coupled with mass spectrometry (GC-MS) was used to scan for the 60 metabolites in our database, encompassing amino acids, organic acids, polyamines, polyols, and sugars. We used the method described in  Serra et al. (2013). Briefly, for each sample, an aliquot of 10 mg of lyophilized powder was homogenized in 600 μl of a solution of ice-cold methanol/chloroform (2:1, v/v).
Statistical analyses were performed with the software R 3. 2. 2 (R Development Core Team 2008) using the package and plugin FactoMineR  (Lê, Josse, & Husson, 2008)  and the interface Rcmdr  (Fox, 2005). Finally, we tried to link metabolites whose concentration varied among the experimental treatments with metabolic pathways. To achieve this goal, we performed pathway enrichment analyses in Metaboanalyst 3. 0  (Xia, Sinelnikov, Han, & Wishart, 2015). Fisher's exact test algorithm was performed for these pathway analyses, with Arabidopsis thaliana as the reference model. In this procedure, the number of hits between the metabolites in our dataset and all metabolites of a given pathway were calculated. Data presented in the figures are untransformed.
g. Posada et al. , 2018). Many examples can be found on the WMO supervised International Data Rescue (I-DARE) Portal (https://idare-portal. org), where actual and potential climate data rescue activities can be reported  (Siegmund, 2014). WMO also publishes guidelines for best practices in the digitization of climate data (WMO, 2016). 2. 2 | Current status of climate data rescue: A survey Who is doing the climate data rescue work presently and why? One of the tasks of C3S DRS is to answer these questions, in order to improve the coordination between different projects and avoid repetition of work. An online survey was launched in late 2017, targeted at the coordinators of climate data rescue projects. This exercise will be repeated every year to compile an evolving inventory of climate data rescue activities that is as complete as possible.
Not only is climate data rescue dealing with geographical data, which often requires knowledge of the past location and place names, but it is also dealing with historical data, which often requires knowledge and expertise about the organization of a local archive. Collecting this context, in turn, is important in itself as a source of information on climate-society interaction  (Brönnimann and Wintzer, 2018). At the same time, there needs to be close collaboration between climate data rescue groups, with networks shared where possible, including know-how on citizen science approaches, imaged library holdings and databases in humanities. What the ACRE initiative  (Allan et al. , 2011a; 2011b; 2016)  has demonstrated is that an international umbrella can provide overarching linkages between various projects, programmes, institutions and organizations operating on various spatial scales-local, regional and global.
| A multi-level effort Our third premise is that efforts must target projects, the scientific community and science administration. For a distributed approach to work, it must be beneficial not only to society, but also to scientists. Or, as  Griffin (2015: 97)  puts it:"Data-rescue projects differ in a fundamental way from most research projects, where prestige and success are strong drivers. By contrast, data rescue is a domain for collaboration, not competition and in the broadest of senses. " For an individual research project to engage in climate data rescue, there must be scientific merit that is acknowledged by the scientific community. Funding bodies should realise the importance of this work for science and the public, thus increasing the rate of acceptance for funding. Focusing on simplifying the required effort, such as through best practice guides, metadatabases and software tools, can reduce the needed effort, personnel, and resources.
In turn, from the point of view of the scientific community, it is important that the individual projects are guided towards delivering data in a common format to the various responsible data repositories. The C3S DRS serves the needs both of projects and of the community, as is outlined in Section 11. At the level of science policy and science administration, effective science requires an open, traceable data policy and the value of climate data science should be acknowledged. During the past 10 to 20 years, much has been achieved in working towards these aims. Datasets can obtain digital object identifiers and, in general, funding bodies are more willing to fund climate data rescue projects, especially when they generate new, high-quality databases and products that aid weather/climate applications and services communities.
SERVICES C3S DRS should address the premises above. It should support climate data rescue as a continuous effort through the Copernicus Programme, and consider climate data rescue in a distributed approach. Therefore, it needs to target individual research groups and projects, provide simple tools and aids, and at the same time guide the projects and contributions towards a common goal and a common data holding/repository. Finally, it must provide access to a community which benefits each contributing project or research group. The functioning of C3S DRS from a user perspective is shown in Figure  5. At every step from the idea of a project to data submission, C3S DRS provides support, ranging from databases, to software user support, community building, best practice reports and capacity building.
The first steps are databases with existing projects (I-DARE) and metadata, where crucial information can be retrieved (e. g. existing metadata on the series, links to archive holdings, contact persons and in the best cases, links to already-imaged data sheets). The annual overview of climate data rescue activities is another example, in that it should help to trigger or better embed activities. Once digitizing is undertaken, several tools may facilitate the work surrounding the digitization process (Figure  5 ). Researchers will find answers to specific technical questions, such as how to digitize thermographs, barographs or pluviographs strip charts. Software tools with a free license as well as online tools will be provided that can help in every phase of digitization, from file naming of the images to the quality control of data and of the metadata (building on the software developed within the European projects ERA-CLIM and UERRA) to the organization of data rescue projects.
| CONCLUSIONS Historical weather and climate data are relevant for climate sciences as well as for society in the context of climate change adaptation and mitigation, and risk assessment. F I G U R E 5 Flowchart that summarizes all phases of a data rescue project (blue rectangles). C3S DRS will provide assistance in every phase, as shown by the red circles Large amounts of data and enhanced databases, vital to improving weather and climate applications and services have not yet been digitized. To promote climate data rescue, data rescue services are now being developed under C3S. In this paper, we report the results of a survey conducted among climate data rescue projects. Based on the results, we formulate three premises, from which follow requirements for such a service. We posit that climate data rescue must be seen as a continuous, long-term activity.
Society is thus best served with an active climate data rescue community that provides expertise on procedures, infrastructure, tools and metadatabases. To maintain this community, ensuring scientific merit and an open data policy will make it attractive for research projects to engage in the process. Data rescue services operate as facilitators for individual projects and collectively push towards these aims. Over the last decade or so, climate data rescue efforts have led to the development of new datasets and databases. As a result, the importance of climate data rescue work has been more and more appreciated, especially as it has been increasingly used to improve the scope, quality and nature of weather and climate applications and services. These efforts must be sustained with the data rescue services under C3S DRS being a vital contribution to this effort.
ACKNOWLEDGMENTS The authors of this paper are the team delivering  Work Package 3 of the Copernicus Climate Change Service 311a Lot1 for Collection and Processing of In Situ Observations -Data Rescue. Jürg Luterbacher  and  Rob Allan acknowledge Climate Science for Service Partnership China (CSSP). Prof. Rob Allan  acknowledges the  University of Southern Queensland, Toowoomba, Australia  and the  Centre for Maritime Historical Studies, University of Exeter, UK  where he is an Adjunct and Honorary Professor respectively. Dr. Compo  was partially supported by the  Physical Sciences Division of the NOAA Earth System Research Laboratory. Stefan Brönnimann  acknowledges funding from the  Swiss National Science Foundation  (project  205121_169676 ).
2011). The atmospheric and surface variables are initialized from ERAinterim and a 1-year spin-up integration is carried out to discard the drifting period, in particular for soil moisture. In addition to lateral and surface boundary conditions, the ALADIN-Climate simulation is also driven by evolving greenhouse gas concentrations (CO2, CH4, N2O, CFC-11, CFC-12, ozone) following CMIP5 recommendations and evolving aerosols using the dataset developed by  Nabat et al. (2013). Note that these external forcings come from observations and are not constant with time. The aerosol forcing prescribed in ALADIN-Climate is a recently-developed dataset covering the Euro-Mediterranean area  (Nabat et al. 1 3 2013) with a spatial and temporal variability for 5 different aerosol classes (sulfate, organic carbon, black carbon, desert dust, sea salt).
For each of the classes, the dataset is constituted by 12 maps of AOD (1 per month) plus a spatially-homogeneous vertical profile. In addition, for the sulfate aerosols, a trend is applied over the period to take into account the brightening effect. The added-value of using this new dataset in ALADIN-Climate was shown by  Nabat et al. (2014 Nabat et al. ( , 2015)). The surface information are defined from the Ecoclimap II database  (Faroux et al. 2013 ) which provides for Europe a 1-km resolution land cover classification including 273 covers, and associated ecosystems and surface parameters. Based on the Corine Land Cover map over Europe  (EEA 2005) , it is a static classification representative of year 2000 and there is no land covers modification over the simulation period. The Ecoclimap II is projected on ALADIN-Climate simulation grid, so that each mesh consists in a combination of land covers.
The fractions of sea, inland water, natural land surface and urban areas are derived from these information. As illustration, impervious fraction map over France is shown in Fig. 1 , with the corresponding values for the French eight largest cities in Table  1. Most of input data required by the surface models are also defined from Ecoclimap II. For TEB, geometric parameters and material properties are assigned to each urban cover. For ISBA, Ecoclimap II provides the vegetation specifications, e. g. vegetation fraction, leaf area index, albedo, emissivity. In addition, the soil texture characteristics (sand and clay fractions), used to compute hydrological and thermal soil properties, are given here by the harmonized world soil database (HWSD, Batjes 2009).
With this aim, three experiments with different descriptions of land uses and land covers for urban areas are compared: (1) CITY experiment corresponding to the explicit description and modeling of cities with TEB, (2) ROCK experiment that is the conventional approach of ALADIN-Climate for which impervious covers are replaced by rocks and computed with ISBA, and (3) VEG experiment for which cities are replaced by the surrounding natural covers (comparable to the case studied by  Trusilova et al. 2007 ) and also modeled with ISBA. Depending on the experiment,  the land cover data come from an adaptation of ECOCLI-MAPII database as described in "Appendix 1".
Evaluation of ALADIN-climate default configuration Model outputs are compared to SAFRAN analyses of daily precipitation rates, incoming solar radiation, and daily minimum (T min ) and maximum (T max ) near-surface temperatures at seasonal scale over the 1980-2009 time period. For this, SAFRAN analyses data have been interpolated on the ALADIN-Climate grid, and 2-m temperature fields have been adjusted for topography differences between SAFRAN and ALADIN-Climate by applying an adiabatic gradient.
For both seasons, the biases do not exceed ± 0. 5 mm day -1 for most of France. A spatial variability of biases is nonetheless noted: precipitation rates are overestimated in winter by 4 mm day -1 in localized mountainous regions (Alps, Pyrenees, Massif Central), and underestimated in summer by -0. 5 to -1 mm day -1 in the southern half of France. As presented in Fig. 2c , it is intesting to emphasize that the inter-annual seasonal variability of precipitation rates (averaged over France) is in adequacy with SAFRAN data for both seasons with temporal correlation coefficient of 0. 86 in summer and 0. 98 in winter. The ALADIN-Climate precipitation rate seem however to indicate a drying trend with time that is not observed with SAFRAN analyses. This could be explained by a brightening effect in the model i.