dataesr/scientific-paragraphs-categorization huggingface dataset:
Land management scenarios While it could be theoretically possible to remove all natural vegetation from the high latitudes, much of the cleared land could not be directly used for bioenergy production unless specific soil and terrain restrictions are eliminated by additional management efforts. As a result we calculate the biophysical bioenergy potentials, using LPJmL for different scenarios on management efforts ranging from conservative  or more plausible where all restrictions are assumed to hold (or there is no management to eliminate these) to idealistic, where no restrictions are considered (or all restrictions are assumed to be eliminated). Soil and terrain restrictions are based on the Global Agro-Ecological Zonal (GAEZ) data set  (Fischer et al. , 2000). The characterization of the suitability of land resources for agricultural production includes all relevant components of soils and landform, which are basic for the supply of nutrients and physical support to plants.
Climatic constraints of the GAEZ data set are ignored in this study as LPJmL already uses climate data as an input and thus crop growth simulated by this model is already restricted by climate. The different land management scenarios used in this study, as tabulated in Table  4, are:  1. MAXL: land with any constraint of unsuitable terrain or unsuitable soil properties, including unsuitable soil fertility, is assumed to be unavailable for farming. Unsuitable terrain mean those areas that have severe terrain constraints (i. e. greater than 16 % slope or areas with greater than slight constraints;  Fischer et al. , 2000). In addition we assume that land currently occupied by built area and cropland  (Erb et al. , 2007 ) is considered to be unavailable for bioenergy plantations. The remaining land is thus available for bioenergy crop plantations.
, 2000). This assumption has also been used in earlier studies, e. g. by  Grünzweig et al. (2004). For the calculation of the biophysical bioenergy potential of individual crop types, we assume that 50 % of crop dry matter is carbon  (Rojstaczer, 2001). The primary energy content per gram of crop dry matter is based on the Energy research Centre of the Netherlands Phyllis database  (ECN, 2007)  and as listed in Table  2. The land use of the area deforested in this experiment is dynamic and depends on which crop would provide maximum energy yield for that particular year. Different crops have different temperature requirements for optimal photosynthesis as shown in Table  3  and the mix of most suitable land use types reflects the heterogeneity in climate. As an example, the land use pattern for the UNLIM scenario for the end of the 21st century is shown in Fig.
02 %. Some discrete samples from Site 1239 were also selected all along the record and measured for their total organic carbon (TOC, wt %) contents at the University Bordeaux 1 using a LECO C-S 125 analyzer after treatment of the sediment with HCl to remove CaCO 3. The precision for TOC is better than ±0. 5 %. The alkenone concentration (C 37 , µg g -1 ) was determined at the University of Kiel at a resolution of ∼ 2-4 kyr between 3. 2 and 0. 5 Myr, at the same time as the U k 37 from Site 1239  (Etourneau et al. , 2010)  with a precision of about ±0. 05 U k 37 units. Evolutionary spectra, or temporal changes in amplitude of signal components in the Milankovitch band, over the last 3. 2 Myr were estimated using TIMEFRQ 4. 3  (Schulz et al. , 1999). Data were prewhitened to a 2 kyr resolution.
Acknowledgements. We acknowledge  T. Blanz ,  I. Billy ,  S. Koch , and  K. Charlier  for geochemical and isotopic analyses and IODP for providing the samples. This research was supported by the  Deutsche Forschungsgemeinschaft through German  projects  SCHN 621/5-2  and  SCHN 621/12-1  ( University of Kiel ), the  French program CNRS-ECLIPSE  to  PhM (University of Bordeaux I) , the  American USSSAC post-cruise support and NSF  funding ( 1060779 ) to  R. S. Robinson.
As a response to public demand for a welldocumented, quality controlled, publically available, global surface ocean carbon dioxide (CO 2 ) data set, the international marine carbon science community developed the Surface Ocean CO 2 Atlas (SOCAT). The first SOCAT product is a collection of 6. 3 million quality controlled surface CO 2 data from the global oceans and coastal seas, spanning four decades. The SOCAT gridded data presented here is the second data product to come from the SOCAT project. Recognizing that some groups may have trouble working with millions of measurements, the SOCAT gridded product was generated to provide a robust, regularly spaced CO 2 fugacity ( f CO 2 ) product with minimal spatial and temporal interpolation, which should be easier to work with for many applications.
, 2010). The latest published global flux map, based on a compilation of approximately three million measurements collected between 1970 and 2007, provides information on the monthly patterns of air-sea CO 2 fluxes during a "normal" non-El Niño year taken to be 2000  (Takahashi et al. , 2009). The tremendous increase in the number of annual observations provides exciting opportunities to look at the patterns of air-sea CO 2 fluxes in greater detail and to understand the seasonal to inter-annual variations and the mechanisms controlling them. As a complement to Takahashi's work to update the CO 2 climatology, there is an ongoing international effort to synthesize all the available surface CO 2 data into a quality controlled database, along with uniform metadata that can be used to examine surface CO 2 variability over a range of temporal and spatial scales (SOCAT; http://www.
socat. info/). The core Surface Ocean CO 2 Atlas (SOCAT) data set (version 1. 5) is a global compilation of underway surface water CO 2 data with 7. 8 million measurements (6. 3 million with f CO 2 values) from 1851 cruises run between 1968 and 2007 by more than 10 countries (Fig. 1 ). SOCAT brings together, in a common format, all publicly available surface ocean CO 2 data, including the Arctic and the coastal seas. All measurements are evaluated for data quality using methods that are transparent and fully documented  (Pfeil et al. , 2013). The observations in the core SOCAT data set are sparse and unevenly distributed in time and space. To simplify exploration of the information from the collection of observations, a standard gridded representation of the SOCAT data with minimal interpolation was generated.
Core data set construction The construction of the core SOCAT data set is described in detail by  Pfeil et al. (2013) , so only a basic description is given here. The SOCAT data set was compiled from data sets of surface CO 2 measurements made through 2007, which were either publically available or obtained upon request. These include measurements from research ships, volunteer observing ships and CO 2 moorings. Once collected, all files had to be put into a common format. This involved not only restructuring the data columns, but recalculating the f CO 2 values using standardized approaches  (Pierrot et al. , 2009). Missing parameters like salinity and atmospheric pressure were extracted from standard global datasets  (WOA 2005 -Antonov et al. , 2006 and NCEP/NCAR -Kalnay et al. , 1996) , in addition to bathymetry added from ETOPO2.
To ensure that each cruise was given a unique identifier in the database, all cruises were given an EXPOCODE that identifies the vessel, country, and date of the first cruise measurements (e. g. , 06MT19910903 = German cruise on the Meteor starting 3 September 1991). An initial set of quality control checks were performed to identify any unrealistic information (e. g. , sample locations on land, dates and times that do not exist), any large "fliers" in the data and any duplicated data or cruise information. Where problems were discovered, the initial quality control checks resulted either in the removal of entire cruises (e. g. , in the case of a duplicated cruise) or in the flagging of individual data points with a bad or questionable value. Although the flagging of individual data points can be somewhat subjective, we made an effort to be very conservative and only flag observations that were well beyond realistic values.
Flag numbers 2, 3, and 4 were used to indicate good, questionable, and bad values, respectively. For the second level quality control checks, the database was divided into regions: North Atlantic (including Arctic), Tropical Atlantic, North Pacific, Tropical Pacific, Indian Ocean, Southern Ocean, and coastal. Small groups of carbon scientists that specialize in each region were given responsibility for examining the data quality in each region. Standardized procedures and tools were used for the 2nd level quality control (2nd QC) in each of the regions. These evaluations included a careful assessment of the metadata to determine if the best practice methods  (Dickson et al. , 2007, http ://cdiac. ornl. gov/oceans/Handbook 2007. html) were followed and properly documented. The f CO 2 data were also examined for consistent patterns and reasonable or expected thermodynamic relationships when compared to measured temperature and salinity.
Data consistency was also assessed by comparing measurements from multiple cruises in the same area at about the same time. The regional groups used their scientific experience in the area to determine the appropriate temporal and spatial scales for the consistency evaluations. Cruises were categorized as A-D, F, S or X based on the evaluations by the regional groups  (Olsen and Metzl, 2010). There are four categories of generally acceptable data that range from A (followed approved methods, metadata documentation complete, 2nd QC performed and deemed acceptable, and comparison with other data performed and deemed acceptable) to D (unsure whether approved methods were completely followed, metadata documentation was incomplete, but 2nd QC was performed and deemed acceptable). There are three categories of data that were not deemed acceptable in their current form.
Category F is for data where the 2nd QC revealed problems with data quality. Category S is for cruises that are temporarily suspended because the data contains unacceptable problems but are in the process of being updated by the data provider. The intent is that these cruises will be included in the next version of SOCAT. Category X is for data that have been excluded, for example if they are a duplicate of an existing SOCAT data set. Because of the extensive amount of work involved in formatting and carefully checking data quality, no data collected after December 2007 were added to this (first) release of the database. After each of the regional groups had categorized all of the cruises, a global group examined the entire database for consistency and resolved occasional conflicts between regional assessments of cruises that crossed regional boundaries.
The choice of grid size and temporal resolution was intended to stay within the average correlation length scale for surface ocean CO 2 and provide a product that could be directly combined with other routine gridded products available at the same resolution (e. g. , World Ocean Atlas). Two types of f CO 2 values are reported: an unweighted mean and a cruise-weighted mean. Measurements collected from different ships can have very different temporal resolutions. Some ships record values every minute and others only take a reading once per hour. If two cruises pass through the same grid cell, one with one minute resolution and another with one hour resolution, the mean of all the measurements would be strongly biased toward the higher resolution data set. In some cases this biasing may not be appropriate.
Therefore, the cruise-weighted mean first averages all the data obtained on a given cruise within a grid cell, and then averages the cruise means within the grid cell. This gives equal weight to all the cruises regardless of the original temporal resolution. Generally these values are very similar, but in some grid cells there are significant differences (e. g. , some cells in the North Pacific). Other diagnostic information is also included to help the user understand any implicit biases in the data. For example, the total number of cruises and the total number of observations within a grid cell are provided. Technically, only one measurement is required to produce a value in a grid cell. One could argue that one measurement cannot adequately represent an entire 1 • × 1 • area over a whole month.
Spatial and temporal coverage The number of CO 2 measurements made in the surface ocean annually has increased dramatically over the past 40 yr. Between the early 1990s and the early 2000s, approximately 900 000 new measurements in total were added to the database. Today, nearly a million measurements are collected every year. Figure  4  shows a map of the total number of surface f CO 2 values per grid cell over the observational period and the percentage of surface ocean grid cells per latitude band sampled over this time. At ∼ 30 • N, nearly 100 % of the surface ocean grids have been sampled at least once within the nearly 40 yr time period covered in this data set. The North Atlantic and North Pacific have at least some measurements in the majority of grid cells. The best coverage appears to be in the high latitude North Atlantic and in the Equatorial Pacific.
A much smaller percentage has been covered in the Southern Hemisphere (generally around 50 %). The map reveals some very significant areas in the South Pacific, Indian and even Atlantic that have no observations over this time period (white spaces in Fig. 4 ). Breaking the  data down into seasons or months shows even more sporadic coverage. The monthly and seasonal variability can be explored in the 12 month climatology data product. This product averages all f CO 2 values in a given grid cell for each month regardless of the year. Figure  5  shows the number of unique months out of the year that a grid cell has observations in the nearly 40 yr database. While spatial coverage has increased dramatically over the last few decades, a majority of the surface ocean has only been sampled for f CO 2 during one month out of the year (blue colors in Fig.
Decadal trends To facilitate the exploration and use of the temporal richness of SOCAT, the monthly gridded data have also been binned into annual and decadal averages. Figure  6  shows histograms of the decadal mean f CO 2 values for each decade globally as well as broken down into the Northern Hemisphere and Southern Hemisphere. As atmospheric CO 2 increases with time so do the peak histogram f CO 2 values. This increase is expected since globally the surface water CO 2 is thought to track the atmospheric increase  (Takahashi et al. , 2009). However, one must be careful to remember that since there is essentially no interpolation in this data set, there are spatial and temporal biases inherent in these gridded products. For example, the data from the 1970s appear to show a bimodal distribution but this is likely an artifact of the limited data coverage at that time.
The SOCAT product has many of the same original data as the Takahashi data set, but the objective was not to generate a climatology representing a single year. The SOCAT gridded product can be used to further investigate the seasonal, inter-annual and decadal variability in the data set. It also allows the community to explore alternative ways of making the time and space interpolations required to generate global flux maps. The grid cell structure was selected to coincide with the World Ocean Atlas and GLODAP gridded products (e. g. ,  Garcia et al. , 2010; Key et al. , 2004). This will allow direct comparison of synthesis products that can be used to investigate controls on surface CO 2 and drivers of variability. Care must be taken, however, when comparing products from different sources that may represent different temporal and spatial scales.
Conclusions The SOCAT gridded data is the second data product to come from the SOCAT project. The first was a quality controlled data set at the originally collected time and space resolution with 6. 3 million f CO 2 observations collected between 1968 and 2007. Recognizing that some groups may have trouble working with millions of measurements, the SOCAT gridded product was generated to provide a robust regularly spaced f CO 2 product with minimal spatial and temporal interpolation, which should be easier to work with for many applications. Gridded SOCAT is rich with information that has not been fully explored yet (e. g. , regional differences in the seasonal cycles), but also contains biases and limitations that the user needs to recognize and address (e. g. , local influences on values in some coastal regions).
Despite this synthesis effort, understanding surface CO 2 variability is still a data-limited problem. More data are being collected every day. Plans are in the works to update the current SOCAT with more data. As future SOCAT data sets become available, the SOCAT gridded product will also be updated. Further automation of data submission and quality control in SOCAT will enable future, prompt SOCAT releases. To keep abreast of the latest developments in SOCAT, please visit www. SOCAT. info.
Acknowledgements. SOCAT is promoted by  IOCCP , the  Surface Ocean Lower Atmosphere Study , and the  Integrated Marine Biogeochemistry and Ecosystem Research program. Douglas Wallace  ( Dalhousie University, Canada  and former SOLAS chair) has strongly encouraged SOCAT. Support has been received from the  University of Bergen (Norway) , the  Bjerknes Centre for Climate Research (Norway) , the  National Oceanic and Atmospheric Administration (United States) , the  University of Washington  ( United States ),  Oak Ridge National Laboratory (United States) , the  University of East Anglia  ( United Kingdom ),  PANGAEA -Data Publisher for Earth & Environmental data (Germany) , the  Alfred Wegener Institute for Polar and Marine Research (Germany) , the  Centre National de la Recherche Scientifique (France) , the  CarboOcean (Norway ,  GOCE 511176-1 ) and  CarboChange (Norway ,  FP7 264879 ) projects of the  European Union , the  US National Science Foundation (United States ,  OCE-1068958 ), the international Scientific Committee on Oceanic Research (SCOR,  United States ,  OCE-0938349 ), and the  UK Ocean Acidification Research Programme  ( NE/H017046/1 ; funded by the  Natural Environment Research Council , the  Department for Energy and Climate Change  and the  Department for Environment, Food  and Rural Affairs).
Acknowledgements. We would like to thank  Joe Mason  and an anonymous reviewer for constructive comments of the discussion manuscript. This study is supported by the  French Agence Nationale pour la Recherche (ANR)  through the  ACTES  project  ANR-08-BLAN-0227/CSD-6  at LMD in Ecole  Normale D. -D. Rousseau  et al. :  A climatic interpretation of the "markers" Supérieure , and through grant  DE-SC0006694  from the  U. S. Department of Energy  and grant  DMS-0934426  from the  U. S. National Science Foundation  at  UCLA. This is LDEO contribution 2013-7725, LSCE contribution 5055, IPGP contribution 3434. Edited by: V. Rath The publication of this article is financed by  CNRS-INSU.
Predicting water-column phytoplankton biomass from near-surface measurements is a common approach in biological oceanography, particularly since the advent of satellite remote sensing of ocean color (OC). In the Arctic Ocean, deep subsurface chlorophyll maxima (SCMs) that significantly contribute to primary production (PP) are often observed. These are neither detected by ocean color sensors nor accounted for in the primary production models applied to the Arctic Ocean. Here, we assemble a large database of pan-Arctic observations (i. e. , 5206 stations) and develop an empirical model to estimate vertical chlorophyll a (Chl a) according to (1) the shelf-offshore gradient delimited by the 50 m isobath, (2) seasonal variability along pre-bloom, post-bloom, and winter periods, and (3) regional differences across ten sub-Arctic and Arctic seas.
Our detailed analysis of the dataset shows that, for the pre-bloom and winter periods, as well as for high surface Chl a concentration (Chl a surf ; 0. 7-30 mg m -3 ) throughout the open water period, the Chl a maximum is mainly located at or near the surface. Deep SCMs occur chiefly during the post-bloom period when Chl a surf is low (0-0. 5 mg m -3 ). By applying our empirical model to annual Chl a surf time series, instead of the conventional method assuming vertically homogenous Chl a, we produce novel pan-Arctic PP estimates and associated uncertainties. Our results show that vertical variations in Chl a have a limited impact on annual depth-integrated PP. Small overestimates found when SCMs are shallow (i. e. , pre-bloom, post-bloom > 0. 7 mg m -3 , and the winter period) somehow compensate for the underestimates found when SCMs are deep (i.
Vertical chlorophyll a database and regional delimitation A large dataset of vertical Chl a profiles collected in the Arctic Ocean was assembled (see details in Fig. 1  and Table 2, and see Table  1  for a list of symbols and units). The major dataset used was the ARCSS-PP (Arctic System Science primary production; http://www. nodc. noaa. gov/cgi-bin/ OAS/prd/accession/details/63065), which was recently compiled by  Matrai et al. (2013). Matrai et al. (2013)  described in details the spatiotemporal heterogeneity of the dataset. It should be noted that intrinsic and potential biases related to the dataset are inevitable and caution is recommended. It consists of a total of 14 791 stations visited once over a 50 yr period. For a significant number of stations in this dataset, only surface or shallow Chl a concentrations were available.
Those stations were not considered in the present study. Other datasets included in this study were collected mainly in the Chukchi Sea, Beaufort Sea, Canadian Archipelago, Hudson Bay, and Baffin Bay (Table  2 ). A rigorous validation of each vertical Chl a profile was achieved according to the following three criteria: (1) the uppermost sample of the profile had to be collected between the surface and a depth of 10 m; (2) the lowermost sample of the profile had to be collected at or below the base of the euphotic zone (1 % of the surface irradiance), or at least at a depth of 75 m or 15 m above the bottom depth (Z BOT ) for shallower stations; and (3) a minimum of four discrete sampling depths were required. Application of these stringent criteria reduced the initial dataset to 5655 profiles.
E d (λ, z) µmol photons m -2 s -1 total downwelling irradiance at λ at z E k PUR µmol photons m -2 s -1 photoacclimation parameter γ Ø spectral dependency of backscattering K d (λ, z) m -1 diffuse attenuation coefficient of downwelling irradiance at λ at z P B max mg C (mg Chl a) -1 h -1 chlorophyll a-specific Because of the difficulty to access the Arctic Ocean in winter, the database mostly covers the sea-ice-free months between April and September (Fig. 2a ). However, several campaigns (e. g. , CASES, CFL) allow a characterization of the Arctic winter conditions (i. e. , polar night and under-ice condition). Baffin Bay, Barents Sea, Beaufort Sea, Bering Sea, Canadian Archipelago, Chukchi Sea, Greenland-Norwegian seas, and the central Arctic Ocean are relatively well covered. The Hudson Bay and the Russian seas are, unfortunately, poorly sampled in both space and time (Figs.