dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of Salinity Bias A similar analysis was done for salinity differences S on the σ 1 surface across the pairs. For the cohorts of CTD type for which there were enough pairs (SBE-41/41CP), any bias in the dataset was much smaller than 0. 01 PSS-78 (Figure  12B ). Across most of the water column, the bias was about 0. 001 PSS-78 for the Druck and Kistler pressure sensor cohorts. There was a small fresh bias that peaked around -0. 002 PSS-78 in the lower thermocline (400-800 dbar), but it was not evident in all the hemispheres. While GO-SHIP profiles do contribute to Argo's reference database used to assess salinity sensor drift, their small number, along with the fact that only about 15% of Argo profiles are adjusted, means that they likely do not dominate these estimates. Thus, it is remarkable that, in aggregate, Argo profiles show such small salinity bias compared to the contemporaneous GO-SHIP surveys.
01 PSS-78 of the GO-SHIP values. The Argo values can be clumped above or below the GO-SHIP estimates, and these are associated with a single float record, suggesting that float salinity can be biased at the 0. 01 PSS-78 level. A similar study was done by  Riser et al. (2008) , which compared salinities from 142 floats with shipboard CTD data collected along 32 • S in the South Pacific. On the 2. 4 • C potential temperature surface, it was found that float-derived salinities agreed with shipboard data to within 0. 01 PSS-78. This salinity accuracy is in accordance with the experience of the Argo delayed-mode teams and their ability to remove sensor drift or offsets. Positions, Subsurface Velocities, and Other Park-Phase Data.
Positions The ARGOS system uses the Doppler shift of received transmissions to estimate positions. As a result, its positioning accuracy depends on the number of satellites within range and the configuration of the constellation at the time the messages are received. ARGOS positions have four levels of accuracy ranging from better than 250-m radius to over 1,500-m radius. Some ARGOS position estimates are accompanied by an error ellipse, which gives a more exact error on individual positions than the broad horizontal error associated with the location classes. Floats that employ the Iridium satellite system for data communications use the Global Positioning System (GPS) to establish their positions. GPS tracking is more accurate than ARGOS tracking, with a typical GPS horizontal accuracy being about 8 m (with a 95% confidence interval).
2 cm s -1 in the Sea of Japan by using this method. In all of these efforts, the common difficulty in estimating velocities from Argo trajectory data results from a lack of timing information from the floats. In addition, for floats that use ARGOS communications, the locations at surfacing and descent are not well-known; the floats wait for unknown amounts of time at the surface prior to connecting with ARGOS satellites passing overhead in order to define a position, and then again wait for an undetermined amount of time after the last position before the float begins its descent. Newer float models that use Iridium communications return more timing information throughout the float mission, typically with a GPS fix at the beginning of the surface interval and a second GPS fix just prior to descending.
HOW TO CITE ARGO DATA: THE DYNAMIC DOI STRUCTURE The citation of Argo data used in scientific studies is a challenging subject since the Argo dataset is "dynamic, " evolving and growing in time. Dynamic data citation is an area of active research. To allow reproducibility of scientific studies that use Argo data, a snapshot of the entire dataset at the GDACs is preserved each month. The snapshot contains all the Argo data available at the time of the snapshot creation. To manage citation of this dynamic dataset, Argo adopted a Digital Object Identifier (DOI) format that gives a single DOI to track data usage, but that also allows users to cite specific time snapshots  (Merceur, 2016). The Argo DOI takes the form http://doi. org/10. 17882/42182#<nnnnn>, where <nnnnn> is the unique identifier for the specific time snapshot being used.
FUTURE CHALLENGES While the Argo Program has made monumental progress in the past two decades on the technical problems relating to the collection of CTD data by profiling floats, work on these issues continues to this day. Individual floats now provide quality data over many years, sending megabytes of data including basic CTD parameters and a myriad of other types of observations to the GDACs, followed by adjustment of the data in a finely tuned delayed-mode process. Yet there remains room for improvement in each of these areas. First, while many present floats provide excellent data for more than 5 years, there are still too many that fail in half of that time. While there are a number of reasons for these early failures, an all-too-often cause is the lack of adequate pre-deployment checks on the part of some float groups.
A central lesson from 20 years of Argo is that there is no substitute for vigilance in making sure floats are operating properly before they are deployed. Argo has spent a considerable effort in recent years in attempting to identify and rectify these group-to-group differences. Second, while many of the CTD data returned by the floats are of good quality, there has been an alarming rise in drift in measured salinity in recent years that is still not well-understood, as noted in section Problems Encountered for Salinity. Work has been underway for several years between float deployers, data users, and manufacturers to characterize and fix this problem, and these efforts continue. In this case, as in the past with the CTD pressure sensor issues, it has been shown to be essential to monitor data quality as closely as possible and for scientists and manufacturers to remain in close communication.
It is likely that there will always be sporadic problems with some components in the float and sensor supply chain that lead to compromised data quality, but the effects of such problems can be minimized with continued vigilance. A third challenge for Argo is the delayed-mode quality control (DMQC) process of the ever-growing and diversifying dataset produced by the float array. To date, DMQC has been conducted by operators at various institutions examining each profile and determining what adjustments are necessary based on comparison with reference data. With an existing dataset comprised of over 2 million profiles, the burden placed on human resources is considerable. Argo seemingly now has an opportunity to make use of recent developments in machine learning (ML) techniques in order to improve quality control procedures, as outlined by  Maze and Morrow (2017).
DISCUSSION AND CONCLUSION The original goal of the Argo Program was to provide a description of the mean state and variability in the upper 2 kilometers of the global ocean on sub-seasonal to decadal timescales. This aspiration was motivated by the success of WOCE in the 1990s to provide a first estimate of the state of the global ocean. The evolution of the float program from WOCE to Argo was not without technical challenges. The basic operation of the extant float buoyancy engine was problematic and needed redesign. The first CTD units in use performed poorly, and an effective alternative needed to be found. There was no systematic way to disseminate and manage real-time data. Furthermore, there were no agreed methods to compare data to reference datasets in order to make adjustments to measurements from floats once they were deployed.
, 2016). Argo data also underpin ocean and climate forecasting services, through their now dominant role in ocean model initialization at most forecasting centers. After 20 years, Argo has exceeded its original aspirations. Science writer Justin Gillis of the New York Times has described Argo as "one of the scientific triumphs of the age"  (Gillis, 2014). In this paper, we have aimed to describe the core Argo dataset collected over the first 20 years of the program. When Argo was first conceived, aspirational uncertainties for the measurements of pressure, temperature and salinity were based on experience with other ocean observing programs, such as hydrographic cruises and moorings, and with the experiences acquired during WOCE. Today, we have been able to estimate accuracies of 0. 002 • C for temperature, 2.
ACKNOWLEDGMENTS The authors would like to acknowledge the following people who have made significant contributions to the core Argo Program in the past 20 years but who have retired or departed:  Jeff Dunn ,  Ann Thresher-Australia ;  Bob Keeley ,  Ron Perkin-Canada ;  Shaohua Lin-China ;  Michel Arhan ,  Fabienne Gaillard ,  Catherine Lagadec ,  Michel Ollitrault-France ;  Sudheer Joseph-India ;  Shinya Minato ,  Keisuke Mizuno ,  Nobuyuki Shikama ,  Kensuke Takeuchi ,  Kazuhiko Hayashi ,  Mizuho Hoshimoto ,  Yoshiaki Kanno ,  Kensaku Kobayashi-Japan ;  HyunSuk Kang ,  SangBoom Ryoo ,  Moon-Sik Suk-South Korea ;  Stephanie Contardo ,  Rebecca McCreadie ,  Lesley Rickards ,  John Lawson-UK ;  Silvia Garzoli ,  Robert Molinari ,  Stephen Piotrowicz ,  Norge Larson-USA. We wish to thank the many individuals who have contributed to float design, fabrication, testing, and deployment.
Abstract. Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO 2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index  (LAI, 1981 (LAI, -2017) )  as well as other proxies for vegetation changes and identify several clusters of significant long-term changes.
To assess observed changes in vegetation over climatic timescales, we make use of a 37-year record of leaf area index (LAI) satellite observations  (1982 ( -2017, Global , Global  Inventory Modeling and Mapping Studies -GIMMS -LAI3g, Sect. 2. 1). The GIMMS LAI3g product is based on the Advanced Very High Resolution Radiometer (AVHRR) sensors, for which there are a number of shortcomings (no onboard calibration, no correction of orbit loss, minimal correction for atmospheric contamination, and limited cloud screening; Sect. 2. 1;  Zhu et al. , 2013; Chen et al. , 2019). To address these shortcomings, we also analyze a total of five different remote sensing products that pursue different strategies for dealing with the issues associated with AVHRR data (Sect. 2. 1). Due to some inexplicable variations in these datasets  (Forzieri et al.
Satellite observations of LAI Our analyses are based on an updated version (V1) of the leaf area index dataset (LAI3g;  Chen et al. , 2019)  based on the methodology developed by  Zhu et al. (2013). The data provide global year-round LAI observations at a 15 d (bimonthly) temporal resolution and 1/12 • spatial resolution. The record covers the period from July 1981 to December 2017. The complete time series of LAI3gV1 was generated using an artificial neural network trained on data of the overlap period of the Collection 6 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI dataset  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  and the latest version (third generation) of the GIMMS group Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data (NDVI3g).
The latter have been corrected for sensor degradation, inter-sensor differences, cloud cover, observational geometry effects due to satellite drift, Rayleigh scattering, and stratospheric volcanic aerosols  (Pinzon and Tucker, 2014). The LAI3g datasets prior to 2000 were not evaluated due to a lack of required field data  (Zhu et al. , 2013; Chen et al. , 2019). After 2000, the quality of the LAI3g dataset was assessed through direct comparisons with ground measurements of LAI and indirectly with other satellite-databased LAI products, as well as through statistical analysis with climatic variables such as temperature and precipitation variability  (Zhu et al. , 2013). Various studies used the predecessor LAI3gV0 and the related dataset of fraction of absorbed photosynthetically active radiation (FAPAR;  Anav et al.
, 2013; Forkel et al. , 2016; Zhu et al. , 2016; Mao et al. , 2016; Mahowald et al. , 2016; Piao et al. , 2014; Poulter et al. , 2014; Keenan et al. , 2016)  and its successor LAI3gV1  (Winkler et al. , 2019a, b; Chen et al. , 2019). Leaf area index is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half of the green needle surface area in needleleaf canopies in both satellite observations and models (Earth system models -ESMs -and LSMs). It is expressed in units of square meters of green leaf area per square meter of ground area. Missing values in the LAI3gV1 dataset are filled using the climatology of each 16 d composite during 1982-2017. We use the annual averaged LAI of each pixel in this study. In addition to the GIMMS LAI3g product, we analyze the MODIS LAI record as well as four other long-term global remote sensing datasets: the Global Land Surface Satellite LAI product (GLASS LAI), the Global Mapping LAI product (GLOBMAP LAI), the NDVI product from the Land Long Term Data Record (LTDR), and a new FAPAR product from the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record Program.
The MODIS LAI data analyzed in this study are based on the combined Terra and Aqua MODIS LAI products (MOD15A2H and MYD15A2H) from Collection 6 (C6;  Myneni et al. , 2015a, b). These LAI datasets are provided at an 8 d temporal resolution with a 500 m sinusoidal projection covering the entire globe. The two LAI datasets are aggregated into 16 d composites by taking the mean of all valid LAI values after an additional data quality assessment is performed (for more details, please see  Chen et al. , 2019). The data are then spatially aggregated to 1/20 • spatial resolution and cover the period from 2000 to 2019. The GLASS LAI dataset  (Xiao et al. , 2014)  is based on AVHRR, MODIS, and Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellite (CY-CLOPES) reflectances and LAI products.
The full time series was generated using an artificial neural network (general regression neural network) that has been trained on the overlap period of AVHRR, MODIS, and CYCLOPES reflectances and LAI products  (Xiao et al. , 2014). We use the latest version of the GLASS LAI dataset, which covers the period from 1981 to 2018 and is provided at an 8 d temporal and a 1/20 • spatial resolution. The GLOBMAP LAI dataset  (Liu et al. , 2012 ) is a reconstruction of the historical AVHRR data by a quantitative fusion with MODIS data. The algorithm inverses a geometrical optical model to establish pixel-level relationships between AVHRR and MODIS LAI for the overlapping period, which are then used to reconstruct AVHRR LAI back to the initial year of the record. We use the latest version of the GLOBMAP LAI dataset, which covers the period from 1981 to 2017 and is provided at a 15 d temporal and a 1/13.
75 • spatial resolution. The NDVI dataset of NASA's LTDR  (Pedelty et al. , 2007)  project is based on a reprocessing of long-term AVHRR reflectances applying improved preprocessing techniques and atmospheric corrections used in the generation of MODIS datasets. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and nearinfrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view  (Pedelty et al. , 2007). Atmospheric corrections include corrections for Rayleigh scattering, ozone, water vapor, and aerosols. We use the recently published version 5 (v5) of the LTDR NDVI dataset, which covers the period from 1981 to 2019 and is provided at a daily temporal and a 1/20 • spatial resolution. The FAPAR product from the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record Program provided by National Centers for Environmental Information (NCEI) is based on carefully calibrated and corrected land surface reflectances from AVHRR sensors  (Claverie et al.
, 2016). The algorithm relies on artificial neural networks calibrated per different land cover type using the MODIS FAPAR dataset. We use the latest version of the FA-PAR dataset from 1981 until 2019, which is provided at a daily temporal and a 1/20 • spatial resolution. We aggregate all datasets to annually averaged values and to spatially area-weighted averages for different biomes as defined by the mask (Sect. 2. 2).
The pixels in the bands 25-50 • N and S remain unchanged because the MODIS land cover product already realistically represents the forested area in these latitudes. Table  S1  provides a detailed overview of the conflation of the MODIS land cover product, GLDAS land cover product, and the MODIS tree cover product. The final biome map (originally resolved at 0. 05 • ) is regridded to the different resolutions of the AVHRR sensor and the models simulations (MPI-ESM and TRENDYv7) applying a largest-areafraction remapping scheme. Based on the observational LAI dataset, we define various clusters for greening or browning in most biomes: North American Tundra (NAm Tundra), Eurasian Tundra (EA Tundra), North American Boreal Forests (NAm Brl F), Eurasian Boreal Forests (EA Brl F), Temperate Forests (Tmp F), Tropical Forests (Trp F), Central African Tropical Forests (CAf Trp F), Northern African Savannas and Grasslands (NAf Sv Gl), Southern African Savannas and Grasslands (SAf Sv Gl), Cool Grasslands (Cool Gl), and Australian Shrublands (Aus Sl).
The forcing data consist of observed atmospheric CO 2 concentrations; observed temporal patterns of temperature, precipitation, and incoming surface radiation from the CRU JRA-55 reanalysis  (Quéré et al. , 2018; Harris et al. , 2014) ; and human-induced land cover changes and management from an extension of the most recent Land-Use Harmonization (LUH2) dataset  (Hurtt et al. , 2011; Quéré et al. , 2018). In this study, we only analyze output for the period 1982-2017 (matching the observational record) from models providing spatially gridded data for all five simulations. A few models provide LAI at the level of plant functional type (PFT). We calculate the average value of all LAI values on the PFT level multiplied by their land cover fraction for each grid cell. All model outputs were spatially regridded to a common resolution of 1 • based on a first-order conservative remapping scheme  (Jones, 1999).
Processing of the gridded data Areas of significant change in LAI are estimated using the non-parametric Mann-Kendall test, which detects monotonic trends in time series. In this study, we set the significance level to p ≤ 0. 1. An alternative statistical test for trend detection  (Cox-Stuart test; Sachs, 1997)  yields approximately the same results. The trends are calculated either for time series on the pixel level or for area-weighted large-scale aggregated time series (e. g. , biome level). Either we define greening (browning) as a positive (negative) temporal trend, or for better comparison among models and observations as well as for a better global comparison across diverse biomes, we express these trends relative to the initial LAI level at the beginning of the observational record (average state from 1982-1984), denoted as (% per decade).
3. 8 The global signal switches to a minor negative trend in the second half of the observational period Natural vegetation shows a slight negative trend for the period 2000-2017 (ca. -0. 4 % per decade; Fig. 4e ). This estimate is within the range of the overall uncertainty and, thus, should be interpreted with caution. Note that the net change in leaf area is still positive when considering only significantly changing pixels (inset in Fig. 2 ). To provide confidence in this result, we analyze three additional remote sensing datasets for LAI (MODIS-LAI, GLASS-LAI, and GLOBMAP-LAI) as well as for the normalized difference vegetation index (LTDR-NDVI) and for the fraction of absorbed photosynthetic active radiation (NCEI-FAPAR), both proxies for leaf area changes (see Sect. 2. 1 for details).
We calculate time series of changes relative to the average baseline value from 1982-1984 to obtain comparability between the conceptually different estimates for changes in natural vegetation (Fig. 5a-c ). Next, we compare the trends for the entire observational period  (1982 ( -2017 ( /2018, Fig. 5d, Fig. 5d ) with trends of the more recent past  (2000 ( -2018, Fig. 5e, Fig. 5e ). Three of the additional four long-term datasets show a weakening of vegetation greening for the second half of the observational period in accordance with the GIMMS LAI3g dataset (GLASS-LAI and especially LTDR-NDVI also show a reversal of the sign to a negative trend). In contrast, the dataset GLOBMAP-LAI depicts a substantial strengthening of the positive trend in LAI for the recent decades. However, the dataset also shows a suspicious jump in the year 2001, which could be an artifact related to problems in the fusion of the AVHRR and MODIS data  (Piao et al.
, 2019). Furthermore, GLOBMAP-LAI generally shows the largest discrepancy among all datasets when compared to ground measurements  (Xiao et al. , 2017). The shorter-term record of MODIS-LAI depicts a stable moderate greening trend for the time span of 2000-2019. Since the MODIS record cannot provide any information on the state of the vegetation in the 1980s and 1990s, we cannot assess whether MODIS would also depict a slowdown of the overall greening trend over this time period. Note that the comparability of relative trends between the long-term remote sensing products (baseline period 1982-1984) and short-term MODIS-LAI (baseline period 2000-2002) is limited. Overall, the analyses of the different remote sensing datasets support to a large extent the findings drawn from the GIMMS LAI3g dataset.
For these reasons, as well as for reasons described earlier in the introduction, we focus on the GIMMS LAI3g dataset in these analyses, but we note that the single-product-centric view may imply some additional uncertainties besides the general uncertainty associated with AVHRR-based datasets. Models reproduce the flattening of the trend and even the reversal in the sign only when the physiological effect of CO 2 is excluded or with a complete absence of CO 2 forcing (Fig. 4e ). A recent study by  Wang et al. (2020)  suggests, by analyzing various observational datasets, that global CO 2 fertilization has declined in recent years and highlights that land surface models are not reproducing the magnitude of this decline, mainly due to the underrepresentation of nutrient limitation. While these results are consistent with ours, we are not convinced that one can infer a decline in or saturation of the CO 2 fertilization effect from these observational datasets.
S4 ). Interestingly, the MODIS record does not exhibit this browning cluster  (Chen et al. , 2019) , though it has been reported in other independent observational datasets  (Zhou et al. , 2014). Also, atmospheric CO 2 inversion studies have identified negative trends in carbon uptake for this region  (Fernández-Martínez et al. , 2019) , which corroborates our results based on the LAI3g dataset.