dataesr/scientific-paragraphs-categorization huggingface dataset:
Trends in SWEmax, which indicates water resource and streamflow potential just before spring melt, are shown in Fig. 4. The figure represents averages from the same datasets used to generate the SCF trends in Fig. 1. Trends are negative over much of Canada, indicating a reduction over time in SWEmax at the onset of the melt season each spring. Because SWE varies with the amount of accumulated snowfall, we may expect a weaker relationship to surface temperature and a stronger connection to precipitation trends. Figure  5  shows trends in annual snowfall estimated from the CAN-GRD dataset  (Milewska et al. , 2005)  based on interpolated adjusted station data from Mekis and  Vincent (2011)  and monthly rain-snow fraction obtained from ERA-Interim 6hourly 2 m air temperature data assuming a 0 • C threshold for rain-snow separation.
g. Kwok and Rothrock, 2009; Haas et al. , 2010; Laxon et al. , 2013; Richter-Menge and Farrell, 2013; Kwok and Cunningham, 2015; Tilling et al. , 2015). These studies indicate thick-  ness declines are greater in the Beaufort Sea compared to the north facing coast of the CAA, which still contains some of the thickest sea ice in the world. Unfortunately, the spaceborne sensors used to obtain sea ice thickness information are not of sufficient spatial resolution to provide reasonable thickness estimates within the CAA. However, the Canadian Ice Service record of in situ landfast ice thickness measurements represents one of the longest datasets in the Arctic,and spans over 5 decades  (Howell et al. , 2016b). However, the seasonal behaviour of landfast ice thickness can provide useful information for understanding the inter-annual variability because ice growth is almost entirely due to thermodynamic forcing.
Historical datasets show the fraction of Canadian land and marine areas covered by snow and ice is decreasing Observations show decreased SIC in all seasons and decreased terrestrial SCF in fall (delayed snow cover onset) and spring (earlier snow melt). There is regional and seasonal variability in the direction and strength of the trends (for example, some increases in spring snow cover across bo-real western Canada) due to seasonal and spatial variability in surface temperature trends resulting from natural climate variability. There is evidence of decreasing annual maximum SWE (reflective of shallower snow depth) consistent with the study of  Mudryk et al. (2015)  and trends of annual maximum snow depth reported in  Vincent et al. (2015). There is only regional evidence (western CAA) of increasing winter season snow accumulation and hence higher SWE across Arctic Canada.
The majority of previous assessments of snow cover trends (i. e. Derksen and Brown, 2012; Hernandez-Henriquez et al. , 2015; Derksen et al. , 2016; Kunkel et al. , 2016)  were based on the NOAA snow chart climate data record (NOAA-CDR;  Estilow et al. , 2015)  selected on the basis of the longest available record. The reliance on individual datasets such as the NOAA-CDR, however, makes trends prone to uncertainty due to the inherent uncertainties in an individual dataset (for instance, see  Brown and Derksen, 2013, and Mudryk et al. , 2017,  for issues related to the NOAA-CDR). It is clear that consideration of multiple datasets either as a means of showing the range of trends from individual datasets  (Mudryk et al. , 2017) , for calculating confidence intervals around an individual dataset  (Brown and Robinson, 2011) , or to benchmark other datasets  (Brown et al.
, 2010; Hori et al. , 2017 ) is a more robust approach. However, in this case, we acknowledge that the availability and use of multiple datasets came at the sacrifice of time series length (the NOAA record extends back to 1967, whereas passive microwave satellite data and reanalyses such as ERA-Interim only begin in 1979). 2. Alpine snow poses a unique challenge to trend analysis because the coarse spatial resolution of gridded products used for climate analysis cannot resolve the high degree of spatial variability (driven by land cover variability and steep topographic gradients) in alpine regions. Wrzesien et al. (2018)  have shown that blended gridded products like the one used in this analysis may significantly underestimate SWE in alpine regions. Because alpine show trends vary with elevation in a complex manner  (Sospedra-Alfonso et al.
, 2015). 4. There is a strong association between the magnitude of warming and snow and ice loss both in observational datasets and climate model simulations, with projected declines in snow and sea ice cover proportional to www. the-cryosphere. net/12/1157/2018/ The Cryosphere, 12, 1157-1176, 2018 the amount of future warming  (Thackeray et al. , 2016; Mudryk et al. , 2017; Notz and Stroeve, 2016). The multi-model mean projections indicate decreasing snow and ice cover because the multi-model mean projects a warmer climate by mid-century. Within this multimodel mean, however, individual climate model realizations contain regions and seasons with cooling trends (see  Mudryk et al. , 2014). It is important to remember, therefore, that we live in a single climate realization, while models produce dozens of potential realizations of a future climate.
Acknowledgements. This work represents " Deliverable 2" of the CanSISE Network. CanSISE was funded under the auspices of the  Natural Science and Engineering Research Council of Canada's Climate Change and Atmospheric Research Program , with additional support provided by  Environment and Climate Change Canada , the  Pacific Climate Impacts Consortium , and the  University of Toronto. The original manuscript was improved thanks to comments from  Christoph Marty  and one anonymous reviewer. Edited by:  Martin Schneebeli Reviewed  by:  Christoph Marty  and one anonymous referee.
Data availability. Environment and Climate Change Canada's Canadian Centre for Climate Modelling and Analysis executed and made publicly available the CanESM2-LE simulations used in this study. Eric Brun provided data from the Crocus snowpack model, and Ross Brown provided the Brown dataset. These data are available from the paper's authors upon request. The remaining datasets are available for download via the links and references provided in Sect. 2. 1 and 2. 2.
, 2016). By the late Holocene, concentrations had risen, and the highest levels were recorded in the 1980s  (Campbell et al. , 2017a). Recommendations. Modern seasonal and annual variability of OCS can be validated with smaller vertical profile datasets, e. g. ,  Kato et al. (2011) , and data from flights, e. g. ,  Wofsy et al. (2011). Interhemispheric variability on millennia timescales requires ice core data from the Northern Hemisphere: all current ice core data are from the Antarctic  (Aydin et al. , 2016).
, 2015). One reason for the gap between the two recent inventories is that the top-down study used an optimization approach in which the result was limited to the a priori range, 150 to 364 Gg S yr -1. Both datasets indicate that most anthropogenic sources are in Asia. Biomass burning is generally accounted for as a category separate from anthropogenic emissions. Several airborne campaigns have observed increases in OCS concentrations in air masses from nearby burning events  (Blake et al. , 2008). The most recent estimate suggests that biofuels, open burning, and agriculture residue are 63, 26, and 11 % of the total OCS biomass burning emissions, respectively  (Campbell et al. , 2015). Recommendations. Anthropogenic OCS emissions experience large year-to-year variation  (Campbell et al. , 2017a).
16±0. 2 for C 4 plants  (Stimler et al. , 2010b)  and 1. 99±1. 44 for C 3 plants (Fig. 2 ). We further assume a 150-day growing season with 12 h of light per day for the purposes of converting between annual estimates of GPP and field measurements calculated in per-second units, though this obviously does not represent the diversity of biomes' carbon assimilation patterns. Additionally, we assume that plants in desert biomes photosynthesize using the C 4 pathway. Converting annual estimated F COS from an annual estimate to a persecond estimate is sensitive to our growing season assumption. The lack of soil OCS flux time series datasets makes a more sophisticated upscaling approach ineffective. Anticipated fluxes from soils and plants are therefore combined in this purposely simple method, scaled to the area of the biome extent, and presented in Table  4  as annual contributions to the atmospheric OCS budget.
Global and regional terrestrial GPP estimates Here we describe work using OCS observations to assemble more information about ecosystem functioning on different scales. Estimates disagree in their diagnoses of global  (Piao et al. , 2013)  and regional  (Parazoo et al. , 2015)  GPP magnitude and spatial distribution in North America  (Huntzinger et al. , 2012) , the Amazon  (Restrepo-Coupe et al. , 2017) , and Southeast Asia  (Ichii et al. , 2013). Feeding observations of OCS uptake over land into transport models informs the spatial distribution and magnitude of GPP. With the suite of OCS flask and satellite data available, we describe studies that examine OCS fluxes with the top-down approach. Finally, we examine GPP estimates on very long temporal scales using the OCS ice core record.
11 ). These results highlight the potential of current in situ OCS measurement to reveal model GPP and respiration biases. Recommendations. While current datasets can support or refute current land surface model GPP data products over North America, evaluating modeled surface GPP fluxes with OCS observations would benefit from a broader network of continuous OCS observations. Unfortunately, satellite data are not currently sensitive to concentrations at the surface. Maintaining a network of tall towers with continuous OCS measurements over more than one continent could, in conjunction with upper-troposphere measurements from satellites, provide the data needed to refine nextgeneration land surface models.
OCS to probe variables other than GPP OCS and CO 2 uptake within plant leaves is partly regulated by the opening of stomata on leaf surfaces. Stomatal conductance is typically determined from combined estimates of transpiration, water vapor concentration, and leaf temperature. That approach can be particularly challenging at the canopy scale, where transpiration is difficult to distinguish from non-stomatal water fluxes (i. e. , evaporation from soil and canopy surfaces) and to upscale from sap flux measurements  (Wilson et al. , 2001). Use of OCS uptake involves the similar but more tractable challenge of distinguishing the canopy OCS uptake from soil OCS uptake or emission, as in  Wehr et al. (2017). OCS data can also look at changes in uptake activity when plants are grown in elevated CO 2 environments  (White et al.
So far, few research schemes have taken advantage of these relationships. 4 Available datasets 4. 1 OCS satellite data products Global OCS concentrations have been retrieved from several satellite instruments, including NASA's TES  (Kuai et al. , 2014) , the Canadian Space Agency's Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS)  (Boone et al. , 2005) , and the European Space Agency's MIPAS  (von Clarmann et al. , 2003; Glatthor et al. , 2017)  and Infrared Atmospheric Sounding Interferometer (IASI)  (Camy-Peyret et al. , 2017; Vincent and Dudhia, 2017). Among these instruments, TES and IASI are nadir-viewing instruments (i. e. , looking downwards from space towards the surface), while ACE-FTS and MIPAS are limb scanners (i. e. , looking through the atmosphere tangentially).
FTIR data Ground-based FTIR retrievals of OCS are sensitive to the altitudes between the surface and 30 km, and can therefore more directly capture the variations near the surface compared to satellite data. There are two networks of FTIR spectrometers: the Network for the Detection of Atmospheric Composition Change (NDACC), recording the mid-infrared spectra including the OCS bands, and the Total Carbon Col-umn Observing Network (TCCON), mainly focusing on the near infrared with only some sites including the OCS bands. The FTIR remote-sensing measurement is an indirect measurement and therefore needs to be calibrated to in situ observations to have the same scale when combining the datasets. For example,  Wang et al. (2016)  added an offset when comparing FTIR retrievals and HIAPER Pole-to-Pole Observations (HIPPO) to the same model.
Published datasets exist for the periods 1993-1997  (Griffith et al. , 1998 ), 1978 -2002 (Rinsland et al. , 2002 ), 2001 -2014 (Kremser et al. , 2015 ), 2005 -2012 (Wang et al. , 2016 ), and 1995 -2015 (Lejeune et al. , 2017)  and by an airborne Fourier spectrometer for the period 1978-2005  (Coffey and Hannigan, 2010). Balloonborne FTIR data are available starting in 1985  (Toon et al. , 2018).
Tower and airborne data Data are available from two kinds of airborne sampling: survey flights and atmospheric chemistry projects. OCS measurements from aircraft began in the late 1980s, using both in situ and flask collection with subsequent analysis by GC-MS (e. g. ,  Bandy et al. , 1992 Bandy et al. , , 1993;; Hoell et al. , 1993; Thornton et al. , 1996; Blake et al. , 2008, etc). The airborne survey flight data are designed to sample background air at set locations on a regular basis over long time periods and are part of the NOAA/ESRL/GMD Global Greenhouse Gas Reference Network's aircraft program (http://www. esrl. noaa. gov/ gmd/ccgg/aircraft/index. html, last access: 6 June 2018; an update of results published in  Montzka et al. , 2007). This data collection started in 1999 at a range of locations and has been used extensively in analysis of the continental US carbon budget (e.
g. ,  Campbell et al. , 2008; Hilton et al. , 2017). OCS has been measured at 10 globally distributed sites in the AGAGE network using the MEDUSA GC-MS. The data for the Jungfraujoch site are presented in  Lejeune et al. (2017). Larger-spatial-scale, shorter-time-interval survey flights include the  HIPPO (2009 HIPPO ( -2011) )  and ATom (2016-2018) airborne programs, which predominantly sample OCS over remote marine locations. Atmospheric chemistry flights are designed to understand chemical processing and pollution transport and include sampling as part of pollution transport across the Pacific (e. g. , Pacific Exploratory Mission-West A (PEM-A);  Thornton et al. , 1996)  or Transport and Chemical Evolution over the Pacific experiment (TRACE-P), which sampled Asian outflow dominated by anthropogenic OCS emissions in 2001  (Blake et al.
, 2004). Other projects included sampling of OCS over continents (e. g. , over the US in 2004;  Blake et al. , 2008). OCS measurements have been made from tall towers using flasks and subsequent analysis by GC-MS. Most long-term tall-tower observations have been conducted as part of the NOAA/ESRL/GMD tower network  (Montzka et al. , 2007). These data from 11-12 sites include continuous sampling from 2000 onward at a daily or twice-daily time basis for most of the record.
Oceanic measurements OCS measurements in the surface ocean comprise about 6000 ship-based measurements. These samples are usually taken at a depth of 0-5 m below the ocean surface and analyzed by gas chromatography with various detectors or offaxis integrated cavity output spectrometry. Table  3  gives an overview on available measurements. A central database for ship-based OCS measurements is desired to derive global patterns and facilitate model comparison. Measurements of the precursor gas CS 2 are scarcer than OCS measurements. Samples for CS 2 are taken usually in a similar way to OCS samples from the same depth range and analyzed using gas chromatography and mass spectrometry detection.
Acknowledgements. This review was initiated at a workshop, "The biosphere-atmosphere exchange and global budget of carbonyl sulfide", held in Hyytiälä, Finland, on 5-9 September 2016. The authors would like to thank  Colm Sweeney ,  Joost de Gouw ,  Mark Zahniser ,  Grayson Badgley ,  Leander Anderegg ,  Ian Baker ,  Ben Miller ,  Murat Aydin , and  James Chalfant  for helpful discussion and data sharing. We acknowledge the integrative activities through an  OCS/CO 2 /SIF  workshop funded by the  Keck Institute of Space Studies. Funding  to support this work included the following:  Mary E. Whelan  was supported by a  National Science Foundation (NSF ) postdoctoral fellowship # 1433257 ;  Mary E. Whelan  and  J. Elliott Campbell  were supported by  NSF  grant # 1600109 ;  Eric S. Saltzman  was supported by  NSF   OPP-1142517 ;  Georg Wohlfahrt ,  Felix M.
Spielmann , and  Florian Kitz  acknowledge support by the  Austrian Science Fund ,  FWF  project # P27176-B16 , and the  Tyrolean Science  fund project # UNI-0404/1801 ;  Huilin Chen  was supported by  NOAA  contract  NA13OAR4310082 ;  Timo Vesala ,  Ivan Mammarella , and  Kukka-Maaria Erkkilä  were supported by the  Academy of Finland Centre of Excellence  grant # 307331 ,  Academy Professor  projects # 284701  and # 282842 ,  ICOS-Finland  # 281255 , and  CARB-ARC  # 286190 ;  Ulli Seibt  and  Wu Sun  were supported by  NSF  grant # 1455381 ;  Julia Marshall  was supported by the  DFG , Project  MA 6668/1-1. ; Teresa E. Gimeno  and  Lisa Wingate  received funding from the  IdEx postdoctoral program  of the  Université de Bordeaux  and by a  Marie Skłodowska-Curie Intra-European Fellowship , grant agreement # 653223 ;  Lisa Wingate  and  Thomas Launois  received funding from the  European Research Council  under the European Union's  Seventh Framework Programme ,  FP7/2007-2013 , grant agreement # 338264 ;  Jérôme Ogée  received funding from the  Agence National de la Recherche ,  ANR  award # ANR-13-BS06-0005-01 ;  Yoko Katayama  received a  Grant-in Aid for Scientific Research  (# 18310020 , # 23310051 , # 16H05884 , # 17H06105 , # 17J08979 ) from the  Ministry of Education, Culture, Sport, Science and Technology, Japan ;  Dan Yakir  was supported by the MINERVA foundation and the  Israel Science Foundation (ISF) ; and the  European Geosciences Union and Aerodyne Research, Inc.
2017; Tietsche et al. 2015; Karspeck et al. 2015; Shi et al. 2017; Valdivieso et al. 2017; Palmer et al. 2017; Masina et al. 2015; Storto et al. 2017 ). These ORA-IP studies have looked at various aspects of global ocean hydrodynamics (steric sea level, air-sea fluxes, ocean heat and salt content among others). The only ORA-IP publication with a polar focus has been  Chevallier et al. (2017) , who compared the representation of the sea-ice cover in the Arctic Ocean in 14 global reanalyses. Using a variety of in-situ and satellite-based observational datasets, they investigated mean states, trends and interannual variability in these reanalyses, focusing on sea-ice concentration (with extent and area), thickness (with volume), velocity and snow depth over sea ice. Chevallier et al. (2017)  showed consistency with respect to sea-ice concentration, which is primarily due to the constraints in surface temperature imposed by atmospheric forcing, and ocean-ice data assimilation.
(2017)  have now been updated and their updates are evaluated in the present paper which allows direct comparisons with their results. In this study, we aim for a comprehensive evaluation of ten selected ORA products (C-GLORS025v5, ECDA3, GECCO2, Glorys2v4, GloSea5-GO5, MOVE-G2i, ORAP5, SODA3. 3. 1, TOPAZ4 and UR025. 4) in the Arctic and Southern Oceans (Table  1 ). For these regions the diagnostics target the following topics: hydrography; ocean heat (OHC), salt content (OSC); ocean transports; mixed layer depth (MLD); sea-ice concentration (SIC) and thickness (SIT); and snow thickness over sea ice. The ORA product biases against observed reference data and their mutual spread are quantified, and possible reasons for discrepancies discussed. The scope of our manuscript is to provide a broad stateof-the-science overview of ocean reanalyses, plus our best estimate of what the truth might look like.
In this context, we will check if the MMM is a useful estimate. As we will repeatedly show, it is a set of fields which is generally most consistent with observations. This is what many users require, although it may not be best suited to analysing dynamical or physical processes, for example. If a user does not want the MMM, but would prefer a single ORA output, for instance to understand the dynamics, this paper does not seek to tell the user which one to use, but in addition to providing a general evaluation, it is able to show which are outliers for certain variables, which can still be very useful. We pay particular attention to the performance of the MMM compared to individual products and the identification of outliers. Notably, as the ORAs assimilate observations they are not independent of some of the reference data they are compared to.
2012). The first AOMIP phase proved that the co-ordinated community approach is the most effective way to address the degree of uncertainty of model results. During AOMIP, ocean-ice models with data assimilation were first introduced to the community (see for example  Kauker et al. 2009 ). Later, FAMOS has been a very productive collaborative effort by producing more than 60 publications including a special issue in the Journal of Geophysical Research  (Proshutinsky et al. 2016 ). The AOMIP/FAMOS modelling studies document, in addition to their scientific results, important ORA developments in the polar regions from the reanalysis methodological perspective. However, a systematic diagnostic analysis of ORA products in the Arctic is missing from the AOMIP/FAMOS studies. This is likely due to the relatively late appearance of ORAs, which have a global scope, in contrast to the regional AOMIP/FAMOS one, and to the strong process focus of AOMIP/FAMOS.
For example,  Xie et al. (2017)  analysed multi-decadal ensemble simulations from the regional TOPAZ4 ocean-ice data assimilation system in the Arctic and found that TOPAZ4 performed better with respect to near-surface ocean variables compared to subsurface ocean and sea-ice thickness due to sparse observations. Furthermore, the TOPAZ4 skill improved as the polar observation network became denser. Specifically, TOPAZ4 has a too cold and diffuse Atlantic water (AW) layer in the Arctic leading to a cold bias of 0. 3 • C at around 400 m, while the Barents Sea is too warm and saline. Although, the decadal reduction of TOPAZ4 sea-ice extent is close to the observed, its regional distribution has a dipole bias-sea-ice concentration is too low close to the ice edge and too high in the central pack, due to the missing seaice heat capacity of TOPAZ4 sea-ice model.
Xie et al. (2017)  also found that the TOPAZ4 sea ice is too thin, on average. Lien et al. (2016)  applied objective statistical methods to assess the added value of data assimilation in three ocean models, including TOPAZ4, for hydrography, volume and heat transports in the Nordic Seas (the Greenland, Iceland, Norwegian and Seas) and the Barents Sea. They found that both data assimilation and higher model resolution improved the model realism. Specifically, high model resolution in ocean and atmospheric forcing improved the representation of variables closely related to forcing, such as sea-ice concentration and sea surface temperature. Hydrographic data assimilation had a tendency to reduce hydrographic biases, but its effect on the liquid ocean transport remained limited  (Zuo et al. 2011).