dataesr/scientific-paragraphs-categorization huggingface dataset:
0. 02-1. 3 mg m -3 , respectively). It should be noticed that this "extreme" value of 1. 3 mg m -3 was recorded in the Peru upwelling. OUTPACE and BIOSOPE data differed in only two spectral bands. OUTPACE and BIOSOPE radiance data with the OCR UV-Vis radiometer differed in only two spectral bands. The PC1 axis was linked to Chla concentration for both cruises. PC2 was linked to another optically active variable, independent of Chla. For OUTPACE, the different behaviors in nL w (565) (yellow) and nL w (490 nm) (green) are significant compared to the sensitivity of the instrument. The significance of PC2 (linked to an increase of nL w (565) for Chla rich waters) is clear. It is absent in the South East Pacific meaning that all these processes were not occurring during the BIOSOPE cruise, i. e.
surface and in-water radiometers were calibrated before the cruise. Mostly cloudy sky conditions existed during the profiles (only a few acquisitions were made under clear skies), Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-570 Manuscript under review for journal Biogeosciences Discussion started: 15 January 2018 c Author(s) 2018. CC BY 4. 0 License. and at SD5 at 17:30-19:00 they were made under a heavy shower. SD3, SD4, and SD13 profiles were not available (night stations). Details of the casts can be found in Appendix A. Determination of nL w (λ) was conducted from values of L u (Z, λ), diffuse attenuation coefficient for upward radiance (K L (λ) in m -1 ), water-leaving radiance [L w (Z, λ)] and E s (λ) (see calculations in Appendix A). The nL w (λ) data presented in this study are average values of two to three upward radiance casts (coefficient of variation < 8% for all the stations concerned).
Proc was the other dominant group impacting the Chla biomass. Two parts of the SWTP ocean at 174°E (SD7): (1) the western part of the MA between New Caledonia and Vanuatu, impacted by a large contribution by Trichodesmium and (2) the eastern part of the transect (FI), more oligotrophic and impacted by Prochlorococcus and other picoplanktonic groups. LDB was the only exception showing a high abundance of both Trichodesmium and other groups, with TChla proportions of Trichodesmium, Syn+Peuk, Micro, Nanoeuk, and Proc of 25, 7, 1. 4, 5 and 45 %, respectively. OUTPACE and BIOSOPE radiometric data show that the South West and South East Pacific surface waters exhibited similar ranges of values for nL w (λ) and Chla (0-0. 58 and Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-570 Manuscript under review for journal Biogeosciences Discussion started: 15 January 2018 c Author(s) 2018.
Acknowledgments. The authors thank the crew of the  R/V L'Atalante  from operation at sea. We acknowledge  Joséphine Ras ,  CNRS , for the  OUTPACE HPLC databasis and Crystal Thomas ,  NASA , for surface and DCM HPLC data. We thank  Mireille Pujo-Pay  for her scientific advices on board  R/V Atalante. We thank  Benjamin Blanc  for sorting and validating images from the  UVP-5. We acknowledge  David Varillon ,  IRD US IMAGO , and  Philippe Gérard  for Chla analyses and the administrative staff of  Center of Noumea. Jacques Neveux  and  Rüdiger Röttgers  are acknowledged for helpful discussions during the elaboration of the manuscript. This is a contribution of the  OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project (https://OUTPACE. mio. univ-amu. fr/)funded by the  French research national agency  ( ANR-14-CE01-0007-01 ), the  LEFECyBER program (CNRS-INSU) , the  GOPS program (IRD)  and the  Centre National d'Etudes Spatiales  ( BC T23 ,  ZBC 4500048836 ).
The OUTPACE cruise (http://dx. doi. org/10. 17600/15000900) was managed by  MIO Institute from Marseilles (France). The  National Science Foundation  supported SD under grant  OCE-1434916. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-570Manuscript under review for journal Biogeosciences Discussion started: 15 January 2018 c Author(s) 2018. CC BY 4. 0 License.
This will require a census survey of plant species abundance in each of the SPs, based on the relative frequency of each species. The Plant List Database (http://www. theplantlist. org) is used as the reference nomenclature for plant species classification, further details can be found in  Pellis and Papale (2017). Further information can be found in the ICOS Ecosystem Instructions for Plant Species Reporting. The visual assessment procedure, if also complemented with hemispherical photographs or phenocam images, is also important for the identification of the occurrence of biotic or abiotic disturbance events, such as the presence of pests or diseased material within the plant canopy as well as recording changes to the canopy structure due to harvest (e. g. thinning of forest canopies) or the influence of wind (e.
The occurrence of disturbance events will be identifiable from the ICOS station meteorological data when compared to the long-term 30 year mean climatological data. The period over which this long-term mean is determined as well as supplementary information including the location of the station from which this data is derived, the measurement variables and data acquisition, management and quality control need to be reported to the ETC. This information can be used for example to identify storm events (based on maximum wind speed and precipitation regimes), drought events, and temperature extremes (heat waves, frost). Site-specific thresholds, for such conditions, need to be defined against long-term (30 year), regional scale meteorological records in order to detect a potential disturbance event that can then be reported within the meta-data.
The meteorological data can also be used to identify periods where there is an increase in the potential probability of an abiotic disturbance event (the possibility of flooding or fire). Where such possibilities exist, changes in site conditions can be assessed using both remote means (webcams), but also by regular manual/visual site assessments. The detection of these events requires the ICOS station PI to consult other key ICOS measurement protocols (EC data acquisition/processing, chamber methodologies and ancillary vegetation measurements, to ensure that they adequately capture the impact of any disturbance on C, N losses as well as GHG emissions. The methodological techniques that can be utilised to assess the impacts of biotic and abiotic disturbances on the aboveground biomass component of terrestrial ecosystems include the use of hemispherical imagery, inclined phenocams  (Wingate et al.
ACKNOWLEDGEMENTS Matthew Saunders would like to acknowledge the  Irish Environmental Protection Agency  in the provision of travel support to attend ICOS meetings. Lutz Merbold  and  Lukas Hörtnagl  acknowledge funds received under the  Swiss National Science Foundation  funded project  ICOS-CH  (grant agreements:  20FI21_148992 , July 2013 to June 2017; 20F121_173691, since July 2017) and of  ETH Zurich  ( 0-23184-15 , June 2015 to December 2017) The authors acknowledge all active members of the ICOS ecosystem working groups and all other people who contributed to the development of the ICOS ecosystem domain and measurement protocols. The authors would also like to recognise the anonymous reviewers for their valuable remarks, constructive comments and corrections that helped to increase the overall quality of the manuscript.
We used a long-term dataset of presence/absence of breeding cormorants, boobies and pelicans on Isla Pescadores, covering 3 periods  (1952-1968, 1977-1990 and 2003-2014)  and coinciding with different regimes in the NHCS, to address 3 main questions: (1) Has the seasonality of the main environmental components, i. e. anchovy productivity and fishing pressure, changed with those regime shifts? (2) Are there any changes in the timing and magnitude of the breeding seasonality of seabirds with regime shifts and do they differ among species? (3) Do the relationships between the onset of breeding and the environmental variables differ between periods?.
Study area and seabird data The study was conducted on Isla Pescadores (11°46' 22. 8" S, 77°15' 36"W, 16. 45 ha, maximal altitude 50 m). The island is arid and uninhabited except by 1 or 2 wardens in charge of the protection and monitoring of seabird colonies. Isla Pescadores has been monitored since 1952 as part of a public program (AGRO RURAL, www. agrorural. gob. pe) to re cord monthly abundance of breeding and non-breeding individuals in the most important colonies of cormorants, boobies and pelicans. Although the program includes more than 30 colonies, only Isla Pes cadores has data available almost continuously since 1952 (Supple ment 1 at www. int-res. com/ articles/ suppl/ m597 p231 _ supp. pdf)  : 1952-1968, 1977-1990 and 2003-2014. The data series for 1952-1968 includes monthly presence/absence of breeders for each species.
Oceanographic data We used monthly oceanographic data collected by IMARPE and international cruises available in the World Ocean Database 2013 (Supplement 2) to build climatologies (monthly averages over years) coinciding with the 3 periods defined above. Oceanographic covariates were sea surface temperatures (SST; °C), surface chlorophyll a concentrations (chl a; mg m -3 ) and oxycline depths (Z 2ml l -1; m). Chl a and SST reflected the annual variations of productivity in the ecosystem. Z 2ml l -1 is the depth at which dissolved oxygen concentration is 2 ml l -1 and is a proxy of the upper limit of the oxygen minimum zone  (Bertrand et al. 2010). Z 2ml l -1 is also a good indicator of the vertical accessibility of forage fish to seabirds. Climatologies were built considering the main nesting sites of seabirds and their foraging range (Supplement 2).
Anchovy data: abundance and catches To explore the availability and abundance of anchovy, we used acoustic survey data (Supplement 3). We estimated regional abundance (s A ), local abundance (s A +), spatial occupation index (ISO), depth and school height of anchovy schools. Although acoustic data were our most direct data to represent the availability of prey, these data were not available at a monthly scale. Therefore, we used anchovy production (in million tons, 10 6 t) from an integrated assessment model (Oliveros-Ramos & Peña 2011) as a measure of potential anchovy abundance available to seabirds and the fishery. Anchovy production is the increase in population biomass due to somatic growth and birth rate without considering natural and fishing mortality and was estimated at monthly time steps.
Anchovy production was built using acoustic estimates of biomass and anchovy length structure from scientific surveys and fishery landings between 7° and 18°S and from the coast to 100 km offshore. Differences between periods for anchovy abundance were tested with a Kruskal-Wallis test. To represent fishing pressure, we used (as a covariate) the monthly landings of anchovy from industrial fisheries divided by the yearly quota. We built climatologies (monthly average over years) of the fishing pressure for the periods  1952-1968, 1977-1990 and 2000-2014. Data were available from articles already published for the periods  1952 -1968 and 1977 -1990 (Tsukayama & Palomares 1987 , Oliveros-Ramos & Peña 2011) , and for the recent period they were provided by IMARPE (www. imarpe. gob. pe/ imarpe/ detallereport.
Acknowledgements. We greatly acknowledge  AGRORU-RAL  for recording and preserving seabird data over the years. We thank  LMI-DISCOH  and the  ANR  project  TOP-INEME  for data and financial support of this work. We thank  GDR 3645 'Statistical Ecology'  for financial support to G. P. This work is a contribution to the cooperation agreement between the IMARPE and IRD. This publication was made possible through support provided by the  IRD-DPF. LITERATURE CITED Afan I, Chiaradia A, Forero M, Dann P ,  Ramirez F  (2015) A novel spatio-temporal scale based on ocean currents unravels environmental drivers of reproductive timing in a marine predator. Proc R Soc B 282: 20150721 Alheit J, Niquen M (2004) Regime shifts in the Humboldt Current ecosystem. Prog Oceanogr 60: 201-222.
The Canadian Sea Ice and Snow Evolution (Can-SISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends.
The current study updates the IPY analysis pertaining to terrestrial snow and sea ice by adding nearly a decade of data and including climate model projections of changes over the next 30 to 40 years. This study is focused on Canadian territory, which is nearly completely covered by snow and sea ice for parts of each year with nearcontinuous coverage over high-latitude and high-elevation regions. Snow and sea ice are recognized as critical components of Canada's natural environment, ecosystems, and economy. With respect to snow cover, real-time information on the amount of snow on the ground (i. e. depth and water equivalent) is used in operational decision making for water resource planning  (Turcotte et al. , 2007) , snow clearing, evaluation of avalanche risk  (Conlan and Jamieson, 2017) , and initialization of Canada's global weather forecast system  (Brasnett, 1999).
Historical snow cover data are used in a wide range of applications including ecological studies  (Luus et al. , 2013) , water resources  (Kang et al. , 2014) , forest management  (Hanewinkel et al. , 2008) , estimation of snow loads for infrastructure design  (Hong and Ye, 2014) , impacts on ground frost penetration  (Zhang et al. , 2008) , and evaluation of climate and hydrological models  (Verseghy et al. , 2017; Ganji et al. , 2017). Snow also makes a significant direct contribution to the Canadian economy through winter recreation  (Archambault et al. , 2003 as cited in Scott et al. , 2007). With respect to sea ice, knowledge of both historical and future sea ice conditions in Canadian waters is important for operational ship navigation to ensure economic and safe shipping, particularly in the Northwest Passage.
Sea ice dynamics (driven by wind and ocean currents) can play a major role in regional sea ice conditions independent of surface temperature  (Howell et al. , 2013a). -Snow-ice forcing of climate anomalies. Variations in snow and ice cover may also generate feedbacks to the atmosphere-ocean circulation that influence climate on seasonal to decadal scales  (Cohen et al. , 2007; Smith et al. , 2010; Scaife et al. , 2014) , although there is no clear consensus on how changes in Arctic sea ice and snow cover influence midlatitude climate  (Francis and Vavrus, 2012, 2015; Francis et al. , 2017; Barnes, 2013; Screen et al. , 2015). The first objective of this paper is to provide an overview of observed changes to seasonal terrestrial snow and sea ice across Canadian territory using the longest available time series of validated gridded datasets.
We use a multi-dataset approach, averaging multiple estimates of terrestrial snow variables together for more robust trends and using an integrated, multi-source dataset for analysis of sea ice change. The second objective is to compare these recent historical changes to projected changes of snow and sea ice over a similar length of time from the near future to the middle of the 21st century (2020-2050). We use simulations from state of the art climate models with confidence levels that account for uncertainty in the regional temperature response (note that the focus of this study is not model evaluation -that analysis is described in  Kushner et al. , 2018 , which is a companion paper to this study). Results are presented in two sub-sections, separated by the observational analysis period (Sect.
Terrestrial snow data Following  Mudryk et al. (2015) , we took a multi-dataset approach to analyze observed snow cover change in order to account for observational uncertainty. We calculated monthly snow cover fraction (SCF) and annual maximum snow water equivalent (SWEmax) using daily SWE data taken from the following four sources over the 35-year period from 1981 to 2015. (1) The Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2)  (Global Modeling and Assimilation Office, 2016; Gelaro et al. , 2017)  is a National Aeronautics and Space Administration (NASA) atmospheric reanalysis product generated with the Goddard Earth Observing System Model, Version 5. 2. 0 (GEOS-5), atmospheric general circulation model and atmospheric data assimilation system (ADAS).
, 2011). The GlobSnow product is masked over regions of complex topography, defined using a high-resolution topographic map (5 arcmin) as regions in which the standard deviation in elevation is larger than 200 m. This criterion affects approximately 20 % of the NH land surface (principally alpine regions) and a similar percentage of the Canadian land surface. We replaced grid cells containing complex topography with a blend of the Glob-Snow data and the mean value from the other three data sources. The weighting for the blend was determined by the fraction of the grid cell area containing complex topography. For grid cells with no complex topography, unaltered Glob-Snow data are used. As the fraction of complex topography increases, the weight applied to the GlobSnow data is linearly reduced, reaching zero for grid cells containing only complex topography.
For a given dataset of daily SWE, we interpolated the data to a regular 0. 25 • grid over Canada and applied a 4 mm threshold to produce a daily binary snow cover field. We averaged this daily field over each month to produce a monthly snow cover fraction. Annual maximum SWE fields were calculated as the maximum value of daily SWE attained at each grid location over a given snow season. For both SCF and SWEmax, we computed trends separately for each of the four datasets and then averaged these together into a final trend representing the mean across the observational ensemble.
Sea ice data For our analysis of changes in sea ice we examined monthly and seasonal changes in sea ice concentration (SIC) and sea ice thickness. SIC is analogous to our SCF derivation, in that it represents the fraction of the surface that is covered by sea ice but can also be interpreted as the fraction of time over which the surface is fully ice-covered. We extracted total and multi-year ice (MYI) area within Canadian Arctic waters from the Canadian Ice Service Digital Archive (CISDA), which is an integration of a variety of datasets including satellite observations, surface observations, airborne and ship reports, operational model results, and the expertise of ice analysts (see Canadian Ice  Service, 2007, and Tivy et al. , 2011a, for complete details). We selected the CISDA record instead of satellite passive microwave data because (1) the CISDA was found to be more accurate in the shoulder seasons during which passive microwave retrievals can underestimate sea ice concentration by as much as 44 %  (Agnew and Howell, 2003)  and (2) the CISDA sea ice record provides homogeneous data back to 1968 for regions of the Canadian Arctic  (Tivy et al.
, 2011a) , almost 10 years earlier than coverage by satellite passive microwave observations. The CISDA data were analyzed over two historical periods: the 1981-2015 period consistent with available snow data and a longer 1968-2016 period. Analogous to SWE, which provides a metric for the total amount of snow, we also analyzed maximum landfast sea ice thickness. We used the Canadian Ice Service record of in situ landfast ice thickness measurements, made regularly at coastal Arctic stations since the early 1950s  (Howell et al. , 2016b). In general, thickness measurements are available at weekly frequency, starting after freeze-up when the ice is safe to walk on and continuing until breakup or when the ice becomes unsafe. Details of this dataset are provided by  Brown and Cote (1992)  and are available on the Canadian Ice Service website at http://ice-glaces.
Surface temperature In light of documented differences in gridded temperature datasets over Canada  (Rapaic et al. , 2015) , surface temperature trends were derived from a blend of six reanalysis products: the European Centre for Mid-Range Weather Forecasting (ECMWF) reanalysis  (ERA-Interim, 2017; Dee et al. , 2011) , the Japanese 55-and 25-year reanalyses (JRA-55, Japan Meteorological Agency (JMA), 2017;  Kobayashi et al. , 2015; JRA-25, Japan Meteorological Agency (JMA)  and the Central Research Institute of Electric Power Industry (CRIEPI), 2017;  Onogi et al. , 2007) , the Modern-Era Retrospective analysis for Research and Applications versions 1 and 2 (MERRA-1, Global Modeling and Assimilation Office, 2017a;  Rienecker et al. , 2011; MERRA-2, Global Modeling and Assimilation Office, 2017b; Gelaro et al.
BCC-CSM1. 1 1 BNU-ESM 1 CanESM2 5 CCSM4 6 CNRM-CM5 5 CSIRO-Mk3. 6. 0 10 FGOALS-g2 1 GISS-E2-R 1 INM-CM4 1 MIROC5 3 MIROC-ESM 1 MPI-ESM-LR 3 MRI-CGCM3 1 NorESM1-ME 1 NorESM1-M 1 2017), and the Climate Forecast System Reanalysis (CFSR, NOAA/National Weather Service, 2017;  Saha et al. , 2010). The average trend was determined by first computing the average of JRA-55 and JRA-25 (resulting in a single JRA trend) and the average of MERRA-1 and MERRA-2 (resulting in a single MERRA trend). The multi-reanalysis mean trend was computed by averaging (ERA-Interim, CFSR, JRA, and MERRA). Reanalysis was used instead of point station data in order to produce spatially continuous trends over both terrestrial and marine areas.
1. This difference may stem from stronger spring trends in the NOAA snow chart data record (the NOAA dataset was not used in this study due to known deficiencies in the fall period;  Brown and Derksen, 2013)  compared to other snow products  (Mudryk et al. , 2017)  and stronger trends across the Eurasian Arctic compared to North America  (Derksen et al. , 2016). SIC trends over Canadian waters for this period are almost exclusively negative in all seasons. Regions with the strongest SIC declines are eastern Canadian waters in winter and spring and the CAA and Hudson Bay in summer and fall, consistent with the warming patterns shown in Fig. 2. The SCF and SIC trends can be viewed collectively as changes in the timing and extent to which highly reflective snow and ice cover the Earth's surface, with important implications for the surface energy budget.
There are no sharp boundaries in trends across adjacent land and ocean regions. This provides confidence in the consistency of the snow and ice datasets as well as evidence of a coherent response of snow and ice to temperature forcing across terrestrial and marine regions. Figure  2  shows seasonal surface air temperature (TAS) trends over the 1981-2015 period computed from a blend of six atmospheric reanalysis datasets (see Sect. 2. 1. 3). TAS trends are generally positive, although no trends are seen throughout the northwestern portion of the country during winter and spring, and there is significant cooling over the Canadian Prairies in spring. Land areas with cooling trends are co-located with positive SCF trends in Fig. 1 , although the region with positive SCF trends is slightly more extensive.