dataesr/scientific-paragraphs-categorization huggingface dataset:
Back Close Full best estimates of the given parameters along with errors and a feedback sheet describing computer specifications, pre-and post-processing steps as well as estimation specifications, computational time, man hours, etc. This allowed for a thorough intercomparison of the various applied methods and thus for finding the optimal ones to be used for deriving the four parameters. The personal invitations gave the highest success-rate, and 26 researchers from Europe and the US responded providing SEC with 11 submissions, IV with 9, CFL with 6, and GLL with 0. The results in focus here are those from SEC in which either ICESat laser or Envisat radar altimetry data could be used over the Jakobshavn Isbrae drainage basin (68-71 • N; 39-52 • W). In case the participants needed an external DEM to carry out the analysis, it was recommended to use the GIMP DEM developed by Howat et al.
One of the 11 submissions was discarded as the results were either comparable with the remaining datasets or independent of the validation data. Table  1  shows the sensor and method used by the participants as well as the submitted output parameters and whether the participants have applied a slope correction  (Scharrer et al. , 2013). In order to anonymise the RR results, the participants are referred to as SEC-1, SEC-2,. , SEC-10, the order in which they are named being random. Three participants used Envisat data and the remaining seven worked with ICESat. Of these, five groups applied the cross-over technique (XO), while the remaining five used repeat-tracks (RT)  (Gunter et al. , 2013; Slobbe et al. , 2008; Moholdt et al. , 2010). Slope corrections were only applied in two cases: SEC-1 used a Point of Closest Approach (POCA) method, while SEC-2 used plane fitting to correct for the slope.
Back Close Full The above differences in the datasets and their formation made it possible to perform an inter-comparison of the methods and approaches. The following parameters were therefore analysed: radar vs. laser altimetry, cross-overs vs. repeat-track. A final part of the RR was to validate the results. This was done using airborne LiDAR data from ESA's CryoVex and NASA's IceBridge campaigns, due to the observations' high accuracy and spatial resolution.
Temporal extent and spatial resolution Table 2 lists the spatial resolution and temporal extent of the submissions. The latter was found to mainly be based on the operational period of the sensor in question. Two Envisat datasets span the period from 2002-2010 corresponding to the 35 day repeat cycle, while the last dataset covers the ICESat observation period from 2003-2009. This is also the case for the ICESat datasets, which, however, are limited by the period of active laser altimeters. No Envisat datasets cover the period from the lowering of the satellite in October 2010 until it ceased operation in March 2012. This, however, makes the submissions more easily comparable, see Sect. 3. 2. The spatial resolution and density of prediction points depend on the applied method. Repeat-tracks have a higher spatial resolution than cross-overs due to the better ground coverage, and the vertical resolution is typically higher for laser rather than radar data as laser altimeters have a higher vertical accuracy and thus lower random errors; e.
, 1983; Ridley and Partington, 1988). The estimates from the two sensors agree well in the interior whereas a small offset is found by the ice margin where ICESat data (SEC-2-SEC-5) show a larger thinning. This is due to its ground tracks agreeing better with the glacier outlet as Envisat misses the smaller ice streams. In addition, differences between the two sensors arise from the ICESat observation period being shorter by two years, ICESat's smaller footprint size (70 m vs. 2-10 km), which allows for a more realistic change detection, as well as errors in the slope correction, the latter being an integral part of the Envisat data processing  (Hurkmans et al. , 2012). The footprint size is particularly important in coastal regions as it determines the amount of topography being included in the signal; for Envisat this along with the slope Introduction.
Inter-comparison of Round Robin results In order to thoroughly analyse the methodologies supporting the submitted results, the following inter-comparisons are made (Fig. 3  and Table  3)  -Radar: Repeat-track vs. cross-overs. Both participants, SEC-1 and SEC-10, used Envisat data. SEC-1 applied the RT technique while SEC-10 used XO differences. -Laser: Repeat-track vs. cross-overs. Both participants, SEC-3 and SEC-7, used ICESat data. SEC-3 applied the RT technique while SEC-7 used XO differences. -RT: Laser vs. radar altimetry. Both participants, SEC-1 and SEC-3, applied the RT technique. SEC-1 used Envisat data and SEC-3 ICESat data. -XO: Laser vs. radar altimetry. Both participants, SEC-8 and SEC-10, applied XO differences. SEC-8 used ICESat data and SEC-10 Envisat data. The applied methodologies are assessed by finding overlapping prediction points and differencing the SEC values herein "diff ").
The mean and root-mean-square-errors (RMSE) of these differences are then found while scatter plots reveal the R 2. The search radius for the overlaps is based on the spatial resolution of the observations (Table  2 , column 4). The analysis of the application of repeat-track vs. cross-overs shows the largest offsets among the submitted results, i. e. the highest RMSE and the lowest R 2. The RT values generally show a larger spread in dH/dt than XO, and the radar data have a smaller R 2 (= 0. 63) than the laser data (= 0. 70). The different results are believed to arise from that mentioned in Sect. 3. 1, namely the gridding of XO measurements into (differently-sized) cells thereby losing information on the SEC values as well as the opposing spatial resolution of the datasets: RT have a high density of measurements along-track, while XO measurements are restricted to overlapping ascending and descending ground tracks, both of which are sparse in time and space.
Discussion Figure  4  outlines the results of the Round Robin exercise: generally agreeing dH/dt values in the interior (high elevations) for all methods and disagreements further out along the ice margin (low elevations). The surface changes in the interior are small, and due to the little amount of topography both laser and radar altimetry perform well, regardless of the method. For the margin regions, the laser data typically indicate lower dH/dt values, due to ICESat's ground tracks agreeing better with the actual outlet than those of Envisat. The XO results are all near-zero, which is due to the observation points being on high elevations, far from the glacier outlet, as well as averaging of the observations. An interesting observation in the ICESat datasets is that although the participants have used the same data release (R33) and some the same method, e.
g. RT (SEC-2-SEC-5), the results still differ. This is partly due to varying processing and estimation schemes, such as different data rejection criteria and linear least squares techniques, i. e. weighted, unweighted and multi-variate approaches, respectively. An additional reason is the inter-observation range biases, the so-called inter-campaign biases, which Figures   2013 ) such one is currently underway  (Hofton et al. , 2012; Schutz et al. , 2011). Along with varying ways of determining the SEC errors (Fig. 2 ) as well as the lack of information submitted regarding exactly this, a difficulty arises in directly comparing the received datasets. In spite of this, the uniquity of the Round Robin exercise is the ability to evaluate the submissions regarding methodology, pre-and post-processing steps, computer specifications, the use of external datasets such as the GIMP DEM  (Howat et al.
, 2012) , etc. SEC-1's RT results illustrate the good potential of using radar altimetry to estimate SEC all the way to the ice margin, the inter-comparison with laser data confirms this, and thus it will be highly beneficial to include data from CryoSat-2 as well as Sentinel-3 once available. The computation efforts, as indicated in the received feedback sheets, reveal that XO typically have the shortest processing times. This is seen in spite of e. g. SEC-4 (RT) and SEC-8 (XO) both applying unweighted linear least squares and one participant not using the tropospheric correction included in ESA's Envisat dataset. The external European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis ERA-Interim correction, derived from surface pressure, is implemented instead. However, the actual computation time depends on the implementation and optimization of the applied methods and hence is not a big issue for large-scale computations carried out on modern-day computers.
Back Close Full The lack of observations also distort mass balance approaches where a flux-balance and seasonal mass balance estimates can be used to infer the vertical surface change. Additional error sources are the poorly known density needed for such estimates as well as the seasonal variability of all of the above. This indicates the difficulty in estimating an accurate vertical correction term to be applied to the LiDAR SEC trends prior to using them for validating the RR datasets. However, when applying the model and using a threshold temperature of T 0 = -5 • C in order to include (nearly) all melt days it is found that the largest elevation difference from either snow or ice is a few meters, i. e. less than the dynamical thinning observed in the area.
Conclusions In order to find the optimal method for determining Surface Elevation Changes (SEC) of the Greenland Ice Sheet, a so-called Round Robin exercise was performed as part of the ESA CCI Ice Sheets project. In this, researchers across Europe and the US submitted their best estimates for a pre-determined area, the Jakobshavn Isbrae drainage basin, derived using either radar (Envisat) or laser altimetry (ICESat) data. In order to evaluate the results, an inter-comparison of e. g. repeat-track (RT) vs. cross-overs (XO) and laser vs. radar data was performed. The submissions were validated against SEC trends derived from temporally consistent airborne LiDAR data from ESA's CryoVex and NASA's IceBridge campaigns. Through this the Round Robin conclusions can be summarised as follows: -The spatial resolution of SEC estimates is higher with RT than with XO, and thus RT allows for better resolving the surface changes along the ice margin, such as along narrow ice streams.
Back Close Full -In spite of ICESat's smaller footprint size, the inter-comparison of laser and radar altimetry revealed that Envisat equally has the potential to map height changes even in regions with high topography. This shows good potential for a future implementation of ESA radar altimetry data from the up-coming Sentinel-3 mission; the first of three satellites is expected to be launched in 2014  (ESA, 2013b). For the ESA CCI SEC generation, we therefore propose a hybrid method in which RT and XO results are combined in order to maximize the spatial resolution and minimize the estimation errors. This will be done using geostatistical interpolation tools, i. e. the optimal gridding procedures known from collocation/simple kriging  (Dermanis, 1984; Goovaerts, 1997; Hofmann-Wellenhof and Moritz, 2005).
Discussion Paper | Discussion Paper | Discussion Paper | Author contributions: J. F. Levinsen performed the analysis and validation of the Round Robin submissions, wrote the paper and produced the figures. K. Khvorostovsky unified the submitted datasets and, along with F. Ticconi, A. Shepherd and R. Forsberg, assisted in data interpretation and in the discussion of how to develop the optimal SEC module. The remaining authors form the team of Round Robin participants and are listed in a random order, based on the affiliation. All co-authors contributed to the discussions leading to the results presented in the paperDiscussion Paper | Discussion Paper | Discussion Paper |.
TCD Acknowledgements. We thank the  Round Robin  participants for their submitted datasets and elaborate feedback sheets. The study was supported by the  European Space Agency (ESA)  through the  Ice_Sheets_CCI  ( 4000104815/11/I-NB ). Both the ICESat GLAS and ATM LiDAR data were downloaded from NSIDC while Envisat data were acquired through  ESA. The Cry-oVex data were available in-house at  DTU Space.
Regarding class 3, variations are opposite across seasons and more important. Maximum occurrence Table  1. Characteristics of the different classes identified over the Observatory of Haute Provence (OHP) site. Class 1 to 3 represent respectively thin cirrus in the middle troposphere (Thin MT), thick cirrus in the upper troposphere (Thick UT) and thin cirrus in the upper troposphere (Thin UT). The references A, B and C correspond respectively to the update analyses  (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) , the analyses performed by  Keckhut et al. (2006)  over the year 2000 and in using the CALIPSO data up to now  (2006) (2007) (2008) (2009) (2010) (2011) (2012).
Current introduced freshwater fishes of Western and south-western Australia Information on current known introduced freshwater fishes in wild aquatic systems in Western Australia was obtained through reviews of  Coy (1979) ,  Allen (1982 Allen ( , 1989)) ,  Allen et al. (2002) ,  Morgan et al. (1998 Morgan et al. ( , 2002 Morgan et al. ( , 2004 Morgan et al. ( , 2011a)) ,  Maddern (2008) ,  Maddern et al. (2011) ,  Morgan and Beatty (2007) ,  Marr et al. (2010) , records from Museum of Western Australia, and unpublished data from our past surveys of >1500 sites throughout the region (see Figure  1 ). Cumulative species introductions in wild aquatic systems (defined here as either naturally occurring lentic or lotic systems or artificial lentic habitats that connect to natural systems) were then plotted against time (of first known presence in the region) to examine temporal rates of introductions.
The extension of the CO 2 FLUXNET measurements and database  62  to CH 4 fluxes is probably achievable at a reasonable cost, and would provide useful constraints for land surface models. For interannual variations in wetland emissions, the sensitivity of emission rates to warming at high northern latitudes and to rainfall changes in the tropics needs to be more consistently quantified in wetland models. The Amazon drought in 2010  63  should have resulted in a drop in wetland CH 4 emissions, and ongoing analyses may allow researchers to test the hypothesis that tropical wetland CH 4 emissions respond strongly to rainfall anomalies and trends. Second, the partitioning of CH 4 emissions by region and process is not sufficiently constrained by atmospheric observations in topdown models. Regional partitioning of total emissions would benefit from denser and more evenly distributed CH 4 concentration data.
Acknowledgements This paper is the result of an international collaboration of scientists organized by the  Global Carbon Project , a joint project of the  Earth System Science Partnership. This work was supported by: the  UK NERC National Centre for Earth Observation ; the  European Commission 's  7th Framework Programme  (FP7/2007-2013) projects  MACC  (grant agreement no. 218793 ) and  GEOCARBON  (grant agreement no. 283080 ); contract  DE-AC52-07NA27344  with different parts supported by the  US DOE IMPACTS and SciDAC Climate Consortium  projects; computing resources of  NERSC , which is supported by the  US DOE  under contract  DE-AC02-05CH11231 ; NOAA flask data for CH 3 CCl 3 (made available by S. Montzka); the  Australian Climate Change Science Program , and  ERC  grant  247349. Simulations from LSCE were performed using HPC resources from  DSM-CCRT  and  CCRT/CINES/IDRIS  under the allocation 2012-t2012012201 made by  GENCI  ( Grand Equipement National de Calcul Intensif ).
Regardless of the frequency range (microwave, visible/near infrared, thermal infrared,. ) or the acquisition type (active vs. passive), satellite data obtained over mountainous terrain are strongly affected by the topography. This can either occur because a too coarse resolution of the acquired images results in a blending of multiple types of surface conditions into the same pixel (this effect is also present in flatter terrain, but the presence of different types of slopes dramatically increases the mixing), or because of issues inherently associated to the sloping conditions, i. e. multiple reflections on neighbouring slopes, etc. As an example of how such effects impact the retrieval of snow properties from satellite images in mountainous terrain, here we describe and compare different methods used to post-process near-infrared (NIR) reflectance data from the MODerate resolution Imaging Spectrometer (MODIS) on board TERRA and AQUA.
Such data feature a high temporal and spectral resolution (daily coverage and about 20-30 nm wide bands) along with a moderate spatial resolution (i. e. , 500 m for the NIR bands). Such data are highly relevant for the surface energy balance of the snowpack. The spectral albedo of snow is determined by the spectral and angular characteristics of the solar irradiance but also by the physical and chemical properties of the snowpack. In the visible part of the spectrum (VIS), snow albedo is mainly influenced by the impurities content. In the NIR, the effect of the snow grain size is predominent  (Warren, 1982). Among numerous and sometimes ambiguous definitions of the grain size of snow  (Wiscombe and Warren, 1980; Aoki et al. , 2000; Fierz et al. , 2009) , for optical radiative transfer applications the notion of an "optical grain radius" is commonly employed.
First successful demonstrations of grain size retrieval were presented in  Nolin and Dozier (1993, 2000)  and  Bourdelles and Fily (1993). Recently,  Painter et al. (2009)  introduced the MODSCAG algorithm to retrieve snow covered area, grain size and albedo on a sub-pixel basis from MODIS data. This method is based on the spectral unmixing of MODIS ground reflectance product (MxD09) whereby a range of snow spectra with various grain sizes are considered as potential end-members. However, although MxD09 data product addresses atmospheric effects  (Vermote and Vermeulen, 1999; King et al. , 2004) , these methods ignore topographic effects, such as the illumination angle or the reflected terrain irradiance, despite their importance in mountainous terrain  (Proy et al. , 1989; Fily et al. , 2000; Sirguey, 2009).
, 2011). Each study departs regarding the number of MODIS spectral bands that were used to retrieve grain size (see  Negi and Kokhanovsky, 2011a)  as well as in terms of the way atmospheric corrections were addressed. For example,  Tedesco and Kokhanovsky (2007)  relied on the standard correction associated with the MxD09 data product, while  Lyapustin et al. (2009)  or  Zege et al. (2011)  used custom corrections. Only  Zege et al. (2011)  accounted for the effect of the presence of soot in the snowpack. Yet, none of these studies addressed the impact of multiple reflections in mountainous areas, and few addressed the impact of the local illumination change due to the slope and the aspect of the studied surface. The goal of this study is twofold: first of all, it aims at evaluating the effect of (1) the local topography, (2) the anisotropy of snow and ice reflection, (3) the assumption on the shape of snow grains, to retrieve snow SSA from MODIS data in mountainous areas.
The second objective is to compare the MODIS retrieved SSA with the output of the detailed snowpack model Crocus  (Brun et al. , 1992; Vionnet et al. , 2012). Following a description of the data and models used, this study introduces in detail the various methods used to retrieve SSA from MODIS data. In the next section, MODIS retrieved SSA from the different methods are com-pared one to each other, to the field measurements and to the outputs of the detailed snow model Crocus. In addition to a detailed discussion on the comparison of the retrieval methods, this study allows for the discussion of the variability of the SSA of surface snow in mountainous terrain in both MODIS and model output data 2 Material and methods.
Application site The study was carried out in a geographical domain located in the centre of the French Alps East of Grenoble covering an area of 46 × 50 km (see Fig. 1a ). The terrain is highly rugged with an elevation ranging from 224 to 3983 m a. s. l. The mean elevation is 1860 m a. s. l. Six cloud-free MODIS/Terra images spanning the same snow season were selected:  2009-01-09, 2009-01-25, 2009-02-26, 2009-03-21, 2009-04-22 and 2009-05-06. This dataset enabled the various methods employed here to be tested for various conditions of snow at several steps of the seasonal evolution of the snowpack.
Digital elevation model (DEM) The DEM used for computing topographic parameters originates from the Shuttle Radar Topography Mission (SRTM,  Farr et al. , 2007). This 3 arc-sec DEM was projected and downgraded using a cubic interpolation from 90 to 125 m (for shadows computation) and 500 m spatial resolution to match the spatial resolution of MODIS data. This downgrading implies a smoothing of the orography, and ignore subgrid local variations in slope and aspect. The relatively loose resolution of 500 m (compared to the very rough nature of the studied area) should be considered as one of the main source of uncertainty in the retrievals. Figure  1b  illustrates the DEM on the studied area. SRTM data have an absolute geolocation error of 8. 8 m and a relative elevation error of 8. 7 m over Europe  (Rodriguez et al.
Satellite data The main features of MODIS data making it an appealing source of information for remote sensing of snow from space are outlined below. First of all, it has a suitable spectral resolution (see Table  1 ), including a detection channel (band 5) centred at 1. 24 µm, which is highly sensitive to SSA and barely affected by light absorbing impurities  (Warren, 1982; Kokhanovsky et al. , 2011). It offers a good compromise between spatial and spectral resolution (respectively 500 m and 0. 02 µm for band 5). Last, it provides at least daily global coverage since 2000, which makes it a valuable dataset for multi-year studies (e. g. Box et al. , 2012)  and, despite a primary goal of being a research-oriented data source, many.
A. Mary et al. : Mountain snow SSA from MODIS operational applications makes increasing use of these data (e. g. Pettinato et al. , 2009). The region of interest is represented by 92 × 100 pixels at 500 m resolution. Note that 500 m is the nominal resolution, which is effective at nadir of the sensor, but data with large sensor zenith angle (on the side of the swath) are affected by a loss of effective pixels resolution. For the studied dates, the absolute sensor zenith angle ranged from 2 to 27 • , with less than a 4 • variation within each image. The Goddard Space Flight Center (MODIS project, http: //modis. gsfc. nasa. gov/) provides several products processed from raw sensor acquisitions. For this study, we used two of these products in order to evaluate the impact of the topographic correction on the retrieved SSA: the MOD02 swath data product (level 1B, obtained with MOD03 geolocation file for reprojection), which contains calibrated top of atmosphere (TOA) radiances; and the MOD09 data product (level 2), which directly provides atmospherically corrected ground reflectances computed by the LSRSCF (Land Surface Reflectance Science Computing Facility: http://modis-sr.
Recent work  (Carmagnola et al. , 2012)  has shown that a recent compilation of the ice refractive index  (Warren and Brandt, 2008)  may not be the more relevant for certain near-infrared wavelengths. However, in the spectral range concerned with MODIS bands (Table  1 ), the agreement between observations and numerical simulations was found extremely satisfactory, so that values from  Warren and Brandt (2008)  are used here. Note that the relevant database is available at http://www. atmos. washington. edu/ice optical constants/.