dataesr/scientific-paragraphs-categorization huggingface dataset:
Different coupling behaviour: this is in particular relevant with respect to the NARR-based results (and to a lesser extent the GLEAM-based results), which might exhibit some model-induced coupling and therefore require validation with observations. This is analyzed in Fig. 4 , where the effect of the dataset selection is isolated, allowing for a direct comparison. As expected from Fig. 2 , datasets display different TFS * patterns, so the NARR-based results are only partially confirmed with the observational datasets.
Overview iii. Time series length and noise: the lengths of the time series considered here range from a few years in FLUXNET to 13 yr (with some gaps) in GLEAM, NEXRAD and NARR. A relatively large number of days is required to estimate TFS * robustly, in particular for noisy observational data. Comparing Fig. 2  with the respective panels of Fig. 4  shows that the decreased sample size in Fig. 4  reduces the number of sites with significant TFS * in NARR and in the GLEAM-NEXRAD combination, although the overall signal remains. Higher noise levels in observational datasets and incomplete sampling due to short record length could also explain their weaker values of the metric. v. Other dataset characteristics, such as temporal resolution and uncertainties, likely also contribute to these differences.
These possible explanations for the observed differences are difficult to disentangle as the selection of days and the length of the time series are linked to the datasets. While the region of strong relationship between before-noon EF and afternoon precipitation in the Eastern US found in NARR and GLEAM-NEXRAD cannot be confirmed with FLUXNET, it is possible that the FLUXNET time series are simply too short or too noisy to detect a robust TFS * in this region. Nevertheless, NARR generally exhibits a stronger (weaker) link between EF and convection triggering over the Eastern (Central and Southwestern) US compared to the observation-based estimates used here (in particular GLEAM-NEXRAD). Hence our results suggest the presence of productdependence in the derived TFS * patterns. Analysis of the differences in the datasets themselves might shed light on main issues impairing the comparison or leading to different TFS * patterns.
Except for the three sites in the middle of the Western US region indicated by empty dots on Fig. 3  (map), which exhibit large differences between NEXRAD and NARR precipitation and point to some issues with NEXRAD over that region, precipitation data from NARR and NEXRAD agree rather well in terms of precipitation occurrence (not shown). We therefore focus on the differences between EF datasets and analyze these in the next section. With the exception of EF analyses (Sect. 4. 2), we exclude FLUXNET data from the subsequent analyses because of the too limited record length of this dataset.
EF time series To analyze the agreement of the spatiotemporal dynamics between the three EF datasets, Fig. 5   most sites and across all dataset combinations. This suggests that the disagreement between the TFS * patterns in different dataset combinations is related to differences in the considered EF datasets (see also Fig. 4 ). Correlations of 10 day and monthly averages of before-noon EF are slightly higher but remain low (Fig. S4  in the Supplement). Correlations of EF anomalies (i. e. after removing the seasonal cycle within JJA) instead of actual values display similar results (not shown). Several reasons might underlie the differences in EF datasets. First, the spatial scale over which EF is estimated, or footprint, is different for each dataset. Measurements from FLUXNET are local, with a footprint typically extending from 100-2000 m  (Schmid, 1994) , while NARR and GLEAM estimate EF over a much larger scale (about 0.
3 • and 0. 25 • , respectively). Differences might thus arise from different environmental conditions over the respective footprints (e. g. , input of water from rainfall in case of very local precipitation events), but also from differences in land cover. Indeed, while wet vs. dry periods might be similar in all datasets, some studies have shown that different vegetation might respond differently to given conditions  (Teuling et al. , 2010). Land cover is in fact different at FLUXNET sites compared to the larger scale in NARR, in particular in regions with cultivated land, as FLUXNET sites are often located over natural vegetation. However, we did not find any systematic link between different land covers and resulting TFS * (not shown). Similarly, soil texture impacts soil moisture dynamics and EF (e.
g. Guillod et al. , 2013)  and differences in local vs. larger scale soil texture could also be a reason for the differences in EF. In order to better characterize the EF time series, Fig. 6  shows the mean, standard deviation, and persistence (quantified by the decorrelation time-scale, τ D , which integrates the autocorrelation function, see von Storch and Zwiers, 1999) of EF for the three analyzed datasets. While we do not find any clear differences between the datasets that can explain the resulting differences in TFS * , the comparison highlights some interesting features. The mean EF is similar in all datasets and exhibits higher values in the Eastern US (wetter climate) compared to the drier climate of the Western US. This pattern is slightly stronger in GLEAM, where the Eastern US display higher Figures mean EF values than in other datasets.
The EF standard deviation is noisy, although similar patterns are found across all datasets, with higher EF variability in the Central US or in the Southern Great Plains (the exact location depending on the dataset). Note, however, that the amplitudes differ widely between datasets. This does not necessarily impact TFS * : the change in the probability of afternoon precipitation with respect to EF is scaled by the standard deviation of EF (see Eq. 4 and  Berg et al. , 2013). Finally, EF persistence is generally lower in the Eastern US, suggesting high variability at a scale of one to a few days in this region of strong relationship in NARR (Fig. 2 , left). Thus, the regions of strong daily correlation between EF and convection triggering correspond, in NARR, to humid regions with low persistence, while in GLEAM-NEXRAD the drier Western region, with higher persistence, displays strongest coupling.
The filters for potentially convective days as well as the binning in CTP and HI low categories in the TFS computation should ideally account for such confounding effects (see Sect. ous day in the TFS * computation. With respect to an explanatory variable X , we denote the change in the probability of afternoon precipitation for high vs. low X as ∆Γ(X ) = Γ(r |X > X Q60 ) -Γ(r |X ≤ X Q40 ). Figure  7  (left) shows ∆Γ(EF) and ∆Γ(P d, prev ) for NARR and the GLEAM-NEXRAD combination, where P d,prev is the precipitation on the previous day (in other words, EF is simply replaced by the daily precipitation from the previous day). Due to short record length, FLUXNET is omitted from this analysis. Note that the patterns of ∆Γ(EF) strongly resemble the patterns of TFS * for these two dataset combinations (Fig.
2 ) as it is the term that leads to most of the TFS * signal: in Eq. (  4 ), σ EF and ∂EF mostly compensate each other. In addition, maps of σ EF (Fig. 6 ) do not display a pattern similar to that of TFS * (Fig. 2 ). Using ∆Γ(X ) allows for a direct comparison between the impact of EF and that of previous day precipitation, shown on the right of Fig. 7  as ∆Γ(P d, prev ). In fact, previous day precipitation is a better predictor for afternoon precipitation occurrence than before-noon EF, which holds for both datasets and across all regions. Given these results, one can wonder if the signal with EF is, in fact, only reflecting precipitation persistence or if EF conveys additional information that can help explain afternoon precipitation. In order to disentangle the impact of EF on precipitation from precipitation persistence, we apply a framework similar to  Salvucci et al.
Nonetheless, for days following rainfree days the clear weakening of the signal suggests a possible strong role of precipitation persistence. In addition, persistence is part of the TFS * signal, which is stronger on days with precipitation on the previous day than on days following rain-free days. This could be due to events lasting a large number of days, leading to very wet conditions and high EF with precipitation likely to occur again, relative to shorter events (1-2 days) that might exhibit lower EF and no further precipitation. Over the Southwestern US, the signal is less sensitive to precipitation persistence and TFS * remains significant over most sites for both datasets. Overall, precipitation persistence plays an important role and thereby affects TFS * in all datasets. Several factors can lead to high precipitation persistence, such as SST forcing e.
Over the Central US, no conclusion can be drawn from NARR as no EF-precipitation relationship is identified (see also Figs. 2  and 3 ). The role of interception can also be investigated using GLEAM. In Fig. 11 , we display TFS * for the GLEAM-NEXRAD combination as shown earlier  (standard version, left)  and when computing EF in GLEAM without including interception evaporation (right, see Eq. 2 in Sect. 2. 4 for details on the computation). The significant positive TFS * signal over the Eastern US vanishes when interception is not accounted for. This shows that, for this observation-based dataset and over this region, interception is the main driver of the relationship between EF and subsequent precipitation, not soil moisture. Conversely, the large TFS * values found in the Central and Southwestern US remain significant after removing interception.
Discussion and conclusions A recent study  (Findell et al. , 2011)  statistically relates the occurrence of afternoon convective precipitation to before-noon Evaporative Fraction (EF) through the TFS metric (Triggering Feedback Strength), based on data from the North American Regional Reanalysis (NARR), and suggests the existence of an extended region of positive land surface-precipitation coupling over the Eastern US. Our study extends that analysis based on a systematic cross validation with additional independent, observation-based data sources and an in-depth investigation of all components contributing to the identified pattern from  Findell et al. (2011). We complement NARR with the use of observational data from FLUXNET stations Comparing the relationship patterns from the different dataset combinations, the FLUXNET-NEXRAD combination does not display the region of positive TFS * in the Eastern US found in NARR, while the GLEAM-NEXRAD combination displays a signal of overall same sign albeit with weaker values in the East.
This suggests that NARR might underestimate a possible EF-precipitation coupling in these regions. In the Southwestern US close to the Mexican border, all datasets agree on the existence of significant relationships between EF and convective triggering. We find that the choice of the EF dataset has a large impact on the relationship between EF and convection triggering, although the patterns of average EF, EF variability and persistence in the different datasets do not provide any clear indication of the source of this discrepancy. We note, however, that regions of strong TFS * can be related to the mean and persistence of EF in two ways: over the Eastern US, high TFS * concurs with high EF and low EF persistence (corresponding to a high day-today variability), while over the Central and Southwestern US, EF is lower and more persistent.
Note here that with the selection of potentially convective days (Sect. 3. 2), days with morning rainfall are excluded from all analyses. Interception therefore comes from rainfall on the preceding night or day. The processes acting in the Central and Southwestern US are different than over the Eastern US. The lower leaf area index in these regions results in lower interception evaporation. Thus, the relationship between EF and precipitation occurrence found in GLEAM-NEXRAD, stronger than in the Eastern US, remains when interception evaporation is removed from the EF estimate, highlighting a likely impact of EF via soil moisture. Similarly in NARR, soil moisture is identified as the primary driver of the relationship at the few sites which exhibit significant TFS * in this dataset for these regions.
The lack of signal in NARR over the Central US while the GLEAM-NEXRAD combination displays some significant relationship suggests that some processes might be misrepresented in this reanalysis product, although issues in the other datasets cannot be excluded in spite of stronger observational components. Note that the Southwestern region is limited to the vicinity of the Mexican border and does not include sites at higher latitudes, where significant TFS * in GLEAM-NEXRAD is likely due to issues in NEXRAD. In the analyses shown here, we divide the US into three large regions over which the processes are likely different due to different evaporation regimes. We note, however, that a number of other processes are not considered. In particular, one cannot exclude strong coupling gradients on scales much smaller than the sub-continental scales considered here.
Given the large range of unresolved issues in the investigation of land-precipitation coupling, further studies are required to pin down this complicated relationship. Analyses of the feedback accounting for precipitation persistence and confounding variables, applied to different temporal and spatial scales and a wide range of datasets, are urgently needed. Simultaneously, improvements in models could possibly allow for more realistic sensitivity studies to tackle this issue. Finally, large-scale soil moisture and EF observations at scales relevant to land-atmosphere coupling (i. e. , 10 km) would help provide additional observational contraints on model results. (Owe et al. , 2008)  night-time overpass (for the S calculation) Vegetation optical depth NASA-LPRM  (Owe et al. , 2008)  daily (for the S calculation) Precipitation NEXRAD (Sect.
Several reasons might contribute to the observed differences between TFS * estimates from the different datasets: i. Spatial scale of the EF data: the footprint of FLUXNET measurements is much smaller than the grid cells of NARR and GLEAM (typically 100-2000 m vs about 25-30 km, respectively, see Sect. 4. 2). In Fig. 4, which displays TFS * for the dif-Discussion Paper | Discussion Paper | Discussion Paper | ferent combinations of EF and precipitation datasets for the same subset of days, namely the potentially convective days according to the NARR selection, sites with positive TFS * using EF from NARR and GLEAM in the Eastern US mostly display smaller TFS * values with FLUXNET EF, although this feature is not completely systematic. Thus, although different TFS * cannot be clearly attributed to differences in footprints, EF data are shown to play a strong role in controlling the convection triggering metric (see also Sect.
We acknowledge the financial support to the eddy covariance data harmonization provided by  CarboEuropeIP ,  FAO-GTOS-TCO, iLEAPS ,  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, University Laval, Environment Canada  and  US Department of Energy  and the database development and tech- ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD.
The observational datasets available are of limited length and geographical extent. Further, they do not show unequivocal support for one or the other of these mechanisms, which both may operate on the ice sheet across different areas or time periods. Previous flow-line modeling of this process (14) used a parameterization based on the Zwally et al. (2)  observations and performed experiments with a degree-day ablation model forced with regional temperature changes based on ×2, ×4, and ×8 modern CO 2 concentrations  (15). By 2500, additional ice mass reductions of 1-7% (for minimal warming) or 4-49% (maximal warming) are created by the incorporation of their lubrication parameterization. These mass changes are likely to have been affected through the altered surface mass balance (SMB) brought about by feedback between flow, surface elevation, and SMB.
During the winter months the GPS instruments had insufficient power to continue logging data. Mean autumn/winter velocities were therefore estimated from the displacement of the GPS receiver between the end of the summer melt season and the start of spring. The University of Utrecht data comprise observations made over the years 2006-2009 at four field sites in the same region. Velocity was derived from GPS measurements logged continuously at hourly intervals throughout the entire year. Fig. 2  shows the locations of the field sites overlaid on the MAR runoff for the year 2010. The location and elevation of the field sites are listed in Table  1. The observed annual pattern of velocity at each site was characterized using a speedup index (S) comprising the ratio of mean-annual velocity to the mean velocity of the 3 mo of lowest velocity (approximating the winter).
5 m•y -1. This value was chosen because it is typical of the maximum runoff predicted by the ECHAM5-forced MAR model at the end of the century for the A1B scenario. Nine evenly spaced samples were selected together with the best fit. Three types of experiment were performed by each model: a control in which there was no change in SMB or speedup forcing (CONTROL), a run with only SMB forcing (SMBONLY), and a variable number of runs with both SMB and speedup forcing (RUNX, in which X refers to the particular parameterization used). Of particular interest are RUN0001 (the best fit), RUN0002 (the minimum response), and RUN1026 (the maximum response). Forcing data were available only up to 2099; the period 2100-2199 therefore implemented forcing from 2090 to 2099 repeatedly with the justification that by this time radiative forcing in scenario A1B begins to stabilize.
This happens in all sectors of the ice sheet but is most noticeable in the west and the north. The spatial pattern of velocity causing these changes is as would be expected from Fig. 3 : For RUN1026, velocity increases monotonically toward the margin (with the pattern of runoff), reaching 120% of the SMBONLY velocities; whereas, for RUN0002, velocity increase is at a maximum ∼200 km from the ice margin and declines to 90% of the SMBONLY velocities at the margin. The latter is a consequence of the form of the minimum parameterization; increases in runoff larger than ∼1. 5 m•y -1 are associated with reduced ice flow. This effect was also noted by Van de Wal et al. (6)  in their long-term  (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006)  observational dataset (only 2006 was used in the development of our parameterization).
ACKNOWLEDGMENTS. This research was funded by the  European Commission's Seventh Framework Programme  through Grant  226375  (ice2sea manuscript no. 121 ). Elmer/Ice simulations were performed using high performance computing resources from  Grand Équipement National de Calcul Intensif -Centre Informatique National de l'Enseignement Supérieur  (Grant /2011016066/) and from the  Service Commun de Calcul Intensif de l'Observatoire de Grenoble. S. F. P. ,  M. J. H. , and M. P. were supported by the  US Department of Energy (DOE) Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research  programs. M. J. H. was partially supported by the  Center for Remote Sensing of Ice Sheets  at the  University of Kansas  through  US National Science Foundation  Grant  ANT-0424589.
In order to increase the understanding of the changing climate, the European Space Agency has launched the Climate Change Initiative (ESA CCI), a program which joins scientists and space agencies into 13 projects either affecting or affected by the concurrent changes. This work is part of the Ice Sheets CCI and four parameters are to be determined for the Greenland Ice Sheet (GrIS), each resulting in a dataset made available to the public: Surface Elevation Changes (SEC), surface velocities, grounding line locations, and calving front locations. All CCI projects have completed a so-called Round Robin exercise in which the scientific community was asked to provide their best estimate of the sought parameters as well as a feedback sheet describing their work. By inter-comparing and validating the results, obtained from research institutions world-wide, it is possible to develop the most optimal method for determining each parameter.
This work describes the SEC Round Robin and the subsequent conclusions leading to the creation of a method for determining GrIS SEC values. The participants used either Envisat radar or ICESat laser altimetry over Jakobshavn Isbrae drainage basin, and the submissions led to inter-comparisons of radar vs. altimetry as well as cross-over vs. repeat-track analyses. Due to the high accuracy of the former and the high spatial resolution of the latter, a method, which combines the two techniques will provide the most accurate SEC estimates. The data supporting the final GrIS analysis stem from the radar altimeters on-board Envisat, ERS-1 and ERS-2. The accuracy of laser data exceeds that of radar altimetry; the Round Robin analysis has, however, proven the latter equally capable of dealing with surface topography thereby making such data applicable in SEC analyses extending all the way from the interior ice sheet to margin regions.
address the most rapidly changing elements of the climate system. The 13 projects were launched in two stages, e. g. aerosol and cloud properties, glaciers and ozone in 2010 followed by sea-ice, soil moisture and ice sheets in 2011/2012. This work is part of the Ice Sheets CCI in which the focus area is the Greenland Ice Sheet (GrIS). The motivation is an increased mass loss  (Sasgen et al. , 2012; Shepherd et al. , 2012; Svendsen et al. , 2013 ) observed e. g. through a lowering of the ice surface mainly in margin regions, as found by  Sørensen et al. (2011)  using ICESat repeat-track data or by  Khvorostovsky (2012) , who used ERS-1, ERS-2 and Envisat cross-overs. In order to increase our understanding of the changes, four parameters are to be determined (ESA, 2013a): -Surface Elevation Changes (SEC): 5 km × 5 km grids made from Envisat, ERS-1 and ERS-2 radar altimeter data.
Once CryoSat-2 and Sentinel-3 data are available, they will be included in the analysis. -Ice Velocity (IV): 500 m × 500 m maps from repeat-pass SAR data over coastal outlet glaciers such as Jakobshavn Isbrae and Upernavik Isstrøm. -Calving Front Locations (CFL): 250 m × 250 m shape-files of marine terminating glaciers or ice streams such as Jakobshavn Isbrae and Kangerdlugssuaq. Optical data from e. g. MERIS and MODIS will be used. -Grounding Line Locations (GLL): 250 m × 250 m shape-files of marine terminating glaciers with a floating-tongue, e. g. the Petermann and 79-fjord glaciers. Optical, altimetric and InSAR data will be used. The work is carried out through a broad collaboration between relevant cryospheric and climate-related research groups across Europe. The international aspect is further increased through the so-called Round Robin (RR) exercise performed in all the 13 projects.