dataesr/scientific-paragraphs-categorization huggingface dataset:
Despite generally smaller values, temporal correlations of anomalies exhibit map patterns that are similar to those described for monthly values. Figure  3  takes the ERA-I correlation as a reference to show the departures of the other forcing datasets. Consistent with the above-mentioned similarities between correlation maps, absolute differences are small: they are below 0. 1 for at least 90 % (monthly averages) and 74 % (anomalies) of the mapped grid cells. Over 94 % of P-ERA monthly and anomaly correlations are within a 0. 05 range of ERA-I values, with small positive differences scattered in the eastern part of the domain. The same is true for 81 % (70 %) and 66 % (44 %) of, respectively, WFDEI and PGF monthly values (anomalies). WFDEI negative departures are mostly located in northern Africa.
Leaf area index Simulated phenology is compared to the satellite-based GIMMS LAI product (Sect. 3. 3. 2). Temporal correlations of monthly averages are large (above 0. 7) over more than 75 % of the pixels where data are available, for all forcing datasets (Fig. 5 ). The largest values are mostly in the central and eastern parts of the domain. Very small or even negative correlations are scattered across the southern part. Irrigation, which is not simulated by ISBA-CTRIP, may be partially account-able for these discrepancies in some areas, such as the Nile delta and the region formerly known as Mesopotamia. Anomalies are systematically less temporally correlated than monthly values in all simulations. Over 90 % of pixels with data availability score values below 0. 5. However, values below 0.
Consistently, WFDEI and PGF, which feature progressively increasing divergences from ERA-I, yield slightly larger correlation differences. These are below 0. 05 for 82 % (69%) and 81 % (61%) for monthly (anomaly) correlations of WFDEI and PGF. While differences from ERA-I correlations are prevalently positive for P-ERA, their signs are more mixed for the other two forcing datasets. The largest clearly distinguishable difference signals, which are yielded by WFDEI and PGF monthly correlations, are located along the Caucasus and Pontic mountain ranges. LAI correlation statistics show a general agreement across forcing datasets. This is also true for monthly correlation coefficients (Fig. 6 ), which follow very distinguishable seasonal patterns. Correlations between monthly averages peak in July-August (0.
85), when LAI reaches its maximum. Instead, anomaly correlations are largest (0. 50-0. 55) in April-May, when the phenological development rate is likely to be at its maximum. Both correlation annual cycles are lowest in winter, when the ISBA-A-gs dynamic vegetation model sets LAI of several vegetation types to prescribed values until the next leaf onset occurs, triggering a new growing season. This does not apply to evergreen trees and winter crops. The prescribed minimum LAI values may be partly accountable for the small winter correlations. They may have a particularly negative impact on anomalies by hindering any winter interannual variability. All correlation statistics are very stable across the forcing datasets. This is similar to what was observed for SSM, with two exceptions: PGF statistics are more aligned with the other forcing datasets, and correlations for monthly averages are generally larger.
River discharge scores Simulated monthly river discharge time series are evaluated using the GRDC dataset (Sect. 3. 4). We use the statistical scores defined in Sect. 4. 3: RE µ , RE σ , MC, AC and KGE. Score values are summarised in exceedance frequency plots (Fig. 7 ) for all 99 selected GRDC gauges. Moreover, they are reported in detail for the 35 most downstream gauges (Tables 1 and 2). To facilitate graphical display and interpretation,  quency plots can be read in the same way: the higher the curve, the better the performance. The simulations yield similar MC and AC summary curves (Fig. 7 ). Approximately 60 % of the gauges have MC and AC values above 0. 6. The WFDEI and P-ERA simulations slightly outperform the others in terms of MC and AC, respectively, but differences are small. The inter-forcing spread becomes larger if we look at relative errors.
Catchment-averaged annual cycles To have an overview of how atmospheric forcing uncertainty affects simulated hydrology at catchment scale, we compare the average annual cycles of forcing and LSM output variables over four catchments: Danube, Rhone, Ebro and Po. These catchments were chosen as they contribute to the water balance of the Mediterranean Sea, even if indirectly, as in the case of the Danube, and because the model reproduces fairly well discharge measurements at their downstream GRDC gauges (Tables  1  and 2 ). The computed catchment-averaged annual cycles are shown and described in detail in Sect. S2 of the Supplement (Figs. S2 to S5). The main findings are summarised in the following paragraphs. Precipitation features the most different annual cycles across atmospheric datasets.
Atmospheric data Precipitation is the most uncertain forcing variable (Figs. S2 to S5). Conversely, the least uncertain forcing variable is air temperature, followed by SW and LW radiation. Both P-ERA and WFDEI forcing datasets are derived from ERA-I. While P-ERA differs from ERA-I only because of monthly precipitation amounts, WFDEI also features differences in SW radiation, air temperature and relative humidity. PGF is independent from ERA-based datasets. In addition to precipitation, wind speed and relative air humidity also show remarkable discrepancies between PGF and the other datasets. Limitations of (and alternatives to) bias correction methods adjusting a sub-set of the forcing variables or not involving statistical moments beyond the mean are discussed by  Haddeland et al. (2012)  and  Sippel et al.
(2016). Some of the reference atmospheric datasets used are not independent from the forcing. For example, CRU air temperature is used as reference and to bias-correct WFDEI and PGF forcing. The extensive use of CRU data is motivated by their relatively high update frequency and resolution (0. 5 • ), which make this dataset attractive for land surface modelling. Moreover, since PGF and WFDEI are based on different reanalyses, their bias corrections yield different subdiurnal variabilities. However, to reduce dataset interdependencies, future work on the evaluation of forcing uncertainties may benefit from using a wider ensemble of state-of-theart meteorological datasets, among others: the Global Soil Wetness Project (GSWP3) forcing dataset  (Yoshimura and Kanamitsu, 2013) , the Goddard Institute for Space Studies Temperature (GISSTEMP) analysis  (Hansen et al.
, 2010; Nasonova et al. , 2011) ; extension of LSM-satellite comparisons to land surface state variables and fluxes driving model simulation or remote sensing retrieval of SSM, e. g. soil brightness temperature  (Parrens et al. , 2014; Muñoz-Sabater, 2015; Barella-Ortiz et al. , 2017)  or radar backscattering coefficient  (Stoffelen et al. , 2017) ; investigating the improvement or inclusion in LSMs of physical processes that are relevant to topsoil hydrology at high latitudes, e. g. snowmelt, flooding and ponding  (Gouttevin et al. , 2013). The SSM statistics yielded by PGF show a slight departure from the range of ERA-based simulations. As PGF is the only forcing dataset not characterised by ERA-I synoptic variability, this result raises questions about the impact of precipitation occurrences on SSM correlations compared to other forcing variables.
Indeed, SSM is likely to be very sensitive to precipitation occurrences, as the modelled and observed surface soil layers are very thin (less than 10 cm). Following investigation steps in this regard should be carried out at finer temporal scales than monthly scales, as monthly averages filter out higher frequency variability. For instance, daily correlations would be more informative about the consistency of forcing precipitation events. Thus one could test SSM sensitivity to precipitation occurrences compared to amounts. For this, LSMs should be forced by several atmospheric datasets with independent precipitation occurrences. As in this study three of four simulations are forced by the same precipitation occurrences, we analysed the output at the monthly timescale. The ESA-CCI RSSM annual cycles display smaller amplitudes than the simulations, whose cycles are very close.
These observations are valid not only for the catchments analysed in Sect. S2  (Figs. S2 to S5) , but for all the areas drained by the chosen 35 downstream GRDC gauges (not shown). We may draw a connection between this result and the spectral analysis performed by  Polcher et al. (2016) , who compared SSM simulated by the ORCHIDEE LSM  (Krinner et al. , 2005)  with SMOS retrievals  (Kerr et al. , 2010)  in the Iberian Peninsula. They found the power ratio of low (seasonal to annual) to high (daily to monthly) frequencies in the SSM signal to be significantly smaller for SMOS than for the LSM. To validate this hypothesis, further large-scale analyses of SSM signals may be needed, also involving several models and forcing datasets to account for as many uncertainty sources as possible. The rescaling (Sect.
Conclusions To assess the hydrological impacts of atmospheric forcing uncertainties, we compared land surface model simulations forced by several meteorological datasets against stationbased and remote sensing estimates. Simulations were run for the 1979-2012 period over the Euro-Mediterranean area, which is widely acknowledged for its drought vulnerability, especially under climate change scenarios. We used the ISBA-CTRIP modelling platform, which simulates dynamic plant above-ground biomass and a multilayer diffusion scheme for the unsaturated soil water and energy balances, together with river discharge, a nonprognostic linear groundwater reservoir, and a non-linear surface reservoir simulating variable-velocity river channel flow. The simulations were forced using four atmospheric datasets and the evaluation of model outputs focussed on surface soil moisture, leaf area index, and river discharge.
The spread between simulations is significantly reduced when considering correlations of monthly discharge and anomalies. Moreover, anomaly correlations are not inferior to those of raw monthly values, thus highlighting the model ability to reproduce the inter-annual variability of river discharge. The impacts of forcing uncertainty are larger on the mean and standard deviation rather than the timing, shape and inter-annual variability of simulated discharge. Moreover, the errors in the seasonal distribution may be due to the lack of model representations of physical processes and anthropic alterations that affect streamflow. Several results point at structural discrepancies between simulations and reference datasets that seem not to be addressable to forcing uncertainty. Therefore, we identify several research directions to improve LSMs and to foster the assimilation of remote sensing retrievals.
Acknowledgements. We are thankful to  Emanuel Dutra  and  Gianpaolo Balsamo  ( European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, UK ) for providing the  ERA-Interim  atmospheric forcing dataset as well as the GPCP-corrected version. The work of  Emiliano Gelati  was supported by the  French   REMEMBER  project ( ANR 2012 SOC&ENV 001 ) within the  HYMEX initiative. The work of  Marie Minvielle  and of  David Fairbairn  was supported by the following European FP7 projects:  eartH2Observe  (grant agreement  603608 ) and  ImagineS  (grant agreement  311766 ), respectively. Graham P. Weedon  was supported by the  Joint DECC and Defra Integrated Climate Program -DECC/Defra  ( GA01101 ). Edited by: Matthias Bernhardt Reviewed by: two anonymous referees.
Introduction At least three recently published papers  (Steinbrecht et al. , 2017; Weber et al. , 2018; Ball et al. , 2018)  show that total ozone and ozone profile trends are consistent with earlier WMO Scientific Assessment of Ozone Depletion (2014) findings. Despite the addition of four more years since WMO Scientific Assessment of Ozone Depletion (2014),  Weber et al. (2018)  show that for most datasets and regions the trends in total ozone, since stratospheric halogens reached their maximum around 1997, are not significantly different from zero. In the case of ozone profile trends, however,  Steinbrecht et al. (2017)  confirmed increasing trends in the upper stratosphere (2 hPa) as first reported in WMO Scientific Assessment of Ozone  Depletion (2014). Due to improved datasets and longer records, the uncertainty in the profile trends reported by  Steinbrecht et al.
Satellite data Solar Backscatter Ultraviolet Radiometer (SBUV) version 8. 6 station overpass satellite data for the period 1980-2015 have been analysed in this work. The SBUV observing system consists of a series of instruments that measure ozone profiles from the ground to the top of the atmosphere (e. g. DeLand et al. , 2012; McPeters et al. , 2013). Measurements are provided as partial column ozone amounts in Dobson   1. The satellite data come from all SBUV-type instruments with data availability from November 1978 to the present (see Table  2  for details). Three versions of the SBUV instrument are used in the series, but the fundamental measurement technique is the same over the evolution of the instrument from the Backscatter Ultraviolet Radiometer (BUV) to SBUV/2  (Bhartia et al. , 2013).
Lidar data Monthly mean ozone profiles from ground-based lidar instruments were obtained by averaging daily profiles from the NDACC database at ftp://ftp. cpc. ncep. noaa. gov/ ndacc/station/ (last access: 2 May 2018; De  Mazière et al. , 2018). Data for lidar stations with long-term measurements, namely Hohenpeißenberg (47. 8 • N, 11. 0 • E), Haute Provence (43. 9 • N, 5. 7 • E) and Table  Mountain (34. 4  • N, 117. 7 • W) in the northern mid-latitudes; Mauna Loa (19. 5 • N, 155. 6 • W) in the tropics and Lauder (45. 0 • S, 169. 7 • E) in the southern mid-latitudes were taken from the NDACC NASA-Ames format files. It should be noted here that all lidar measurements are given as number density (molec cm -3 ) versus altitude. From these measurements the column densities in m atm cm (DU) were calculated for the corresponding SBUV layers using the following equation: Column density (in DU) = z 1 z 0 [O 3 (in molecules cm -3 )] • z (cm) 2.
69 × 10 16 , (1) where z 0 is the base, z 1 is the top of each SBUV layer and z is the height interval between two successive lidar measurements. The relation between height and atmospheric pressure is derived from ERA-Interim reanalysis data interpolated at each station. 3 Representativeness of single station ozone profiles in comparison to zonal means The comparison between lidar and SBUV station overpasses on common days throughout the record was based on deseasonalized monthly mean lidar and SBUV ozone profiles. Figure  1a  shows the resulting correlation coefficients which were found to be all statistically significant at the 99. 99 % confidence level. Concerning the correlations between lidar data and SBUV overpasses, we calculated monthly averages when at least three common days were available.
The data were deseasonalized by subtracting the long-term monthly mean pertaining to the same calendar month. All correlation coefficients (r) were calculated using the Pearson productmoment correlation and were tested for significance using the t test formula for the correlation coefficient with n -2 degrees of freedom  (von Storch and Zwiers, 1999) : t = r n -2 1 -r 2. ( 2 ) Recalculation after removing the strong trends before 1998 does not alter the significance. We have confined our analysis to the SBUV layers from 8 (40-25 hPa) up to 14 (2. 5-1. 6 hPa). This was imposed by the fact that at the highest altitudes lidar data quality is reduced, while at the lowest altitudes SBUV data quality is also reduced. The average distance between the subsatellite point and a lidar station was 500 km at middle latitude stations (Hohenpeißenberg, Haute Provence and Lauder) and 700 km for lower latitude lidar stations (Table  Mountain  and Mauna Loa) with collocation time criterion being sub daily.
The correlation coefficients in Fig. 1a  show a structure in the vertical. This is a result of using different instruments and different sampling times (SBUV data are daytime drifting orbit, lidar data are night-time). The declining signal-to-noise ratio for the lidars above 35 to 40 km also plays a role. Larger atmospheric variability at higher latitudes tends to increase correlations, e. g. at Lauder and Hohenpeißenberg, as does the very regular and large Quasi Biennial Oscillation (QBO) signal at Mauna Loa. To check the effect of different sampling, we also calculated the correlation between monthly mean SBUV overpasses averaged over all ≈ 30 days in a month, with SBUV overpasses averaged only over those days when lidar measurements were available. These results are presented in Fig. 1b.
Regression analysis model Multivariate linear regression (MLR) analysis has been applied both to SBUV and lidar datasets (e. g. WMO Scientific Assessment of Ozone Depletion, 2011;  Nair et al. , 2013; Harris et al. , 2015). Historically, long-term trends in ozone have been investigated with the use of simple linear trends. More sophisticated methods allowing for the estimation of a change in the long-term trend (such as the piecewise linear trend, PWLT), or directly using the EESC as a proxy to estimate the rate of change in ozone losses due to the evolution of ozone depleting substances (ODSs), have been used, e. g. by  Reinsel et al. (2005) ,  Newman et al. (2007)  or in the ozone assessments (WMO Scientific Assessment of Ozone Depletion, 2014). In this work we have used the statistical model in two ways, using either (a) the PWLT method, with January 1998 selected as inflection point, or (b) EESC and its orthogonal function as proxies  (Damadeo et al.
Data are deseasonalized prior to the analysis by removing the long-term monthly average  for each calendar month (January, February,. December). The other terms are the following: µ which corresponds to a constant term. -For the QBO term, equatorial zonal winds at 30 and 50 hPa as given by the standardized NOAA -CPS indices for 30 and 50 hPa were used (http://www. cpc. ncep. noaa. gov/data/indices/, last access: 2 May 2018). -SOLAR accounts for the solar cycle effect in ozone, using the 10. 7 cm wavelength solar radio flux (F10. 7) as a proxy. -Similarly, ENSO accounts for the ENSO effect on ozone, using the MEI (Multivariate ENSO Index) as a proxy http://www. esrl. noaa. gov/psd/enso/mei/table. html (last access: 2 May 2018). -The AOI term is used to describe the Arctic (or Antarctic) Oscillation effect on ozone.
The AO index is used for the Northern Hemisphere and the AAO index for the Southern Hemisphere. Both come from NOAA: http://www. cpc. ncep. noaa. gov/products/ precip/CWlink/daily_ao_index/teleconnections. shtml (last access: 2 May 2018). trop pres is the term used to describe the effect of tropopause changes on ozone. This index is constructed from NCEP reanalysis tropopause pressures. It is filtered to remove ENSO, solar, QBO, long-term trend and volcanic effects through MLR analysis. The index is calculated separately for every dataset used here, either as a zonal mean for the SBUV zonal averages or for each station (lidar or SBUV overpasses). -AOD is used to describe volcanic effects: the zonal mean 525 nm stratospheric aerosol optical depth integrated from the tropopause upwards is used from the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) dataset  (Thomason et al.
MLR results and discussion Figure  4  shows the amplitude (maximum value minus minimum value all divided by two) of ozone variability attributed to each proxy for the seven vertical layers and for Hohenpeißenberg, Mauna Loa and Lauder. Amplitudes are given in percent of the long-term ozone mean. The upper panels of Fig. 4  show results for Hohenpeißenberg as a northern midlatitude example, the middle panels for Mauna Loa as a tropical latitude and the bottom panels for Lauder as a southern mid-latitude example. The left plots refer to monthly mean SBUV overpasses for the whole period 1980-2015, the middle plots refer to SBUV data for the period common with lidar measurements and the right plots refer to the lidar monthly mean ozone profiles. The amplitude of QBOrelated variations below 10 hPa, down to 40 hPa, is on the order of 2 % of the mean.
The effect of tropopause height variations is most evident in the lower stratospheric layer 8, where it reaches 4 % for the SBUV data at Lauder and Hohenpeißenberg, but only 2 % for the lidar data. The lidars have better altitude resolution than SBUV in the lower-and mid-stratosphere, and do not include a substantial contribution from levels below 40 hPa or 26 km. In the upper levels, tropopause-height-related ozone variations generally decrease. Transient effects from large AOD of volcanic origin (El Chichon, Mount Pinatubo) can contribute substantially to the ozone variability, from 4 to 6 % of the mean, but for shorter time periods (2 to 3 years) after the volcano. Finally, the EESC proxies representing halogen chemistry carry the largest and most significant ozone variations, up to 5 % of the mean in the upper stratosphere.
Conclusions This paper investigates the representativeness of single lidar stations to calculate trends in the vertical ozone profiles. From 40 hPa to the upper stratosphere, single or grouped stations correlate well with zonal means calculated from SBUV overpasses. A good correlation (> 0. 4) with zonal means is found within ±15 • of latitude north or south of any lidar station with little dependence on height. This is because at the highest altitudes lidar data quality is reduced, while at the lowest altitudes SBUV data quality are reduced; we have confined our analysis to the SBUV layers from 8 (40-25 hPa) to 14 (2. 5-1. 6 hPa). Ozone trend profiles are very similar over the different stations and their corresponding zonal means. A detailed analysis of proxy footprints in the vertical ozone profiles also shows large similarities between lidar time series at the stations, the SBUV overpass time series and the SBUV zonal means.
gsfc. nasa. gov/pub/MergedOzoneData/ Ind_Inst_HDF/ (last access: 2 May 2018;  McPeters et al. , 2012; Bhartia et al. , 2013). Ground-based lidar ozone profiles were obtained from the Network for the Detection of Atmospheric Composition Change (NDACC) database at ftp://ftp. cpc. ncep. noaa. gov/ ndacc/station/ (last access: 2 May 2018; De  Mazière et al. , 2018). Competing interests. The authors declare that they have no conflict of interest.
In addition, the application of the EC tracer method is not straightforward to data collected during cold periods. In this last case, datasets must be thoroughly examined to determine the days when secondary formation of particulate OC is expected to be negligible. Usually, parameters used for the determination of "primary emission predominance" conditions are low solar radiation, low temperature and low O 3 concentration levels, and/or the occurrence of high NO and low NO 2 concentrations. Based on these criteria, Lonati et al. [97]  obtained a [OC/EC] p ratio of 9. 5, for the subset of data collected in Milan (Italy) during several cold seasons. However, this value seemed too high to be assumed as representative of primary ratio compared to previously reported values in several other studies  [21, 94, 99].