dataesr/scientific-paragraphs-categorization huggingface dataset:
data series. On Fig. 7b are plotted all the (a) 1261 and (b) values corresponding to the calibrations reported on Fig. 7a. The correlation coefficient given 1262 for all Porites species is high: b = -27. 24 x a -4. 92, R 2 = 0. 999, n = 30, p<0. 001. All dots showing (a) 1263 > -0. 19, the slope value derived from the theoretical δ 18 O-temperature relationship at equilibrium 1264 (Kim et al. , 2007) correspond to New Caledonia coral cores developed at mitigated temperatures, with 1265 fibers in greater amounts compared to COC, all other ones showing (a) < -0. 19 are associated to corals 1266 grown at high temperature, with reverse microstructures relative amounts. ic eq ui lib riu m (K im et al. , 20 07 ) Ne w Ca led on ia Cli pp ert on Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-433 Manuscript under review for journal Biogeosciences Discussion started: 19 October 2018 c Author(s) 2018.
1169 and annual δ 18 O ranges corresponding to the intersection of the calibrations. This feature is made 1170 possible by the homogenous light influence on calibrations. , https://doi. org/10. 5194/bg-2018-433 Manuscript under review for journal Biogeosciences Discussion started: 19 October 2018 c Author(s) 2018. CC BY 4. 0 License. , https://doi. org/10. 5194/bg-2018-433 Manuscript under review for journal Biogeosciences Discussion started: 19 October 2018 c Author(s) 2018. CC BY 4. 0 License. calibrations, δ 18 Ocarbonate = a x SST (°C) + b. Weber and Woohead (1972)  data series provided coral data. Fig. 3adisplays constants values from the 44 coral genera of Table1. (a) is considered as the disequilibrium indicator compared to -0. 19, the slope value derived from the theoretical δ 18 Otemperature relationship at equilibrium (Kim et al.
5194/bg-2018-433 Manuscript under review for journal Biogeosciences Discussion started: 19 October 2018 c Author(s) 2018. CC BY 4. 0 License. Comparison of δ 18 O measured on coral core collected at Moorea (French Polynesia) 1272 (Boiseau et al. , 1998) and measured and estimated temperatures. On left hand Fig. 8a, between 1980 1273 and 1990, seasonal measured data are compared to instrumental seawater temperature (Boiseau et al. , 1274 1998). On right hand Fig. 8b, over the last century, annual averaged measured data, originated from 1275 the same data series than seasonal data, are compared to estimated temperature in the (1°, 1°) grid 1276 containing Moorea.
Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO 2responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25-75. 5 • N, 11. 5 • W-62. 5 • E, at 0. 5 • resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I).
The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties.
For example, most GHMs do not simulate explicitly the surface energy fluxes that are crucial for evaporation, while most LSMs solve water and energy balances together  (Haddeland et al. , 2011; Pokhrel et al. , 2012). Physically consistent descriptions of such fluxes are necessary for modelling land surface feedbacks into the atmosphere, which can be represented by coupling LSMs with AGCMs  (Koster et al. , 2004; Betts, 2007; Campoy et al. , 2013). The outlined calibration trade-off should be carefully considered when choosing the modelling approach. In this study, we chose a LSM for assessing the hydrological impacts of atmospheric forcing uncertainties in the Euro-Mediterranean area. LSMs can be coupled to AGCMs  (Widén-Nilsson et al. , 2007)  or run offline, forced by gridded atmospheric datasets that are generally obtained from AGCM reanalysis or simulation.
, 2011; Muerth et al. , 2013; Papadimitriou et al. , 2017). Uncertainty impacts can be assessed by comparing simulation outputs with available independent datasets of land surface state variables and fluxes. Such datasets should be representative at the current spatial scales of LSMs (10 1 -10 2 km). River discharge is useful for validating large-scale LSM simulations as it is the integral of all hydrological processes occurring in a catchment  (Fekete et al. , 2012). Moreover, observed river discharge time series are globally available for a large number of catchments  (Hannah et al. , 2011). LSMs can be coupled with river routing models that simulate horizontal channel water movement from headwaters to oceans  (Ducharne et al. , 2003; Pappenberger et al. , 2010; Decharme et al. , 2010). Anthropic alterations of the natural hydrological regime (e.
ISBA-A-gs is a CO 2 -responsive LSM that simulates vegetation dynamics and provides prognostic estimates of LAI (in the configuration described by  Gibelin et al. , 2006). It also features a multi-layer diffusion scheme for the soil water and energy balances  (Decharme et al. , 2013). The latter allows the simulation of soil moisture over a surface layer whose thickness is compatible with the generally acknowledged penetration depth of L-band microwave radiometers used to remotely sense SSM (10 -2 -10 -1 m, according to  Escorihuela et al. , 2010, and Kerr et al. , 2010). To account for forcing uncertainties, LSM simulations are driven by four atmospheric datasets. Three of them are obtained from the ERA-Interim reanalysis  (Dee et al. , 2011)  produced by the European Centre for Medium Range Weather Forecasts (ECMWF; Reading, UK).
ERA-Interim is the first dataset, while its two derivatives are a version corrected using the Global Precipitation Climatology Project (GPCP) monthly precipitation data  (Huffman et al. , 2009)  and WATCH Forcing Data ERA-Interim (WFDEI;  Weedon et al. , 2014) , obtained by correcting precipitation, air temperature and downward shortwave (SW) radiation with monthly data from the Climatic Research Unit (CRU; University of East Anglia, Norwich, UK;  Harris et al. , 2014). These three forcing datasets are characterised by the same large-scale synoptic variability and precipitation occurrences. The fourth dataset is chosen to have a LSM simulation independent from ERA-Interim: PGF  (Sheffield et al. , 2006)  developed by the Terrestrial Hydrology Research Group at Princeton University (Princeton, NJ, USA).
The evaluation provides an overview of how atmospheric forcing uncertainties influence the simulation of surface hydrology. To frame uncertainties and their impacts, model input and output variables are compared to state-of-the-art reference datasets. Forcing data are compared to the following: E-OBS (van der Schrier et al. , 2013) air temperature and precipitation; Global Precipitation Climatology Centre (GPCC; Deutscher Wetterdienst, Offenbach, Germany;  Schneider et al. , 2014)  precipitation; CRU air temperature; and NASA/GEWEX Surface Radiation Budget (SRB;  Zhang et al. , 2015)  and Clouds and the Earth's Radiant Energy System (CERES;  Wielicki et al. , 1996)  for long-wave (LW) and SW downward radiation. Some of these datasets are used to correct biases of atmospheric forcing (see Sect.
3. 1) and thus are not independent references. Simulated LAI and SSM are compared to the remotely sensed Global Inventory Modeling and Mapping Studies (GIMMS;  Zhu et al. , 2013)  and European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA-CCI;  Dorigo et al. , 2014)  datasets. River discharge simulations are evaluated using the recorded time series from 99 gauges of the Global Runoff Data Centre (GRDC, 2018) dataset. The presented assessment involves the atmospheric conditions driving the simulation (forcing variables), the distributed land surface dynamics controlling fluxes at the interface with the atmosphere (LAI and SSM) and the integral of all catchment hydrological processes (river discharge).
E. Gelati et al. : Hydrological assessment of atmospheric forcing uncertainty Moreover, we use long time series of remote sensing retrievals: GIMMS LAI data are available from 1981 to 2011, and ESA-CCI SSM covers the whole simulation period. Several studies evaluated the impacts of forcing uncertainties on large-scale LSM simulations:  Guo et al. (2006)  tested the sensitivity of soil moisture simulations forced by several meteorological forcing datasets;  Decharme and Douville (2006b)  and  Szczypta et al. (2012)  looked at the impact of forcing precipitation errors on river discharge simulations;  Materia et al. (2010)  analysed the sensitivity of simulated river discharge to changes in the atmospheric forcing data;  Nasonova et al. (2011)  investigated the impacts of forcing and surface parameter uncertainties on simulated runoff and evapotranspiration;  Liu et al.
(2016)  ran multi-forcing and multi-model experiments to compare simulated and observed evapotranspiration. To our best knowledge, the extensiveness of the presented assessment exceeds those of previous studies focussed on assessing LSM sensitivity to atmospheric forcing uncertainty. The large spectrum of comparisons and the length of the benchmark time series contribute to the completeness of the exercise. The remainder of the paper is organised as follows. Section 2 describes the ISBA-A-gs LSM and CTRIP river routing model. Section 3 presents the atmospheric forcing datasets as well as all data sources used for evaluating the simulations. Section 4 outlines the experimental design of simulations and evaluation of model input and output. In Sect. 5 we show the results, which are discussed further in Sect.
(1985)  modified by  Jacobs et al. (1996)  that describes the relation between photosynthesis and leaf stomatal aperture in the absence of water scarcity. ISBA-Ags simulates two types of response to soil moisture stress for both herbaceous and forest vegetation  (Calvet, 2000; Calvet et al. , 2004) : the drought-avoiding response increases water use efficiency at the emergence of the stress, while the drought-tolerant response decreases or keeps the water use efficiency stable throughout the stress period. ISBA parameters depend on soil and vegetation types. Land surface classifications are provided by the ECOCLIMAP-II/Europe database  (Faroux et al. , 2013)  that covers Europe at 1 km resolution and is based on satellite observations. We use the following ISBA-A-gs settings: 1. coupling between above-ground biomass dynamics and soil water balance ("NIT")  (Calvet and Soussana, 2001; Gibelin et al.
Data In this section we describe all datasets used as input and to evaluate model simulations, namely atmospheric forcing and reference data (Sect. 3. 1 and 3. 2), SSM and LAI remote sensing retrievals (Sect. 3. 3), and river discharge measurements  (Sect. 3. 4). The simulation grid covers the continental surfaces of the Euro-Mediterranean area (25-75. 5 • N, 11. 5 • W-62. 5 • E) with a homogeneous 0. 5 • longitude-latitude resolution (Fig. 1 ).
Atmospheric forcing ISBA-A-gs simulations were forced by 3-hourly time series of precipitation, downward LW and SW radiation, air temperature and humidity, wind speed, atmospheric pressure, and CO 2 concentration. The atmospheric datasets used to force model simulations are briefly described in the following list: 1. ERA-I: ERA-Interim reanalysis  (Dee et al. , 2011). 2. P-ERA: ERA-I  (Balsamo et al. , 2015)  whose precipitation amounts are bias-corrected so that their monthly values match those of GPCP data. 3. WFDEI: WATCH Forcing Data ERA-Interim  (Weedon et al. , 2014). It is obtained from ERA-I by correcting precipitation, air temperature and downward SW radiation with monthly CRU data. Air temperature, pressure and humidity as well as downward LW radiation are sequentially elevation-corrected.
Moreover, air temperature is also corrected using mean monthly diurnal temperature ranges. Downward SW radiation is corrected for cloud cover and aerosol loading. Precipitation amounts are corrected to match CRU monthly averages. 4. PGF: Global Meteorological Forcing Dataset for land surface modelling. It is based on the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR; Boulder, CO, USA;  Kalnay et al. , 1996)  reanalysis. Precipitation and air temperature are bias-corrected so that monthly averages match those of CRU data; as for WFDEI, air temperature, pressure and humidity as well as downward LW radiation are sequentially elevationcorrected; downward LW radiation is corrected with the NASA/GEWEX Surface Radiation Budget (SRB) monthly data  (Zhang et al.
, 2013 (Zhang et al. , , 2015)) , and downward SW radiation is corrected using both SRB and CRU data. P-ERA and WFDEI inherit large-scale synoptic atmospheric circulation patterns and precipitation occurrences from ERA-I. Therefore, in the following we refer to ERA-I, P-ERA and WFDEI as ERA-based forcing. While WFDEI and PGF were available at 0. 5 • (latitude-longitude) horizontal resolution, ERA-I and P-ERA were interpolated from their original 0. 75 • resolution to 0. 5 •. All datasets span the experiment period 1979-2012. We used an experimental version of PGF, as the stable version available when simulations were run covers the period  1948 -2008 (PGF, 2018)). While ERA-based forcing provides rainfall and snowfall, PGF precipitation values were partitioned using an air temperature threshold of 1 • C.
The choice of the forcing datasets was motivated by two rationales. First, we wanted to compare the performance of a reanalysis product (ERA-I) with several bias-corrected versions. This type of atmospheric forcing is likely to be affected by biases, which in turn constitute a major source of uncertainty for land surface and hydrological simulations  (Berg et al. , 2003; Sheffield et al. , 2004; Ngo-Duc et al. , 2005; Weedon et al. , 2011). Second, we included a forcing dataset based on a different reanalysis than ERA-I, i. e. PGF.
Atmospheric reference datasets Reference datasets were processed for the following atmospheric forcing variables: precipitation, air temperature, and downward LW and SW radiation. To account for uncertainties in state-of-the-art climatological data, we used two datasets per variable. Precipitation data were obtained from two station-based gridded datasets: the E-OBS version 11. 0 product  (Haylock et al. , 2008; van der Schrier et al. , 2013)  covering the 1950-2015 period; and merging the 1900-2010 version 6 reanalysis and the 2011-2014 version 4 monitoring products of GPCC. The air temperature datasets are E-OBS, and CRU version 3. 21 (see Sect. 3. 1), spanning the 1901-2012 period. The chosen reference datasets for downward LW and SW radiation monthly data are SRB (see Sect. 3. 1) versions 3.
0 and 3. 1 for SW and LW, respectively, covering the 1983-2007 period, and Clouds and the Earth's Radiant Energy System (CERES) version 2. 8 from 2000 to 2014  (Wielicki et al. , 1996). E-OBS, GPCC reanalysis and CRU datasets were available at 0. 5 • resolution. GPCC monitoring, SRB and CERES products were interpolated from the original 1 to 0. 5 • resolution via nearest-neighbour estimation.
Remote sensing retrievals To evaluate simulated SSM and LAI, we used the  ESA-CCI 1978 -2014 dataset version 02, and the GIMMS 1981 -2011  dataset version 1. 3. These data sources were chosen since their temporal coverages are comparable to those of the forc- ing datasets. While the annual missing observation rates of GIMMS LAI are stationary, those of ESA-CCI SSM exhibit a decreasing trend due to the progressive inclusion of different microwave sensors. Despite its non-homogeneous time coverage, we decided to use ESA-CCI SSM for the whole simulation period  to perform a long-term evaluation of LSM output.
ESA-CCI surface soil moisture The ESA-CCI SSM product merges retrievals from several passive and active microwave satellite sensors  (Liu et al. , 2011; Dorigo et al. , 2014). Although the sensing depth depends on soil moisture itself among other factors, microwave-based retrievals are regarded as informative of the moisture in the top few centimetres of soil  (Escorihuela et al. , 2010). The source dataset consists of daily estimates (including missing values) spanning the 1978-2014 period at 0. 25 • horizontal resolution. First, daily maps were interpolated to the 0. 5 • model grid by averaging only over model pixels for which at least 50 % of the possible retrievals were available. Retrievals flagged for insufficient quality were regarded as missing. As topographic relief is known to negatively affect remote sensing estimates of soil moisture  (Mätzler and Standley, 2000) , we discarded the time series for pixels whose average altitude exceeded 1500 m a.
GIMMS leaf area index The GIMMS LAI product is based on an artificial neural network algorithm. The algorithm was trained to map the GIMMS normalised difference vegetation index (NDVI) to fraction of absorbed photosynthetically active radiation (FA-PAR) and LAI retrieved by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS;  Yang et al. , 2006). The training was carried out on the overlapping 2000-2009 period. The algorithm was then used to generate LAI and FA-PAR fortnightly estimates for the 1981-2011 period at 1/12 • spatial resolution  (Zhu et al. , 2013). As for ESA-CCI SSM, each retrieval map was interpolated only on those model pixels where missing data were below 50 %. The average missing rate in processed data ranges from 34 % in summer to 41 % in winter, when snow cover hinders the retrievals in mountainous areas and in the northernmost part of the domain.
Experimental design As our objective is the impact assessment of atmospheric forcing uncertainties on land surface hydrology, we performed a model simulation for each of the four forcing datasets considered (Sect. 3. 1). All simulations span the 1979-2012 period, which is the longest overlapping time interval across the forcing datasets. Model input and output are compared with a number of datasets, which are described in Sect. 3. 2 to 3. 4. Input val-ues of precipitation, air temperature, and downward LW and SW radiation are compared to station-and satellite-based estimates. Among the model outputs, particular attention is dedicated to river discharge, SSM and LAI: river discharge can be seen as the integral of all catchment hydrological processes; SSM and LAI represent key components of the land surface biophysical state, as they are important controls on water, heat and carbon fluxes at the land-atmosphere interface.
While modelled SSM and LAI are compared to remote sensing retrievals, simulated river discharge time series are evaluated with gauge measurements. To have an overview of how the model simulates large-scale hydrological processes, average annual cycles of forcing and simulated variables relevant to the water cycle are computed over large catchments. ISBA-A-gs and CTRIP can produce outputs at half-hourly and daily time intervals, respectively. However, since three of the four forcing datasets are characterised by the same (ERA-I) synoptic variability, we choose to perform our analysis at the monthly timescale. Indeed, we believe that a hydrological evaluation of the uncertainty deriving from the considered forcing datasets at (sub-)daily temporal scale may not add significant further information, because the ERA-based forcings have the same precipitation occurrence processes.
Moreover, a daily timescale analysis may be severely affected by the lack of model representation of anthropic river regulation, which affects most major European rivers. To compare model input-output variables with corresponding datasets, monthly series are computed using only the time steps where reference data are available. Monthly anomalies are computed from monthly averages: at each grid cell, the climatological mean and standard deviation are estimated for each calendar month. Then monthly averages are subtracted the climatological mean and divided by the climatological standard deviation to obtain normalised monthly anomalies. Anomaly statistics measure how well the model simulates departures from the annual cycle, i. e. the interannual variability.
Surface soil moisture The water content of the ISBA top soil layer is compared to the ESA-CCI SSM remote sensing retrievals. We use the top layer, which is 1 cm thick, to take full advantage of the fine vertical soil discretisation of the DF scheme. The soil column is represented with 14 layers over a 12 m depth following  Decharme et al. (2013) , who simulated soil moisture only over the root zone. Our choice is also based on the conclusions reached by previous studies. Escorihuela et al. (2010)  studied the penetration depth of L-band microwave radiometry to estimate SSM: they found that brightness temperature, which is measured directly by the radiometer, was best correlated with soil moisture in the top 2 cm for dry soils and in the top 1 cm for wet soils. Dorigo et al. (2014) , who validated ESA-CCI SSM data using ground-based measure- To ensure comparability between model output and remotely sensed data, each SSM time series is linearly rescaled to the interval [0, 1] with respect to its minimum and maximum values.
Catchment-averaged annual cycles We compute catchment-averaged annual cycles for the following forcing variables: downward LW and SW radiation, air temperature at 2 m, wind speed, relative air humidity, and total precipitation. Of the simulated land surface states and fluxes, we show river discharge, evapotranspiration, LAI and RSSM. To be consistent with river discharge, which is the integral of upstream hydrological processes, annual cycles are computed using only the time steps when river discharge measurements are available. The evaluation of model simulations against the datasets described in Sect. 3 is reported in the following. Catchmentaveraged simulated annual cycles of hydrologically relevant variables are described in the Supplement for four river basins (Danube, Rhone, Ebro and Po).
Surface soil moisture We compare the simulated water content of the top soil layer of the ISBA model with the ESA-CCI SSM remote sensing retrievals (Sect. 3. 3. 1). To ensure comparability, each SSM time series is linearly rescaled with respect to its minimum and maximum, as outlined in Sect. 4. 1. Global correlation values are computed using the entire datasets and are reported in the upper-left corners of the maps in Fig. 3. As expected, similar scores are obtained for the different simulations, among which the PGF-forced SSM is slightly less correlated with ESA-CCI data. Looking at this small difference, we must recall that the atmospheric synoptic variability of PGF is different from that of ERA-based forcing. ERA-I, P-ERA and WFDEI are characterised by the same precipitation occurrences, while the intensities of individual precipitation events may differ according to the applied bias-correction (Sect.