ERA-Interim / Land : A global land water resources 1 dataset

Introduction Conclusions References


Introduction
Multi-model land-surface simulations, such as those performed within the Global Soil Wetness Project (Dirmeyer, 2011;Dirmeyer et al., 2002Dirmeyer et al., , 2006)), combined with seasonal forecasting systems have been crucial in triggering advances in land-related predictability as documented in the Global Land Atmosphere Coupling Experiments (Koster et al., 2011(Koster et al., , 2009(Koster et al., , 2006)).The land-surface state estimates used in those studies was generally obtained with offline model simulations, forced by 3 hourly meteorological fields from atmospheric reanalyses, and combined with simple schemes to address climatic biases.Bias corrections of the precipitation fields are particularly important to maintain consistency of the land hydrology.The resulting land-surface data sets have been of paramount importance for hydrological studies addressing global Introduction

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Full ERA-Interim reanalysis, by incorporating recent land model developments at ECMWF combined with precipitation bias corrections based on GPCP v2.1.
To produce ERA-Interim/Land, near-surface meteorological fields from ERA-Interim were used to force the latest version of the HTESSEL land-surface model (Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land).This scheme is an extension of the TESSEL scheme (van den Hurk et al., 2000) that was used in ERA-Interim, which was based on a 2006 version of ECMWF's operational Integrated Forecasting System (IFS).HTESSEL includes an improved soil hydrology (Balsamo et al., 2009), a new snow scheme (Dutra et al., 2010), a multi-year satellite-based vegetation climatology (Boussetta et al., 2013a), and a revised bare-soil evaporation (Balsamo et al., 2011;Albergel et al., 2012a).
The next section describes the various data sets used for production and verification of ERA-Interim/Land.Section 3 describes the offline land-surface model integrations.Section 4 presents the main results on verification of land-surface fluxes, soil moisture, snow, and surface albedo.The land-surface estimates from ERA-Interim/Land are a preferred choice for initializing ECMWF's seasonal forecasting system (System-4, Molteni et al., 2011), as well as the monthly forecasting system (Vitart et al., 2008), since both systems make use of ERA-Interim/Land scheme.A summary and recommendation for the usage of the ERA-Interim/Land product is reported in the conclusions.

Dataset and methods
The experimental set-up makes use of offline (or stand-alone) land simulations, which represents a convenient framework for isolating benefits and deficiencies of different land surface parameterizations (Polcher et al., 1998).In addition, in terms of computational cost, given the complexity of the coupling with the atmosphere, offline simulations are much more cost-effective (faster) to run than a full atmospheric-land assimilation system.Introduction

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Full In this study, offline runs are performed both at the global and point scales.All the 3 hourly meteorological forcing parameters were linearly interpolated in time to the land surface model integration time step of 30 min.The land-use information has been derived from the United States Geophysical Survey -Global Land Cover Classification (USGS-GLCC) and the United Nations -Food and Agriculture Organization (UN-FAO) data set at the same resolution as the forcing data.A comprehensive description of the land surface model and the ancillary datasets is given in the IFS documentation (2012, Part IV, chapters 8 and 11, http://www.ecmwf.int/research/ifsdocs/CY37r2/index.html).

Validation and supporting datasets
The quality of ERA-Interim/Land builds upon an error correction methodology applied to the forcing precipitation and on a comprehensive verification applied to the different compartments of the water and energy cycle at the surface.In the following the datasets entering the ERA-Interim/Land generation and its verification are briefly presented.

ERA-Interim meteorological reanalysis
ERA-Interim (Dee et al., 2011) is produced at T255 spectral resolution (about 80 km) and covers the period January 1979 to present, with product updates available approximately 1 month delay from real-time.The atmospheric forcing data was gridded on the original reduced Gaussian grid (with a resolution of 0.7 • at the Equator) with a 3 h time interval.ERA-Interim precipitation and radiation fields (incoming long-and short-wave components) are generated by the forecast model in 3 hourly accumulations, and present some initial spin-up (Kållberg, 2011).To avoid possible spin-up effects, the 3 hourly surface fluxes correspond to the 09-21 h forecast intervals from initial conditions at 00:00 and 12:00 UTC.ERA-Interim temperature, surface pressure, humidity and wind fields are instantaneous values representative of the lowest model level corresponding to a height of 10 m above the surface and are extracted from the Introduction

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Full 03-12 forecast-range intervals and from both 00:00 and 12:00 UTC runs.The forecasts are then concatenated to produce a continuous 3 hourly meteorological forcing data set that can be used to drive land surface simulations.The ERA-Interim 3 hourly precipitation is rescaled to match the GPCP monthly averages, as detailed by Balsamo et al. (2010a).

GPCP v2.1 precipitation
The GPCP dataset merges satellite and rain gauge data from a number of satellite sources including the Global Precipitation Index, the Outgoing Long-wave Radiation, Precipitation Index, the Special Sensor Microwave/Imager (SSM/I) emission, the SSM/I scattering, and the TIROS Operational Vertical Sounder (TOVS).In addition, rain gauge data from the combination of the Global Historical Climate Network (GHCN) and the Climate Anomaly Monitoring System (CAMS), as well as the Global Precipitation Climatology Centre (GPCC) dataset which consists of approximately 6700 quality controlled stations around the globe interpolated into monthly area averages, are used over land.More details on the datasets and the method used to merge these data are provided by Adler et al. (2003).
Compared to earlier versions the version 2.1 of the GPCP used in this study takes advantage of the improved GPCC gauge analysis and the usage of the OPI estimates for the new SSM/I era.Thus, the main differences between versions 2.1 and 2.0 are introduced by the use of the new GPCC full data reanalysis (Version 4) for 1997-2007, the new GPCC monitoring Product (version 2) thereafter and the recalibration of the OPI data to a longer 20 yr record of the new SSM/I-era GPCP data.Further details on the 2.1 version can be found in Huffman et al. (2009).

FLUXNET land energy fluxes
Available observational data for the year 2006 from the Boreal Ecosystem Research and Monitoring Sites (BERMS, Betts et al., 2006), the FLUXNET project (Baldocchi Introduction

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Full As part of the CEOP program, reference site observations from the Amazonian region also belonging to the LBA experiments (the Large Scale Biosphere-Atmosphere Experiment in Amazonia) are available for scientific use.In this study, observations are taken from flux towers located within an evergreen broadleaf forest (Manaus) and a woody savannah region (Brasilia).
The FLUXNET observations used in this study are part of the LaThuile dataset, which provides flux tower measurements of latent heat flux (LE), sensible heat flux (H) and net ecosystem exchange (NEE) at high temporal resolution (30 to 60 min).For verification purposes, hourly observations from the year 2004 were selected from the original observational archive (excluding gap filled values) with high quality flag only (see Table 1).

ISMN soil moisture observing network
In-situ soil moisture observations are valuable to evaluate modelled soil moisture.In the recent years huge efforts were made to collect observations representing contrasting biomes and climate conditions.Some of them are now freely available on the Internet such as data from The International Soil Moisture Network (ISMN, Dorigo et al., 2011, http://www.ipf.tuwien.ac.at/insitu/).The ISMN is a new data hosting centre where globally available ground-based soil moisture measurements are collected, harmonized and made available to users.This includes a collection of nearly 1000 stations (with data from 2007 up to present) gathered and quality controlled at ECMWF.Albergel et al. (2012a-c) have used these data to validate various soil moisture estimates produced at ECMWF, including from ERA-Interim as well as from offline land simulations.Data from 6 networks are considered for 2010: NRCS-SCAN (Natural Re-  3 gives a full list of reference for each network as well as the main statistical scores for the comparison.

The GTS-SYNOP observing network
The SYNOP datasets provide daily observations of the main weather parameters and selected land surface quantities such as snow depth, at a large number of sites worldwide.The snow data are acquired at a minimum frequency of once a day and represent the only quantitative snow-depth measurement on the ground (remote sensing observations have difficulties in representing snow properties).These data are operationally used at ECMWF for the daily global snow analysis as described in Drusch et al. ( 2004) and de Rosnay et al. (2013a).

The satellite surface albedo
The Moderate Resolution Imaging Spectro-radiometer (MODIS) albedo product MCD43C3 provided data describing both directional hemispheric reflectance (blacksky albedo) and bi-hemispherical reflectance (white-sky albedo) in seven different bands and aggregated bands.Data from the Terra and Aqua platforms are merged in the generation of the product that is produced every 8 days, with 16 days acquisition, and available on a 0.05 • global grid.The accuracy and quality of the product has been studied by several authors (e.g.Roman et al., 2009;Salomon et al., 2006).The MODIS product has served as a reference for model validations (e.g.Dutra et al., 2010Dutra et al., , 2012;;Wang and Zeng, 2010;Zhou et al., 2003).In this study, we compare the white-sky broadband shortwave albedo (2000-2010) with ERA-Interim and offline simulations.Introduction

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Full MODIS albedo was averaged for each month and spatially aggregated to the simulation grid.

The GRDC river discharge dataset
The Global Runoff Data Centre (GRDC) operates under the auspice of the World Meteorological Organization and provides data for verification of atmospheric and hydrologic models.The GRDC database is updated continuously, and contains daily and monthly discharge data information for over 3000 hydrologic stations in river basins located in 143 countries.Over the GSWP-2 period the runoff data of 1352 discharge gauging stations was available and used for verification of the soil hydrology (Balsamo et al., 2009).Pappenberger et al. (2009) and Balsamo et al. (2010b) used the GRDC daily discharge to evaluate a coupled land surface -river discharge scheme for river flood prediction.
In this study river discharges from GRDC data set, clustered by large continental areas, are used to evaluate the overall hydrological consistency and improvements.

Soil hydrology
A revised soil hydrology in TESSEL was proposed by van den Hurk and Viterbo (2003) for the Baltic basin.These model developments were in response to known weaknesses of the TESSEL hydrology: specifically the choice of a single global soil texture, which does not characterize different soil moisture regimes, and a Hortonian runoff scheme which produces hardly any surface runoff.Therefore, a revised formulation of the soil hydrological conductivity and diffusivity (spatially variable according to a global soil texture map) and surface runoff (based on the variable infiltration capacity approach) were operationally introduced in IFS in November 2007.Balsamo et al. (2009) verified the impact of the soil hydrological revisions from field site to global atmospheric coupled experiments and in data assimilation.

Snow hydrology
A fully revised snow scheme was introduced in 2009 to replace the existing scheme based on Douville et al. (1995).The snow density formulation was changed and liquid water storage in the snow-pack was introduced, which also allows the interception of rainfall.On the radiative side, the snow albedo and the snow cover fraction have been revised and the forest albedo in presence of snow has been retuned based on MODIS satellite estimates.A detailed description of the new snow scheme and verification from field site experiments to global offline simulations are presented in Dutra et al. (2010).
The results showed an improved evolution of the simulated snow-pack with positive effects on the timing of runoff and terrestrial water storage variation and a better match of the albedo to satellite products.

Vegetation seasonality
The Leaf Area Index (LAI), which expresses the phenological phase of vegetation (growing, mature, senescent, dormant), was kept constant in ERA-Interim and Introduction

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Full assigned by a look-up table depending on the vegetation type; thus vegetation appeared to be fully developed throughout the year.To allow for seasonality, a LAI monthly climatology based on a MODIS satellite product was implemented in IFS in November 2010.The detailed description of the LAI monthly climatology and its evaluation is provided in Boussetta et al. (2013a).

Bare soil evaporation
The bare soil evaporation included in the HTESSEL model in conjunction with the LAI update as reported in Balsamo et al. (2011) has been extensively evaluated by Albergel et al. (2012a) over the US.The evaluation was based on data from the Soil Climate Analysis Network (SCAN) as well as Soil Moisture and Ocean Salinity (SMOS) satellite data.The bare ground evaporation has been enhanced over deserts by adopting a lower stress threshold than for vegetation.This is in agreement with previous experimental findings (e.g.Mahfouf and Noilhan, 1991) and results in a more realistic soil moisture for dry lands, as was largely confirmed by Albergel et al. (2012a).

Results
The quality of ERA-Interim/Land builds upon reduced errors in the meteorological forcing and land surface modelling.In the following resources dynamical range in the past 3 decades associated with snow and unsaturated soil layers.The evolution of ERA-Interim/Land along the 32 yr of this dataset and its differences with respect to ERA-Interim are illustrated in Figs. 3 and 4 for both soil moisture and snow water equivalent.The stability and the differences with respect to ERA-Interim can be appreciated in the plots of Figs.3a and 4a for snow water equivalent and Figs.3b and 4b for the top 1 m soil moisture.The snow changes in Fig. 4a are mainly consequence of the new snow scheme and highlight both a snow mass increase in high latitudes and a slight reduction in mid-latitudes.The soil moisture presents large differences in Fig. 4b than can be attributed to the soil hydrology revisions.In these runs observational constraints on the snow and soil water reservoirs such as those applied by data assimilation is totally absent, however the resulting water reservoirs and the fluxes both towards the atmosphere (heat and moisture) and the river discharges, are shown to improve with respect to the original ERA-Interim output.In the following sections a selection of results to prove the added value of ERA-Interim/Land is presented.

Land fluxes verification
The land surface fluxes resulting from the offline-driven land simulations are validated against two categories of land-controlled fluxes, the land-atmosphere turbulent heat and moisture and the river discharges.

Latent and Sensible heat flux
The fluxes are measured over 34 FLUXNET, CEOP and BERMS flux-towers, as listed in Table 1.Correlation, mean bias and root mean squared differences are improved using the ERA-Interim/Land surface scheme, indicating a higher skill in reproducing the land atmosphere fluxes.Introduction

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Full A detailed evaluation of the ERA-Interim (TESSEL) and ERA-Interim/Land (HTES-SEL) surface schemes in offline driven simulations for each site confirms a general improved representation of both the latent and sensible heat fluxes (Fig. 5).
An overall quantitative estimate of the improvements is reported in Table 2.Both Latent and Sensible heat fluxes indicate an average improvement of 8 %, when adopting the ERA-Interim/Land surface scheme instead of the ERA-Interim surface scheme, evaluated as root-mean-square-error differences.

River discharge
River discharge is used here to provide an integrated evaluation of the continental water cycle for verifying improvements in the representation of land hydrology.The ERA-Interim/Land discharges are compared to those obtained from ERA-Interim by consideration of their correlation to observed GRDC monthly river discharges clustered by continent.Figure 6 shows the cumulative distribution function of the correlations between simulated and observed monthly river discharges ERA-Interim/Land (blue dashed line).A general improvement over ERA-Interim (red solid line) is evident since the correlations are higher at all levels in nearly all cases (blue line is nearly always above the red line and area under the blue curve is greater).Although there is still some way to go in effectively representing river discharge in land surface schemes such as this (Pappenberger et al., 2012) what is particularly encouraging is the average improvement of river discharge correlations of ERA-Interim/Land over ERA-Interim occurring on all continents which therefore encompass different rivers and water balance regimes.

Land water resources verification
The water reservoirs verification aims at assessing the daily performance of ERA-Interim/Land in reproducing the top metre of soil water content and the snow water equivalent, which are responding to the diurnal, synoptic and seasonal variations.The Introduction

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Full slower and deeper soil layers, such as the water table, are not considered in the present verification since they are not yet properly represented in the model.

Soil moisture
The changes in the land surface parameterization have largely preserved the mean annual soil moisture, which ranges around 0.23-0.24m 3 m −3 as global land average on the ERA-Interim period.However the spatial variability has greatly increased with the introduction of the revised soil hydrology (Balsamo et al., 2009).In order to verify the soil moisture produced by the offline simulations we make use of the International Soil Moisture Network (ISMN) ground-based observing networks.This has been applied by Albergel et al. (2012b) to validate soil moisture from both ECMWF operational analysis and ERA-Interim.Offline land surface simulations were also used by Albergel et al. (2012a) to evaluate the new bare ground evaporation formulation mentioned in Sect.2.2.4.Considering the field sites of the NRCS-SCAN network (covering the US) with a fraction of bare ground greater than 0.2 (according to the model), the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 to 0.087 m 3 m −3 when using the new formulation in offline experiments (and from 0.110 to 0.088 m 3 m −3 in operations).It also improves correlations.Figure 7 illustrates the effect of the model changes for one site located in Utah.ERA-Interim and ERA-Interim/Land soil moisture are shown to illustrate the differences in soil moisture and the contribution of GPCP correction.
In the TESSEL formulation used in ERA-Interim, minimum values of soil moisture are limited by the wilting point of the dominant vegetation type, however ground data indicate much drier conditions, as is clearly observed from May to September 2010.The new soil hydrology and bare ground evaporation allows the model to go below this wilting point so the new analysis is in much better agreement with the observations than in ERA-Interim.The better correlations and reduced RMSD are explained by a more realistic decrease in soil moisture after a precipitation event due to its higher water Introduction

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Full holding capacity and are attributed to soil hydrology revisions (Balsamo et al., 2009;Albergel et al., 2012b).The ability of ERA-Interim/Land and ERA-Interim to reproduce soil moisture is also presented by Fig. 8.This illustrates also the gain in skill in reproducing the observed soil moisture in dry land as a function of vegetation cover.These improvements are attributed to the bare ground evaporation revision (Balsamo et al., 2011;Albergel et al., 2012).
The correlation of ERA-Interim/Land soil moisture with the various observed soil moisture networks varies depending on the network selected (Fig. 9).This variation is similar in manner to that seen with ERA-Interim but the correlation is not significantly improved.However, in Fig. 10 a Taylor diagram is used to illustrate a more detailed statistical comparison of ERA-Interim/Land (in red), ERA-Interim (in blue), and in situ observations for 2010.In this figure the distance to the point marked "In situ" has been reduced with the ERA-Interim/Land, which indicates a more realistic soil moisture variability (better reproduction of the standard deviation of observations).

Snow
The verification of snowfields considers two different observational datasets to evaluate the snow evolution in ERA-Interim and ERA-Interim/Land: (i) the SYNOP daily snow depth and (ii) datasets from the former USSR.The 1979-1993 former USSR dataset was used in Brun et al. (2013) to evaluate simulated snow properties, such as density, which is not routinely measured at SYNOP stations.Dutra et al. ( 2010) attributed the largest improvement in the new snow scheme to the snow density representation.This is confirmed by the verification results on a large number of sites where snow density was measured for the typical Northern latitudes snow season (October to June) average for 1979-1993 period (Balsamo et al., 2012).In ERA-Interim, as well as ERA-Interim-Land, the snow density is not at all constrained by data assimilation due to a lack of observations and therefore it relies solely on the capacity of the land surface Introduction

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Full Simulations of snow water equivalent with and without the GPCP V2.1 rescaling have been evaluated against observations, which are available from 1979 to 1993 over the USSR.A significantly lower bias in this case is obtained without the GPCP rescaling (9.7 mm vs. 33.8mm) confirming the general difficulties in measuring snowfall with gauges.
The capacity of detecting the presence of snow on the ground is examined using the SYNOP network in more recent years considering two snow seasons 2005/06 and 2009/10.Two scores are adopted: a robust indicator and is more resilient to model biases (in case snow abundance the SDR may favour a biased snow scheme).The MODIS land surface albedo is used to show improvements in the snow representation in forest areas (Fig. 12). Figure 12c points to a substantially reduced albedo bias in the ERA-Interim/Land attributed to the snow scheme revision described in Dutra et al. ( 2010) Dedicated land surface reanalyses, such as the ERA-Interim/Land evaluated here, are becoming established added-value products within the reanalysis efforts worldwide (Dee et al., 2013).They allow computationally effective testing of new land surface developments, including improvements to the process representation and parameterisation of the hydrological and biogeochemical cycles that contribute to a fast-track land surface model developments as identified by van den Hurk et al. ( 2012).Future research into improved representation of the land surface is high priority, and work already underway in this area includes land carbon exchanges (Boussetta et al., 2013b) and hydrological applications such as global water-bodies reanalysis (e.g.Balsamo et al., 2012) and used in applications such as global flood risk assessment (e.g.Pappenberger et al., 2012).More sophisticated rescaling methods (e.g.Weedon et al., 2011) are envisaged to bias correct the meteorological forcing and to permit a high resolution downscaling of land reanalysis.In addition, consideration of land surface parameterisation uncertainty could be used to further improve predictive skill (e.g.Cloke et al., 2011).
Important developments with advanced land data assimilation methods such as the Extended Kalman Filter (Reichle et al., 2013;de Rosnay et al., 2013b;Drusch et al., 2010) can be combined with offline surface simulations.The experimental equivalence of offline and atmospheric coupled land data assimilation (Balsamo et al., 2007;Introduction Conclusions References Tables Figures

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Full within the EU-funded ERA-CLIM project, moreover it can open up new possibilities of considering more advanced data assimilation schemes (e.g.Fowler and van Leeuwen, 2012), especially designed for non-linear systems.
The skill of an ERA-Interim/Land variant (with no precipitation readjustment) together with other model-based and remote-sensing datasets for the detection of soil moisture climate trends in the past 30 yr is evaluated in Albergel et al. (2013).This study, using the methodology described in this paper, represents an attempt to gain insights on soil water reservoirs and its evolution in response to natural and anthropogenic forcing.

Conclusions
This paper documents the configuration and the performance of the ERA-Interim/Land reanalysis in reconstructing the land surface state over the past 3 decades.The ERA-Interim/Land is produced from the ERA-Interim meteorological forcing offline landsurface model simulations.In this paper it has been demonstrated that the ERA-Interim/Land dedicated land surface reanalysis is of added value over the standard land component for the ERA-Interim reanalysis product.The ERA-Interim/Land runs are an integral part of the ERA-Interim on-going research efforts and respond to the need to re-actualize the land surface initial conditions of ERA-Interim, following several model parameterization improvements.The newly produced land-surface estimates benefit from the latest land surface hydrology schemes used operationally at ECMWF for weather, monthly, and seasonal forecasts.The ERA-Interim/Land added value components encompass soil, snow and vegetation description upgrades, as well as a bias correction of the ERA-Interim monthly-accumulated precipitation based on GPCP v.2.1.In the Northern Hemisphere the precipitation correction is shown to be effective in reducing the bias over US and rather neutral over Eurasia, while in the tropical land benefits are visible in the river discharge.
The new land surface reanalysis has been verified against several datasets for the main water reservoirs, snow and soil moisture, together with the energy and water Introduction

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Full fluxes that have direct impact on the atmosphere.The verification makes use of both in-situ observations and remote sensing products.Improved match to observations largely attributed to the land surface revisions in the latest ECMWF land surface scheme, is found in the latent and sensible heat fluxes and in soil moisture and snow.
The overall water balance is verified with the observed river discharge from the GRDC river network showing an enhanced correlation to the observations with respect to ERA-Interim as combined effect of the GPCP precipitation correction and the land surface improvements.Finally, the impact of adopting ERA-Interim/Land as initial condition in retrospective forecasts has also been verified with a generally positive effect of the new land initial condition, more evident in longer lead times of the forecasts (Balsamo et al., 2012).
The ERA-Interim/Land dataset has been used operationally at ECMWF since 2010 for the initialization of the past reforecasts needed for the monthly forecasting (Vitart et al., 2008) and the seasonal prediction systems (Molteni et al., 2011).Ongoing research effort includes the extension of this dataset beyond 2010 using a different dataset for precipitation based on the latest GPCC collections and application of the described methodology to future ECMWF reanalyses.

Dataset access
The ERA-Interim/Land dataset is freely available and it can be downloaded from: http: //apps.ecmwf.int/datasets/.

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Full  Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | et al., 2001) and the Coordinated Energy and water cycle Observations Project (CEOP) were used in this study.
Discussion Paper | Discussion Paper | Discussion Paper | -Interim/Land differs from the land component of ERA-Interim in a number of land surface parameterization improvements introduced in the operational ECMWF forecast model since the frozen cycle used in the ERA-Interim reanalysis.These are briefly described in the following subsections.The ERA-Interim meteorological forcing with the GPCP-rescaled precipitation as described in Sect.2.1.2is used to drive a 11 yr spinup run that generates plausible initial conditions for the 1st of January 1979.Those are obtained as average of land conditions valid on the 1st of January 1980-1989 derived from the spin-up run.A single continuous 32 yr simulation starting on the 1st of January 1979 is then realised with the latest ECMWF land surface scheme, which includes several updated modelling components.These are briefly described in the following subsections.Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , selected verification results are included, showing the added value of ERA-Interim/Land in reproducing the main land water reservoirs and fluxes towards the atmosphere and river outlets.The two most active water reservoirs are the root-zone soil moisture (here the top 1 m of soil is considered) and the snow accumulated on the ground.These global reservoirs in its median of the distribution calculated on the period 1979-2010 are shown in Fig. 1 for soil moisture and snow water equivalent (both expressed in mm of water or equivalently in kg m −2 ) for two representative dates for mid-winter and mid-summer.The 95th percentile of the distribution is shown for comparison in Fig. 2 to illustrate the water Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | model to represent the seasonal evolution, from about 100 kg m −3 at the beginning of the winter season to more than 300 kg m −3 towards the end of the snow season.
sources Conservation Service -Soil Climate Analysis Network) and SNOTEL (short for SNOwpack TELemetry) over the United States, with 177 and 348 stations, respectively; SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Introduction

Table 2 .
Summary of mean latent heat (LE) and sensible heat (H) statistics averaged over the 34 sites (units of W m −2 ).Statistical indices shown are, mean bias (bias), root mean squared error (rmse) and correlation (corr).

Table 3 .
Comparison of surface soil moisture with in situ observations for ERA-Interim/Land (fiv8)[bold]and ERA-Interim in 2010.Mean correlations (R), bias (in situ measurements minus products) root mean square differences (RMSD), normalized standard deviation (SDV) and the centred RMSD model and in situ patterns, normalized by the in situ standard deviation are given for each network.Scores are given for significant correlations with p values < 0.05.For each R estimate a 95 % Confidence Interval (CI) was calculated using a Fisher Z transform.