Appraising the capability of a land biosphere model as a tool in modelling land surface interactions: results from its validation at selected European ecosystems

In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Speciﬁcally, SimSphere’s performance in predicting Net Radiation ( R net ), Latent Heat (LE), Sensible Heat ( H ) and Air Temperature ( T air ) at 1.3 and 50 m 5 was examined. Model simulations were validated by ground-based measurements of the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEu-rope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation. Overall, model performance can largely be described as satisfactory for most of the 10 experimental sites and evaluated parameters. For all model parameters compared, predicted H ﬂuxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36 W m − 2 . LE ﬂuxes and R net also agreed well with the in-situ data with RSMDs of 62.75 and 64.65 W m − 2 respectively. A good agreement between modelled and measured LE and H ﬂuxes was found, especially for 15 smoothed daily ﬂux trends. For both T air 1.3 m and T air 50 m a mean RMSD of 4.14 and 3.54 ◦ C was reported respectively. This work presents the ﬁrst all-inclusive evaluation of SimSphere, particularly so in a European setting. Results of this


Introduction
Global climate change is currently facilitating large scale changes within the atmosphere, biosphere, geosphere and hydrosphere (Steinhauser et al., 2012).Quantification and management of such changes and a better understanding of the interactions between different components of the Earth system has been identified nowadays as an important and urgent research direction to be addressed within numerous scientific disciplines (Coudert et al., 2008;Petropoulos et al., 2013a).It also serves as essential information for policy makers and the wider global community (IPCC, 2009).Accurate monitoring of water and vegetation stress is now of prominent global concern and it is regarded as a high priority issue within several European Union (EU) frameworks.This is particularly so for communities in water limited environments, or areas which rely on rain fed agriculture, such as some regions in the Mediterranean basin (European Commission, 2009;Amri et al., 2014).
Accurate estimation of energy fluxes and their partitioning has never been more important in the face of increasing climate change (WMO, 2002;ESA, 2014).The terrestrial boundary layer and its vegetation play a critical role in regulating the partitioning of incoming energy (into latent (LE), sensible (H), and ground (G) heat fluxes) and in the land-atmosphere exchange of carbon dioxide (CO 2 ), and the close relationship between photosynthesis and the energy and water vapour cycles (Prentice et al., 2014).On this basis, the need to develop a thorough understanding of how heat and water fluxes are characterised in different ecosystems is imperative.This is due to the profound contribution these parameters make to various biogeophysical processes at the planetary boundary layer (Feddema et al., 2005).Currently, the physical interactions behind land surface processes are relatively well-documented within the global scientific community.However, there is a need for further research towards improving our understanding of temporal and spatial dynamics of energy and water fluxes (Quintana-Segui et al., 2008) and the complexity of regional energy and water exchanges (Braud et al., 1995).Also, there is a requirement to provide, at increased estimation accuracy, Figures parameters characterising the energy and water cycles at different observations scales (Anderson et al., 2008;Amri et al., 2014).
Research undertaken to improve our understanding on the representation of land atmosphere interactions has lead to the development and exploration of a wide variety of modelling schemes.Since the 1970's the global scientific community has developed numerous land surface models (LSMs) to assess a multitude of parameters associated with land surface interactions with varying degrees of complexity and applicability (Olchev et al., 2008).LSMs have evolved from simple bucket models without vegetation consideration (e.g.Manabe, 1969) into credible representations of the exchanges of energy, water and carbon dioxide in the soil-vegetation-atmosphere continuum.The use of Soil Vegetation Atmosphere Transfer (SVAT) models represent one of the most common approaches in studying land surface processes and the interactions between the Earth's system components.SVAT models are mathematical representations of vertical "views" of the physical mechanisms controlling energy and mass transfers in the soil-vegetation-atmosphere continuum.Those models are able to provide deterministic estimates of the time course of soil and vegetation state variables at time-steps compatible with the dynamics of atmospheric processes.
Those models have arisen as a convergence of several needs (Petropoulos et al., 2009a), namely: (i) to better understand land/atmosphere boundary transfers, (ii) to investigate how vegetation responds to climate change and (iii) to assess hydrological balances and measure conditions at a given boundary level.One of their main relative advantages, compared to traditional techniques, is the ability to simulate at a fine temporal resolution (often less than 1 h); this subsequently allows simulations to be in satisfactory agreement with the timescale of the physical process being simulated.In addition to this, SVATs comprehensively analyse a large array of parameters associated with the hydrological, radiative and physical domains of the Earth's energy and water cycles.To this end, such models are widely regarded as the most suitable tool to analyse various complex land surface interactions.SVAT models can be employed as "decision making tools" within policy implementation because of their ability to holisti-Introduction

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Full cally and accurately assess numerous parameters in past, present, and future environments.Yet, their predictions have an undefined spatial coverage and are limited in their ability to simulate energy and water transfers only within an area representative of their initial parameterisation.Therefore, surface heterogeneity presents itself as a pertinent problem in the application of those models to more fragmented landscapes, where the high levels of internal biophysical variability cannot be fully represented within the model's parameterisation (Oltchev et al., 2002;Falge et al., 2005;Olioso et al., 2005;Samaali et al., 2007).Additionally, SVAT models often require a large amount of input parameters for initialisation.This makes the widespread application and transferability of those models in some cases troublesome.This is because obtaining site specific parameters in remote and data scarce areas is often very difficult (Oltchev et al., 2002).Current research has led to the development of SVATs incorporating sub-grid scale heterogeneity and with improved representation of plant physiological processes.Evidently, the incorporation of these additional processes has further increased the complexity and number of input parameters required to implement such models.
It is important to note, however, that uncertainty is inevitable in any model since it will never be as complex as the reality it portrays (Denti, 2004).As such, the process of validating a mathematical model is an essential step in its development.Generally speaking, the validation of a model consists of determining how well the model performs when comparing its simulated results with those from the real world.Numerous model validation techniques exist; for a comprehensive overview see Bellocchi et al. (2010).A common strategy is to quantitatively compare the model's predictions vs. actual in-situ observations on the basis of various appropriate statistical metrics.Validation techniques are often also implemented over numerous land cover types, helping to further identify how energy and water fluxes are characterised within different ecological settings.Such techniques help develop confidence in the model's ability to be used within these settings and also contribute to our overall understanding on how land cover types characterise local energy and water fluxes (Coudert et al., 2008).Sensitivity analysis (SA) can also be performed as a key component of any Introduction

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Full model evaluation, including SVATs.SA utilises mathematical techniques which aim to quantify the relative influence of each input parameter on the model's output variability (Tomlin, 2013;Vanuytrecht et al., 2014).It allows for an objective assessment of model structure and coherence (Petropoulos et al., 2013a;Gan et al., 2014).In addition, Kramer et al. (2002), in an attempt to holistically assess the capability of a model in portraying a real world system, has proposed a set of model assessment criteria, namely: accuracy, generality and realism.Accuracy is described as the "goodness of fit" of a models estimations to in-situ measurements.Generality is described as the applicability of the model in numerous ecosystems.Realism is described as the ability of the model to address relationships between modelled phenomena.It is widely agreed however, that sometimes discrepancies between the modelled and observed datasets can be partly attributed to uncertainty within the observational dataset itself (Denti, 2004;Wang et al., 2004;Verbeeck et al., 2009).Therefore validation attempts not only require a highly accurate observational dataset (Wang et al., 2004), but also a wider understanding of problems associated to equifinality, insensitivity and uncertainty when assessing biophysical models (Verbeeck et al., 2009).
SimSphere is one example of a SVAT model, developed by Carlson and Boland (1978) to increase our understanding of boundary layer processes.Since its original development, the model has diversified and become highly varied in its applicational use (for a comprehensive overview of the model use refer to Petropoulos et al., 2009a).
SimSphere's development as a research, educational and training tool is currently expanding within several universities worldwide.Furthermore, its use synergistically with Earth Observation (EO) data is at present being considered by several Space Agencies towards the development of spatio-temporal estimates of evapotranspiration (ET) rates and surface soil moisture (Mo) products at an operational scale globally (Chauhan et al., 2003;ESA STSE, 2012).These investigations have been based around the implementation of a data assimilation technique termed the "triangle" on which Sim-Sphere is used synergistically with EO data (Carlson, 2007;Petropoulos and Carlson, 2011).Furthermore, a variant of this "triangle" approach is already in use in Spain to Figures

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Full deliver an operational product of surface soil moisture at 1 km spatial resolution from the Soil Moisture and Ocean Salinity (SMOS) satellite launched by the European Space Agency (ESA) (Piles et al., 2011).Thus, it is understandable that it is of primary importance to perform a variety of validatory tests to appraise SimSphere's adequacy and coherence in terms of its ability to realistically represent Earth surface processes.In this respect, a series of SA experiments have already been conducted on SimSphere (Petropoulos et al., 2009b(Petropoulos et al., , 2013a(Petropoulos et al., , b, 2014a, b), b).Those studies provided for the first time independent evidence to enhance our understanding of the model's behaviour, coherence and correspondence to what it has been built to simulate.Yet, validation studies performing direct comparisons of model predictions against corresponding in-situ data on the basis of statistical metrics proposed in the classic literature have been scarce and incomprehensive, only performed over a very small range of land use/cover types (e.g.Todhunter and Terjung, 1987;Ross and Oke, 1988).This despite the fact that this type of validation approach is a common strategy in examining the accuracy of model predictions (e.g.Falge et al., 2005;Giertz et al., 2006;Marshall et al., 2013).Given SimSphere's current global expansion, this type of validation is both timely and of fundamental importance in further establishing the model's structure, coherence and representativeness.
With regards to the elements discussed above, this paper investigates the applicability of SimSphere in reproducing a series of observed parameter validations characterising land surface interactions at a total of 7 European ecosystems.The objective was to thoroughly understand the model's ability to simulate, at a local scale, key parameters characterising Earth's energy and water budgets, namely: net Radiation (R net ), Latent Heat (LE), Sensible Heat (H), and Air temperature (T air ) at 1.

Model formulation
This work deals with the SimSphere 1-D boundary layer model devoted to the study of energy and mass interactions of the Earth system.Formerly known as the Penn-State University Biosphere-Atmosphere Modeling Scheme (PSUBAMS) (Carlson and Boland, 1978;Lynn and Carlson, 1990), it was considerably modified to its current state by Gillies et al. (1997) and later by Petropoulos et al. (2013c).It is currently maintained and freely distributed by the Department of Geography and Earth Sciences at Aberystwyth University (http://www.aber.ac.uk/simsphere).This section aims at providing an overview of the model architecture, based on the most recent implementation by Gillies et al. (1997).
SimSphere represents various physical processes taking place in a column that extends from the root zone below the soil surface up to a level well above the surface canopy, the top of the surface mixing layer.Essentially, SimSphere is a 1-dimensional two-source SVAT model with a plant component.Three main systems are represented within SimSphere's structure, namely the physical, the vertical and the horizontal layers (Fig. 1).The physical components ultimately determine the microclimate conditions in the model and are grouped into three categories, radiative, atmospheric and hydrological.The primary forcing of this component is the available clear sky radiant energy reaching the surface or the plant canopy, calculated as a function of sun and earth geometry, atmospheric transmission factors for scattering and absorption, the atmospheric and surface emissivities and surface (including soil and plant) albedos.The vertical structure components, effectively correspond to the components of the Planetary Boundary Layer (PBL) which is divided into three layers -a surface mixing layer, a surface of constant flux layer and a surface vegetation or bare soil layer, where the depths of the first layer is somewhat variable with time, growing throughout the day as H flux is added from below.The depth of the constant flux and vegetation layers are set in the model input, although the depth of a bare soil transition (between soil and air) layer is variable in time depending on the wind speed and the surface roughness.Introduction

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Full In addition, the vertical structure also contains a fourth layer, the substrate layer, which refers to the depth of the soil over which heat and water is conducted.The vegetation component is dormant at night, that is, after radiation sunset.The night-time dynamics for the surface fluxes differ from those during the day time.LE and H fluxes are exchanged between both the ground and foliage, between plant and interplant airspaces through stomatal and cuticular resistances in the leaf (for water vapour) and the air, between soil and the interplant air spaces and between the entire vegetation canopy and the air.A separate component exists for the bare soil fluxes between the surface and the air.Vegetation and soil fluxes merged at the top of the vegetation canopy.Their relative weights depend on the fractional vegetation cover, specified as an input to the model.As such, SimSphere is referred to as a form of two-stream or twosource model.An important factor in controlling, in particular, the partitioning between LE and H is the stomatal resistance component within the vegetation parameterisation settings.SimSphere provides a choice of two stomatal resistance parameterisations, Deardoff (1978) and Carlson and Lynn (1991).The first is inclusive of the stomatal resistance behaviour that is affected by soil, water and sunlight.However, the inability to measure plant hydraulics (a major attributing factor to vegetation transpiration) is seen to be a prominent disadvantage.The second measures stomatal resistance as a function of leaf-atmosphere vapour pressure difference.This is measured by the difference within the mesophyll and epidermal leaf water potentials, as the stomatal resistance is directly proportional to the vapour pressure difference.The main advantage of this parameterisation setting is the ability to analyse the transpiration plateau effect, described in more detail in Carlson et al. (1991).Further details about the model architecture can be found in Gillies (1993).
The processes and interactions simulated by the model are allowed to develop over a 24 h cycle at a chosen time step, starting from a set of initial conditions given in the early morning (at 05:30 LT -local time) with a continuous evolving interaction between soil, plant and atmosphere layers.A large amount of input parameters are required for the model parameterisation, 53 in total, categorised into 7 defined groups; time and Introduction

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Full location, vegetation, surface, hydrological, meteorological, soil and atmospheric (Table 1).From initialisation, over a 24 h cycle SimSphere assesses the diurnal evolution of more than 30 prognostic variables associated with the radiative, hydrological and atmospheric physical domains.Numerous physical processes are simulated and all parameters are evaluated as a function of time and their diurnal evolution.Outputs of the model include, between others, the surface energy fluxes (LE and H fluxes) below and at the soil surface, around and above the vegetation canopy and the transfer of water in the soil and in the plants.It also simulates the CO 2 (carbon dioxide) flux between the atmosphere and the plants and the surface O 3 (ozone) flux.Several meteorological parameters are also assessed such as the radiometric surface temperature, wind velocity, air temperature, and humidity at various levels in and above the canopy, plus a number of other plant parameters, such as stomatal resistance and leaf water potential.

Materials and methods
This section provides a synopsis of the methodology followed in evaluating Sim-Sphere's ability to simulate key parameters characterising land surface interactions.An overview of the main steps included in this process is furnished in Fig. 2.

In-situ datasets collection
Reliable data is needed to calibrate and evaluate the predictions of any model (Wang et al., 2004).Therefore, in this study, in-situ data from selected sites belonging to the CarboEurope ground monitoring network were obtained.The latter is part of a larger observational network, FLUXNET (Baldocchi et al., 2001), which is currently the largest global network acquiring ancillary information of micro-meteorological flux and a number of ancillary parameters.Once the data reaches FLUXNET, it is quality controlled and gap-filled using techniques described by Papale et al. (2006) (2007).As a result, the in-situ data can be provided to the end users community at different processing levels.
In this study, SimSphere's ability to provide estimates of key parameters characterising our water and energy balance was evaluated at 7 CarboEurope sites.These sites were representative of different ecosystem types with markedly different site characteristics (Table 2).All available in-situ data for each site was obtained for the year 2011, allowing for a sufficient database for model parameterisation and validation to be developed.All data was acquired from the European Fluxes database Cluster (http://gaia.agraria.unitus.it/).In particular Level 2 data was obtained across all selected sites for consistency.This product includes the originally acquired insitu measurements from which only the removal of erroneous data caused by obvious instrumentation error has been undertaken.In addition, atmospheric profile (i.e.radiosonde) data were obtained for each site/day by the University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html).This data included the atmospheric profile of temperature, dew point temperature, wind direction, wind speed and atmospheric pressure.

Validation days selection
Further analysis was implemented to identify the specific days for which SimSphere would be parameterised and validated for each experimental site.Initially, for each site, cloudy days were identified and subsequently excluded from further analysis.Judgment on which days (or time-periods) were cloud-free was based on analysis of the diurnal observation of shortwave incoming solar radiation (Rg).Cloud-free days were flagged as those having smooth and symmetrical Rg curves, a property signifying clear-sky conditions (Carlson et al., 1991).
Subsequently, for the cloud-free days, the energy balance closure (EBC) was evaluated.EBC evaluation has been accepted as a valid method for accuracy assessment of the turbulent fluxes derived from eddy covariance measurements (Wilson et al., 2002;Li et al., 2005).Introduction

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Full Evaluation of EBC using the above equation is only directly relevant to the assessment of LE and H fluxes, and not to other scalar fluxes such as CO 2 (e.g.Wilson et al., 2002;Foken et al., 2006).Energy imbalance derived from implementation of the EBC principle has been found to have implications for the way these energy flux measurements should be interpreted, and therefore, on how they should be compared with model simulations (e.g.Twine et al., 2000;Culf et al., 2002).
EBC was evaluated herein principally by performing a regression analysis (e.g.see Wilson and Baldocchi, 2000;Wilson et al., 2002;Oliphant et al., 2004).The linear regression coefficients (slope and intercept) as well as the coefficient of determination (R 2 ) were calculated from the ordinary least squares (OLS) relationship between the half-hourly estimates of the dependent flux variables (LE + H) and the independently derived available energy (R net − G − S).In addition to this, the Energy Balance Ratio (EBR) was computed by cumulatively summing R net − G − S and LE + H from the 30 min mean average surface energy flux components, and then rationing each of the cumulative sums as follows (e.g.Oliphant et al., 2004;Liu et al., 2006): This index ranges generally from zero to one, with values closer to one highlighting a satisfactory diurnal energy closure, indicating a good quality of in-situ measurements.
Further constraints were subsequently employed to ensure that selected days were of the highest possible quality in terms of in-situ data quality.Firstly, all days selected were within the growing season of April-October; this eliminated the main effects ascribed to the inter-annual variability in vegetation phenology.Secondly, selected simulation days were assessed for atmospherically stable conditions, namely low wind speeds and small available energy (Maayar et al., 2001).Such conditions were identified by the evaluation of the in-situ dataset, where direct measurements of wind speed Introduction

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Full and energy flux amplitude and diurnal trend were used as indicators of atmospherically stable conditions.However, it should be noted that for the IT_Ro3 site no in-situ measurements of air temperature were available for August and September.As a result it was not possible to evaluate the model's performance for this period.In the end, a final set of a total of 70 non-consecutive days from the 7 different CarboEurope sites were identified as being suitable to proceed with the SimSphere validation.

SimSphere parameterisation and implementation
As already stated (Sect.2), SimSphere has been developed to simulate the various physical processes that take place as a function of time in a column that extends from the root zone below the soil surface up to a level higher than the surface vegetation canopy.In the horizontal domain, SimSphere implicitly refers to a horizontal area of undefined size that can be composed of a mixture of bare soil and vegetation.Thus, it is conceivable that the horizontal scale for the model is defined by the degree to which the model's initial conditions are representative of the horizontal area to be simulated.
In theory, this scale should also be used for the validation process.Consequently, Sim-Sphere parameterisation was carried out at the measurement scale of the flux tower observations.That is a function of the area of the fetch around which the tower is built and the footprint of the turbulent flux measurements, representing an area of ∼ 1 km 2 for the test sites as they are relatively homogeneous.On this basis SimSphere was parameterised to the daily conditions existing at the flux tower for each of the selected days.Initial conditions for T air , humidity, wind velocity and direction soundings were acquired at 06:00 GMT from the University of Wyoming database to correspond to the model's initialisation and were used within the parameterisation.Ancillary information on vegetation and soil parameters (e.g.Leaf Area Index (LAI), Fractional Vegetation Cover (FVC), vegetation height, soil type etc.) was also Introduction

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Full  , 1991).The soil type parameters were obtained from the classifications of Clapp and Hornberger (1978) and Cosby et al. (1984), using the soil texture data provided at each CarboEurope test site and information supplied in some instances by the site managers for each experimental site.Similarly, this was also the case for the topographical information that was required in model initialisation.Upon the model initialisation, the latter was executed for each site/day and the 30 min average value of each of the evaluated parameters per site for the period 05:30-23:30 LT was subsequently exported in SPSS for comparisons against the corresponding in-situ data.

Validation approach
Six statistical metrics were used to evaluate how well the SimSphere predictions matched the observed data for each day on which the model was parameterised and executed.The model's coherence to the observational data was undertaken using the statistical terms suggested by Wilmott (1982).These specifically included the Root Mean Square Difference (RMSD), the linear regression fit model coefficient of determination (R 2 ), the Bias or Mean Bias Error (MBE), the Scatter or Mean Square Difference (MSD), the Mean Absolute Error (MAE) and the NASH index.The MBE term expresses the accuracy of the model outputs in relation to the in-situ measurements (i.e.low bias = high accuracy) and is used to correct for systematic errors.The MSD term expresses model precision (i.e.low scatter = high precision) and is used to correct for non-systematic errors.The sum of both can be utilised to evaluate overall model accuracy.Table 3 lists the formulae that express the above statistical terms; a detailed description of which can be found for example in Silk (1979), Burt and Barber (1996) and Wilmott (1982).These statistics have also been widely used in similar validation experiments carried out previously (e.g.Wang et al., 2004;Falge et al., 2005;Giertz et al., 2006;Marshall et al., 2013).
In addition, SimSphere's ability to reproduce the diurnal evolution of the examined parameters was evaluated according to the Kramer et al. (2002) criteria described earlier (Sect.1).All statistical metrics were computed from comparisons performed at identical 0.5 hourly intervals between the two datasets for each day of comparison.In addition, the same statistical parameters where computed as a summary per experimental CarboEurope site to provide an overview of the model performance per site.

Net Radiation (R net ) flux
Table 3 summarises the results of the statistical analysis concerning the comparisons of Net Radiation between the SimSphere estimations and the in-situ measurements.Furthermore, Fig. 3 illustrates the agreement between the in-situ and the predicted R net for all days of comparisons from all experimental sites.Generally, the diurnal variation of the simulated R net was in close correspondence with the observed R net in both shape and magnitude for most of the compared days (although results not shown here for brevity).In overall, SimSphere was able to simulate R net relatively satisfactorily with an average RMSD of 64.65 W m −2 and a correlation coefficient of 0.95.A minor underestimation of the in-situ data was also evident for all sites and days combined (MBE = −2.07W m −2 ).The correspondence between predicted and observed R net fluxes was variable between the individual sites and days included in our study.Indeed, R net showed a significant range of agreement, with RMSD ranging from 24.38 to 98.26 W m −2 between the different validation days.Notably, there were increased periods within a number of test sites where simulation accuracy increased depending on the period in which the simulation days were located.For example, for the IT_Ro3 days, suggesting a satisfactory agreement between both datasets, also illustrated by the distribution of the points around the 1 : 1 line in Fig. 3.This was also reflected within the NASH index values reported (0.897-0.999).
As can be seen from All in all, SimSphere was able to reproduce the evolution of R net reasonably well in terms of both amplitude and trend which is reflected in the low MSD values of all sites (55.01-68.03W m −2 ), particularly so at sites such as IT_Lav (55.01 W m −2 ) and ES_Agu (60.92 W m −2 ).Generally, sites which recorded higher scatter results also exhibited higher RMSD results -notably in sites IT_Col (68.03 W m −2 ) and FR_Pue (66.60 W m −2 ).Throughout, consistently high NASH values further confirmed the high correspondence between model predictions and observed data.

Latent heat (LE) flux
Results for the comparison between SimSphere estimated LE flux and the CarboEurope in-situ LE measurements for all days combined exhibited an overall average RMSD error of 62.75 W m −2 and a correlation coefficient value of 0.542 respectively Introduction

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Full (Table 4).Figure 4 plots the LE flux from the in-situ measurements against the corresponding predicted fluxes from SimSphere for all simulation days from all experimental sites.Although RMSD for the LE parameter showed a better agreement in comparison to the R net parameter (Sect.4.1), R 2 was significantly lower (a decrease of 0.408).As can be seen from Fig. 4, the distribution of points shows an increased dispersion from the 1 : 1 line in comparison to the R net parameter.There was also an apparent overestimation of the in-situ measurements by the model for this parameter (MBE = 15.78W m −2 ).R 2 values varied significantly between all simulation days from 0.020-0.961(Table 4), suggesting notable discrepancies between the predictions and observations.Additionally, daily RMSD values also varied significantly, reflecting the trends observed in the R 2 statistics.RMSD varied from 22.08 to 86.45 W m −2 between all days of simulation.When analysed on a site by site basis, average RMSD exhibited comparable ranges to those reported for the individual simulation days, with RMSD varying from 37.25 W m −2 (ES_Agu -Shrubland) to 75.36 W m −2 (IT_Col, deciduous broadleaf forest).On a per site basis, in overall, there were noticeable differences in the magnitude of the daily evolution of simulated LE when compared to the in-situ measurements.Specifically, the ES_Agu shrubland site, consistently demonstrated above average alikeness to the in-situ measurements with the lowest RMSD and MAE values of all sites, 37. respectively.Yet, for the IT_Mbo site, a moderate overestimation of 16.41 W m −2 was reported, suggesting land cover type may be related to simulation accuracy, which can be subject of future investigations.

Air temperature at 1.3 m (T air 1.3 m )
Results obtained confirmed the ability of the model to simulate T air 1.3 m well, indicating a low average RMSD of 4.1 • C and an average correlation coefficient of 0.631 for all sites and days (Table 6, Fig. 6).Notably, results for R 2 for the specific test days and study sites exhibited significant variance, ranging from 0.237 to 0.939.Such results suggest that time of year and land cover type, and in particular their effect on vegetation, has a noticeable effect on the model's capability to predict T air 1.3 m .RMSD results also exhibited variation between different test days and sites, with values ranging from 1.32 to 7.13 • C. When simulation accuracy was assessed on a site by site basis, average RMSD ranged from 3.15 • C (IT_Ro3) to 5.12 • C (IT_Col).All sites showed an overestimation of T air 1.3 m , with an average MBE of 3.33 • C. In addition to this, all sites reported low MSD, with an average of just 2.30 • C.This appraises the model's ability to repetitively simulate T air 1.3 m to a highly acceptable accuracy.The results for the specific sites varied markedly.Simulation over the ES_Agu and IT_Ro3 sites exhibited minor overestimation of the in-situ measurements, with an MBE of 0.72 and 1.01 • C for both sites respectively.Scatter results for both the IT-Lav and It Ro3 sites were very low (and 2.84 and 2.99 • C), appraising the model's ability to produce accurate and stable outputs over these sites.Furthermore, the IT_Ro3 site also produced the highest correlation coefficient (R 2 = 0. spectively indicating weaker model stability over these sites for all days combined.Results for the ES_Lju site also exhibited lower NASH (−0.054) and R 2 (0.517) values in comparison to all other sites.The latter indicated that the model had some difficulty in reproducing the conditions represented by the in-situ data over the olive orchard experimental site.

Air temperature at 50 m (T air 50 m )
Figure 7 shows the agreement difference in the simulated T air 50 m and corresponding in-situ from all experimental sites/days included in this study.The results from the statistical comparisons between the simulated and the measured diurnal T air 50 m for all the days of the experiment for which observational data were available are summarised in Table 7.As can be observed, the model showed slightly superior performance in predicting T air 50 m compared to T air 1.3 m , with a decrease of 0.45 R 2 values per study day for T air 50 m showed an increased variability in comparison to the T air 1.3 m parameter, with the overall range in values increasing by 0.173 (0.055 to 0.930).However, daily average RMSD exhibited significantly less variability between sites, ranging from 1.06 • C (IT_Lav) to 6.49 SimSphere.Both parameters were consistently simulated to high statistical accuracy over the IT_Lav study site.Less satisfactory simulation accuracy was exhibited within the ES_Lju (olive orchards) site for both.As a whole, the diurnal course of the temperatures predicted by SimSphere was also found to be largely realistically reproduced by the model for most days.A minor overestimation of T air 50 m was reported for all validation sites used in this study, with an overall MBE of just 1.35 • C for all days simulated.The extent to which each site overestimated T air 50 m was comparable, with a very low range in MBE results from 0.03 • C (ES_Agu) to 2.66

Discussion
This study evaluated the ability of the SimSphere land biosphere model to simulate key parameters characterising the Earth's energy and water budget in several European ecosystems.The model was parameterised for a total of 7 CarboEurope sites, representative of a range of ecosystem and environmental conditions.A total of 70 days (10 days per site) from the year 2011 were selected to validate the model's ability to predict Net Radiation (R net ), Latent Heat (LE), Sensible Heat (H), and Air temperature (T air ) at 1.3 and 50 m.The agreement between the two datasets was evaluated based on a series of computed statistical metrics.At all sites, R net was systematically well represented by the model, with an average overall RMSD of 64.65 W m −2 .In comparison to previous similar validation experiments conducted on earlier SimSphere versions, simulation accuracy of R net reported here is higher, for example more than 20 W m −2 in comparison to Ross and

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Full  2014) noted that R net prediction accuracy is also largely dependable on the vegetation and surface characteristics of the respective site and model performance is highly reliant on its representation of the surface vegetation and soil optical properties, most notably surface albedos and emissivities (Falge et al., 2005).
SimSphere showed increased model performance in simulating both LE and H fluxes in comparison to R net ; this is confirmed by the low average RMSD and high overall R 2 as reported in Tables 4 and 5 The shrubland site ES_Agu consistently showed remarkably low average RMSD in all parameters assessed, particularly so for LE and H fluxes.This is likely to be a function of the site's location within a water limited environment, where transpiration effects are much lower in amplitude and thus more predictable, especially given the site's rel-Introduction

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Full ative homogeneity (Maayar et al., 2001).Marshall et al. (2013) have also suggested that ecosystems which exhibit increased stand complexity and heterogeneity, such as forested environments (particularly those with understory vegetation) can have a profound effect on the overall exchange of mass and energy.The latter cannot be fully represented within the model's parameterisation, therefore accounting for poorer simulation accuracies of LE and H. Additionally, it is widely reported that soil water content is an imperative control to the simulation accuracy of LE and H (Oltchev et al., 2002;Falge et al., 2005).Within our study, soil moisture availability and root zone moisture availability, two of the most sensitive parameters to LE and H flux partitioning (see for example SA study of Petropoulos et al., 2013aPetropoulos et al., , 2014a)), were acquired directly from the corresponding daily in-situ measurements.Akkermans et al. (2014) stated that underestimations of LE can largely be attributed to overestimations of H fluxes.Such effects were seen most prominently in our validation site ES_Lju, where a general underestimation of LE (MBE = −17.17W m −2 ) partly contributed to the significant overestimation The model also consistently indicated a satisfactory capability in simulating T air 1.3 m and T air 50 m in all ecosystems in which it was assessed, with average RMSD similar to values reported in other analogous studies (Ross and Oke, 1988).Poorer simulation accuracies of T air 1.3 m were reported in stands where vegetation height exceeds 1.3 m; this is most noticeable in sites ES_Lju, IT_Col and FR_Pue.This suggests that the in-situ data at 1.3 m has a limited representation of the overall transfer of energy and heat seen within the stand; this can explain in part why the model often portrays a general overestimation of T air 1.3 m at these particular sites.However, when model predictions are evaluated at 50 m the agreement between modelled and predicted T air is much stronger, with an average RMSD error of 0.6 • C lower than T air 1.3 m .Ross and Oke (1988) noted that peak values of air temperature should be observed between 10:30-14:30 LST, this is in close correlation to this present study, further appraising SimSphere's representation of T air at both 1.3 and 50 m.Introduction

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Full It is also apparent that SimSphere fulfils all 3 of Kramer et al.'s (2002) model assessment criteria, namely accuracy, generality and realism.No significant prediction errors occurred within all of the parameters analysed, further appraising the model's ability to represent numerous environments accurately.Temporal patterns of the predicted parameters were consistent with the patterns found in the corresponding field data, indicating a strong influence of environmental forcing variables (such as global radiation or vapour pressure deficit) on model output.This result is also in agreement to previous SimSphere validation studies (Ross and Oke, 1988).SimSphere has shown high levels of generality, with acceptable simulation accuracies attained in all evaluated sites.In order to improve the model's generality, the inclusion of more northern European sites would act to further test the models applicability within European ecosystems.Realism has been most notable in the simulation of LE and H fluxes, where slight changes in the vegetation phenology or soil surface moisture was accountable for characterising the diurnal evolution of fluxes in all validated sites.On this basis, SimSphere has shown itself to be highly capable of simulating the observed fluxes in both terms of trend and amplitude, with systematically accurate representation of the seasonal effects of vegetation change to flux characteristics.
In the overall evaluation of the results reported, instrumentation uncertainty in the measured parameters themselves should also be partially taken into account when attempting to explain the disagreement between the simulated and observed parameters (Baldocchi et al., 2001;Oncley et al., 2007;Verbeeck et al., 2009).Generally, R net measurement accuracy error is in the order of 10 %, although, an additional 10 % instrumentation uncertainty should be added due to limited view angle/measuring volume (especially in the case of rugged terrains) (Baldocchi et al., 2001).Typical uncertainty in the estimation of the LE and H fluxes using the eddy covariance method generally varies between 10 to 20 % but can be much higher during periods of low flux magnitude and/or limited turbulent mixing such as at night (Petropoulos et al., 2013c).For example, Hollinger and Richardson (2005) showed that uncertainty in flux measurements are inversely proportional to magnitude; the smaller the flux the greater the relative Introduction

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Full uncertainty.Also, it should be noted that for some days included in our comparisons, a characteristic of the acquired in-situ data for those days was the presence of many spikes (indicative of very high or very low values).Probable reasons for those spikes could be instrumental errors, horizontal advection of H 2 O and CO 2 , footprint changes as well as a non-stationarity of turbulent regime within the atmospheric surface layer (Papale et al., 2006;Olchev et al., 2008).For those days, comparisons resulted in a somewhat lower accuracy of model predictions as such conditions cannot be replicated by the model which assumes homogeneity of vegetation canopy and ignores horizontal advection.In terms of SimSphere parameterisation, it is important to note that understory effects of vegetation is a critical influence missing from the model's parameterisation, along with the model's representation of multiple vegetation types.The latter can have a significant effect in more complex vegetation stands (for example the increased presence of understory vegetation in forested environments).This might also be in part responsible for the comparatively poorer overall simulation accuracies exhibited by the model at times.
On the whole, despite the occasionally inferior performance of the model in simulating the examined parameters for some days/sites, SimSphere predictions are significant in terms of the representation of the physical and dynamic processes involved in the interactions of the complex nature of the soil-land-atmosphere system.Moreover, it is important to recognise that uncertainty is inevitable in any model, as a model will never be as complex as the reality it portrays (Denti, 2004).In this way, SimSphere fulfils its objective as a tool to identify expected patterns of change, if not always the magnitudes.The latter indicates its usefulness in practical applications either as a stand-alone tool or in combination with EO data, as done for instance through the implementation of the "triangle" data assimilation technique of Carlson (2007).

Concluding remarks
In this paper, key findings from a large scale validation of the SimSphere land biosphere model in numerous European environments are reported.In total, 7 different ecosystems were chosen for validation with 70 simulations made for cloud free days in 2011.A systematic statistical analysis was employed to assess the agreement between model predictions and corresponding in-situ measurements.To our knowledge, this is the first study of its kind, reporting results from an in-depth validation of this models' ability in accurately simulating key parameters characterising land surface processes, particularly so in European ecosystems.
In overall, model performance can largely be described as satisfactory for most of the experimental sites and parameters which were evaluated.Results were also largely comparable to other similar validation attempts of earlier versions of the model performed in dissimilar experimental settings (Todhunter and Terjung, 1987;Ross and Oke, 1988).SimSphere was found to be able to reproduce the diurnal evolution of key parameters at accuracies similar to those reported by others evaluating different SVAT models (Ridler et al., 2012;Marshall et al., 2013;Akkermans et al., 2014).Many factors were identified as having a noteworthy effect on simulation accuracy.Model comparisons similar to the one conducted in this study can advance our understanding on the amount of complexity required for adequate representation of land surface processes and interactions between different components of our Earth system.
An evaluation and analysis of a model performance allows for an increased understanding of the model's representation and helps to identify possible misrepresentations within the observational data.Thus, reported discrepancies found in any validation study such as ours should indeed be regarded as a positive step when evaluating model performance (Denti, 2004;Verbeeck et al., 2009).However, as noted by Denti (2004), any land surface model, by its definition, will never be as complex as the reality it portrays.Nevertheless, in overall, the validation results of this study provide further

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Full independent evidence that SimSphere has a high capability of simulating parameters associated with the Earth's energy balance.Further efforts should be made to validate SimSphere to numerous global ecosystems to assess its applicability as a universally applied SVAT model.Moreover, as SimSphere's use is being explored synergistically with EO data, perhaps future efforts should be directed towards performing a detailed error budget assessment and evaluating the overriding effects of SimSphere predictions to the overall prediction error of the spatio-temporal estimates of energy fluxes and soil moisture derived from its implementation within the "triangle" technique.These topics and results will be discussed in the next issues.

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Full  Full  Full  Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 and 50 m.Model validation is performed through a comparison of the model predictions against corresponding data belonging to CarboEurope, the largest in-situ monitoring network in Europe which provides validated measurements of key micrometeorological parameters.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.
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Discussion Paper | Discussion Paper | Discussion Paper | cropland site, error ranges decreased for the period between late April (21 April 2011) and late August (28 August 2011), before increasing in early September (9 September 2011).However, the periods of increased accuracy varied on a per site basis and were only prevalent within the olive plantation (ES_Lju), grassland (IT_Mbo), cropland (IT_Ro3) and deciduous broadleaf forest (IT_Col) sites.Daily R 2 values exhibited less variance with generally more comparable ranges (0.909-0.998) between all the study Introduction Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 5
Figure 5 depicts the scatterplot of observed vs. simulated H flux for all experimental sites, whilst Table 5 summarises the relevant statistics concerning the comparisons between the simulated and observed H fluxes for all the days/sites.Results consistently indicated a high ability of the model to accurately simulate H fluxes, with an average RMSD of 55.36 W m −2 and an R 2 value of 0.83.A significant improvement in accuracy of this parameter in comparison to both the R net and LE parameters was evident.H flux results exhibited a decrease in overall RMSD of 9.29 and 7.39 W m −2 respectively.Similar trends were also evident in both the MBE (−0.08 W m −2 ) and MSD (55.36 W m −2 ) results for this parameter, where model performance was better in comparison to both the R net and LE parameters.Although with regards to R 2 , the H flux parameter exhibited a minor decrease in correlation (0.83) compared to the R net parameter.When examining the R 2 values for the individual simulation days, there was a significant variation in both correlation coefficients (R 2 = 0.607-0.982)and RMSD (RMSD = 20.03-91.07W m −2 ).Notably, there was no clear trend between simulation accuracy and simulation day.Values ranged from 35.50 W m −2 (ES_Agu) to 80.41 W m −2 (IT_Ro3) on a site by site basis.Similarly to LE flux, the ES_Agu site reported the highest simulation accuracy (RMSD = 35.50W m −2 , R 2 = 0.944, MBE = −7.01W m −2 , MSD = 34.80W m −2 ).On the contrary, the cropland site IT_Ro3 consistently reported a less satisfactory agreement between model prediction and in-situ data for H flux. Generally, SimSphere was often unable to represent the peak of H flux across all sites diurnally;this is shown by a scatter of peak values as reported in Fig.4.However, the model did neither consistently overestimate nor underestimate H flux, but produced a range 769) and NASH index (0.769) of all sites.Results for the IT_Col and ES_Lju sites exhibited an increased overestimation of the in-situ measurements (MBE = 3.49 • C) compared to all other sites, with MSD values of 3.74 and 3.95 • C re-Discussion Paper | Discussion Paper | Discussion Paper | • C in overall RMSD to an average value of 3.54 • C.There was a minor overestimation of T air 50 m by the model (1.40 • C); however, again, an improvement on the results exhibited by the T air 1.3 m parameter was apparent.
• C (FR_Pue), an improvement of 0.038 • C on the ranges displayed by the T air 1.3 m results.On a site by site basis, the average range in RMSD in comparison to T air 1.3 m decreases considerably again, with RMSD ranging from 2.87 • C (IT_Mbo) to 4.25 • C (ES-Lju) for the T air 50 m parameter.When considering simulation accuracy on a per site basis, it was evident that there were significant differences between the accuracy of the model in simulating both the T air 1.3 m and the T air 50 m parameters over the different sites included in our study.Highest simulation accuracy was reported within the It_Mbo (grassland) and IT_Lav (evergreen needle leaf) sites for the T air 50 m parameter, whereas in comparison, IT_Ro3 (cropland) and ES_Agu (shrubland) were the most accurate sites in terms of T air 1.3 m prediction by Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |Oke (1988) who validated R net over an urban environment.Respectively, ecosystems which presented high inter-annual change of vegetation phenology, namely olive plantation (ES_Lju), grassland (IT_Mbo), cropland (IT_Ro3) and deciduous broadleaf forest (IT_Col) sites all exhibited distinct periods where model performance was increased.However, these results are significantly better in comparison to those reported byMarshall et al. (2013), who in a similar validation study of the model reported average RMSE of up to 118.46 W m −2 .Akkermans et al. ( . Apart from the general overestimation of LE (MBE = 15.78W m −2 ), results reported show largely acceptable simulation accuracies compared to other analogous studies.Ross and Oke (1988) performed a validation of a previous version of SimSphere over an urban environment of Vancouver, BC.Authors reported acceptable agreement between model output and observed in-situ for H flux (average RMSE = 56 W m −2 ) but significant average error distributions for LE fluxes (RMSE = 107 W m −2 ).Todhunter and Terjung (1987) further described in detail how earlier versions of the SimSphere model dissipated too much of R net as LE and too little to be lost to H, this correlates well to Ross and Oke's (1988) findings but also the findings reported within; where average bias values indicate general net overestimations of LE flux in the order of 15.78 W m −2 , compared to the slight average underestimation of H at −0.08 W m −2 .
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Denti, G.: Developing a desertification indicator system for a small Mediterranean catchment: a case study from the Serra De Rodes, Alt Emporda, Catalunya, NE Spain, PhD thesis, University of Girona, Girona, 2004.European Commission: White Paper, Adapting to climate change: towards a European framework for action, COM, Brussels, 1-16, 2009.
Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | using a GSA approach, in: 7th International Conference on Sensitivity Analysis of 25 Model Output, 1-4 July 2013, Nice, France, 2013a.Petropoulos, G. P., Griffiths, H., and Tarantola, S.: Sensitivity analysis of the SimSphere SVAT model in the context of EO-based operational products development, Environ.Modell.Softw.Discussion Paper | Discussion Paper | Discussion Paper | Samaali, M., Courault, D., Bruse, M., Olioso, A., and Occelli, R.: Analysis of a 3-D boundary layer model at local scale: validation on soybean surface radiative measurements, Atmos.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Willmott, C. J.: Some comments on the evaluation of model performance, B. Am.Meteorol.
balance closure atFLUXNET sites, Agr.Forest Meteorol., 113, 223-243, 2002.Wilson, K. B. and Baldocchi, D. D.: Seasonal and inter-annual variability of energy fluxes over Discussion Paper | Discussion Paper | Discussion Paper | Table 1.Summary of the main SimSphere inputs.The units of each of the model inputs are also provided in parentheses where applicable.Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 2 .
Figure 1.The three facets of SimSphere Architecture.
Table 3, when averaged per site, RMSD showed significantly less variance, exhibiting a range from 55.86 W m 2 (IT_Lav) to 68.19 W m −2 (IT_Col).This trend was also reflected by lower variance in correlation coefficients (R 2 = 0.936-0.970)and NASH index values (0.943-0.981) for the per site averages.The evergreen needle-leaf forest site, IT_Lav, consistently demonstrated the highest model performance in simulating R net with a mean absolute error value of 55.86, 8.79 W m −2 lower than the overall average.A weaker agreement was apparent between model predictions of R net and the corresponding observed data in the deciduous broadleaf site, IT_Col (68.19 W m −2 ), which exhibited the highest RMSD of all sites.MBE between sites showed significant variability, ranging from a moderate underestimation of the insitu measurements over the evergreen broadleaf forest site (−15.99W m −2 ), to a moderate overestimation within the shrubland site (15.02W m −2 ).No clear trends in model prediction accuracy dependent on site or land cover type could be identified in our study results.
• C (FR_Pue), further appraising the model's ability to produce accurate outputs.Furthermore, such results are a significant improvement on those reported earlier for the T air 1.3 m parameter.For all days of simulation, low MSD values were also obtained, with an average MSD of just 3.15 • C.Although there was a slight increase on values reported for the T air 1.3 m parameter, results reported still indicate a satisfactory agreement with the in-situ data.
Heise, E.: A new multi-layer version of the DWD Soil Model TERRA_LM, Cosmo Technical Report, No. 2, available at: www.cosmo-model.org(last access: 16 De-

Table 2 .
Some of the main characteristics of the selected CarboEurope sites used for Sim-Sphere validation.

Table 3 .
An overview of the statistical measures implemented in this study to evaluate Sim-Sphere's outputs against the corresponding in-situ data.

Table 4 .
An overview of R net simulation accuracy.

Table 7 .
An overview of T air 1.3 m simulation accurancy.

Table 8 .
An overview of T air 50 m simulation accurancy.