dataesr/scientific-paragraphs-categorization huggingface dataset:
240 CCI Land cover dataset. The original 300 m ESA-CCI land cover classes were spatially aggregated to the 0. 25º×0. 25º regular grid, generating fields with the fraction of each of the ESA-CCI 22 land cover class. These fractions were computed by counting the number of 300 m pixels of each class occurring within each of the 0. 25º×0. 25º grid-cells. These fractional classes were then converted to the CHTESSEL land cover classes using a cross-walking table adapted from  Poulter et al. (2014). These new land cover fractions are then processed to compute the fractional cover of low.
265 H_CCI_cl_LAI used the LAI fields from the CGLS database. For this purpose, the 10-daily version 2 CGLS LAI was processed for the period 1999-2018 to generate the mean monthly climatology computing the monthly mean for each calendar month in the full 20-years period. This generated a climatology at 1 km that was then aggregated to the regular 0. 25ºx0. 25º resolution by mapping the 1 km grid into the coarser grid. The motivation for updating the LAI fields in H_CCI_cl_LAI is provided by the high sensitivity of maximum daily LST on cveg reported by JO19.
Simulation evaluation metrics An overall evaluation of the skin temperature estimates derived from the considered model-based datasets is shown in Table  2 , using the LSA-SAF LST product as reference. Since LSA-SAF LST is a clear-sky variable, the comparison against model skin temperature (or model LST) must be performed for model clear-sky cases as well. Restricting the 275 analysis to summer months (June-July-August, JJA) ensures a reasonable sample of both model and satellite LST values, since cloud coverage remains relatively low in Iberia during summer months, as shown by JO19. Additionally, for the comparison between simulated and observed LST to be consistent, we performed an upscaling of the LSA-SAF LST hourly data, by computing the median of the whole group of LSA-SAF LST pixels (at 3 km resolution) inside each 0.
25ºx0. 25º grid cell. The fraction of valid pixels (each grid cell and time) was retained during the 280 upscaling procedure and used as a proxy to compute clear sky fraction. Subsequently, for each 0. 25ºx0. 25º grid cell over Iberia, only the instants where the percentage of valid LSA-SAF pixels was greater than 70% and the ERA5 total cloud cover was below 30% were considered for dataset comparison. These two thresholds were chosen based on JO19, corresponding to a balance between ensuring most of the grid cell to be cloud-free while keeping a large amount of valid data for the JJA 2004-2015 period over Iberia.
290 Although the sample of LSA-SAF LST estimates was reduced outside the summer months, we compared the seasonal cycle of daytime maximum and night-time minimum LST amongst the different model-based datasets considered in the present investigation. This comparison provides a quantitative measure of the impact of the use of different landsurface models and different vegetation coverage formulations on all seasons. Additionally, we estimated the maximum monthly FCOVER over Iberia in the different simulations considered here satellite corresponds to 1 minus the permanent bare soil coverage. In turn, the percentage of bare soil may be estimated from land-surface models from by employing equation 2. Notice that in other months (where FCOVER is not maximum), the dependence of the fraction of green vegetation coverage on the LAI, which is not represented in the original CHTESSEL formulation, may render this approximation invalid.
We also compared the fractions of low and 300 high vegetation (i. e. Cl and Ch, respectively) amongst the different simulations. When analysing these comparisons amongst the vegetation coverage in the different simulations, it is important to keep in mind that the CGLS FCOVER is tightly related to the CGLS LAI (they are derived from the same observations) which is given as input for H_CCI_cl_LAI. Additionally, the ESA-CCI vegetation cover is also used in constructing the ECOCLIMAP-II used by SURFEX. Nonetheless, these comparisons provide relevant insights into the differences in the LST fields amongst 305 the different simulations as discussed below. Finally, we also compared the surface evaporation from the different simulations considered here and from the GLEAMv3b dataset over the different seasons.
340 (Fig. 4b ) largely overestimated the CGLS FCOVER (Fig. 4a ) over most of the regions of Iberia where large daily https://doi. org/10. 5194/gmd-2020-49 Preprint. Discussion started: 13 March 2020 c Author(s) 2020. CC BY 4. 0 License. maximum LST bias (cf. Fig. 1 ) and RMSE (cf. Fig. 2 ) value were found for these datasets. We point out that the vegetation cover fields are identical in ERA5 and CTR. In contrast, SFX displayed lower values for both LST bias and absolute maximum monthly FCOVER error, and with a correlation coefficient of only 0. 2 between these two errors (Fig. 3d ). Furthermore, the maximum FCOVER pattern estimated from SFX (Fig. 4c ) was closer to the CGLS 345 dataset, although SFX also tends to overestimate the FCOVER. Given these results, it is hypothesized that, at least part of the error in daily maximum LST during summer over Iberia by CHTESSEL (and thus in ECMWF products) was due to its misrepresentation of the vegetation coverage in this region.
This hypothesis was also recently put forward in JO19, which reported a similar relation between the summer LST bias and the misrepresentation of summer FCOVER over Iberia. Here, this hypothesis was further supported by the significantly improved representation of the 350 vegetation coverage over Iberia in SURFEX (largely due to its use of the ECOCLIMAP-II database), corresponding to a drastic reduction of the summer LST errors over Iberia. Subsequently, we compared the fractions of high and low vegetation (i. e. Ch and Cl) between the original CHTESSEL based products (ERA5 and CTR) and SFX during JJA. On the one hand, the original CHTESSEL based products overestimated Cl over wide portions of Iberia, particularly in the North (Fig. 5a ) compared to SURFEX (Fig. 5c ). On.
365 The results presented in Section 4 highlight the large cold bias affecting summer daily maximum LST over Iberia in the ECMWF products. In contrast, given the same atmospheric forcing, SURFEX displayed a lower magnitude, and positive, LST bias. One of the key differences between the two models was the representation of vegetation coverage over Iberia. In this section, we evaluate the impact of the changes in vegetation coverage in the LST simulations. First, in simulation H_CCI the high and low vegetation fractions and types were modified based on ESA-CCI database.
1f ) and, consequently, also reducing the RMSE (Fig. 2f ). The overall summer LSTmax bias averaged over entire Iberia was -4. 4ºC in H_CCI_cl, -5. 0ºC in CTR, and -5. 8ºC in H_CCI. The overall RMSE reduced from 5. 7ºC in CTR (and 6. 2ºC in H_CCI) to 5. 0ºC in H_CCI_cl. The bias and RMSE boxplots in Fig. 6  also illustrate this reduction, although they 395 also evidence that the errors in summer LSTmax in SFX were lower than in H_CCI_cl. The maximum monthly FCOVER from H_CCI_cl (Fig. 4e ) still represents an overestimation of the CGLS dataset (Fig. 4a ), although slightly improved compared to CTR (Fig. 4b ) and H_CCI (Fig. 4d ). The pattern of summer fraction of high vegetation in H_CCI_cl (Fig. 5h ) was similar to H_CCI (Fig. f ) and SFX (Fig. 5d ). However, the large overestimation of the summer fraction of low vegetation in H_CCI (Fig.
5e ) was only moderately decreased in 400 H_CCI_cl (Fig. 5g ), and still larger than the values found in SFX over most of Iberia (Fig. 5c ). Updating the vegetation fractions and types based on ESA-CCI resulted in major changes in the areas dominated by high and low vegetation between CTR and H_CCI or H_CCI_cl (Fig. 4 ). However, the LAI field used in H_CCI_cl to compute the vegetation density parameters was not updated, and corresponds to the original vegetation distributions in CTR. We argue that this can generate some inconsistencies and limits the potential improvement in the 405 representation of summer LST in H_CCI_cl. Consequently, a third simulation was introduced (denoted H_CCI_cl_LAI), which includes the revised vegetation fractions and types based on ESA-CCI, the clumping parameterization and updated LAI fields from CGLS database.
, 2016). We compared the full diurnal cycle of the summer LST in all datasets considered here, in order to assess the differences for LSA-SAF. We also notice that SFX did not display phase shift, peaking at 13 UTC. The time delay to reach maximum temperature in all CHTESSEL simulations is likely related with a too strong coupling with the underlying soil, but further investigation of this issue is beyond the scope of this study. During the afternoon the cooling rate tends to be slightly slower in all models than in observations, but the resulting differences in night-time LST are relatively small amongst all datasets (below 2ºC), and within the typical uncertainty associated with the LSA-SAF.
Impact of updated vegetation on LST outside summer The seasonal cycle of LSTmax and LSTmin averaged over Iberia during the 2004-2015 period amongst all modelbased datasets is shown in Fig 8a. Notice that outside JJA, the comparison with LSA-SAF LST was not performed 445 due to frequent cloud cover over Iberia, reducing the satellite sample and increasing the probability of cloud contamination, as discussed above. However, one may still assess the impact of the different model formulations in simulating the Iberia LST outside of summer. This is particularly relevant for simulations employing clumping (H_CCI_cl, H_CCI_cl_LAI and SFX), since equation 3 introduces a seasonal cycle in the vegetation density parameters.
450 The monthly mean LSTmax averaged over Iberia was identical between ERA5 and CTR throughout the entire year (Fig. 8a ), providing further robustness to the use of the offline simulations to assess the errors of LST in ERA5 dataset and their main sources. Fig. 8a  also shows that the differences in monthly averaged daily maximum LST between H_CCI and CTR and between H_CCI_cl and CTR were below 1ºC throughout the entire year. Large differences emerged between H_CCI_cl_LAI and CTR during the summer months, up to 3. 8ºC. It should be noticed that this 455 comparison differs from the diurnal cycle results in Figure  7 , since it represents all weather conditions while the diurnal cycle in Figure  7  represents clear-sky only conditions in both LSA-SAF and ERA5. However, these differences reduced significantly over the colder months, being below 1.
Discussion Simulation H_CCI_cl_LAI features an updated representation of vegetation over Iberia compared to the original 470 CHTESSEL formulation, namely with revised LAI and vegetation fractions and types, and a clumping parameterization for the vegetation density parameters. Compared to the CTR simulation, H_CCI_cl_LAI resulted in a closer agreement to the fractions of low and high vegetation during summer in SFX, derived from ECOCLIMAP-II. Additionally, it also resulted in a closer match to the maximum monthly FCOVER from the CGLS dataset. Both these comparisons must be interpreted with care. On the one hand, H_CCI_cl_LAI uses the CGLS LAI, which is turn 475 is tightly related to the CGLS FCOVER. On the other hand, ECOCLIMAP-II uses information on the vegetation cover from the ESA-CCI dataset.
Furthermore, SFX also uses a clumping parameterization for the low vegetation density parameter (but not for the high vegetation parameter). https://doi. org/10. 5194/gmd-2020-49 Preprint. Discussion started: 13 March 2020 c Author(s) 2020. CC BY 4. 0 License. Nonetheless, a robust support to the added value of the corrections implemented in H_CCI_cl_LAI was provided by comparing the simulated LST amongst datasets. Our results showed a large reduction of the summer daily maximum.
480 LST cold bias in H_CCI_cl_LAI when compared to CTR. Additionally, we found relatively small differences (within typical LST observation uncertainty; Göttsche et al, 2016) amongst these two datasets during winter and during nighttime. This suggests that the implemented changes are robust, in the sense that the simulated LST is improved during daytime in warm months, with negligible impacts in all other months and hours of the day. Furthermore, the fact that SFX displayed similar performance in the simulation of LST provides further support to the results of H_CCI_cl_LAI.
525 Thus, evaluating the simulated surface water variables and water and energy fluxes is required for a robust validation of the implemented changes. However, these fields are strongly dependent on the surface-atmospheric feedbacks and its validation based on offline simulation alone is not robust. Furthermore, there are very few reliable observational datasets over Iberia for these variables with appropriate accuracy to perform an effective model validation. In order to illustrate this problem, we compared the surface evaporation amongst the simulation datasets and with GLEAMv3b.
Conclusions We used the LSA-SAF satellite product to evaluate the summer LST over Iberia simulated by two LSMs -CHTESSEL 545 and SURFEXduring the 2004-2015 period. Both LSMs were ran offline forced by the same atmospheric forcing fields obtained from the recently released ERA5 reanalysis. The results show a large cold bias of the JJA LSTmax simulated by CHTESSEL, reaching magnitudes larger than 10ºC over wide portions of central and southwestern Iberia. This large cold bias was shown to be tightly linked to a misrepresentation of the Iberia vegetation cover in CHTESSEL, when compared against ESA-CCI land cover dataset,.
550 CGLS FCOVER and ECOCLIMAP-II dataset. We show that this misrepresentation includes significant differences between the areas dominated by high and low vegetation types, but also the amount of bare soil and the fractions of green cover. This result agrees with recent investigations reporting a tight link between the misrepresentation of the vegetation coverage and the errors in LST simulated by different LSMs over different regions of the globe  (Zheng et. , 2012; Trigo et al. , 2015; Gu et al. , 2019; and JO19). Accordingly, the summer LSTmax over Iberia simulated by.
555 SURFEX displayed a much smaller magnitude (positive) bias, which was within the observational uncertainty associated with LSA-SAF LST. One of the key reasons for this difference is the fact that SURFEX uses the updated land cover dataset ECOCLIMAP-II, and it includes interactive vegetation evolution, with a clumping parameterization for the low vegetation density parameter. We propose a methodology to improve the representation of vegetation over Iberia in CHTESSEL by combining 560 information from the ESA-CCI land cover dataset with the CGLS LAI. The proposed improvement in vegetation includes an update of the LAI data and high and low vegetation fractions and types, together with introducing a clumping parameterization for both high and low vegetation density. The clumping introduces seasonality to the vegetation coverage due to an exponential dependence on the LAI.
Finally, we point out that in the present study the proposed formulation was only tested for the simulated LST results. It is important assess the results of the revised vegetation coverage in other components of the surface water and 585 energy budgets. However, as shown here for evaporation, this requires coupled land-atmosphere simulations which will be the subject of a subsequent investigation. Furthermore, it requires accurate validation datasets which are not always available. Additionally, the proposed methodology must be tested over other domains outside Iberia. Nonetheless, the present work has important implications. First, LST plays a central in the surface-atmosphere energy and water exchanges and, thus, its accurate representation in LSMs is crucial for accurate Earth System Models.
https://doi. org/10. 5194/gmd-2020-49 Preprint. Discussion started: 13 March 2020 c Author(s) 2020. CC BY 4. 0 License. types (needle leaf trees, evergreen broadleaf trees, deciduous broadleaf trees, C3 crops, C4 crops, C4 irrigated crops, 215 herbaceous, tropical herbaceous and wetlands). The land cover parameters (including vegetation coverage) in SURFEX are derived from the ECOCLIMAP-II database (Faroux et al. , 2013), which is developed at a resolution of 1 km over Europe. ECOCLIMAP-II provides a static (representative of year 2000) land cover classification and associated surface parameters based on Corine Land Cover map over Europe. It also uses other auxiliary data sources to derive the land cover classification and parameters, including the leaf area index (LAI) from MODIS (MODerate 220 resolution Imaging Spectroradiometer) and the normalized difference vegetation index (NDVI) from SPOT/Vegetation satellite mission.
in the Iberia summer LST amongst datasets outside of the time of daytime maximum. Fig. 7shows the JJA LST at each hour of the day averaged over the 2004-2015 period and over all Iberia grid-points (only considering clear-sky conditions in both satellite and simulations). The average warming rate during the morning in LSA-SAF was overestimated in SFX, resulting in a slight warm bias of LSTmax, but underestimated by CHTESSEL, resulting in the cold bias found for CTR and ERA5. H_CCI displayed a slightly larger underestimation of the morning warming rate 430 compared to CTR, thus increasing the magnitude of the cold bias. H_CCI_cl showed only slight increase of the morning warming rate compared to CTR, but still well below the LSA-SAF warming rate. Finally, in H_CCI_cl_LAI the morning warming rate was much closer to LSA-SAF, resulting in larger reduction of the LSTmax cold bias, of similar magnitude to the warm bias in SFX.
Acknowledgements This research was funded by  Fundação para a Ciência e a Tecnologia (FCT)  grant number  PTDC/CTA-MET/28946/2017  (CONTROL). E. Dutra was funded by  FCT  research grant  IF/00817/2015. The authors would also like to acknowledge the financial support of  FCT  through project  UIDB/50019/2020 -IDL.
425 in the Iberia summer LST amongst datasets outside of the time of daytime maximum. Fig. 7shows the JJA LST at each hour of the day averaged over the 2004-2015 period and over all Iberia grid-points (only considering clear-sky conditions in both satellite and simulations). The average warming rate during the morning in LSA-SAF was overestimated in SFX, resulting in a slight warm bias of LSTmax, but underestimated by CHTESSEL, resulting in the cold bias found for CTR and ERA5. H_CCI displayed a slightly larger underestimation of the morning warming rate 430 compared to CTR, thus increasing the magnitude of the cold bias. H_CCI_cl showed only slight increase of the morning warming rate compared to CTR, but still well below the LSA-SAF warming rate. Finally, in H_CCI_cl_LAI the morning warming rate was much closer to LSA-SAF, resulting in larger reduction of the LSTmax cold bias, of similar magnitude to the warm bias in SFX.
3. 3. Identification procedure In this study, an identification procedure based on Finite Element Modelling Updating (FEMU)  [NEG15]  was developed using Cast3m  [CAS07]  and Matlab. This method (FEMU) is based on the minimisation of an error function built with the difference between data gathered from experimental work and numerical results. The identification flow chart is presented in Figure  3. It can be divided into three parts. The first is related to the experimental data obtained in this study. Then, a second part is related to the FE simulation. The last part is associated with the minimization algorithm, developed on Matlab. Figure  3.
M = N : O PI J Q O PI J - O PI < (S ) Q O PI < @ < FT J< + N : O UV J Q 100 -O UV J - O UV < (S ) Q 100 -O UV < @ <JX<YA< ( 18 ) where C e and C s are the vectors of experimental and modelling results for respectively the relative mass variation (ML) and the relative humidity values (RH). S is a vector composed of model's input parameters. It is possible to add weighting factors in this error function. Finally, the minimisation is calculated, based on Levenberg-Marquart's optimisation method implemented in Matlab. The framework of the algorithm is presented in Figure  4. The minimized parameters resulting from this process are entered in CAST3M as input parameters. The identification runs until it reaches a criterion based on the relative variation of the identified parameters increments. If this variation remains below the limit of 0.
4. 2. Identification of model parameters The model described in section 2 is implemented in the CAST3M finite element software. The geometries are meshed with discretisation along the x-axis. More elements are placed in direct proximity with the drying interface due to the higher humidity gradient in that area. The meshing of G1 is given as an example in Figure  8 , where only half of the prism is meshed since the test is symmetrical. The RH value is computed as the average value of the elements related to the position of the corresponding sensor S1, S3 or S3. Due to the large number of parameters in the drying model, several solutions can be found, all of them satisfying the mass loss curves of G1, G2 and G3. This is the case of sets of parameters P1, P2 and P3 (Table  2 ), all of which provide good superimposition of the experimental curves.
Fifty-one contiguous axial slices of 2. 5 mm thickness were acquired in the anterior commissure-posterior commissure (AC-PC) plane. Twenty-four diffusion gradient directions (b = 1000 s/mm 2 ) were applied with a nominal isotropic resolution of 2. 5 mm 3. The b0 image was acquired four times to increase signal to noise ratio while the other directions were acquired twice. Diffusion data preprocessing was performed using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL)  15. First, data were corrected for subjects' motion and Eddy currents and nonbrain voxels were removed using FSL-BET. From transformation matrices generated during the Eddy current correction, we obtained a mean patient displacement value of 1. 26 ± 0. 52 mm (mean ± SD), which is half of a voxel size (2.
5 mm). For each subject, the tensor model was then fitted on diffusion data using FSL-FDT  16  and four diffusion maps (FA, MD, AD, and RD) were extracted. Fiber-bundle analysis. Based on the Illinois Institute of Technology (IIT) atlas (see Supplementary Material), large WM fiber-bundles were defined including the forceps major (Fmajor), the forceps minor (Fminor), the superior and inferior longitudinal fasciculus (SLF and ILF respectively), the inferior fronto-occipital fasciculus (IFOF), the uncinate fasciculus (Unc), the cingulum (Cing), and the cortico-spinal tract (CST)  17. The IIT atlas FA map was co-registered to each subjects' FA image using a non-rigid transformation performed with NiftyReg 18 and the deformation field was applied to all fiber-bundles. Since fiber-bundles masks of IIT atlas contained the probability of each voxel to belong to a specific fiber-bundle, a threshold of 35% was used to discard all voxels having a lower probability to belong to the fiber-bundle of interest and the resulting masks were binarized.
At a nodal scale, the degree (k i ) represents the number of connections of each node. The clustering coefficient (C i ) defined as the ratio between the number of triangles and the number of triplets around each node, measures the network's tendency to form dense local clusters. The betweenness centrality (B i ), defined as the ratio of the number of the shortest paths comprising the node to the total number of shortest paths in the graph, measures the hub property of the node. The nodal efficiency (E i ) defined as the mean of the inverse of the distance vector, measures the signal transmission efficiency. All the metrics, except graph density, were computed based on the binarized connectivity matrices using the brain connectivity toolbox on Matlab 21.