dataesr/scientific-paragraphs-categorization huggingface dataset:
Rockfall events Rockfall is an impact that can be triggered by extreme precipitation. A logistic regression model describing the probability of rockfall in the Central European low mountain ranges was fitted to the  Rupp and Damm (2020)  database. The model incorporates the compound nature of such events by including several parameters (multivariate compounding) and taking into account preconditions (temporal compounding). The predictors for the statistical model are the across-site percentile of a fissure water proxy D (precipitation minus potential evaporation determined for the last 5 days), the local percentile of daily precipitation and the binary information if a freeze-thaw cycle has occurred within the last 9 days. Details of the statistical model can be found in  Nissen et al. (2022).
Soil moisture of the dry summer of 2018 To address sectoral impacts, we focus on the effects of the 2018 drought on agriculture and forestry in Germany. For this purpose, the temporal evolution of soil moisture deficits at different depths from the ERA5 dataset and agricultural data from German national institutes are analysed. Focusing on the temporal evolution of soil moisture, a deficit developed during the spring and early summer of 2018  (Rousi et al. , 2023) , which also reached the lowest soil layer with a temporal delay of about three months, as shown in Fig. 4. The dryness in 2018 was more intense than the usual soil moisture variability in the period 1991-2020, as shown by the soil moisture dropping below the range of ±1 standard deviation of the 1991-2020 mean soil moisture (shaded area).
Rockfall events Another hazard with CE triggers observed in connection with Friederike was a rockfall event. Although wind is normally not considered as one of the triggering factors  (D'Amato et al. , 2016) , such events may also occur as the consequence of the precipitation associated with the windstorm. It is well known that heavy precipitation can initiate landslides and rockfall events. Slope susceptibility is influenced by pore water/fissure water preconditions, rendering them events with multivariate and temporally compounding triggers. With respect to rockfall, another potentially triggering factor are freeze-thaw cycles prior to the event. In terms of reported hill slope failures, storm Burglind was more effective than storm Friederike. For storm Friederike, only one rockfall event is registered in the landslide database for Germany.
Conclusions -Lessons learned and future steps The case studies presented in this paper illustrate the role of CEs for impact studies exemplified with the calendar year of 2018, as an exceptional year of various multi-variate extremes. Our approach follows a user-oriented climate change research path which addresses the socio-economic effects of the meteorological changes (IPCC, 2022). Within climXtreme, a variety of statistical approaches and datasets have been explored and implemented. The showcases presented in this paper include multivariate, pre-conditioning, temporally and spatial CEs (see  Zscheischler et al. , 2020; Bevacqua et al. 2021). Combinations of heat and drought stress as well as windstorms, convective storms and heavy precipitation led to unprecedented impacts on ecosystems, infrastructure and societies.
Acknowledgements This paper is a collaborative effort within the  BMBF climXtreme  project, for which the authors acknowledge funding (grant numbers  01LP1901A ,  01LP1903C ,  01LP1901F ,  01LP1903A ,  01LP1902J ,  01LP1903F ,  01LP1902M ,  01LP1901E ). EX, NL, OV acknowledge support by the  EU   Horizon 2020  Project  CLINT  under Grant Agreement number  101003876. EX acknowledges support by the  BMWK  project  DAKI-FWS  (grant number  01MK21009I ) and the  EU Horizon Europe  project  MedEWSa  under Grant Agreement number  101121192. JGP thanks the  AXA  research fund for support. LSG has also received funding from the  European Union 's  Horizon Europe Framework Programme  under the  Marie Skłodowska-Curie  grant agreement No  101064940. SMVB acknowledges funding from the  DFG training group NatRiskChange  (grant No  GRK 2043/1 ).
S. Department of Energy 's Program  for Climate Model Diagnosis  https://doi. org/10. 5194/egusphere-2023-1460 Preprint. Discussion started: 11 August 2023 c Author(s) 2023. CC BY 4. 0 License. and  Intercomparison , the  European Network for Earth System Modelling  and other partners in the  Global Organisation for Earth System Science Portals (GO-ESSP ). We acknowledge the E-OBS dataset from the  EU -FP6 project UERRA (http://www. uerra. eu) and the  Copernicus Climate Change Service , and the data providers in the ECA&D project (https://www. ecad. eu).
Data availability The ERA5 (https://doi. org/10. 1002/qj. 3803,  Hersbach et al. , 2020)  reanalysis data are publicly available via the Copernicus Climate Change Service (https://doi. org/10. 24381/cds. adbb2d47,  Hersbach et al. , 2023). The gridded observational datasets E-OBS (https://doi. org/10. 1029/2017JD028200,  Cornes et al. , 2018)  are publicly available on the European Climate Assessment & Dataset website (https://www. ecad. eu/download/ensembles/download. php, ECA&D, 2023). The observational datasets (https://doi. org/10. 5194/asr-10-99-2013, Kaspar et al. , 2013)  and the phenological data from the German Weather Service (DWD; https://doi. org/10. 5194/asr-11-93-2014,  Kaspar et al. , 2015)  are publicly available on the DWD website under their Open Data Portal (https://opendata.
dwd. de/, DWD, 2023). The yield productivity data (http://dx. doi. org/10. 22029/jlupub-7177, Ellsäßer and Xoplaki, 2022a), the yield anomaly catalogue (http://dx. doi. org/10. 22029/jlupub-7176, Ellsäßer and   Xoplaki, 2022b)  and supplementary data (http://dx. doi. org/10. 22029/jlupub-7203, Ellsäßer and Xoplaki, 2022c)  are publicly available at the JLUpub research data repository. Historic climate data from the GSWP-W5E5 dataset used for LPJmL5 simulations are available from https://doi. org/10. 48364/ISIMIP. 982724  (Lange et al. , 2022). The historical data of atmospheric N deposition and atmospheric CO2 concentrations can be obtained from https://doi. org/10. 48364/ISIMIP. 600567  (Yang and Tian, 2020)  and https://doi. org/10. 48364/ISIMIP. 664235. 2 (Büchner and Reyer, 2022), respectively.
Land surface processes, crucial for exchanging carbon, nitrogen, water, and energy between the atmosphere and terrestrial Earth, significantly impact the climate system. Many of these processes vary considerably at small spatial and temporal scales, in particular in mountainous terrain and complex topography. To examine the impact of spatial resolution and quality of input data on modeled land surface processes, we conducted simulations using the Community Land Model 5 (CLM5) at different resolutions and based on a range of input datasets over the spatial extent of Switzerland. Using high-resolution meteorological forcing and land-use data, we found that increased resolution not only improved the representation of snow cover in CLM5 (up to 52% enhancement) but also propagated through the model, directly affecting gross primary productivity and evapotranspiration.
These findings highlight the significance of high spatial resolution and high-confidence input datasets in land surface models, enabling better quantification and constraint of process uncertainties. They have profound implications for climate impact studies. As improvements were observed across the cascade of dependencies in the land surface model, high spatial resolution as well as high-quality forcing data becomes necessary for accurately capturing the impacts of recent climate change. This study further highlights the utility of multi-resolution modeling experiments when aiming to improve process-based representation of variables in land surface models. By embracing high-resolution modeling, we can enhance our understanding of Earth's systems and their responses to climate change.
Model experiments with CLM5 Figure  1  provides a general overview of the experimental setup, which includes three main aspects. Firstly, we varied the spatial resolution, ranging from 0. 5 • (10x6 grid cells) to 0. 25 • (19x11 grid cells) to 1 km (365x272 grid cells) over the study domain. Secondly, we used different meteorological forcing datasets, including a globally available coarse-resolution dataset (Clim CRU ), the same global dataset with lapse-rate corrected temperature (Clim CRU* ), and a high-resolution regional dataset (Clim OSHD ). Lastly, we considered two options for land-use information: a global dataset (LU Gl ) and a high-resolution dataset (LU HR ). This approach is intended to cover multiple facets of resolution: on one hand, the spatial resolution of the CLM5 simulations themselves; on the other hand, the 'native' resolution, or level of detail, of the input datasets, with higher resolution implying better quality of the datasets.
Different CLM5 configurations were set up to cover the variations in spatial resolution, meteorological forcing, and land-use information. At the 1km scale, CLM5 was run with six different configurations, each using different combinations of meteorological forcing and land-use information. At the 0. 5 • and 0. 25 • resolutions, CLM5 was run with three configurations corresponding to the respective meteorological forcing datasets and using the global land-use dataset. These regional CLM5 simulations across the spatial extent of Switzerland and adjacent watersheds of neighboring countries, covering an area of 44,050 km 2 , were set up in an identical way as global simulations. Additionally, point-scale simulations were conducted at 36 snow-monitoring station locations within the model domain.
At the snow monitoring stations, we focus on the impact of meteorological forcing and land-surface input on CLM5 simulations by running the same six configurations as for the 1km gridded experiment. Land surface information for each site location was thereby extracted from the nearest 1km tile of the gridded dataset (either LU Gl1km or LU HR1km ). Model performance evaluation was carried out based on in-situ observations at these stations (see Section 2. 4. 1 and 2. 5. 1 for more information). We also set up simulations at 6 FLUXNET tower locations  (Pastorello et al. , 2020) , setup and results of which can be found in Appendix B. The performance of all gridded CLM5 configurations in simulating seasonal snow cover was assessed against simulations obtained with a the dedicated snow model (see Section 2.
Input datasets Each CLM5 model configuration requires the following meteorological driving data: incident short and long-wave radiation, air temperature, relative humidity, wind speed, pressure, and precipitation. Additionally, a land surface information file is required. CLM5 simulations were set-up to run between January 2016 and December 2019, in order to maximize the temporal overlap between the various meteorological forcing datasets and available data for model bench-marking. We further performed 10 years of spin-up in accelerated decomposition mode, followed by a final spin-up of 10 years, both by re-cycling through the https://doi. org/10. 5194/egusphere-2023-1832 Preprint. Discussion started: 26 September 2023 c Author(s) 2023. CC BY 4. 0 License. available input-data. A spin-up was necessary to ensure soil moisture and soil temperature were in approximate equilibrium and not affecting temporal dynamics and physical properties e.
Meteorological Forcing To assess the impact of meteorological input data quality, we considered three meteorological forcing datasets with increasing level of detail. As an example of a standard global dataset, we used the recent state-of-the-art dataset CRU-JRA (University of East Anglia Climatic Research Unit; of East Anglia Climatic Research Unit;  Harris (2019) ), which provides near-global (excluding Antarctica) six-hourly meteorological data on a 0. 5 • latitude x 0. 5 • longitude grid. CRU-JRA is a merged product of the monthly Climate Research Unit (CRU) gridded climatology  (Harris et al. , 2014)  with the Japanese Reanalysis product (JRA,  Kobayashi et al. (2015) ). We selected CRU-JRA due to its large timespan , which includes recent years and hence ensures sufficient overlap with our high-resolution forcing dataset (see below), data as well as due to its application in the annual Global Carbon Budget assessments (e.
g. , TRENDY,  Friedlingstein et al. (2020) ) in the Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP,  Hurk et al. (2016) ). The original 0. 5 • CRU-JRA dataset was first projected to our model domain using nearest neighbor techniques (Clim CRU0. 5 • ), before re-gridding it to 0. 25 • and 1km using bilinear interpolation to obtain Clim CRU0. 25°a nd Clim CRU1km. As a dataset representing an intermediate level of detail, we upgraded the Clim CRU1km dataset by downscaling temperature data using a temperature lapse rate of -6. 5K/1000m, which resulted in the Clim CRU* dataset. This approach was intended to account for variations of air temperature within the complex topography of the Swiss Alps and subsequent refinement of the partitioning of precipitation into snow and rain.
The updated 1km fields were upscaled back to 0. 25 • and 0. 5 • to inherit this correction also to the coarser-resolution simulations. This resulted in the Clim CRU* dataset. All other forcing variables were left identical for Clim CRU1km and Clim CRU*1km simulations. As input datasets with the highest level of detail, we used meteorological forcing generated according to methods developed by the Operational Snow Hydrological Service (OSHD), at 1km spatial and 1hour temporal resolution as well as all point locations at 1 hour temporal resolution. Necessary meteorological input variables were all provided by MeteoSwiss (COSMO1 and COSMOE product), and specific downscaling routines were applied e. g. , to incoming solar radiation and wind velocity to optimally capture the influence of complex topography.
Of particular relevance to this study is the correction of snowfall input fields by assimilation of station data according to  Magnusson et al. (2014). In the context of this study, this dataset can be considered a meteorological input specifically optimized for accurate gridded snow cover simulations. The 1km forcing data was then upscaled to the desired target resolution (0. 25 • and 0. 5 • ) with no smoothing applied. We refer to  Mott et al. (2023)  for further details with regards to the Clim OSHD product.
Land-use information Input datasets for the land surface are based on the global-scale input dataset commonly used in CLM5, where percent plant functional type for each grid cell are derived from MODIS satellite data  (Lawrence and Chase, 2007) , as are monthly LAI and SAI values. These global-scale surface input-datasets have an initial resolution of 0. 05 •. We firstly re-projected and re-https://doi. org/10. 5194/egusphere-2023-1832 Preprint. Discussion started: 26 September 2023 c Author(s) 2023. CC BY 4. 0 License. gridded the dataset to the model domain using Earth System Modelling Framework (ESMF) regridding tools, which resulted in our "global info" land surface dataset (LU Gl , see Figure  1 ) and represents a land surface dataset equivalent to that which would be used in a typical large scale LSM/General Circulation Model application.
This step was performed separately for each target resolution, resulting in the LU Gl0. 5 • , LU Gl0. 25 • as well as the LU Gl1km dataset. To obtain an alternative land-use input dataset with a higher level of detail, the LU Gl1km dataset was updated based on a combination of the official land use and land cover Data of Switzerland (Arealstatistik, 100m resolution, updated every 6-8 years and derived by visual interpretation of aerial photographs  (Swiss Federal Statistical Office, 2001) , forest mixing ratios (100m resolution, Swiss Federal Statistical Office (  2013 )) and Copernicus Sentinel-3 data (333m resolution). More specifically we merged arealstatistics Switzerland and Copernicus Sentinel-3/OLCI, PROBA-V @333m and forest mixing ratios to obtain vegetation, lake, urban, glacier, cropfraction at the land-unit level and monthly LAI, SAI, fraction per PFT (incl.
Snow dynamics using different meteorological forcing We observed distinct differences in performance using different meteorological forcing datasets in our CLM5 experiments (see Figure  2 ). The CLM5 model using global meteorological forcing data (Clim CRU1km +LU Gl/HR 1km ) showed poor performance in modeling seasonal snow development. RMSEs exceeded 1m for mid-elevation stations and only marginally improved for highand low-elevation stations. When the lapse-rate based downscaled temperature input was used (Clim CRU*1km +LU Gl/HR 1km ) instead, the model's performance improved significantly, particularly at low elevations. The CLM5 model forced with OSHD data (Clim OSHD1km +LU Gl/HR 1km ) demonstrated the best performance across all three elevation bands, with only minor errors in low-and mid-elevation locations (e.
Snow dynamics using different land use information Regarding the effects of the land-use information dataset, we observed that the choice of land-use information had a smaller impact compared to the meteorological forcing data (Figure  2 ). While a slight improvement was seen when using the highresolution land-use information dataset (LU HR1km ) at high elevations for all three sets of meteorological forcing data (reducing RMSE by -0. 13m/-0. 02m/-0. 06m for Clim CRU1km / Clim CRU*1km / Clim OSHD1km simulations, respectively), no significant differ-.
Meteorological forcing data had a slightly smaller with relative overestimations of GPP throughout the model domain (up to 14% during peak growing season when averaged over the model domain, see Figure  C5 ). For the coarse-scale runs, we see that non-resolved surface heterogeneity (e. g. , lakes) has a large effect on simulated GPP, underlying the effect of resolution. The choice of land surface information datasets, on the other hand, only showed marginal effects on simulated ET, but the effect of meteorological forcing results in substantial differences in simulated ET (up to 26% when averaged over the entire model domain, see Figure  C5  in the Appendix). This effect is especially pronounced along the Swiss Alps, where complex terrain leads to differences in precipitation patterns captured by the two forcings (see Figure  C6  in the Appendix for a direct comparison of precipitation patterns in the forcing datasets).
Such differences can arise e. g. due to discrepancies in summer precipitation captured by the different meteorological forcings, or because inconsistencies in vegetation parameters derived from different land use datasets result in different magnitudes of ecophysiological processes. The variations in estimated peak summer GPP between different CLM5 configurations exceeded 200 gC m-2 month-1, equivalent to a 8,810 tC month-1 difference in peak growing season GPP across Switzerland. These differences are noteworthy because errors in simulated GPP can propagate through LSMs and introduce additional errors in simulated biomass and other fluxes  (Schaefer et al. , 2012). They have substantial implications for climate predictions and underline the challenges that current LSMs face in predicting carbon exchanges  (Beer et al.
These findings are underlining local-scale results of e. g. , Harpold (2016), which demonstrate the importance of snow-disappearance date for water stress across 62 sites in the Western US. They are further consistent with observations of greening as a result of reduced snow cover in Arctic  (Myers-Smith et al. , 2020)  and Alpine  (Rumpf et al. , 2022)  regions. At the same time, there is evidence that longer growing season is not always associated with increased productivity  (Phoenix and Bjerke, 2016) , highlighting the relevance of processes beyond the snow season diagnosed in our analysis as well. Our study thus suggests the potential of LSMs applied at high spatial resolutions and fed with accurate input datasets to complement observational studies, as they allow us to quantify and better understand these still poorly constrained process dependencies across larger spatial and temporal extents, including predictions of future conditions.
Conclusions Using multi-resolution modeling experiments to quantify and potentially constrain uncertainties in land surface modeling, we highlight the importance of input data quality and spatial resolution in accurately representing processes across scales. By using regionally optimized datasets, we enhance the accuracy and applicability of LSM simulations, enabling a more comprehensive understanding of ecosystem responses to environmental changes. We could demonstrate the accuracy of simulated snow-cover in CLM5 simulations based on high-quality/high resolution meteorological forcing data and with landscape heterogeneity fully resolved at 1km and show how performance differences between different CLM5 configurations propagate through the model to result in substantial differences in gross primary production as well as evapotranspiration.
with global land surface information (LUGl1km). For these 3 selected examples, the HighRes case performs better for the low and high station location all 3 shown station locations, whereas the global case shows slightly better performance during the melt period for the mid-elevation station. Ultimately, it can be seen though that the effect of meteorological forcing data is substantially larger in comparison to differences arising from the choice of land surface information. Table  B1. Name, Location, site characteristics and the selected CLM5 plant functional type for each FLUXNET site used for model performance evaluation. The FLUXNET tower sites were used to evaluate the performance of the various CLM5 configurations regarding evapotranspiration (latent heat flux) and ecosystem carbon balance (gross primary production).
Generally, performance differences between the various CLM5 simulations are small (Figure  B1 ), especially when compared to the pronounced effects for the snow-cover development shown in Figure  2  in the main manuscript. However, an ANOVA reported p-values <0. 001 for LH and GPP at all sites, revealing significant differences in performance (absolute error) means between CLM5 configurations. A Tukey post-hoc test confirmed this (Figure  B2 ). resolution). Error in GPP simulations showed little variation with the different resolutions and meteorological input datasets, including overlapping extents of the confidence intervals between the different configurations. was GM (project: P500PN, 202741). We further thank the team of the operational snow hydrologic service at SLF for providing input data.
Code and data availability. All scripts used for simulation setup and analysis can be found at https://github. com/johanna-malle/CLM5_CH. FSM2 snow simulation results can be downloaded from https://www. envidat. ch/dataset/seasonal-snow-data-wy-2016-2022. Upon publication, all CLM5 simulation results presented in this study will be available from the WSL data repository Envidat at their website under https://www. envidat. ch/.
Context. The third Gaia data release (Gaia DR3) contains, beyond the astrometry and photometry, dispersed light for hundreds of millions of sources from the Gaia prism spectra (BP and RP) and the spectrograph (RVS). This data release opens a new window on the chemo-dynamical properties of stars in our Galaxy, essential knowledge for understanding the structure, formation, and evolution of the Milky Way. Aims. To provide insight into the physical properties of Milky Way stars, we used these data to produce a uniformly derived all-sky catalogue of stellar astrophysical parameters: atmospheric properties (T eff , log g, [M/H], [α/Fe], activity index, emission lines, and rotation), 13 chemical abundance estimates, evolution characteristics (radius, age, mass, and bolometric luminosity), distance, and dust extinction.