dataesr/scientific-paragraphs-categorization huggingface dataset:
Water resources in Central Asia strongly depend on glaciers, which in turn adjust their size in response to climate variations. We investigate glacier runoff in the period 1981-2019 in the upper Naryn basin, Kyrgyzstan. The basins contain more than 1,000 glaciers, which cover a total area of 776 km 2. We model the mass balance and runoff contribution of all glaciers with a simplified energy balance melt model and distributed accumulation model driven by ERA5 LAND re-analysis data for the time period of 1981-2019. The results are evaluated against discharge records, satellite-derived snow cover, stake readings from individual glaciers, and geodetic mass balances. Modelled glacier volume decreased by approximately 6. 7 km 3 or 14%, and the majority of the mass loss took place from 1996 until 2019.
The contribution of snow and glacier melt to total runoff, however, is often poorly constrained. On the one hand, large uncertainties in the meteorological datasets limit the application of hydrological/glaciological models for estimates on snow and glacier dynamics. This is the result of discrepancies in climate forcing datasets, as well as a scarcity in meteorological stations for validation in Central Asia (e. g. Armstrong, 2010; Palazzi et al. , 2013; Hoelzle et al. , 2017; Pohl et al. , 2017; Zandler et al. , 2019). As an alternative, geodetic mass balance estimates (e. g. Brun et al. , 2017; Shean et al. , 2020)  are limited in their temporal resolution, which prevents exploitation of required information for meltwater contribution. Higher temporal resolutions are obtained by incorporating additional Earth observation datasets (e.
g. Barandun et al. , 2021)  and allow to obtain annual estimates on glacier mass balance. However, these approaches share a common short temporal coverage that limit an analysis of meltwater contribution in a climatological context. In this study, we present simulated glacier melt contribution for the Big and Small Naryn headwater catchments, for the time period 1981-2019. The work is based on a glacier mass balance model  (Machguth et al. , 2009 (Machguth et al. , , 2013)) , using meteorological forcing from the European Centre for Medium range Weather Forecast (ECMWF) reanalysis dataset ERA5  (Hersbach et al. , 2020) , and calibrating the model with direct observations on glacier melt as well as geodetic mass balance estimates for the time period 2000-2018  (Barandun et al. , 2021). Validation and analysis of obtained results is performed using available runoff observations for the two catchments.
For model calibration geodetically-derived mass balance estimates for the time period 2000-2018 were used  (Barandun et al. , 2021) , which are available for all glaciers larger than 1 km 2. For glaciers smaller than 1 km 2 , the mean value of the geodetic mass balance dataset was taken. The mass balance model is driven by meteorological data from the ERA5-Land re-analysis  (Hersbach et al. , 2020)    Frontiers in Environmental Science | www. frontiersin. org precipitation, and shortwave radiation for the years 1981-2019. Cloud cover was derived from the ECMWF ERA-Interim Re-Analysis  (Dee et al. , 2011)  cloud cover data set. For model validation, point mass balance observations for the glaciers No. 354, Batysh Sook and Sary-Tor (Figure  1 ) were available for the time period of 2010-2018.
Data from the glaciers No. 354 and Batysh-Sook were obtained within the framework of the CATCOS and CICADA projects  (Hoelzle et al. , 2017) , while data for Sary-Tor glacier were obtained from the World Glacier Monitoring (WGMS) database  (Zemp et al. , 2020). For validation of the calculated glacier melt, mean monthly runoff data was used from three gauging stations, situated at the confluence of the Big and Small Naryn rivers and in the Naryn city provided by the Kyrgyz Hydromet (Figure  1 ). ERA5-Land data products were compared with available daily precipitation and temperature data measured at the Tien Shan meteorological and Kumtor automatic weather station (AWS), located in the north eastern part of the study area (Figure  1 ).
Cloudiness is simply up-sampled from the re-analysis grid to the much finer DEM resolution. For more details on the downscaling we refer to the two aforementioned studies. Note that these studies also applied debiasing of RCM data based on weather station data from within the model domains. Such de-biasing was not applied here as the study area includes very few weather stations. 2013 ) and visualizes the process of mass balance model calibration in the context of the full modeling chain. Calibration of the mass balance was carried out in two steps. In the first step, albedo of ice, firn and snow (α s , α f , α i ) and constants of the longwave radiation balance and turbulent exchange (C 0 and C 1 ) were tuned to match observed point data of the three mass balance glaciers: No. 354, Sary-Tor and Batysh-Sook (Figure  3 ).
, 2020). We will use this emergence level range in this study to refer to significant changes. In order to validate modelled glacier runoff and identify possible meteorological causes for differences between catchment-wide discharge, we incorporate monthly snow cover data that provide an independent data source from the utilized meteorological forcing dataset ERA5. For this we use monthly snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset MOD10CM version 6  (Hall et al. , 2016). The dataset provides independent qualitative indications of snow precipitation events. MOD10CM is generated from Normalized Difference Snow Index (NDSI), utilizing the differences in reflectance in the visible (high reflectance) and shortwave infrared (low reflectance) of snow-covered land detected by MODIS.
It provides the difference in relative terms in the range of 0-100%. MOD10CM provides monthly averages for 0. 05 °× 0. 05 °(approx. 5 km) cells of daily maximum snow cover extent. The dataset is filtered to only utilize clear surface views and remove likely erroneous snow detection. We first calculate monthly snow cover anomalies as deviation from the multi-annual monthly means. From these anomalies we calculate cumulative sums. The display of cumulative instead of individual monthly anomalies facilitates the identification of long and short-term periods of snow cover build-up and melting phases in a temporal context. The cumulative anomalies are calculated for 100 m elevation bands to showcase if snow accumulation anomalies occurred preferably in the lowlands or on the glaciers.
Snow Cover Analysis The cumulative monthly snow cover anomalies using the full 2000 to 2020 period available from the dataset as a reference for the snow evolution are displayed in Figure  9  and Figure  10. The strong negative anomalies in late 2007 (falling limbs in the graphs) for all but the highest permanently snow covered elevation bands indicate a lack of snow precipitation. This deficit corresponds to the most negative mass balance in 2008 for both catchments (Figure  5 ). The early negative anomalies for 2014 indicate an earlier onset in snow melt. The continuing falling limbs throughout the year to the beginning of 2015 indicate also a weaker snow build-up phase for 2015. The year 2014 marks again a strong negative mass balance year for both catchments. The 2015 mass balance is, however, only strongly negative for the Big Naryn (see Figure  5 ).
Both catchments experienced a reduction in streamflow during the 1970's and 1980's, which is also reflected in the modeled mass balances. The Tien Shan weather station data suggest that this period was cooler resulting in a reduction of streamflow in spring and autumn. Snow cover analysis is only covering the last two decades and we cannot identify a particular trend. Dietz et al. (2013)  have shown that since 1985 snow cover fraction in winter has mostly increased although with a later onset of snow cover buildup and earlier melting onset for most regions of the Syr-Darya and Amu-Darya catchments. Glacier melt simulations for High Mountain Asia are sparse, limiting our understanding of the future impact of glacier retreat on river runoff and its implications to water resources management.
CONCLUSION The modelling of all glaciers of the upper Naryn catchment suggests an overall negative glacier mass balance since 1996 induced by an increase in summer temperatures in the study area. From 1977 to present, the glaciers lost 7. 1 km 3 or 15% in volume and 56. 2 km 2 (9%) of their initial surface area. Runoff analysis of three runoff gauges suggest an increase in runoff in winter since the early 1990's in both catchments. In the Small Naryn catchment, there is also a slight increase in summer and autumn runoff. In the Small Naryn catchment, glacier meltwater contributing to the discharge shows an increasing trend, while no trends are observed for the Big Naryn catchment, but the melt intensity of the shrinking glaciers is increasing. Supported by data from the Tien Shan weather station, it seems probable that increasing precipitation and temperatures can explain the observed changes in runoff.
Figure2is modified after Machguth et al. (2013)  and visualizes the process of mass balance model calibration in the context of the full modeling chain. Calibration of the mass balance was carried out in two steps. In the first step, albedo of ice, firn and snow (α s , α f , α i ) and constants of the longwave radiation balance and turbulent exchange (C 0 and C 1 ) were tuned to match observed point data of the three mass balance glaciers: No. 354, Sary-Tor and Batysh-Sook (Figure3). Albedo values were used as principal adjustment parameter to fit the melt model to the ablation stake readings of the three glaciers and were compared to in situ albedo measurements and satellite derived albedo maps (Petrakov et al. , 2019; Barandun et al. , 2021). The resulting longwave radiation constants used in the calculation were C 0 -56 and C 1 13.
ACKNOWLEDGMENTS We thank the project  CICADA (Cryospheric Climate Services for improved Adaptation ), with contract no. 81 049 674  between  Swiss Agency for Development and Cooperation  and the  University of Fribourg. The authors are grateful to  Agency of Hydrometeorology  under the  Ministry of Emergency Situations of the Kyrgyz Republic (KyrgyzHydromet)  for the provision of data.
DATA AVAILABILITY STATEMENT KH 9 Hexagon satellite images can be accessed here: https:// www. usgs. gov/centers/eros/science/usgs-eros-archive-declassifieddata-declassified-satellite-imagery-2. ERA5 Reanalysis data is available at: https://https://cds. climate. copernicus. eu/cdsapp#!/ dataset/reanalysis-era5-land?tab form. ERA-Interim Reanalysis data is available at: https://apps. ecmwf. int/datasets/data/interimfull-daily/. Landsat imagery and SRTM is available at: https:// earthexplorer. usgs. gov/. ASTER imagery is available at: https:// earthdata. nasa. gov/. MODIS MOD10CM data is available at: https://nsidc. org/data/mod10cm. Detailed discharge data for the Gunt catchment is available from the Tajik Hydromet http://www. meteo. tj/ upon request, discharge for the Naryn is available from the Kyrgyz Hydromet http://meteo.
Present research With two studies, we address three main hypotheses. The first one is that when people are exposed to a threatening message that highlights the negative impacts of climate change for the French system, they will have a greater tendency to justify it and deny environmental problems (Study 1). The second hypothesis is that when people are exposed to a threatening message that highlights the growing pressure to change one's habits, it would encourage them to deny environmental problems (Study 2). In both studies, we also expected that right political orientation will be associated with system justification tendencies and environmental denial. Data collection was undertaken between March and May 2019, before the COVID-19 pandemic. Data, materials and codes are available on the OSF repository.
The two TL and classical approaches are then compared in 3D with in-field measurements in Section 5, by analyzing two distinct measurement cam-paigns: the CSK experimental data in the French coasts, and the OOW NOFO experiment off Bergen, Finland. Comparing the classical GO and SPM models with the unified SSA1 model makes it possible to highlight the limitations of the former two models, showing in particular that the widely used Bragg scattering model is not always enough to describe the scattering mechanisms. We will see in the following that, in general, these two approaches have rather complementary validity domains: The TL approach is always valid for small observation angles, which makes it suitable for near nadir sensors such as altimeters, whereas the classical approach is valid for moderate observation angles, which makes it suitable for most satellite applications.
3. The third case study concerns the images in Figure  4 , taken on 11 February 2011, 5 h 41. One area is concerned by 1 potential oil spill: area about 3. 42 km 2 -width 2. 67 km. The incidence angle is equal to 50. 9 • in this area. The average estimated wind speed is u 10 = 2. 5 m/s. Note that, in Section 5. 1, this area will be referred to as scenario 3. All these 3 images correspond to CSK data in Huge Region mode, with a spatial spacing (pixel resolution) of 100 m. The second data set corresponds to measurements that have been acquired during the oil pollution experiment called oil-on-water (OOW) NOFO exercise  [28]  in June 2011 off Bergen (Finland). In this context, 3 images of interest have been retained. They are all in VV polarization. These 3 case studies (scenarios) are: 1. The first case study concerns the images in Figure  5 , taken on 8 June 2011, 17 h 58.
2. The second case study concerns the images in Figure  6 , taken on 9 June 2011, 21 h 28. It is an ASAR/ENVISAT (C band) image in VV polarization. The incidence angle is equal to about 34 • in the area of the pollution, where the average estimated wind speed is u 10 = 4. 5 m/s. 3. The third case study concerns the images in Figure  7 , taken on 12 June 2011, 21 h 18. It is an ASAR/ENVISAT (C band) image in VV polarization. The incidence angle is equal to about 25 • in the area of the pollution, where the average estimated wind speed is u 10 = 5. 8 m/s. The first case corresponds to CSK data in Wide Region mode, with a spatial spacing (pixel resolution) of 30 m. Cases 2 and 3 correspond to ASAR/ENVISAT data in Wide Swath Medium Resolution mode, with a spatial spacing (pixel resolution) of 75 m.
| Database query and literature search We reviewed and updated the ACTN2 page on the Leiden Open Variation Database (LOVD V. 3. 0) to include, at the best of our knowledge, all the ACTN2 variants previously reported in literature. We also reviewed the ACTN2 variants identified in previously unpublished, large resequencing projects that were conducted at mostly with an adult-onset distal myopathy, who were analyzed using a targeted gene panel  (Evila et al. , 2016). All patients and their relatives provided written informed consent. F I G U R E For each variant, we collected available clinical and familial segregation data. The pathogenicity of the variants was evaluated based on the guidelines provided by American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP)  (Richards et al.
| Characterization of the ACTN2 c. 1840-2A>T variant We performed a segregation analysis on the available family members (primers available on request). For splicing characterization, we extracted RNA from the muscle biopsy (tibialis anterior) using the Qiagen RNeasy Plus Universal Mini Kit (Qiagen) according to the manufacturer's instructions. Strandspecific RNAseq library was prepared using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs), and sequenced on HiSeq. 4000 (Illumina), generating over of ~40 million reads (75 bp paired-end sequencing). Trimmed sequences were mapped with STAR 2. 7. 0d  (Dobin et al. , 2013)  using the two-pass method. Gene level read summarization and quantification was done by featureCounts  (Liao et al. , 2014). To characterize the splicing defect due to the previously detected nucleotide change, we compared ACTN2 spliced reads ("junctions") in the patient with an internal control group (RNAseq data from over 200 myopathy patients and over 30 non myopathy controls).
| Variants identified in patients with a skeletal muscle disorder Five studies reported eight causative ACTN2 variants in patients with a skeletal muscle disorder, including seven heterozygous mutations with de novo occurrence or dominant disease inheritance, and one homozygous mutation with recessive disease transmission (Table  1 ). Two heterozygous de novo variants, p. Leu727Arg and p. Ala732_Ile742del, absent in the genome aggregation database (gnomAD), were identified in patients with congenital myopathy  (Lornage et al. , 2019). The phenotype included facial muscle weakness and ophthalmoplegia, ptosis, and high palate. Creatinine Kinase (CK) levels were normal. The histological analysis and electron microscopy imaging of muscle biopsies showed muscle fibers containing small structured cores and jagged Z-lines.
ACKNOWLEDGMENTS We thank  Xaviere Lornage  for sharing preliminary data. We also thank  Prof. Den Dunnen  and the team of the Leiden Open Database for establishing and maintaining the freely accessible data set. M. S. is supported by  Association Française contre les Myopathies  (grant # 23281 : "The expanding spectrum of ACTN2-related myopathies"),  Academy of Finland  (grant # 339437 : " Improving the clinical interpretation of sequence variants ") and Sydäntutkimussäätiö. P. H. is supported by  Erkko Foundation. B. U. is supported by  Academy of Finland. M. O. is supported by  Fondo de Investigaciones Sanitarias (FIS) ,  Instituto de Salud Carlos III, Spain  and  FEDER  funds "A way to make Europe"/" Investing in your future " under Grant  PI21/01621.
Renewable energy development is growing fast and is expected to expand in the next decades in West Africa as a contribution to addressing the power demand and climate change mitigation. However, the future impacts of climate change on solar PV and the wind energy potential in the region are still unclear. This study investigates the expected future impacts of climate change on solar PV and wind energy potential over West Africa using an ensemble of three regional climate models (RCMs). Each RCM is driven by three global climate models (GCMs) from the new coordinated high-resolution output for regional evaluations (CORDEX-CORE) under the RCP8. 5 scenario. Two projection periods were used: the near future (2021-2050) and the far future (2071-2100). For the model evaluation, reanalysis data from ERA5 and satellite-based climate data (SARAH-2) were used.
As stated by Tebaldi and Knutti  [28] , the ensemble mean of a multi-model approach in climate modelling often presents fewer biases compared to the individual models, especially for first-order statistics. Moreover, the new high-resolution (25 km) CORDEX-CORE climate models could provide significant added value in terms of spatial resolution as compared to the standard simulations of the CORDEX initiative, which were conducted in 50 km resolution. The study is complemented by a detailed model evaluation of the RCMs for the target variables using state-of-the-art reanalysis and satellite global datasets as reference products. The following section provides a description of the CORDEX-CORE simulations and the methodology used. The model evaluation results and the projected changes are discussed in Section 3.
Data The data used in this study are the current CORDEX-CORE simulations from the Word Climate Research Program (WCRP), the atmospheric reanalysis dataset (ERA5,  [37] ), developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as a satellite-based climate dataset (SARAH-2,  [38] ). The CORDEX-CORE simulation data used in this study are based on three RCMs namely: RegCM4, REMO, and CCLM, each driven by three GCMs (NorESM1-M, MPI-ESM-MR, and HadGEM2-ES) from the Coupled Model Intercomparison Project 5 (CMIP5,  [39, 40] ). According to Giorgi et al. [41] , these GCMs are among the best.
Data The data used in this study are the current CORDEX-CORE simulations from the Word Climate Research Program (WCRP), the atmospheric reanalysis dataset (ERA5,  [37] ), developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as a satellite-based climate dataset (SARAH-2,  [38] ). The CORDEX-CORE simulation data used in this study are based on three RCMs namely: RegCM4, REMO, and CCLM, each driven by three GCMs (NorESM1-M, MPI-ESM-MR, and HadGEM2-ES) from the Coupled Model Intercomparison Project 5 (CMIP5,  [39, 40] ). According to Giorgi et al. [41] , these GCMs are among the best performing models in the CORDEX domains within the CMIP5 ensemble (e. g. ,  [42, 43] ). They roughly span the range of global climate sensitivity of the CMIP5 ensemble, with HadGEM2-ES having high sensitivity, and MPI-ESM-MR and NorESM1-M having medium and low sensitivity, respectively, in terms of increase in global mean temperature compared to the pre-industrial level.
To study the future solar PV and wind energy potential, the climate variables taken from the CORDEX-CORE simulations via the ESGF nodes (https://esgf-data. dkrz. de/search/cordex-dkrz/ Accessed on 6 December 2021) are monthly surface downwelling shortwave radiation (RSDS), near-surface air temperature (Tas), and near-surface wind speed at ten meters above ground level (WSPD). To validate the RCM simulations, monthly surface wind speed (10 m above the ground level) and the ambient air temperature were taken from the ERA5 dataset, which is the fifth generation ECMWF reanalysis for the global climate and weather produced by the Copernicus Climate Change Service at ECMWF. The ERA5 dataset provides estimates of atmospheric variables at a resolution of approximately 31 km worldwide. The monthly shortwave solar radiation was received from the second version of the Surface Solar Radiation Dataset-Heliosat (SARAH-2).
SARAH-2 is a satellite-based climate data record of the solar irradiance, the direct irradiance, the sunshine duration, spectral information, and the effective cloud albedo derived from satellite observations of the visible channels of the MVIRI and the SEVIRI instruments onboard the geostationary Meteosat satellites  [38]. Details about the quality of this dataset were demonstrated in many studies, including Mueller et al. [51]  and Pfeifroth et al. [52]. The SARAH and the ERA5 data cover the period from 1985 to 2014. More details about the model configurations and the data used in this study are given in Table  1. Tiedtke  [54]  Nordeng  [62]  Pfeifer  [63]  Tiedtke  [54]  being modified by D. Mironow (DWD) Ritter and Geleyn  [70]  Interactive aerosols Organic and black carbon, SO4 (Solmon et al.
Methods Figure  2  indicates the conceptual framework of the study with the two state-of-the global datasets used for RCM evaluation (left side) and the different RCM/GCM combinations of the CORDEX-CORE ensemble (right side). To estimate the solar PV and wind energy potential, the PV potential (PVP) and the wind power density (WPD) are calculated from the respective RCMs and reference datasets. In addition, further variables such as wind speed at 100 m and cell temperature (Tcell) are calculated from the datasets, which are needed for the estimation of the main target variables. Afterwards, a model evaluation of the RCMs of CORDEX-CORE is performed for a 30-year period based on the Mean using the reference datasets. In addition, the projected changes of PVP and WPD are calculated from the CORDEX-CORE ensemble for future periods.