dataesr/scientific-paragraphs-categorization huggingface dataset:
In this paper we analyze data from a new high-altitude network of automatic weather stations (AWSs) deployed in April and May 2019, as part of National Geographic and Rolex's Perpetual Planet Everest Expedition (hereafter 2019 Everest Expedition). Coupled with existing stations  33, 66  and regional-scale reanalyses, these data facilitate an event-level assessment of the physical processes associated with precipitation, enabling improved understanding of the potential impacts of climate warming on this critical component of the regional hydrological cycle. We use reanalysis data to place the AWS observations in context, but caution that the former are known to exhibit positive biases for precipitation over the Himalayas  67  and the adjacent Tibetan Plateau. 68, 69 Our specific objectives of this paper are to: (1) place the first complete year of precipitation observations from the new AWS network in a regional and longer-term context; (2) characterize the temporal patterns, intensity, and phase of precipitation across the region; (3) categorize moisture source regions and synoptic-scale circulation associated with precipitation; and (4) identify characteristics of heavy precipitation events.
Everest-Camp II (6,464 m asl), South Col (7,945 m asl), and Balcony (8,430 m asl)-and thereby provide an unprecedented dataset of in situ surface observations to characterize vertical profiles of temperature, moisture, and wind speed during precipitation events. For each precipitation event at Phortse, we used the Lagrangian particle dispersion model FLEXPART  70 (v. 10. 4 ) to simulate 72-h backward air trajectories that group into six clusters representing dominant moisture transport regimes using the NOAA HYSPLIT model trajectory clustering algorithm. 71 e analyze low-, mid-, and upper-level flow patterns associated with each trajectory cluster by creating composite synoptic plots and analyze differences in in situ surface and ERA5 atmospheric parameters between heavy and light precipitation events.
74 ur reported significant rise in mean JJAS freezing-level height since 2005 (Figure  2E )-with attendant impacts on precipitation Location information can be found in Table  S1. phase and glacier accumulation-is likely a major factor in the acceleration of glacier retreat and thinning throughout the Khumbu region and broader HKH 8 because of decreased accumulation and albedo feedback (e. g. , rain results in lower albedo and enhanced absorption of short-wave radiation). The wider network of operational AWSs (IRD/TU stations at Pheriche and Pyramid), longer-term DHM climate stations, and reanalysis datasets enable important aspects of the variability across space and time to be analyzed. An altitudinal and south-north gradient in precipitation is evident from the longerterm DHM datasets, which are generally consistent with previous studies,  21  with an $50% reduction in precipitation moving from Chaurikhark to Namche Bazaar, further decreasing upslope to Syangboche and Khumjung (Table  1 ).
Second, our analysis was largely restricted to the first year of the newly installed NGS AWS network, and some periods of missing data from Pheriche, Pyramid, and Everest Base Camp precluded a more complete characterization of the vertical and south-north precipitation gradients. Third, our Lagrangian assessment infers moisture source regions only through 72-h backward air trajectories and does not explicitly quantify where it originates (i. e. , where evaporation occurs  94  ). Future research is therefore needed to physically confirm the inferred moisture source regions. Additional process-based research is needed to investigate the physical mechanisms driving the nighttime maximum and vertical structure of precipitation. In this paper we have placed the first complete year of the new AWS network in a regional and long-term context, characterized the temporal patterns and moisture source regions, and identified some key characteristics of heavy precipitation events.
Automatic Weather Stations As part of the 2019 Everest Expedition, five weather stations were installed along an elevational transect in the Khumbu region and were described by Matthews et al. 13  Of particular relevance for this paper, Phortse and Everest Base Camp precipitation sensors consist of two Pluvio 2 1500 weighing precipitation gauges surrounded by Belfort double-alter windshields. Table  S5  summarizes the data sources and time periods used for this study. In situ manual daily precipitation data were obtained from the DHM for six stations in the Solo-Khumbu region (Figure  1 ) with a minimum of 5 years of data and completeness >80%. No records exist for stations >4,000 m asl that meet these criteria and the only two stations currently in operation are the lowest: Salleri (2,383 m asl) and Chaurikhark (2,642 m asl).
34, 97, 98 he Parsivel 2 present weather sensor  99, 100  was used to classify the timing, phase, and intensity of precipitation at Phortse. Data were available for Everest Base Camp AWS only from 10 October 2019 through 31 May 2020, thereby missing the critical monsoon period of JJAS 2019. Precipitation gauges were not included on higher stations because of the considerable logistical challenges of transport, installation, and maintenance that rendered them impractical. Satellite telemetry for the Balcony AWS was lost on 20 January 2020 and redundant wind measurements at the South Col AWS failed on 5 January 2020 in an extreme windstorm. We also obtained data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land and ERA5-Single Level and Pressure Height products for two grid cells in the Khumbu (Figure  1 ).
Daily precipitation totals used for the 1981-2019 trend analyses were calculated from the ERA5-Land hourly precipitation dataset. JJAS freezing-level heights were determined using 1981-2019 hourly ERA5 data as the altitude of the 0 C isotherm derived using temperature and geopotential height data on pressure levels corresponding to the Phortse grid cell. We also obtained 500 hPa specific humidity data for the same time period. Additional ERA5-Single Level and Pressure Height data (Table  S5 ) were obtained for 1 June 2019 to 31 May 2020 to complement the observations from the Phortse AWS. Hourly precipitation data from the Phortse AWS were used to identify 171 separate precipitation events of >0. 1 mm during the period 1 June 2019 to 31 May 2020 employing established approaches. 79, 101 We defined the beginning of a precipitation event as the hour measurable precipitation was first reported, the maturation of the event as the hour with the highest precipitation total, and the ending as the last hour measurable precipitation occurred.
A precipitation event remained active if measurable precipitation was reported during a 6h period; precipitation breaks over 6 h resulted in the identification of separate precipitation events. In addition to the surface meteorological data from the Phortse AWS, we included total precipitation at Pheriche, Pyramid, and Base Camp during the event. Other meteorological parameters from Camp II, South Col, and Balcony AWSs and ERA5-Single Level and Pressure Height products were likewise included in the event-level statistics. We derived lapse rates (mean value of À5. 4 C km À1 during precipitation events) by calculating the difference in the mean temperature for each event between Phortse (3,810 m asl) and Camp II (6,464 m asl) and used this in conjunction with Phortse temperatures to estimate freezing-level heights (mean value of 4,897 m asl during precipitation events).
ACKNOWLEDGMENTS This research was conducted in partnership with the  National Geographic Society ,  Rolex , and  Tribhuvan University , with approval from all relevant agencies of the government of Nepal. We acknowledge  Conrad Anker  and  Peter Athans  as inspirational mountaineers who prepared the 2019 Everest expedition team and kept them safe. We also wish to thank the communities of the  Khumbu Region, Xtreme Climbers Treks and Expedition P. Ltd. , and  Jiban Ghimire  and  Shangri-La Nepal Trek Pvt. Ltd. We further recognize and greatly appreciate the extraordinary efforts of our entire Sherpa climbing team, whose physical and mental prowess at altitude made installation of the AWSs possible. The core AWS-installation team members were  Panuru Sherpa  (Sirdar),  Phu Tashi Sherpa ,  Pemba Sherpa ,  Urken Lendu Sherpa ,  Ila Nuru Sherpa ,  Fura Chetten Sherpa ,  Lakpa Gyaljen Sherpa ,  Pasang Sona Sherpa ,  Pasang Kami Sherpa ,  Nima Rita Sherpa ,  Tenzing Gyanjen Sherpa ,  Nawang Phinjo Sherpa ,  Phinjo Sherpa , and  Gyaljen Dorji Sherpa.
Dawa Yangzum Sherpa  and  Tenzing Gyalzen Sherpa  provided helpful assistance during the installations. We thank  Kyler Abernathy ,  Mike Hughes , and  Dana Greene  for AWS design considerations; the team of engineers at  Campbell Scientific, Inc. ( Mike Hansen ,  Jared Campbell ,  Steve Gunderson , and  Gary Roberts ) for designing the AWSs;  Sandra Elvin  and  Alex Tait  for help during installation; and  Richard Rigby  for assistance in setting up and running FLEXPART. Additional funding supporting this research was provided by the  National Science Foundation  through grant  AGS-1347179  (L. B. P. ). The Pyramid and Pheriche stations have been funded by the  French Service d'Observation GLACIOCLIM  (part of IR OZCAR) and by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ) and have been operated with the support of the  JEAI HIMALICE  and the Ev-K2- CNR  Project in collaboration with the  Nepal Academy of Science and Technology and Tribhuvan University.
e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study  and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov-Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent.
On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (  2004 ), temperature distributions have emerged by 50 %-60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period  (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions.
It is a perfect example of a permafrost landscape that is prone to and highly sensitive to the impacts of climate change. Available air temperature and precipitation records in this region extend back more than 100 years and provide a database to investigate local trends and variability in climate in more detail. Despite a general warming trend, a strong spatial and temporal variability is apparent over northeastern Eurasia  (Desyatkin et al. , 2015; Fedorov et al. , 2014b; Gorokhov and Fedorov, 2018)  and in the high latitudes in general  (Mahlstein et al. , 2011). A few locations show no apparent trend over the available long-term records  (Fedorov et al. , 2014b). Gorokhov and Fedorov (2018)  focus on the region of Yakutia (Sakha Republic) and find positive temperature and precipitation trends for the region as a whole for the period 1966-2016.
As a result, considerable effort has been put in the development of methods to investigate and identify when climate departs or emerges from its natural state or variability (time of emergence -ToE). ToE studies cover a wide spectrum of applications, from the most common climate variables like near surface air temperature and precipitation  (Giorgi and Bi, 2009; King et al. , 2015; Lehner et al. , 2017; Mora et al. , 2013)  to climate extremes  (King et al. , 2015; Maraun, 2013; Scherer and Diffenbaugh, 2014)  and sea level rise  (Lyu et al. , 2014). There are several methods to calculate the ToE (e. g. Sui et al. , 2014, and references therein) , depending on the available data sources and the specific purpose of the study. Two major aspects are at the front line of research. The first concerns the methodology, and the second concerns the database on which to perform the analysis.
The KS test is based on a continuous distance metric, i. e. the maximum difference between two cumulative density functions, but it has so far always been used in combination with a significance level. All existing ToE methods are by definition a test, either on the exceedance of an S/N ratio threshold or based on a statistical significance level. As such, they require a parameterization, which can be a drawback in terms of objectivity and transferability. For instance, dealing with a set of different climate variables may lead to different distribution models, where different dataset record lengths affect the behaviour of statistical tests and filtering operations. The development of a non-parametric approach is appealing because results are not impeded by the choice of parameters as in the case of parametric approaches.
Data basis for ToE studies The second major aspect of ToE research concerns the data basis. Observational datasets facilitate ToE studies that focus on changes which have already occurred. Direct observational data are the most accurate estimates but come with the downside of data gaps and limited spatial coverage. Reanalysis datasets assimilating observational data provide extended spatiotemporal coverage. Their continuous spatiotemporal coverage is an advantage over meteorological station data, but this comes at the cost of some biases with respect to the real observations  (Khan et al. , 2008; Serreze and Hurst, 2000). Possible ToE methods for these data types rely on a statistical analysis of their signal's evolution over time. In some cases, including the present study, continuous time series are compulsory, excluding data from meteorological stations with interrupted observations.
(2015) , to identify anthropogenic climate change and the window width to filter out natural and decadal climate variability of the climate signal. Finally, we present our conclusions on how the presented method provides a versatile tool for ToE studies and how it https://doi. org/10. 5194/hess-24-2817-2020 Hydrol. Earth Syst. Sci. , 24, 2817-2839, 2020 can reduce uncertainty by the incorporation of observational and reanalysis datasets.
Methods In the following section we present the methods for ToE detection, sensitivity analysis, and data selection. Our ToE method is a non-parametric metric and thus differs from previous approaches that are parameterized tests for emergence. Our metric describes emergence by measuring how data distributions in continuous target periods have changed with respect to a reference period. Like other approaches, it requires meta-parameter choices, like the start and end point of a reference period and window widths for target periods, for which we will present a sensitivity approach. Finally, the availability of actual long-term observations in the Lena River catchment (Fig. 1 ) allows for validating reanalysis and climate model simulation datasets for their potential to represent the same climate change evolution.
2 ). The HD will increase both if a PDF with a same shape is shifted to higher or lower values and if its shape changes. The HD is calculated for each time step after the reference period stops. This results in a continuous time series of HD values or the level of emergence. This time series serves three purposes: (1) a level of emergence can be derived for any given time step, (2) the ToE can then be inferred based on a posteriori applied thresholds, and (3) different competing datasets can be tested for consistency based on their HD evolution (Fig. 2 ). We additionally calculate the sign of change because emergence could also occur towards lower values (e. g. less precipitation). The sign (positive or negative) of change is calculated from the summed differences of the PDF densities ( RQ): RQ = d i=1 (R i -Q i ) • bc i , (3) where bc i values are the actual values at the position i along the extended value range used in Eq.
(  2 ). The sign is 1 for RQ > 0, -1 for RQ < 0, and 0 for QR = 0. We set the reference period to 1901-1921 and take values for the target periods in moving windows of 21 years too. We test different reference periods and number of years in a sensitivity analysis (see next section). The reference period contains the earliest 21 years commonly available for all datasets. The target periods are taken as a two-sided moving window around each year after the reference period stops, providing a distribution for each time step thereafter. The ToE method is applied independently to the reanalysis data and each individual CMIP5 CS. We follow previous studies by running our analysis not only at annual scale but also on the seasonal scale (winter -November to March -and summer -May to September) to highlight seasonal differences.
Dataset selection In order to obtain the most reliable estimates for the ToE, the best data choice would be measurements from long-term operating meteorological stations in the Lena River catchment. However, data gaps and a poor spatial coverage demand for alternative data sources to provide a spatially and temporally comprehensive analysis. We thus test three commonly used state-of-the-art reanalysis datasets for their actual representation of in situ temperature and precipitation records. In order to investigate the evolution of climate over the 21st century, we include a collection of CMIP5 climate simulations  (Taylor et al. , 2012)  and test their performance by means of HD evolution (in the past) with respect to the reanalysis data. The reanalysis datasets are tested against the records from the meteorological stations for near-surface air temperature (T ) and precipitation (P ) using ordinary least-square regression analysis.
For each of the 49 stations in the Lena River catchment (Fig. 1 ), the corresponding pixel-based time series of either reanalysis dataset is extracted, and the performance in terms of explained variance (r 2 ) is evaluated. The best-performing dataset is used in the subsequent steps. For the analysis of the ToE in the future, we use both the whole set (n = 65) of model simulations but also a subset (n = 10). The subset is used to test whether it reduces uncertainty for ToE estimates compared to the use of the entire ensemble. The subset is chosen based on a comparison between the HD of reanalysis and climate model simulations. By comparing the HD evolution (0 %-100 %) instead of the actual values, we avoid possible bias issues in temperature and precipitation estimates within the CS. We use the Nash-Sutcliffe efficiency  (Nash and Sutcliffe, 1970; Moriasi et al.
, 2007)  as objective function for the selection. In contrast to the r 2 , the Nash-Sutcliffe efficiency (NSE) adds a penalty for offsets between HD evolutions, according to NSE = 1 -     n i=1 Y R i -Y CS i 2 n i=1 Y R i -Y R mean 2     , (4) where Y R i is the ith HD value of the used reanalysis dataset, Y CS i is the ith HD value of a climate model simulation, and Y R mean is the mean of the HD of the reanalysis dataset  (Moriasi et al. , 2007). As we will show in the results, we had to question the validity of reanalysis data in some cases. To ensure confidence in the data, we made a further refinement by choosing five pixels within the Lena River catchment domain where meteorological stations provide long-term observations and allowed us to verify the quality of the reanalysis.
Observational data For observational data we use the All-Russia Research Institute of Hydrometeorological Information -World Data Centre (RIHMI-WDC; http://meteo. ru/, last access: 19 May 2020) dataset, compiled by  Bulygina and Razuvaev (2012). The dataset comprises 49 stations within the catchment area of the Lena River (Fig. 1 ). Data were obtained as daily values and averaged and summed to monthly values of T and P , respectively. The longest records are available for site Yakutsk starting in 1834. All stations within the dataset have record gaps. The dataset provides data only for the locations of the meteorological stations.
CRUNCEP v7 CRUNCEP v7 is a global forcing product (ds314. 3; Viovy, 2018) used, for example, in the ORCHIDEE-MICT (ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature) land surface model  (Guimberteau et al. , 2018). The dataset is derived through a combination of the annually updated CRU TS (Climatic Research Unit Time-Series) v3. 24 monthly climate dataset  (New et al. , 2000)  and NCEP (National Centers for Environmental Prediction) reanalysis  (Kalnay et al. , 1996). The CRU TS datasets are based on surface climate data anomalies from different quality-controlled datasets. They are combined with monthly climatologies and interpolated to provide full spatial and temporal coverage. The time coverage is from 1901 to 2016 in 6-hourly temporal and 0.
20th Century Reanalysis (V2c) (20CR) The 20CR: Monthly Mean Single Level (Analyses and Forecasts) dataset (ds131. 2; Compo et al. , 2011 ; http://www. esrl. noaa. gov/psd/data/gridded/data. 20thC_ReanV2c. html, last access: 19 May 2020) contains objectively analysed 4-dimensional weather maps and their uncertainty from the mid-19th century to 21st century. The dataset has a temporal coverage from 1851 to 2011 with a monthly temporal and a 2 × 2 • spatial resolution.
ERA-20C reanalysis (ERA20) ERA-20C is a reanalysis product (ds626. 0; ECMWF, 2014) of the European Centre for Medium Range Weather Forecast (ECMWF) of the 20th century, from 1900 to 2011. It assimilates observations of surface pressure and surface marine winds only. A coupled atmosphere-land-surface-oceanwave model is used to reanalyse the weather, by assimilating surface observations. Data in monthly temporal resolution (monthly means of daily means) in 2 × 2 • spatial resolution were obtained.
Climate model data We use a set of global climate scenarios from the Coupled Model Intercomparison Project phase 5 (CMIP5;  Taylor et al. , 2012) , obtained through the R package "esd"  (Benestad et al. , 2015). The model predictions are bias-corrected through an empirical downscaling approach described in  Benestad (2001). All models have historical natural and anthropogenic forcing and land use for the period 1861-2005 and the concentration pathway 8. 5 (RCP8. 5) thereafter until 2100. An overview of these model simulations is given in Table  S1  in the Supplement. The most striking difference between the HD-based ToE approach and previous ones is the continuous character of the obtained metric. However, the KS test also utilizes a continuous metric, namely the maximum distance between the cumulative density functions (CDFs) of two samples, in the following referred to as the KS metric.