dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to evaluate the additional value of the HD-based approach, we showcase how these two distance metrics compare to each other in terms of sensitivity and accuracy (Figs. 3  and 4 ). For this, we generated synthetic data with a controlled onset and strength in signal changes. We first use two datasets, one closely resembling a temperature time series of the utilized climate model datasets (type 1) and one that serves to showcase detection sensitivity (type 2). The type 1 and type 2 data are normally distributed data with a fixed mean and standard deviation (SD) until the breakpoint year  (1960). Thereafter, type 1 data have a fixed linear change (slope derived from an arbitrarily chosen pixel of one of the climate model simulations), while the SD stays constant. The type 2 data have a constant mean value after the breakpoint year but a continuous increase in standard deviation, reaching 2 times the reference SD at the end of the time series.
We generate 5000 time series of each type of dataset and calculate the HD and KS metric. Figure  3  showcases one representation of each of the synthetic time series (panels a and b). The distance plots (panels c-f) show the median (bold line), interquartile range (shading), and the 5th-95th percentiles (points) to give a representative assessment of how the two distance metrics perform. Generally speaking, the HD has a crucial advantage over the KS metric in terms of continuous change description and also in terms of accuracy. The left panel in Fig. 3  shows the co-evolution of the HD and KS metric for a time series with a pronounced trend. A step-function-like evolution is visible for the KS metric. This becomes even clearer if the change in the original signal is smaller (Fig. 3 -right ).
Dataset selection The comparison of in situ data with CRUNCEP, 20CR, and ERA20 data shows differences in the reanalysis datasets' performances (Fig. 5 ). T estimates of the datasets explain more than 95 % of the variance, but only CRUNCEP's P estimates show a high correlation (r 2 = 0. 85) and limited bias with the observational data. Apart from the poor representation of the other datasets for P , 20CR also shows a systematic T underestimation (overestimation) in spring and autumn (summer and winter; Fig. 5 ). CRUNCEP provides the best estimates from the tested datasets for both target variables and is used in the following. As CRUNCEP results partially from direct station measurements, the best match is not surprising, even though we did not test which stations and which periods of the station data are incorporated or rejected.
However, some artificial precipitation signals are apparent in the CRUNCEP dataset. These occur mainly in the northwestern part, where no stations with data records in the reference period exist (Figs. 1  and S1 ). For this region, the CRUNCEP P data show a strong artificial, annual repetitive pattern, with probable recycling of the same year, resulting in a very low inter-annual variability (Fig. S3 ). Here, the HD rapidly emerges to more than 40 % (Figs. 6 and 7 and Video 2). While the CRUN-CEP T signals do not show a similar pattern that would be easy to identify, the inter-annual variability is also lower in the northeastern part compared to the rest of the study area (Fig. S3 ). Whether this implies an area-extensive bias in the CRUNCEP dataset for T is difficult to assess. The resulting differences in the HD for CRUNCEP based on the full dataset vs.
the reduced dataset (pixels with validated long-term observations) are displayed side by side in Figs. 6 and 7 as solid and dashed green lines, respectively. The identified 10 bestperforming model simulations based on either dataset are shown in Figs. S4 and S5, and the obtained NSE statistics derived from this analysis are shown in Table  1. The resulting HD differences are less than 10% emergence for T but in some cases more than 20 % for P (Figs. 6  and 7 ). The obtained NSE values are presented in Table  1  (the corresponding graphs for HD evolution are available in Figs. S6  and S7 ). The NSE for T attests to a very good representation of the HD for some of the climate model simulations (0. 73 to 0. 81 for annual values), contrasting with a rather poor representation for P (below 0; Table  1 ).
Temporal evolution of temperature and precipitation emergence The evolutions of area-averaged annual T and P by means of CRUNCEP and the 65 CMIP5 CSs, as well as the model ensemble mean, are shown in Fig. 6. The CMIP5 ensemble mean temperature is in close agreement with CRUNCEP at the annual scale. The ensemble mean for precipitation overestimates the CRUNCEP signal, but some individual CSs are close to the CRUNCEP P estimates. To highlight the effect of our sub-selection method for CSs, we present the study-area-averaged HD for the different data sources (1) CRUNCEP, (2) individual CSs, (3) average of the HD of all individual CSs, (4) ensemble mean, and (5) average of the HD of the 10 best CSs (Fig. 6 ). Video 1 and Video 2 show the spatiotemporal evolution for each of the datasets and seasons, respectively.
In particular the HD of the ensemble mean is progressing very differently compared to the other datasets and shows decades earlier emergence (Fig. 6 ; see Sect. 6. 1 for discussion). In contrast, the HD differences for both T and P between the average of all individual CSs and the average of the 10 best CSs are the lowest and show a similar evolution. Individual CSs may show a very different evolution and different regional patterns, which is also highlighted in Video 1 and Video 2. The videos include the single-best-performing CS to showcase the higher spatiotemporal variability of individual CSs compared to the averaged ones. The T signals show the most prominent evolution and the most significant emergence. The emergence patterns for CRUNCEP and all individual CSs are very similar (Fig.
6 ). The HD shows a continuous increase starting in the 1960s. For CRUNCEP this increase is preceded by an initial HD increase at the beginning of the target period and stagnation thereafter until the 1960s. In contrast, the HD increase based on individual CSs indicates little change (< 30 %) until the 1960s and 1970s with respect to the reference period. The CRUNCEP signal emerges above 60 % by 2004 (last data point). The average HD of individual CSs and the 10 best CSs reach 90 % emergence in the 2040s and near 100 % emergence by the end of the time series (2089). In stark contrast to that is the HD based on the ensemble mean, which shows a 100 % emergence already by 2004. For P , the evolution of the CRUNCEP signal and individual CSs, as well as the corresponding HD, show more significant uncertainties and are less well-defined (Fig.
Method The ToE method applied in this study provides an innovative way to investigate climate change evolution and its emergence. It is different from existing ToE methods that rely on tests and are based either on the exceedance of an S/N threshold (e. g. Hawkins and Sutton, 2012)  or a statistical significance level (e. g. Mahlstein et al. , 2011). The new approach provides a continuous measure of emergence. This has advantages and disadvantages compared to previous methods. Striking benefits are that it facilitates the comparison of the evolution for different datasets (Figs. 6  and 7 ), allowing to rank and select climate simulations whose emergence signatures correspond the closest to observational data. This is a big difference with respect to preselection procedures based on statistical comparison (e.
In the present case, this muting is inconclusive because it results from the averaging over differ-ent climate models with different internal variability. As our method is designed to specifically detect the change of a signal with respect to its natural variability, the presence of variability is a prerequisite. The development of our method was made with the intention to have a wide range of applications, including nearly all types of time series data. Like the application of the KS test  (King et al. , 2015; Mahlstein et al. , 2012) , PDFs can be obtained for any data distribution, and their overlap as a measure for emergence can be easily understood. The resulting emergence as a time series provides the advantage over previous methods to investigate how a signal has emerged in detail. How different datasets and the utilization of different temporal resolutions (e.
Data Our initial selection of reanalysis data through comparison with observational data has shown good agreements for T but, with the exception of CRUNCEP, a very weak repre- sentation for P. In combination with the systematic bias for the warmest and coldest temperatures for 20CR (Fig. 5 ), this also requires a cautious selection of CSs based on observational data in the region. As CRUNCEP results from interpolated observational data, the good match is not surprising. How well this dataset represents the actual conditions for the times where there are no measurements available remains unsolved. The apparent recycling of a single year in the CRUN-CEP time series (Sect. 5. 1) and the resulting standard deviation close to zero (Fig. S3 ) indicate that the data are biased or unreliable in the northwestern part.
S10 ) shows similar patterns compared to the ensemble of CSs (Fig. 12 ). This pattern match can be interpreted as both datasets having a similar variability and distribution of extreme values, as well as an overall similar trend, as discussed in Sect. 6. 2. However, the presented results for P should be treated with caution. Climate model simulations and reanalysis data need to be improved to derive regionally reliable estimates, which in turn are needed to investigate the physical processes in the Earth system that can aid decision making.
, 2014b)  and precipitation trends  (Gorokhov and Fedorov, 2018)  are somewhat complicated due to the different starting points of the datasets. Trends in  Gorokhov and Fedorov (2018)  are with respect to the 1966-2016 period. As indicated in Fig. 6 , the study-area-wide precipitation signal shows relatively high values in the 1960s, with a positive emergence for CRUNCEP and a decline thereafter (Figs. 6  and 7 ). The derived trends in  Gorokhov and Fedorov (2018)  start in this positive emergence and are consequently depicting a negative trend in the northern regions (∼ -8 mm per decade), where precipitation changes according to the CS are lowest (Fig. 11 ). Gorokhov and Fedorov (2018)  still find increasing positive trends towards the south (∼ 16 mm per decade). This north-south gradient is reflected by our results (Fig.
10 ) even though we cannot associate any trend value with a derived emergence level. Fedorov et al. (2014b)  reported generally stronger positive trends for temperatures in the eastern and southern mountain regions in our study area and lower trends in the lowlands and towards the east. A general overlay of earlier ToE values (Fig. 10 ) is visible for stronger trends, and vice versa. However, weaker trends in the most northern part and one of the strongest trends for Yakutsk in the lowland render a conclusive comparison difficult. Fedorov et al. (2014b)  use a dataset with variable station record length, which might explain to some degree the discrepancies. In the end, such differences are expected given the variability in the CMIP5 model simulations and individual offsets to CRUNCEP (Fig.
7 ). Taking into account the mutual interactions between temperatures and precipitation, which results in snow cover and ground thermal insulation as well as snow stocks for melt  (Karlsson et al. , 2011; Westermann et al. , 2017) , systematic changes should occur as a result of the two. The onset of winter P emergence in the 1990s and more strongly thereafter would provide a possible explanation. It would also not contradict the strong positive emergence for P in the 1950s and 1960s (Fig. 7 ) that has not resulted in detectable flood events. The HD and the signal of change for the CRUNCEP data show that more precipitation (positive signal) occurred alongside more negative temperatures (negative signal), which would counteract strong melting events. The implied changes in T and P at different emergence levels will certainly have significant impact on various environmental and socio-economic aspects.
Conclusions We developed a novel method for the determination of climate change emergence. Its non-parametric character allows for its application on data with different types of data distributions, which we showcased for T and P in the Lena River catchment, and use with synthetic datasets. The strongest biases were found in a synthetic dataset for low changes in PDFs when the distributions are strictly positive and heavily skewed, which might be expected for high-frequency data, like hourly precipitation. Even then, once the distributions show an HD of 0. 3, these biases fall below 10 % and attest a large application range of the approach. Unlike other ToE methods that rely on a threshold or statistical test, our method provides a continuous signal of emergence. This facilitates an extended analysis of the progression of climate change signals and provides a useful tool for comparing datasets regarding their similarity in describing climate change.
It comes with the need for applying a threshold a posteriori. Comparison with ToE estimates from other stud-ies indicates that an equivalent ToE occurs at an emergence level of around 30 % for both T and P. A comparison of three commonly used state-of-the-art reanalysis datasets with observational data from meteorological stations has revealed a generally good agreement for T , but only the tested CRUNCEP data provided P estimates with little bias. Even within this dataset, we found artificial behaviour in the time period 1901-1921 for the P estimates, probably due to the limited number of meteorological stations operating at that time. In combination with the P intensity bias of many of the CSs, conclusions on the emergence of P are rendered uncertain. Our method allowed us to compare the evolution of emergence of T and P from CRUNCEP with those of 65 climate model simulations taken from a CMIP5 ensemble.
This provides an alternative to preselection methods based on dataset statistics or weighting schemes for climate models and simulations. We obtain surprisingly similar emergence times independent of using either the mean emergence of all simulations or from our sub-selection of the 10 best-performing simulations. On the contrary, individual models show estimate differences of up to 70 years at low emergence levels. This provides confidence in using ensembles which are large enough rather than somehow-chosen sub-selections to identify the ToE if no or insufficient observational data are available. Nonetheless, the selection method presented here might provide means to discriminate the most reliable data sources in other more documented regions or contexts. The conclusion to include full climate ensembles rather than single simulations is supported by a consistent similarity between the full set and the subset of CSs in all applied cases (T and P for annual, summer, and winter).
The differences in derived emergence for reanalysis and climate simulations, however, stress the need for model improvements and an effort for continuous observational data, which can be comprehensively utilized in the presented approach. Finally, the methodology should be explored in the future to analyse further-impacted variables (e. g. ground temperatures and hydrological conditions) in the complex cryohydrological system to identify spatiotemporal links. Ultimately, these are needed to derive an understanding of how and when climate change will impact the numerous aspects of this system. 19 May 2020)  (Compo et al. , 2011). CRUNCEP v7 data are available through registration at the website https://rda. ucar. edu/datasets/ ds314. 3/ (last access: 19 May 2020)  (Viovy, 2018). ERA20 data are available from ECMWF data servers through the Python module "ecmwfapi" at https://pypi.
org/project/ecmwf-api-client/ (last access: 19 May 2020)  (ECMWF, 2019). The RIHMI-WDC observational dataset used in this study can be obtained through the website https://cdiac. ess-dive. lbl. gov/ndps/russia_daily518. html (last access: 19 May 2020)  (Bulygina and Razuvaev, 2012). Video 2 (https://doi. org/10. 5446/42825) is the same as Video 1 but for precipitation. Supplement. The supplement is added as additional document and provides information about the spatiotemporal variability of datasets and gives a more detailed view on some statistics. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-24-2817-2020-supplement. Author contributions. EP and CG designed the study. EP did the calculation and produced the figures, maps, and the toolbox. EP wrote the outline.
Acknowledgements. We thank the IPSL-EUR postdoc initiative that initiated a workshop on the ToE issue. We acknowledge the discussions with  Pascal Terray ,  Goulven Laruelle ,  Marco Gaetani , and  Vincent Thieu. Special thanks go to  Alexander Fedorov  and  Pavel Konstantinov  from the  Melnikov Permafrost Institute, Yakutsk , for providing data and discussion. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table S1 of this paper) for producing and making available their model output. For CMIP the  US Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led the development of the software infrastructure in partnership with the  Global Organization for Earth System Science Portals.
Support for the 20th Century Reanalysis project dataset is provided by the  US Department of Energy Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) programme and Office of Biological and Environmental Research (BER)  and by the  National Oceanic and Atmospheric Administration Climate Program Office. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-11-IDEX-0004-17-EURE-0006 ). Review statement. This paper was edited by  Thomas Kjeldsen  and reviewed by  Wolfgang Schwanghart  and one anonymous referee.
Code availability. The main code to process and analyse the data is available in the scripting language Python under the GitHub repository https://github. com/pohleric/toe_tools (last access: 19 May 2020)  (Pohl, 2020). Data availability. 20th Century Reanalysis V2c data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www. esrl. noaa. gov/psd/ (last access: https://doi. org/10. 5194/hess-24-2817-2020 Hydrol. Earth Syst. Sci. , 24, 2817-2839, 2020.
A wide variety of observation data sets are used to assess long-term simulations provided by chemistry-climate models (CCMs) and chemistry-transport models (CTMs). However, the upper troposphere-lower stratosphere (UTLS) has hardly been assessed in the models yet. Observations performed in the framework of IAGOS (In-service Aircraft for a Global Observing System) combine the advantages of in situ airborne measurements in the UTLS with an almost global-scale sampling, a ∼20-year monitoring period and a high frequency. If a few model assessments have been made using IAGOS database, none of them took advantage of the dense and high-resolution cruise data in their whole ensemble yet. The present study proposes a method to compare this large IAGOS data set to long-term simulations used for chemistry-climate studies.
For this purpose, a new software (named Interpol-IAGOS) projects all IAGOS data on the 3D grid of the chosen model with a monthly resolution, since generally chemistry-climate models provide 3D outputs as monthly means. This provides a new IAGOS data set (IAGOS-DM) mapped at the model's grid and time resolution. As a first application, the REF-C1SD simulation generated by MOCAGE CTM in the framework of CCMI phase-I has been evaluated during the 1994-2013 period for ozone (O 3 ) and the 2002-2013 period for carbon monoxide (CO). This comparison is exclusively based on the grid cells sampled by IAGOS, thus the assessed model output (MOCAGE-M) is obtained by applying a corresponding mask onto the grid. First, climatologies are derived from the IAGOS-DM product. Good correlations are reported between IAGOS-DM and MOCAGE-M spatial distributions.
They are characterized by a high spatio-temporal resolution and a wide coverage with most data gathered at cruise levels (9-12 km above sea level). Thus, IAGOS database is suited to assess long-term simulations in this altitude range. Recently, its ozone data have been used to evaluate simulations from the models CESM1 CAM4-Chem  (Tilmes et al. , 2016)  and GEOS-Chem  (Hu et al. , 2017)  during the periods  1995 -2010 and 2012 -2013 , respectively. Tilmes et al. (2016) )  used the IAGOS measurements gathered in the vicinity of Narita airport (Japan) only, and the comparison made by  Hu et al. (2017)  only spread over 2 years, while IAGOS ozone data are available since 1994 and covering a wide area, especially in the northern mid-latitudes from Western North America to East Asia. Gaudel et al.
It would be very expensive computationally and not meaningful to interpolate monthly-mean model data onto very high frequency (a few seconds) IAGOS measurement locations. Alternatively, the comparison could be performed after mapping the high resolution IAGOS data on the model grid, on a monthly basis. Several gridding methods already exist for in situ measurements. Some of them consist in interpolating the neighbouring measurements points onto each gridpoint (e. g. New et al. , 2000). However, it requires to memorize all the measurement locations for a whole month. It is thus convenient for measurements with regular locations only, but their use on the IAGOS database would be expensive computationally as well. Variational methods are also widely employed (e. g. Bourassa et al. , 2005)  but they concern integration, which is not our purpose.
The present study aims at providing a new methodology designed to generate a gridded monthly data set from the IAGOS measurements, in order to evaluate REF-C1SD types of simulations. We also propose a set of relevant diagnostics for the model evaluation against IAGOS data mapped on the model grid. These diagnostics originate from  Cohen et al. (2018)  that studied climatologies and trends in ozone and CO, based on the analysis of the quasi-totality of the IAGOS database. The use of such a high spatial and temporal resolution data set allows to account for inter-regional differences that could not be highlighted with zonal means. Its projection into a model grid suits well to the constraint of working on monthly outputs from multi-decadal simulations like REF-C1SD. In order to demonstrate the interest of the new methodology and its associated diagnostics, we perform the assessment on one of the REF-C1SD simulations, that of the MOCAGE CTM model.
The IAGOS observations (referring to the IAGOS-Core database hereafter) sample frequently the whole troposphere nearby airports, measuring vertical profiles during ascent and descent phases, and the UTLS during the cruise phases, mostly in the northern mid-latitudes where most of the flight observations are gathered. In these latitudes, a recent analysis of O 3 and CO climatologies and trends based on almost two decades of IAGOS cruise measurements has been performed in  Cohen et al. (2018). In addition to global climatologies, the same analysis also focused on eight well-sampled regions in the UT and the LS separately. In order to generate comparable results with the latter, this study focuses on the same time period  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013)  and, where relevant, on the same regions.