dataesr/scientific-paragraphs-categorization huggingface dataset:
09 +0. 11   -0. 14 • C from 2011-2020 compared to the period from 1850 to 1900  (IPCC 2021). Already now, anthropogenic climate change has an effect on extreme weather and climate events everywhere on Earth  (IPCC 2021). It is a fact that anthropogenic greenhouse gas emissions drive the rapid increase in global mean surface temperature  (IPCC 2021). Studies considering the impact of climate change on site characteristics of ground-based telescopes started to emerge only recently. Hellemeier et al. (2019)  analysed the site characteristics' cloud cover, 200-hPa-wind-speed, PWV, vertical wind velocity, and aerosol index of 15 well-established astronomical sites using the FriOWL database  (Graham et al. 2008). The FriOWL database combines results from two reanalysis data sets: the ERA-40 reanalysis of the European centre for medium-range weather forecasts (ECMWF) covering the period from 1958-2002  (Kållberg et al.
2004) , and the national centers for environmental prediction/national center for atmospheric research reanalysis covering the period since 1948  (Kalnay et al. 1996). A reanalysis assimilates archived observational data into a continuous weather simulation and is therefore a valuable tool for studying past climatic changes. To explain trends in long-term site conditions, especially in cloud cover, PWV, and 200-hPa wind speed,  Hellemeier et al. (2019)  explored the possibility of anthropogenic climate change as a cause. However, the low horizontal resolution of ERA-40 of approximately 278 km limits the reliability of this study. The first study, to the best of our knowledge, to look explicitly at the influence of climate change on ground-based telescopes was done by  Cantalloube et al. (2020).
Model skill score To evaluate the skills of the GCMs, we use  Taylor (2001)  diagrams, which combine correlation coefficient, centred root mean square error (RMSE) differences and standard deviation of GCMs and in situ observations. Figure  2  shows a fictitious diurnal cycle with its corresponding Taylor diagram. The observational data (black solid line in Fig. 2a ) constitute the reference dataset used to evaluate the test data (coloured lines). In the example plot, we have four test data sets, the first is simply offset from the ground truth by a constant, the second overestimates the amplitude of the diurnal cycle, the third demonstrates a delay in phase and the fourth includes both biases of the second and third case. First of all, we note that the Taylor diagram (Fig. 2b ) does not account for the bias of the test dataset compared to the reference dataset, so in the first case where the test data set is shifted on the y-axis in Fig.
5. 2. 5. To eva combin ror (RM situ ob its corr solid li evaluat have fo truth b the diu fourth all, we for the so in th in Fig. a = 0, b = 0, c = 0 hexagon (red): a = 0, b = 0, c = 0. 8 pentagon (orange): a = 1. 5, b = 0, c = 0 diamond (dark-orange): a = 0, b = 1. 2, c = 0 up-triangle (maroon): a = 1. 5, b = 1. 2, c = 0. diagram. The third dataset showcases a decreased correlation due to the phase delay, but exhibits the same standard deviation as the observation. Finally, the fourth dataset shows the same correlation as the third dataset and the same standard deviation as the second dataset. In general, the shorter the distance to the reference point in the Taylor diagram, the better the model skill. There are different approaches to quantify the skill of a model. Taylor (2001)  suggests two formulas for the calculation of a skill score, one emphasising the correlation and the other highlighting the pattern variation.
We use a Gaussian probability density function as the Bayesian likelihood function for our data points h i ∼ N(µ i , σ), (18) with the standard deviation σ chosen as a weak prior with a uniform distribution with non-zero values between 0 and 10. For the mean µ i , we chose to infer a sine-shaped seasonal cycle, which could increase, decrease or stagnate linearly with the monthly time series. This progression with time, namely the historical and future yearly trends, is implemented in the model by an added linear function with slope b and intersect a. With this, we arrive at µ i = a + b • x i + C • sin 2π 12 • (x i + ϕ) , (19) where the slope b, the amplitude C and the phase ϕ were chosen to have weak Gaussian prior distributions, which work with every dataset, centred around 0 with standard deviations of 3, 6 and 6, respectively.
13 ± 0. 12% per decade until 2050 for coupled simulations and -0. 11 ± 0. 10% per decade for atmosphere-only simulations for the selected sites (Fig. 11d  and Table A. 9). Since PRIMA-VERA shows poor to mediocre agreement with ERA5 and in situ data for the historical period (Fig. 11c ), PRIMAVERA projections of changes in relative humidity over the sites may not be reliable. Nevertheless, the trends found are in agreement with the literature. Byrne & O'Gorman (2018)  found a slight absolute decrease of -0. 22 ± 0. 20% per decade from 1979 to 2016 within 40 • N and 40 • S, using the HadISDH dataset  (Willett et al. 2014). Moreover, the IPCC Fifth Assessment Report  (Collins et al. 2013)  states with medium confidence that small reductions in relative humidity over land are likely and linked to faster rising temperatures over land than over the ocean.
Steve Lee, senior technician based at the AAT, Michael Sharrott, project manager at Siding Spring observatory, Dr. Daniel Cunnama, science engagement astronomer at the southern African large telescope (SALT), and Dr. Myha Vuong De Breuck, astronomical database specialist at the European Southern Observatory (ESO). M. -E. Demory would like to thank Professor O. Romppainen-Martius and Professor K. Heng for hosting her at the University of Bern. We would like to thank Dr. Alexander Kashev from the faculty science IT support of the University of Bern for offering storage and computing power. We implemented code with python using numpy  (Harris et al. 2020) , scipy  (Virtanen et al. 2020) , matplotlib  (Hunter 2007) , pandas  (McKinney 2010) , netCDF4 (Unidata 2020), xarray  (Hoyer & Hamman 2017) , and the skillmetrics package developed by Peter Rochford 28.
Acknowledgements. This work has been carried out within the framework of the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation  under grants  51NF40_182901  and  51NF40_205606. The authors acknowledge the financial support of the  SNSF. The  PRIMAVERA  project is funded by the  European Union 's  Horizon 2020 programme , grant agreement no. 641727. We thank all the  PRIMAVERA climate modelling groups  ( Met Office, CERFACS, MPI-M, CMCC, ECMWF, KNMI, SMHI, BSC, CNR ) for producing and making their model output available. We also acknowledge the Earth system grid federation infrastructure (ESGF), an international effort led by the  U. S. Department of Energy's program  for climate model diagnosis and intercomparison, the European network for Earth system modelling, and other partners in the global organisation for Earth system science portals (GO-ESSP).
The relation between warm absorber (WA) outflows of AGN and nuclear obscuration activities caused by optically-thick clouds (obscurers) crossing the line of sight is still unclear. NGC 3227 is a suitable target to study the properties of both WAs and obscurers, because it matches the following selection criteria: WAs in both ultraviolet (UV) and X-rays, suitably variable, bright in UV and X-rays, good archival spectra for comparing with the obscured spectra. To investigate WAs and obscurers of NGC 3227 in detail, we used a broadband spectral-energy-distribution model that is built in the first paper of our series and the photoionization code of SPEX software to fit the archival observational data taken by XMM-Newton and NuSTAR in 2006 and 2016. Using unobscured observations, we find four WA components with different ionization states (log ξ [erg cm s -1 ] ∼ -1.
With these considerations, we firstly built a broadband SED model from the near infrared (NIR) to hard X-rays for NGC 3227 in our Paper I. In this paper, we use this broadband SED model and a robust photoionization code (pion model;  Mehdipour et al. 2016)  in the SPEX package  (Kaastra et al. 1996)  v3. 05. 00  (Kaastra et al. 2020)  to analyze the archival XMM-Newton and NuSTAR data taken in 2006  (Markowitz et al. 2009 ) and 2016  (Turner et al. 2018). SPEX is currently the only code that enables the SED and the ionization balance to be fitted simultaneously, while all other codes have to pre-calculate the ionization balance on a given SED. The pion model is a self-consistent model that can simultaneously calculate the thermal/ionization balance and the plasma spectrum in photoionization equilibrium.
In this work, we focus on properties of the WAs and obscuration events of NGC 3227 with the archival 2006 and 2016 data. The detailed analysis of the 2019 obscuration events will be presented in Paper III by  Mao et al. (in prep). The discussion about how the obscurer changes over the course of the 2019 observations with the XMM-Newton/EPICpn data will be presented in Paper IV by  Grafton-Waters et al. (in prep). This paper is organized as follows. In Sect. 2, we present the archival data that are used in this work and the data reduction process. In Sect. 3, we introduce the spectral analysis based on the broadband SED model. In Sect. 4, we present and discuss the results about WAs and obscurer components. In Sect. 5, we give a summary of our conclusions. In this work, Cash statistic (Kaastra 2017, hereafter C-stat) will be used to estimate the goodness of fit and statistical errors will be given at 1σ (68%) confidence level.
Observations and Data Reduction In Table  1 , we list the archival data that are used in this work. These data include six XMM-Newton observations  (Markowitz et al. 2009; Turner et al. 2018 ) and five NuSTAR observations  (Turner et al. 2018). We do not use the archival XMM-Newton observations taken in 2000 and on 2016 November 29, because the spectrum of the 2000 observation has a lower signal to noise ratio (S/N) owing to its short exposure time, and the observation.
XMM-Newton data The data reduction was done using XMM-Newton Science Analysis Software (SAS) version 18. 0. 0, following the standard data analysis procedure 2. The cleaned event files of EPIC-pn data were produced using the epproc pipeline and flaring particle background larger than 0. 4 count/s was excluded. The EPICpn spectra and lightcurves were extracted from a circular region with a radius of 30 arcsec for the source and from a nearby source-free circular region with a radius of 35 arcsec for the background. Response matrices and ancillary response files of each observation were produced using the SAS tasks arfgen and rmfgen. Following the standard procedure, the first-order data of RGS1 and RGS2 were extracted using the SAS task rgsproc and flaring particle background larger than 0.
NuSTAR data For the two telescope modules (FPMA and FPMB) data of NuSTAR, level 1 calibrated and level 2 cleaned event files were produced using the standard procedure of the nupipeline task of HEASoft v6. 27. The level 3 products including lightcurves, spectra and response files were extracted using the task nuproducts from a circular region with a radius of 90 arcsec for the source and from a nearby source-free circular region with the same radius for the background. Finally, we produced combined spectra of FPMA and FPMB data using tasks mathpha and produced combined response files using tasks addrmf and addarf. 2 See https://www. cosmos. esa. int/web/xmm-newton/sas-threads for details.
Spectral Analysis For the 2016 archival data, we will consider each set of XMM-Newton and NuSTAR observations taken on the same date as a single dataset (see Obs2 to Obs6 in Table  1 ), where XMM-Newton (OM UVW1 filter at ∼ 2910 Å, RGS data in the 6-37 Å wavelength range, and EPIC-pn data in the 2-10 keV energy band) and NuSTAR (combined FPMA and FPMB data in the 5-78 keV energy range) data are used simultaneously for the spectral analysis. For Obs1 that was taken on 2006 December 03, only XMM-Newton observation is available (see Table  1 ), and we used Obs5 to verify that the best-fit parameters of the WAs do not significantly change without NuSTAR observation. In Figs. 1  and 2 , we show the XMM-Newton/EPIC-pn light curves, softness ratio curves (the ratio of count rates between 0. 3-2 and 2-10 keV bands), and the correlation between the softness ratio and 0.
The lower limit of the radial location of each WA component is estimated to be 0. 03 pc for WA 1 , 0. 2 pc for WA 2 , 0. 3 pc for WA 3 , and 4 pc for WA 4 (see Table  4 ). The third method to estimate the radial location of WAs is based on the recombination timescale. Following  Bottorff et al. (2000) , the recombination timescale of the ion X i is defined as t rec (X i ) = α r (X i )n e f (X i+1 ) f (X i ) - α r (X i-1 ) α r (X i ) -1 , (7) where α r (X i ) is the recombination coefficient (recombination rate from the ion X i+1 to X i ), n e is the electron number density of the absorbing gas, and f (X i ) is the fraction of ion X i. We select the ions that contribute significantly to the spectral fit for each WA component as the indicators of this component. Some of these parameters can be obtained from SPEX code  (Mao & Kaastra 2016 ): α r (X i ) and α r (X i-1 ) come from atomic physics, and f (X i ) and f (X i+1 ) are estimated from the ionization balance.
SUMMARY AND CONCLUSIONS The relation between WA outflows of AGN and nuclear obscuration activities is still unclear. NGC 3227 is a suitable target to study the properties of both WAs and obscurers, which might help us understand their correlation. To investigate the WA components of NGC 3227 in detail, we use a broadband SED model (Paper I) and the photoionization model in SPEX software to fit the unobscured spectra of the archival and previously published XMM-Newton and NuSTAR observations taken in 2006 and 2016. Based on the broadband SED and WAs parameters, we also study the X-ray obscuration events in the archival observations. We detect four ionization phases for the WAs in NGC 3227 using the unobscured observations: log ξ (erg cm s -1 ) ∼ -1. 0, 2. 0, 2. 5, 3. 0. The highest-ionization WA component has a much higher hydrogen column density (N H ∼ 10 22 cm -2 ) than the other three WA components (N H ∼ 10 21 cm -2 ).
Acknowledgements. We thank the referee for helpful comments that improved this paper. This research has made use of the  NASA/IPAC Extragalactic Database (NED) , which is funded by the  National Aeronautics and Space Administration  and operated by the  California Institute of Technology. YJW gratefully acknowledges the financial support from the  China Scholarship Council. YJW and YQX acknowledge support from  NSFC-12025303 ,  11890693 ,  11421303 , the  CAS Frontier Science Key Research Program  ( QYZDJ-SSW-SLH006 ), the  K. C. Wong Education Foundation , and the science research grants from the  China Manned Space  Project with NO. CMS-CSST-2021-A06. SRON is supported financially by  NWO , the  Netherlands Organization for Scientific Research. JM acknowledges the support from  STFC (UK)  through the  University of Strathclyde UK APAP  network grant  ST/R000743/1.
GP acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  865637 ). E. B. is funded by a  Center of Excellence of THE ISRAEL SCIENCE FOUNDA-TION  (grant No. 2752/19 ). SB acknowledges financial support from  ASI  under grants  ASI-INAF I/037/12/0  and n. 2017-14-H. O. , and from  PRIN MIUR  project " Black Hole  winds and the  Baryon Life Cycle of Galaxies : the stone-guest at the galaxy evolution supper", contract no. 2017PH3WAT. BDM acknowledges support via Ramón y Cajal Fellowship RYC2018-025950-I. SGW acknowledges the support of a PhD studentship awarded by the  UK Science & Technology Facilities Council (STFC). DJW acknowledges support from  STFC  in the form of an  Ernest Rutherford fellowship.
Therefore, we only need to check whether these clock constraints are schedulable or not. In other words, if unsat is returned in the schedulability analysis, the safety requirements are inconsistent. As a model checker, MyCCSL only supports the core CCSL expressions. Not all the clock constraints used in this paper are defined in MyCCSL. Constraint Alternate needs to be transformed. The Alternate (C 1 ∼ C 2 ), could be expressed using the DelayFor($), i. e. , C 1 = C 2 $1. It means between C 1 [i] and next C 1 [i + 1], C 2 [i] could be inserted. Before the verification, one needs to input the running parameter: the program timeout period to prevent the running time from being too long to terminate; the SMT solver can choose Z3 to solve the SMT formulae; the boundary value, 0 represents unbounded verification, usually we will enter a positive integer as the bound of verification; and whether this is periodic or not.
e. its consistency with a class diagram and a state diagram. Shen et al. present a template semantics, for describing the operational semantics of behavioral notations, especially the combined fragments and other constructs of sequence diagrams  [22]. Muram et al. translate sequence diagrams into temporal logic based constraints (LTL) and formal behavior specifications (i. e. , symbolic model language (SMV)), respectively  [23]. The model checker NuSMV is used to verify the containment relationship. Various petri nets are also used. For example,  [7]  defines and explains the relationship between sequence diagrams and normal Petri nets. Cunha et al. also translate UML sequence diagrams to Petri nets and verify deadlock freeness, reachability, safety and liveness properties by using a model checker  [24].
Acknowledgements This research was supported by the  National Natural Science Foundation of China  ( 62192731 ),  Shanghai Science and Technology Committee ( STCSM 20DZ1100300 ,  20ZR1416000 ), and the  French National Research Agency SIM   ANR-19-CE25-0008-02. The author  Qin Li  is supported by the foundation of  Shenzhen Institute of Artificial Intelligence and Robotics for Society  ( AC01202005020 ).
Introduction Indirect observations of climate from proxies such as tree-ring width (hereafter TRW) or isotopic content in ice cores and coral inform on past climate variability beyond the instrumental era  (Jones et al. , 2009). Those proxy records have been used in numerous studies devoted to climate variability over the last millennium. This has been classically achieved by using empirical  (i. e. , statistical)  relationship between climate variables of interest (e. g. , surface air temperature) and proxy observations (e. g. among other inputs, which represents a major advantage for taking into account the potential effect of the recent exponential increase of CO 2 on tree growth  (Myhre et al. , 2013; Boucher et al. , 2014). In  Rezsöhazy et al. (2021) , the MAIDEN model has been successfully applied to the PAGES2k TRW database using the calibration protocol developed in  Rezsöhazy et al.
The Southern Hemisphere also constitutes an interesting study case, as few reconstructions have been performed in this region so far  (Neukom et al. , 2011 (Neukom et al. , , 2014)). The DA experiments are based on both TRW and ice core (δ 18 O and snow accumulation) proxy data to ensure the consistency of the reconstructed large-scale circulation pattern by avoiding to only reconstruct smallscale features. They both represent the best available continental annually resolved proxies in the SH  (PAGES 2k Consortium, 2017). First, DA experiments are performed and evaluated against state-of-the-art gridded climate datasets over the last century to evaluate the impact of the assimilation of the tree-ring data and the associated PSMs on the skill of the reconstruction. In Sect. 4. 1, we compare the performance of the DA-based reconstructions using a regression-or a process-based dendroclimatic PSM in our framework over the last century.
Climate model simulations We use the ensemble of three simulations performed with the isotope-enabled Community Earth System Model version 1 (iCESM1;  Brady et al. , 2019; Stevenson et al. , 2019 ; publicly available at https://www. earthsystemgrid. org/dataset/ucar. cgd. ccsm4. CESM_CAM5_LME. html; last access: 12 May 2021) as a basis for building the prior of our data assimilation experiments. The iCESM1 is a coupled atmosphere-ocean-sea-ice model at 1. 9°latitude and 2. 5°longitude resolution for the atmosphere component and around 1 • for the ocean-sea-ice component. The model simulations include the anthropogenic (aerosol emissions and land use changes) and volcanic aerosol forcings, the greenhouse gas emissions, solar irradiance variations and Earth's orbital changes  (Brady et al. , 2019; Stevenson et al.
Tree-ring width data The PAGES2k database (hereafter PAGES2k2017) includes 692 proxy records (PAGES 2k Consortium, 2017) selected for reconstructing the temperature of the last two millennia. The records are from a variety of archives (tree rings, ice cores, lake sediments, coral, speleothems, documentary evidence, etc). TRW records represent a bit more than the half of the database with 354 records, among which 12 are located below 30 • S, in New Zealand (four records), Tasmania (four records) and South America (four records). The previous version of the PAGES2k TRW database (PAGES 2k Consortium, 2013; hereafter PAGES2k2013) contains 28 TRW records below 30 • S. Nine records have been excluded from the recent version of the PAGES2k database because they were too short (less than 300 years).
Those records are also included in this study and are all located in South America. The location of the 21 TRW records from the two databases can be seen on Fig. S1  (blue stars). The location of the 15 TRW records that are actually used in this study can be observed on Fig. 1  (see Sect. 2. 4. 1 and 2. 4. 2 for more details on the included sites). The TRW records are normalized (i. e, with a null mean and a unity standard deviation) relative to the 1900-2000 CE time period for a use with the MAIDEN model (Sect. 2. 4. 1) while for the regression-based model (Sect. 2. 4. 2), the anomalies relative to the 1900-2000 CE time period are subtracted from the TRW time series.
90°S 60°S 30°S 0°0 °60°E 120°E 180°120°W 60°W 0°F igure 1. Location of the 15 tree-ring width (TRW) records (blue stars) from the PAGES2k databases  (PAGES 2k Consortium, 2013 , 2017)  used in this study; the 49 ice core snow accumulation records (purple triangles) from  Thomas et al. (2017)  and  Medley et al. (2018) ; and the 29 δ 18 O records (green diamonds) from  Stenni et al. (2017) (Sect. 2. 3). Background map from  Hunter (2007). from the database of  Thomas et al. (2017)  spanning the last millennium  (Medley and Thomas, 2019). The B40 ice core snow accumulation record of  Medley et al. (2018)  has also been added as this record has been published after the publication of the  Thomas et al. (2017) 's database. Finally, as in  Dalaiden et al. (2021) , we use the 29 annually resolved δ 18 O records in Antarctica from  Stenni et al.
We must first determine where the MAIDEN model can be skillfully applied among the 21 available PAGES2k sites (Sect. 2. 3. 1). MAIDEN was calibrated at the 12 SH sites from PAGES2k2017 in  Rezsöhazy et al. (2021)  driven by the daily maximum and minimum temperature and precipitation from the Global Meteorological Forcing Dataset for land surface modelling (v2) (http://hydrology. princeton. edu/data. php, last access: 5 May 2021;  Sheffield et al. , 2006)   et al. , 2019). This Bayesian approach is used to calibrate the most sensitive parameters of the model from the photosynthesis and carbon allocation modules (18 parameters). MAIDEN was found to be robustly calibrated with significant (95% confidence level) calibration and verification correlations greater than 0. 3 at five sites south of 30 • S.