dataesr/scientific-paragraphs-categorization huggingface dataset:
ERA5, NCEP and IASI temperatures are used to compile stratospheric temperature above DDU. Using these temperatures, a number of potential PSC days is computed, i. e. the number of days where PSCs could occur based only on temperature. The number of PSC days per year is the variable considered in order to circumvent the challenging issue of delimiting PSCs both in time and space. Mainly due to chemical kinetic concerns, PSCs generally form a few degrees below T NAT  (Dye et al. , 1992). Using the condition T -T NAT < 0 to state if a day is a potential PSC day The number of PSCs detected by the lidar in Fig. 9  is consistently and logically below the one estimated by the temperature datasets. The three datasets were considered for the trend calculation to show that despite the discrepancies they all indicate an approximately similar trend.
Acknowledgements. The author wishes to thank  MC Alvarez-Castro ,  J Riboldi ,  M Galfi ,  M Vrac ,  A Hisi ,  E Coppola  and  R Vautard  for the discussions. The authors acknowledge the support of the  INSU-CNRS-LEFE-MANU grant  (project DINCLIC), as well as the gran  ANR-19-ERC7-0003  (BOREAS). This work has received support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101003469  (XAIDA), by the  European Research Council (ERC)  grant agreement No. 948309  (CENAE), and the  Marie Sklodowska-Curie  grant agreement No. 956396  (EDIPI).
( , 1770 Qamili et al. ( , 1810 Qamili et al. ( , 1868 Qamili et al. ( , 1870 Qamili et al. ( , 1888 Qamili et al. ( , and 1900, when , when  analysing the temporal behaviour of the difference between predicted and actual geomagnetic field model values for successive intervals from 1600 to 1980, based on the gufm1 geomagnetic model  (Jackson et al. , 2000). The typical SV time scale spans years to centuries, and its typical magnitude is 10 -100 nT. yr -1. These values help to define the observational requirements for magnetic observatories: they should run for many years, achieve measurement accuracy in all components of the field vector of around 1 nT, and maintain the long-term stability needed to resolve the typical secular change signal. With the above recommendation the geomagnetic observatories represent a unique dataset to understand the secular variation over long-term periods.
Each gvo datum is derived using satellite measurements in the near vicinity of a given gvo location, and within a chosen time window. As for ground-based records, by taking annual differences of these locally derived gvo field estimates, SV time series can be obtained. To address issues of insufficient local time sampling and contamination from external sources such as the magnetospheric ring current and ionospheric current systems  (Olsen and Mandea, 2007; Beggan et al. , 2009; Shore, 2013) , additional data processing steps, changes to the modelling scheme and de-noising procedures have been incorporated into the gvo algorithm  (Hammer et al. , 2021a). In order to make robust gvo field estimates, a pre-whitening step is applied wherein main field predictions B(r GV O ) for each data point of position r GV O are subtracted off, and at a later stage added back for the gvo epoch under consideration.
4 : Comparison of SV times series at three example ground observatories for the East (φ), Radial (r), and South (θ) components respectively. Shown are SV from annual differences of revised monthly means (black dots) (see  Olsen et al. , 2014) , and 4-monthly gvos (mapped to ground) derived from champ (red dots), CryoSat-2 (purple dots) and Swarm (blue dots) measurements. Also shown are predictions of the CM6  (Sabaka et al. , 2020)  (cyan line) and chaos-7. 5  (Finlay et al. , 2020)  (green line) models. Pacific region around 2017. gvos are thus suitable for studying inter-annual and longer field changes at satellite altitude. These likely originate from changes in the outer core fluid motions (see section §4). The gvo dataset, together with their error estimates, are suitable for core flow inversions  (Whaler and Beggan, 2015; Kloss and Finlay, 2019; Rogers et al.
Acknowledgements We thank the anonymous reviewer for remarks that help us improving the manuscript. This work was partially supported by the  CNES. It is based on observations with magnetometers embarked on Swarm mission. NG contribution has been also funded by  ESA  in the framework of  EO Science for Society , through contract  4000127193/19/NL/IA  (SWARM + 4D Deep Earth: Core). MM: the research leading to these results has received funding from the  European Research Council (ERC)   GRACEFUL Synergy Grant  No  855677. MDH was supported by the  European Research Council.
Classification of spatio-temporal observations The input parameters in our classification scheme are the spatio-temporal distributions of β 532 , δ 532 and G F , which are presented as matrices , 532 ij  , , 532 ij  , , ij F G , where i=1… N T ; j=1… N H. Values N T and N H are the numbers of temporal and height intervals in the analyzed dataset. In a single measurement we accumulate 2×10 3 laser pulses, so temporal resolution of the measurements is about 100 s, while the height resolution is 7. 5 m. The particle intensive parameters cannot be evaluated reliably when the backscattering coefficient is low. Thus, we set a threshold value for β 532 (normally 0. 2 Mm -1 sr -1 ); namely, when , 532 ij  <0. 2 Mm -1 sr -1 the elements of the matrices , 532 ij  and , ij F G , are classified as "low signal" and ignored.
Methods Proxy-model comparison. Our proxy compilation builds on previous efforts to compile Pliocene hydroclimate records. These include the compilations created by refs. 18, 40, 41. The records included in these sources span a variety of proxy types, from sedimentological indicators of lacustrine environments, palynological indicators of vegetation composition, faunal remains, and stable isotope records from organic and inorganic materials. We added to these compilations by including new records of terrestrial hydroclimate dating to the Pliocene archived on the NOAA Paleoclimatology Database (https://www. ncdc. noaa. gov/data-access/ paleoclimatology-data) and Pangaea Database (https://www. pangaea. de/). To identify the average hydroclimate signal during the mid-Pliocene, we filtered available records based on the precision of their age models.
These filtering criteria allowed us to retain a compilation of 62 records. Of the 62 records included, 30 come from paleobiological indicators like faunal remains or pollen, while the other 34 records are drawn from interpretations of sedimentary sequences or stable isotopic analyses of organic and inorganic materials (SI). A supplementary excel file (Supplementary Data 1) with details on the proxy records, including methods, chronology, and original citation, is included with this manuscript. Because of the broad, continental-scale coherence of the model signal, proxy-model synthesis results are similar when using the compilations found in other sources e. g. , ref. 41. We rely on the author's original interpretation about whether the record reflects, on average, wetter or drier conditions, or no change in hydroclimate during the mid-Pliocene compared to late Quaternary/modern conditions.
However, in cases with records showing opposite signs of change, we retain both records since in many cases there is not enough information to determine which record is more reliable, and excluding both would decrease the data coverage of our proxy compilation in key regions like East Asia (Fig. S2 ). In the absence of a priori evidence that proxies reflect hydroclimate in a particular season, patterns of the proxy-model agreement are assessed using annually averaged P-E. We independently assess the fit between proxies and models for winter (December-February) and summer (June-September) separately (Fig. S4 ). The fit is much higher across all models and the multi-model mean for June-September rainfall. Proxy-model agreement for winter rainfall alone shows very low values of Gwet's AC statistic, suggesting that winter rainfall changes do not explain a significant component of the pattern seen in the proxy record.
Furthermore, intermodel spread in Gwet's AC values on annually averaged rainfall show a strong correlation with intermodel Gwet's AC results calculated for June-September P-E (Fig. 2 ) at nearly all threshold values of P-E. This suggests that the summer seasonal signal, which is also the largest signal across model simulations (Fig. 1 ), drives the overall pattern of proxy-model agreement across models. PlioMIP2 ensemble. We use a suite of model simulations conducted as part the 2nd Pliocene Model Intercomparison Project (PlioMIP2)  35, [81] [82] [83] [84] [85] [86] [87]. Boundary conditions are derived from the PRISM4 dataset  38. PRISM4 boundary conditions include information on land distributions, topography and bathymetry, vegetation, soils, lakes, and land ice cover. Two experimental protocols were developed, one implementing Pliocene conditions with a modern land/sea mask, and the enhanced experiment that included all boundary conditions  37, 38.
Acknowledgements The authors would like to thank all modeling groups who provided PMIP4 outputs for this analysis, WCRP, CMIP panel, PCMDI, ESGF infrastructures for sharing data, WCRP, and  CLIVAR  for supporting the  PMIP  project. R. F. , T. B. , B. L. O. , and E. C. B acknowledge support from  U. S. National Science Foundation  grant numbers 1814029 and 1903650 (R. F. ), 1903148 and 2103015 (T. B. ) and 1852977 (B. L. O. and E. C. B. ). X. L. and N. T. acknowledge support from the  National Science Foundation of China  grant numbers  42005042  (X. L. ) and 41888101 (N. T. ). D. L. acknowledges support from  NERC  ( Natural Environment Research Council ), SWEET Large Grant number  NE/P01903X/1. C. C. acknowledges support from France  ANR HADoC  grant number  ANR-17-CE31-0010. Q. Z. acknowledge support from  Swedish Research Council (Vetenskapsrådet)  grant numbers.
Data availability The PlioMIP2 simulations analyzed in this study that are part of the Climate Model Intercomparison Project 6 (CMIP6) have been deposited to the Earth System Grid Federation with the access link: https://doi. org/10. 22033/ESGF/CMIP6. 4804 for EC-Earth3, https://doi. org/10. 22033/ESGF/CMIP6. 5230 for IPSL-CM6A-LR, https://doi. org/ 10. 22033/ESGF/CMIP6. 7227 for GISS-E2-1-G, https://doi. org/10. 22033/ESGF/ CMIP6. 7675 for CESM2, and https://doi. org/10. 22033/ESGF/CMIP6. 12130 for HadGEM. The processed multi-model ensemble of PlioMIP2 and CESM2 data are available at Zenedo with the access link: https://zenodo. org/record/ 5706370#. YZbc6y1h0eY.
Acknowledgments This work was supported by the  Ministry of Economy and Competitiveness, Spain , and  FEDER EU  through the  EXPLORA Ciencia National R + D Plan  under  TURBOMIX  project ( CGL2015-73100-EXP ) and through the projects  PROMISES  ( ESP2015-67549-C3-1-R ) and  COSMO  ( CTM2016-79474-R ). This work was supported in part by the  Spanish   R&D  project  L-BAND  ( ESP2017-89463-C3-1-R ), which is funded by  MCIN/AEI/10. 13039/501100011033and   ERDF. A way of making Europe, and project  INTERACT  ( PID2020-114623RB-C31 ), which is funded by  MCIN/AEI/10. 13039/501100011033. We also acknowledge support from  Fundación General CSIC (Programa ComFuturo ). This work acknowledges the " Severo Ochoa Centre of Excellence " accreditation ( CEX2019-000928-S ). This work is a contribution to  CSIC PTI Teledetect.
InSAR time-series processing We process Synthetic Aperture Radar (SAR) data from the ESA Sentinel-1 data archive using the New Small Baseline Subset (NS-BAS;  Doin et al. 2011 Doin et al. , 2015;; Grandin 2015; Grandin et al. 2016)  processing chain [based on the ROI PAC software;  Rosen et al. (2004) ]. Data from a single descending (D064) and ascending (A159) track covering the MKDF (>300 km in the azimuth/alongtrack direction, Fig. 1 ) are processed in interferometric wide-swath (IW) mode (swath width is ≈250 km in range/across-track). The interferogram (IFG) networks span October 2014, to November 2019, and we construct 392 and 290 IFGs, from 126 and 96 acquisitions, for the ascending and descending track, respectively, with temporal baselines ranging from 12 d to over 1 yr. The interferometric networks are constructed semi-automatically; these initially include all possible date pairs within the same season (i.
The residual ramp may reflect residual long-wavelength signals, such as ionospheric, turbulent tropospheric, orbital or plate motion drifts that have not been accounted for. (3) To ensure phase closure is maintained over the IFG network, we invert the ramp parameters individually for each epoch in the time-series assuming no covariance between the parameters. (4) For each IFG, we reconstruct the tropospheric phase-delay using the inverted ramp parameters. (5) We then apply the correction by subtracting tropospheric phase-delay from the individual IFGs, with both the data and model referenced to a 50 × 50 pixel area, using the mean of the phase weighted by the colinearity, assumed to be a stable non-deforming zone (away from known active structures, high topographic gradients, anthropogenic deformation sources and basin interiors;  Maubant et al.
M O D E L L I N G To model surface deformation from interseismic motion along the MKDF, we use Flower2D  (Daout et al. 2016a, b) ; a geodetic tool package for the 2-D exploration of fault geometry and slip-rates within a Bayesian framework. For each model, we run 100 000 synthetic surface displacement models with the PyMC Monte Carlo Markov Chain (MCMC) algorithm (not using a back-slip approach). The results present the posterior distribution of parameters, having discarded the first 50 per cent of these models (burn-in period). Initially we model the resolved deformation as a dislocation on a vertical strike-slip fault in a 2-D elastic half-space, before progressively increasing and justifying structural complexity from geodetic and geological observations. Combining the descending and ascending LOS, we observe two components of the 3-D surface deformation field.
DATA AVA I L A B I L I T Y InSAR data are provided by the ESA Copernicus Sentinel-1 Satellite Mission (scihub. copernicus. eu). InSAR time-series processing and analysis uses the NSBAS processing chain (forge. osug. f r). Tropospheric path-delay corrections are provided by the Generic Atmospheric Correction Online Service for InSAR (GACOS; www. gacos. net), with data from the European Centre for Medium-Range Weather Forecasts (ECMWF; operational high-resolution tropospheric products). LOS velocity fields are provided as electronic supplements (. grd files for both descending and ascending LOS at 250 m resolution in UTM Zone 40N; included are the average velocity, standard error, LOS heading and LOS incidence data sets).
Abstract. Vertical ozone profiles from combined spectral measurements in the ultraviolet and infrared spectral range were retrieved by using data from the TROPOspheric Monitoring Instrument on the Sentinel-5 Precursor (TROPOMI/S5P) and the Cross-track Infrared Sounder on the Suomi National Polar-orbiting Partnership (CrIS/Suomi-NPP), which are flying in loose formation 3 min apart in the same orbit. A previous study of ozone profiles retrieved exclusively from TROPOMI UV spectra showed that the vertical resolution in the troposphere is clearly limited  (Mettig et al. , 2021). The vertical resolution and the vertical extent of the ozone profiles is improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The combined retrieval particularly improves the accuracy of the retrieved tropospheric ozone and to a lesser degree stratospheric ozone up to 30 km.
An increase in the degrees of freedom (DOF) by 1 DOF was found in the UV + IR retrieval compared to the UV-only retrieval. Compared to previous publications, which investigated combinations of UV and IR observations from the Ozone Monitoring Instrument and Tropospheric Emission Spectrometer (OMI and TES) and Global Ozone Monitoring Experiment version 2 and Infrared Atmospheric Sounding Interferometer (GOME-2 and IASI) pairs, the degree of freedom is lower, which is attributed to the reduced spectral resolution of CrIS compared to TES or IASI. Tropospheric lidar and ozonesondes were used to validate the ozone profiles and tropospheric ozone content (TOC). In their comparison with tropospheric lidars, both ozone profiles and TOCs show smaller biases for the retrieved data from the combined UV + IR observation than from the UV observations alone.
065 nm. The spatial resolution depends on the channel and on the position in the swath. At the nadir-viewing points it is 28. 8 × 5. 6 km 2 (cross track × along track) in UV1 and 3. 6 × 5. 6 km 2 in UV2. The smaller TROPOMI pixels are binned together to match the coarser spatial resolution of CrIS. Using the cloud-cleared radiance L2 product from CrIS, the spatial resolution ends up being 42 × 42 km 2. TROPOMI, like other instruments of this type, shows drift and degradation effects in the UV channels and needs an extensive pre-and post-launch calibration  (Ludewig et al. , 2020). For this study, we use the Level 1B version 2 data. In our UV-only retrieval, additional calibration steps as part of the profile retrieval are needed  (Mettig et al. , 2021). The version 2 data set is limited to 12 weeks distributed over the period from July 2018 to October 2019, and all evaluations in this study are based on data from this period.
CrIS CrIS aboard Suomi-NPP is a Fourier-transform spectrometer which provides soundings in the thermal IR spectral range. Suomi-NPP moves in the same orbit as S5P in a loose formation with TROPOMI. The time difference between the measurements from both instruments above the same location is around 3 min. CrIS covers three wavelength ranges with 2211 spectral points in the long-wave, middle-wave, and short-wave infrared: LWIR (9. 14-15. 38 µm), MWIR (5. 71-8. 26 µm), and SWIR (3. 92-4. 64 µm)  (Han et al. , 2013; Strow et al. , 2013; Tobin et al. , 2013). The spectral resolution is 0. 625 cm -1, which is coarser than that from instruments like TES or IASI. But in comparison to IASI, CrIS has lower noise. For this study, we use a spectral window in LWIR between 9. 35 and 9. 9 µm from the level 2 CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) full spectral resolution cloud-cleared radiance V2 data product  (Barnet, 2019b).
The ozone profile used in the validation part of this work and the surface temperature are taken from the level 2 CLIMCAPS atmosphere cloud and surface geophysical state V2 data product  (Barnet, 2019a). Using the cloud-cleared radiances allows us to avoid cloud handling in the retrieval process and including cloudy pixels provides more collocated pixels for TROPOMI. CrIS has a field of view consisting of 3 × 3 circular pixels of 14 km diameter each (nadir spatial resolution). In conjunction with the cloud-clearing algorithm and due to the subsequent L2 processing, the nine field-of-view pixels are combined, resulting in an effective spatial resolution of 42 × 42 km 2.
5-3. 5 km  (Livesey et al. , 2020). In the MLS user guide  (Livesey et al. , 2020) , the precision is estimated to be 4 %-7 % with an accuracy of 5 %-10 % above 18. 5 km. From the lower stratosphere downward to the troposphere, the precision of the individual profiles decreases up to 5 %-100 % (depending on the latitude) with an accuracy of 7 %-10 %. To validate the ozone profiles in the lower stratosphere and in the troposphere, in situ ozonesonde measurements and ground-based ozone lidar data are used. The ozonesondes are provided by the World Ozone and Ultraviolet Radiation Data Center (WOUDC) (WOUDC Ozonesonde Monitoring  Community et al. , 2022)  and the Southern Hemisphere Additional Ozonesondes (SHADOZ)  (Witte et al. , 2017 (Witte et al. , , 2018;; Thompson et al. , 2017; Sterling et al.
, 2018). Those measurements have a high vertical resolution of 100-150 m and are well validated. The precision is on the order of 5 %, and the accuracy 5 %-10 %  (Deshler et al. , 2008; Johnson, 2002; Smit et al. , 2007). Around the tropopause layer in the tropics, the uncertainties peak and reach about 15 %-20 %  (Witte et al. , 2018). During the time when TROPOMI data are available, 242 collocated ozonesonde measurements from 30 different sites were found. The collocation criteria are a maximum distance of 100 km and a maximum time difference of 24 h. The exact locations can be found in the Supplement (Table  S1 ). To validate the lower levels of the atmosphere, tropospheric lidars are a valuable option, because of their great vertical resolution and stable and precise ozone profile measurements.
Unfortunately they are not as widely distributed as ozonesonde and stratospheric lidar sites. For the limited TROPOMI/CrIS data set, ozone profiles from three different locations are available:   (Newchurch et al. , 2016). The vertical range of the ozone profiles from Huntsville and Observatoire de Haute-Provence (OHP) is 3-14 km with a precision of better than 10 %  (Kuang et al. , 2013; Gaudel et al. , 2015). The tropospheric lidar measurements are done during daytime in Huntsville and after sunset in OHP. For Table  Mountain , where ozone profiles during daytime and nighttime are available, the vertical range is increased up to 25 km during the night. The overall precision reaches from 5 % in the free troposphere to up to 15 % above 20 km  (Leblanc et al. , 2016). For a comparison of the lower vertically sampled retrievals to the fine sampled tropospheric lidar and ozonesondes, a pseudo-inverse (linear) regridding  (Rodgers, 2002, Sect.
, 2020)  Convergence criteria 2 % change of the ozone profile or the spectral fit rms same as in the TOPAS UV-only retrieval are not explained in detail here. The corresponding information can be found in  Mettig et al. (2021). Compared to the ozone profile retrieval from TROPOMI UV data described in  Mettig et al. (2021) , there are two main changes: the way the measurement error covariance matrix S y and an altitude-dependent first-order Tikhonov regularisation is constructed (see Table  1 ). For S y , the fit residuals from the pre-processing step are used instead of instrument SNRs. Tests have shown that this approach works better for combined UV + IR retrievals. The underlying problem is the different SNR of TROPOMI and CrIS and the different spatial resolution of the instruments measurements before binning the pixels.
IR RTM and pre-processing In the IR wavelength range, the intensities are simulated using a line-by-line model, which is also part of SCIATRAN-V4. 5. The HITRAN (HIgh-resolution TRANsmission molecular absorption database) 2020  (Gordon et al. , 2021)  spectroscopy database is used. A continuous spectrum between 9350 and 9900 nm with a sampling of 0. 05 nm is modelled containing atmospheric trace gases O 3 , H 2 O, and CO 2 in the forward model. Because CO 2 does not affect the ozone profile retrieval, it is kept constant using a climatological CO 2 profile calculated with B2D chemistry-transport model  (Sinnhuber, 2003). The change of water vapour is taken into account by retrieving the integrated column value and scaling the climatological H 2 O profile. The rotational Raman scattering and polarisation are not taken into account, as the contribution of scattered solar radiation is negligible.
4  (left). This approach is based on the concept of data density  (Purser and Huang, 1993)  and is explained by the definition of DOF and the resulting assumption that the diagonal of the AK matrix is a "measure of the number of degrees of freedom per level, and its reciprocal is a number per degree of freedom, and thus a measure of resolution"  (Rodgers, 2002, Sect. 3. 3, pp. 54). As is known from previous studies, the UV-only retrieval from TROPOMI measurements (blue) has high vertical resolution above 20 km and reduced vertical resolution between 10 and 15 km  (Mettig et al. , 2021). The IR-only retrieval from CrIS measurements (orange) has a vertical resolution of around 10 km between 5 and 25 km. The combined UV + IR ozone profile retrieval shows a vertical resolution of about 10 km from 5 to 55 km.