dataesr/scientific-paragraphs-categorization huggingface dataset:
Code and data availability The COASTLOOC data is provided as a collection of comma-separated values (CSV) files that are regrouping measurements associated with a particular type of measure. To aid the user to merge these files, there is a lookup   Core Team, 2022). The code used to produce the figures and the analysis presented in this paper is available under the GNU GPLv3 license (10. 5281/zenodo. 7708653, Massicotte 2023).
Acknowledgements. A great number of people contributed to the generation of the COASTlOOC database as described herein, be it by contributing to project implementation, supporting the field campaigns, performing lab work, or providing scientific guidance. In this respect, we would like especially to thank  James Aiken ,  Jean-François Berthon ,  Annick Bricaud ,  Hervé Claustre ,  Roland Doerffer ,  Giovanni Massimo Ferrari ,  Hans Hakvoort ,  Geir Johnsen ,  Khadija Oubelkheir ,  Matt Pinkerton ,  Rainer Reuter ,  Marcel Wernand  and  Grigor Obolensky. We are also grateful to the crews of the ships (NO Thetis-2, RV Poseidon,  RV Victor Hensen ) and helicopter ( Commerc'Air SA ) we used for.
Bulk rock geochemical data for major oxides was analysed by ALS, Co. , Galway, Ireland, by inductively coupled plasma atomic emission spectroscopy of lithium borate fusion glasses dissolved in nitric acid (ME-ICP06). This technique measured SiO 2 , Fe 2 O 3 , CaO, MgO, Na 2 O, K 2 O, Cr 2 O 3 , TiO 2 , MnO, P 2 O 5 , SrO and BaO. In addition, Nb and Zr were analysed by using Trace Level XRF Analysis (ME-XRF05) by ALS. Inductively coupled plasma mass spectroscopy analysis for trace elements was carried out at both the University of Brighton (UoB; Agilent 7900 ICP-MS) and the British Geological Survey (BGS, Keyworth; Agilent 7500 ICP-MS). Samples analysed at BGS were prepared using a sodium peroxide fusion and those at UoB with a lithium metaborate fusion technique; in both cases fusion was followed by dissolution in concentrated nitric acid.
Major element compositions of amphibole and pyroxene were determined on carbon-coated (30 μm) polished thin sections using a JEOL JXA-8200 electron microprobe (EMP) with wavelength dispersion spectrometry at Camborne School of Mines, University of Exeter, UK. Typical beam conditions used were a 15-kV accelerating voltage, 10-nA beam current, and 10-μm beam diameter. These data were collected over three visits. Analytical conditions for the EMP are given in Supplementary Material S1. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) was used to collect a suite of trace element analyses for amphibole and pyroxene using a New Wave Research UP193FX excimer (193 nm) laser ablation system coupled to an Agilent 7500 series quadrupole ICP-MS at the British Geological Survey, Keyworth.
The NIST SRM-610 glass was used as a standard and Si from EMP analyses used as the internal standard. Samples were presented as polished thin sections to LA-ICP-MS; an ablation time of 30 s at 5 Hz with a 50-μm spot size was used to produce a ∼15-μm deep crater. Data reduction was done using Iolite v2. 5 software  (Paton et al. , 2011). The NIST SRM-612 glass used for QA/QC and error was <5% for the elements analysed.
ACKNOWLEDGEMENTS Rocky Lowell Rakotoson  and  Fetra Rasolonirina  are thanked for their invaluable assistance in organising fieldwork and field expertise in Madagascar. Tantalus Rare Earths  AG provided assistance with field logistics and access to the drill core. Pete Lyons  and  Dr Magda Grove  provided assistance and support in the development of ICP-MS procedures at the  University of Brighton. Joe Pickles  and  Gavyn Rollinson  enabled access and provided assistance in the collection of EMP dataset at the  Chemical, Imaging and Mineralogical Facility (CIMF), Camborne School of Mines, University of Exeter. The  British Geological Survey 's  Charles  Gowning and  John Wheeler  provided access and assistance to the BGS rock crushing facilities;  Gren Turner ,  Alicja Lacinska  and  Jeremy Rushton  provided assistance and expertise for SEM imagery collection; and  Simon Chenery  provided assistance and expertise during the collection of LA-ICP-MS datasets.
Impact on rotation modeling for Mars We now explore how the time transformation developed above can impact Mars rotation modeling in BCRS. We first consider a rotation model expressed in Mars's local reference frame. Such a model includes a uniform rotation and periodic terms, that is, ϕ local (τ) = ϕ 0 + φlocal 0 τ -ψ nut cos ε 0 + 4 j=1 (ϕ c j cos j l ′ + ϕ s j sin j l ′ ). ( 23 ) On the other hand, one needs a similar modeling expressed in terms of TDB in order to perform the data analysis. Using Eq. (  22 ), this modeling is given as: ϕ(t) = ϕ local (τ(t)) = ϕ 0 + φlocal 0 t -ψ nut cos ε 0 + 4 j=1 (ϕ c j cos j l ′ + ϕ s j sin j l ′ ) + ϕ GR (t), (24) where ϕ GR (t) = φlocal 0 [τ-t] GR • (25) When analyzing data from orbiting spacecraft or surface landers, the timescale used in the data reduction is TDB, so that the model for Mars rotation needs to include the relativistic contributions, as presented above.
Impact of the time transformation on Mars rotation modeling In this section, we study the various contributions to the [τ-t] GR relationship that arise from the integration of Eq. (  17 ) and their corresponding impacts on the Mars BCRS rotation model through Eq. (  25 ). In order to cross-check and validate our results, we developed three independent approaches. The first approach consists of developing a simple toy model to analytically integrate Eq. (  17 ). This provides a good physical intuition with regard to the various terms obtained and the process is relatively pedagogical. The second approach consists of numerically integrating Eq. (  17 ) using the DE440 planetary ephemerides  (Park et al. 2021)  provided by the NAIF-SPICE software  (Acton 1996; Acton et al. 2018). In a subsequent step, analytical series consisting in various harmonic terms are fitted to the result of the numerical integration.
Numerical solution using the DE planetary ephemerides In this section, we present the result of a numerical integration of Eq. (  17 ) using the DE440 planetary ephemerides  (Park et al. 2021)  provided by the NAIF-SPICE software  (Acton 1996; Acton et al. 2018). The integration was performed starting from J2000 and lasting 30 yr backward and forward. In a second step, analytical series consisting of various harmonic terms were fitted to the result of the numerical integration. This procedure is similar to the one developed to produce the Time Ephemeris for Earth (see  Fukushima 1995 Fukushima , 2010;; Irwin & Fukushima 1999; Harada & Fukushima 2003).
1b. The analysis of the smoke emitted by these fires and its easterly transport across the Atlantic Ocean is performed using state-of-the-art measurements of (1) CO total column provided by the space-based Infrared Atmospheric Sounding Interferometer (IASI) instruments on polar orbiting platforms and (2) aerosol optical depth (AOD) provided by a combination of optical imagers on five different geostationary platforms. Satellite data are compared with analyses of CO total column and AOD from the CAMS operational system to check how well the model represents trace gases and aerosols during the emission and long-range transport of biomass burning plumes. In addition, lidar observations from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and ground-based instruments in Europe are used to verify the consistency of remotely sensed passive observations and model outputs at different stages during the transport of smoke, especially with respect to their altitude.
Long-range transport is observed until the 21st, with massive plumes traveling from Western US to the Atlantic Ocean. Smoke from a previous peak in emissions is observed to reach the northwest of Europe on September 11th (see Fig. 3  and Supplementary Fig. S3  online) after it had been transported over more than 9000 km. On this date, transported CO and AOD attained values of 4 × 10 18 molecules/cm 2 and 0. 4 in average along the coast of Belgium and the Netherlands. Overall, all four datasets show a good agreement throughout the selected dates despite coming from different sources (model versus satellite) and representing different physical quantities (CO total column versus AOD). Some minor differences can be however observed in Fig. 3  on the location, shape, and magnitude of some smoke plumes.
These dissimilarities are discussed in the following section. It is worth mentioning that while the assimilation of IASI retrievals into the CAMS model explains in part the good agreement between the two CO data sets, we observe some differences that may come from the assimilation of Data assessment during the intercontinental transport of smoke. We further assess the agreement among the four datasets by inspecting their mutual correlation over three geographical domains (hereafter referred as West, Central, and East) that were defined according to longitude as shown in Fig. 4a. The period of study is extended here from September 3rd to September 26th, 2020. Satellite retrievals are first compared to CAMS analyses, in terms of CO total columns in Fig. 4b  and AOD in Fig. 4c. Regarding CO, IASI and CAMS agree well with each other with an average correlation scores of 0.
90 and 0. 83 for the Central and East domains, respectively. A lower correlation (average of 0. 59) is however observed in the West domain, especially during the first half of the period of study, which could be associated with the generally higher biases in the two data sets in the occurrence of intense smoke emission. While errors in the CAMS analyses may be related to uncertainties in the estimates, injection height, and diurnal cycle of fire emissions considered in the model, satellite observations may be biased due to the erroneous cloud masking of thick smoke plumes. Regarding aerosol load, the GEO-ring AOD product shows a fairly good agreement with CAMS analyses (average correlation between 0. 53 and 0. 79) despite being two unconnected datasets (i. e. , GEO-ring AOD is not assimilated in CAMS) and the shorter lifetime of AOD compared to CO.
The maximum correlation value of 0. 88 is observed for the Central domain around September 19th, when vast smoke plumes were traveling across the Atlantic (see Fig. 3 ), which validates the way the transport of plumes is implemented in the model. CO and AOD are compared in Fig. 4d  for CAMS and in Fig. 4e  for the satellite products. The three domains are inspected sequentially. First, the CAMS analyses in the West domain show a high correlation (average of 0. 80) between CO and AOD, peaking during the dates of maximum aerosol loading. However, the satellite data show a generally lower correlation (average of 0. 53) for the West domain, which may come from specific limitations of the retrieval processes (e. g. , cloud filtering) or the different overpass times. Second, high correlation between CO and AOD can be observed in the Central domain, especially from September 14th to 21st for both CAMS (average of 0.
77) and the satellite products (average of 0. 75). This comes from the transport of huge plumes across the Atlantic Ocean as seen in Fig. 3. Finally, results for the East domain show a generally lower correlation (average of 0. 39 and 0. 21 for CAMS and satellite data, respectively), potentially coming from the contribution to AOD of other aerosol species in addition to smoke. However, several correlation peaks between AOD and CO are observed on September 11th, 15th, and 22nd, which we attribute to the arrival of biomass burning smoke plumes in Europe. These findings are reported both from satellite and model data, underlining again the consistency among data sets.
5 , were detected on September 14th, 17th, and 18th (not shown here). We analyze air parcel back trajectories generated from the HYSPLIT model for the four plumes observed on September 11th, 14th, 17th, and 18th. To define back trajectories starting points, a time-height grid was set over lidar profiles obtained at Villeneuve d' Ascq ground site. Detected plumes were gridded with a temporal and altitude resolution of 1 h and 500 m, respectively. Each grid cell was then used to initialize calculation of a given back trajectory. In total, 303 back trajectories were established but only those tracing back to Western US are considered here (corresponding to 27% of the dataset). Figure  6a  shows the back trajectories with an indication along the paths of the elapsed time to their detection by the ground lidar station (i.
Evolution of AOD and CO along smoke trajectories. Figure 7a, b show the mean temporal evolution of AOD and CO from the satellite observations and CAMS analyses along the back trajectories. To limit the impact of the potential uncertainties from HYSPLIT, we considered the means of several trajectories to infer the results. To avoid mixing signals from very fast or slow transport, we only take into account back trajectories that lasted 8 days from the time the air parcels leave the Western US to their detection over Europe. The absolute values are different for faster or slower transports, but the general trends are similar and the number of data points is the highest considering 8 days allowing robust statistics (not shown). Here, we use the daily AOD and CO values from GEO-ring and IASI while CAMS atmospheric analyses are used at their original temporal resolution of 6 h (see "Methods" section for details on the data).
From Fig. 7a , we observe that the AOD from CAMS differs from the satellite observations by about 0. 14 but both show a consistent decrease in AOD with time. Indeed, the mean AOD ranges from 0. 5 and 0. 4 for the analyses and the observations, respectively, when the air parcels leave Western US to 0. 2 for both datasets when the air parcels are observed over Europe. From Fig. 7b , we observe that CO from CAMS differs from the satellite observations by about 4. 42 × 10 17 molecules/cm 2. Similar to AOD, CO decreases over time along transport, from 3. 02 × 10 18 to 2. 26 × 10 18 molecules/cm 2 for IASI and from 2. 30 × 10 18 to 1. 96 × 10 18 molecules/cm 2 for CAMS. Figure  7c  shows the ratio of the logarithms of AOD and CO along the back trajectories from Western US to Villeneuve d' Ascq in Northern France.
The logarithm is used here to make these two quantities more comparable. While the ratio is overestimated by CAMS analyses by 0. 010 compared to the satellite observations, both datasets show a ratio that slightly decreases over time. For CAMS (satellite observations), the mean ratio decreases from -0. 016 (-0. 023) to -0. 035 (-0. 042). We finally analyze 200 forward trajectories that were computed for air parcels in September 2020 associated with a CAMS AOD greater than 2. 3 over the Western US, corresponding to the upper quartile of AOD for that region. This threshold is applied to ensure that the air parcels are initially associated with a significant biomass burning plume. Figure  7d-f  show the temporal evolution of AOD, CO, and the ratio of the logarithms of AOD and CO for air parcels along forward trajectories that originated from Western US and were transported to Europe.
The low values of the mean AOD when air parcels arrive over Europe suggest that aerosols have been removed or dispersed during the transport for most of the trajectories, unlike Fig. 7a-c. Figure  7d  shows that AOD decreases by an absolute value of 3. 1 over time for CAMS and by 0. 5 for the satellite data. Figure  7e  shows that CO decreases by 13. 6 × 10 18 molecules/cm 2 for CAMS and by 3. 0 × 10 18 molecules/cm 2 for the satellites. Finally, Fig. 7f  shows that the ratio of the logarithms of AOD and CO decreases by 0. 047 for CAMS and by 0. 043 for the satellites. The trends from Fig. 7d -f are the same as for Fig. 7a -c but the differences in the AOD, CO, and the ratio of both over time are larger. The similarities in the temporal variation of CO and AOD and the ratio of the two quantities show that the analysis from CAMS can accurately reproduce processes of dispersion of gases and aerosols with respect to the satellite data used in this work.
Conclusions The increasing number and intensity of wildfires worldwide induced by global warming have devastating ecosystem, social, and economic impacts. Biomass burning is nowadays identified as the predominant source of air pollution in many regions of the world including large parts of Canada, Siberia, Africa, South America, and Australia  28. Emitted smoke plumes can be transported across long distances and affect the local air quality in downwind regions thousands of kilometers away, which makes it essential to better understand their transport. In the summer 2020, wildfires burning in Western US carried harmful pollutants across the country and reached Europe as demonstrated for example by local lidar station measurements. This study illustrates the usefulness of satellite and model data to monitor the rapid intercontinental transport of smoke plumes through the joint use of total AOD and CO total columns.
CAMS data are open access and freely available from the Atmosphere Data Store at https:// ads. atmos phere. coper nicus. eu/ cdsapp# !/ datas et/ cams-global-atmos pheric-compo sition-forec asts? tab= overv iew. CO from CAMS was found to be in good agreement when compared with continental measurements  49  and shipborne measurement  50. The AOD from CAMS has also been validated on several events. For example, Akritidis et al. 51  found a correlation coefficient of 0. 77 when comparing CAMS AOD with MODIS, CALIOP, and ground-based observations. CAMS analyses of AOD were recalculated at 640 nm to match the wavelength of the GEO-ring satellite retrievals using the Ångström exponent between 550 and 670 nm calculated from CAMS. In this study, analyses (at T + 0) were preferred over forecasts (at T + x) to prove the usefulness of assimilating additional satellite observations (at T + 0 as well) in the CAMS operational system.
Methodology for data analysis. All above-mentioned data were made available from the 3rd to the 26th of September 2020. Data sets of IASI CO, GEO-ring AOD, and CAMS analyses of AOD and CO were collocated in time and space. This was done by averaging the IASI CO data over a 0. 5° × 0. 5° grid on a daily basis. CAMS AOD and CO were linearly interpolated a 0. 5° × 0. 5° grid. Satellite AOD and cloud fraction maps were down sampled by block averaging to match the resolution of 0. 5°. To account for the fact that GEO-ring AOD is derived from daytime measurements only, a weighting factor of 2 and 1 was applied to IASI data corresponding to 9:30 and 21:30, respectively, when doing the daily average. Analogously, 6-hourly analyses from CAMS were interpolated to provide a single daily value centered at local noon.
Acknowledgements IASI is a joint mission of  EUMETSAT  and the  French "Centre National d'Etudes Spatiales " ( CNES). The IASI Level 1C data are distributed in near real time by  EUMETSAT  through the EUMETCast system distribution. The GEO-ring product has been produced by  AERIS/ICARE Data and Services Centre  based on geostationary data made available by  EUMETSAT ,  NOAA , and  JMA. The  SATMOS/CMS center  is acknowledged for providing the satellite radiances and cloud masks for the five geostationary satellites. The authors also gratefully acknowledge the  NOAA Air Resources Laboratory  for the provision of the HYSPLIT transport and dispersion model used in this publication. The data have been generated using Copernicus Atmosphere Monitoring Service Information 2022 and computational support provided by the  ICARE/AERIS Data and Services Center.
Data availability The IASI-CO used in this study are retrieved from the Aeris data infrastructure (https:// iasi. aeris-data. fr/ CO/). We used NCEP-NCAR Reanalysis 1 data provided by the NOAA PSL from their website at https:// psl. noaa. gov. The CAMS data used are retrieved from the Copernicus Atmosphere Data Store (https:// ads. atmos phere. coper nicus. eu/). The GEO-ring AOD data used in this investigation can be accessed through the following link upon free registration on the ICARE Data and Service Center: https:// www. icare. univ-lille. fr/ data-access/ data-archi ve-acces s/? dir= PUBLI CATION_ DATA/ 10. 1029/ 2021J D0349 06/.
uncinata and Betula pendula) and shrub (Calluna vulgaris, R. ferrugineum and J. communis) encroachment of open grasslands occur rapidly across the valley and are driven by changing pastoral practices and under-grazing  (Galop et al. , 2011). At 1800 m asl, average annual temperature over the period 1981-2010 was 5. 3 • C (June-August mean temperature was 12. 3 • C) and mean annual precipitation was 1245 mm (May is the moistest month with 133 mm and July the driest, with 73 mm precipitation) according to the SAFRAN-Crocus dataset  (Durand et al. , 2009). Precipitation falls as snow generally as of November-December  (Marti et al. , 2016)  and snow melt-out timing occurs around the second week of May (See Fig. S1 ).
The images were merged automatically with the Zeiss Zen 2011 software to cover entire cross-sections. Because ring widths are roughly three times larger in J. communis than in R. ferrugineum, the preparation of microsections is not necessary and we simply air-dried sections and sanded them with gradually finer sandpapers according to standard procedures used in dendrochronology  (Bräker, 2002). Similarly, for P. uncinata, increment cores were air-dried and sanded. Subsequently, sections and increment cores were scanned with a resolution of 1200 dpi using a high-resolution Epson 11000XL scanner.
Ring-width chronologies For all species, growth rings were measured from digital images with the CooRecorder 7. 6 software (CYBIS Elektonik & Data AB). For shrubs, ring widths were measured at three radii to detect partial rings. Crossdating was done with Cdendro (CYBIS Elektonik & Data AB) following the three-step procedure described in  Francon et al. (2017)  for shrubs and the traditional dendrochronological protocol for trees  (Bräker, 2002). Visual cross-dating was statistically validated using COFECHA  (Holmes, 1994). Only carefully dated sections were retained for further analysis. Individual tree and shrub-ring series were standardized using the R (R Core Team, 2016) dplR package  (Bunn, 2008)  to eliminate non-climatic trends (e. g. , age related growth trends, effects of natural or human-induced disturbances) and to maximize climatic information.
We used a cubic smoothing spline with a 50% frequency response at 25 years  (Cook and Peters, 1981)  to detrend each series. Besides the standardized series, we also calculated residual (or pre-whitened) time series where auto-correlation was removed. After standardization, ring-width indices (RWI) were averaged for each individual with a bi-weighted robust mean so as to reduce the influence of outliers  (Cook and Peters, 1981). Descriptive statistics including standard deviation (SD), mean sensitivity (MS), and autocorrelation (AC) were computed using the R (R Core Team, 2016) dplR package  (Bunn, 2008)  for the three chronologies. In addition, the subsample signal strength (SSS), expressed population signal (EPS;  Wigley et al. , 1984)  and mean inter-series correlation (rbar) were computed using dplR to evaluate their robustness.
Trend analysis We used the Age-Class Isolation (ACI) method to derive growth trends from tree and shrub ring-width series  (Peters et al. 2015). This method compares growth rates within fixed age classes, thereby removing age trends from the raw growth rates. In our case, we used 20-year age classes. For each individual, we calculated the average raw ring width in each age class, centered on the 10-, 20-, 30-etc. cambial age. We discarded age classes as soon as the number of individuals included in the age class dropped below 10. To detect significant positive or negative growth trends by age class, we calculated linear regressions and Pearson's correlation coefficients between time and mean ring width (significance level p < 0. 05, see  Cook and Peters, 1981). We also used linear mixed models (LMMs) available from the R (R Core Team, 2016) nlme package  (Pinheiro and Bates, 2000)  to test the significance of trends among age class.