dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Processing and Statistics Climatic series of precipitation and temperature were decomposed into seasonal and trend components to highlight long-term climatic patterns. This was achieved by computing the "STL" function using the default configuration in the R programming language, according to the Seasonal Trend decomposition procedure from Loess  (Cleveland et al. , 1990). Since the seasonal component of climatic variables was stationary, it was not removed from the climatic dataset before the computation of Pearson correlations coefficients. The temporal evolution of the climate-growth relationship was analyzed according to DeSoto et al. (  2014  A temporal framework of 24 consecutive months was considered as the x-axis of the dendrochronology/climate figures to identify the relationships between wood properties and key climatic factors from the previous to the current year of treering xylogenesis.
Though correlations were calculated for each climatic variable initially considered, here we only present graphs of the main common key climatic factors involved in P. pinaster's wood ring growth and density. We computed monthly SPEI with time-scales spanning from 1 to 24 months, which were related with maritime pine radial growth and density. We then calculated the correlations between SPEI and the corresponding dendrochronological time-series over the period 1965-2010 and for each time-scale (1 to 24 months). Therefore positive values of SPEI correspond to wet conditions while negative values indicate drought. The impact of climatic variables on dendrochronological traits was ranked by scoring the total number of significant correlations (P < 0. 05, |r| > 0. 515) divided by the total number of correlations obtained in dendrochronology/climate figures, The results were expressed as the percentage of significant correlations obtained over a total of 912 (24 months * 38 years = 912) calculated correlations in each dataset.
Recent Climate Evolution in Central Portugal The recent evolution of climate variables in southern Portugal is in agreement with the recent Fifth Assessment Report of the IPCC (2013). The latter shows similar temperature increase trends at the global scale since 1958, with a decline until 1978 followed by an increasing trend since. This led to the disappearing of cold days in our sampling location. This trend was confirmed by the IPCC report describing the decline of cold days since 1950 and attributed to an anthropogenic cause with a very high likelihood. While a slight annual precipitation decline has often been reported in mainland Portugal between 1951 and 2010  (Sousa et al. , 2011; Santo et al. , 2014b) , our results, using the ECAD climatic dataset, do not present a clear evidence of such trend.
pinaster's productivity and commercial benefits in Portugal in the near future. adapted her Matlab algorithm for the computation of the SPEI multi-scalar drought index. FV shared and adapted the methodological approach used in a previous work. RT conceived the moving correlations methodology and supervised the team effort. CG provided complementary fundings for the work to be completed. She supervised the use of SPEI index in dendrochronological correlations and their related interpretation. CM performed a thorough detached analysis of the data interpretation, spotting unclear contents. Each of the co-authors performed a thorough revision of the manuscript, provided useful advices on the intellectual content and improved the English language.
ACKNOWLEDGMENTS The data was host-processed in the computational facility in the framework of the FCT SHARE project (RECI/GEO-MET/0380/2012). The authors also wish to acknowledge the FCT projects QSECA (PTDC/AAG-GLO/4155/2012), PINUS RAIN (PTDC/AGR-CFL/099614/2008) and SOLAR (PTDC/GEOMET/7078/2014), which shared preliminary data without which this work would not have been possible. The authors additionally thank FCT for Ana Russo's grant (SFRH/BPD/99757/2014) and Eng. Rui Alves for all logistic support in the field of Companhia das Lezírias S. A.
5) Space-borne aerosol depolarization ratio from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Lidar aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) platform along orbits close to the Dome C station. The time evolution of the atmosphere has also been evaluated by considering the outputs from the meso-scale AROME and the global-scale ARPEGE meteorological models. Two distinct periods are highlighted by all the datasets: the warm and wet periods (24-26 March 2011 and 4 March 2013) and the cold and dry periods (5 April 2011 and 5 March 2013). Combining radiation and active measurements of aerosols with nebulosity calculations, a thick cloud is detected during the warm and wet periods with high depolarization ratios (greater than 30%) from the surface to 5-7 km altitude associated with precipitation of ice particles and the presence of a supercooled liquid water (depolarization of about 10%) cloud.
Chem. Phys. Discuss. , doi:10. 5194/acp-2016 -815, 2016  Manuscript under review for journal Atmos. Chem. Phys. Published: 15 September 2016 c Author(s) 2016. CC-BY 3. 0 License. reach up to 0. 85. And finally 3) "ARPEGE micro-physics", as in "Operational with ice tuning" but includes the physics of ARPEGE and a state-of-the-art scheme to represent the snow pattern taking into account the roughness length. And finally, we will use the globalscale ARPEGE meteorological analyses in 2013 selected over the Dome C station. The manuscript is structured as follow. Section 2 presents all the data sets used in our study. Section 3 investigates the episode 1, namely the thick cloud and the diamond dust episodes during the 1-month period in 2011 considering the temporal evolution of the different parameters above and in the surroundings of the Dome C station.
The HAMSTRAD Radiometer The HAMSTRAD (H 2 O Antarctica Microwave Stratospheric and Tropospheric Radiometers) instrument is a state-of-the-art microwave radiometer to probe the troposphere in very cold and very dry environments in order to retrieve temperature and absolute humidity vertical profiles, and IWV. Temperature profiles are obtained from the 51-59 GHz spectral range, centered on the oxygen line. Absolute humidity profiles are retrieved from the  GHz spectral range, centred on the water vapour line. IWV is calculated from the water vapour profile integrated along the vertical. Integration time is 7 min. The radiometer is presented in  Ricaud et al. (2010). The instrument was sent to Dome C in January 2009. It has been running automatically since January 2010. Science and validation studies using HAMSTRAD data are detailed in  Ricaud et al.
The ARPEGE Model The ARPEGE model is the global model used for the numerical weather prediction (NWP) at Météo-France. In the present study, the operational configuration has been used with a stretched grid at high resolution over France (10 km) and a coarser grid over Australia of 60 km. At the South Pole, the horizontal resolution is about 50 km and the vertical grid has 70 levels with a first level at around 16 m above the ground. The assimilation tool is based on an incremental 4-dimensional variational (4D-Var) method. The physical package used in the ARPEGE model is at the state-of-the-art, with a Turbulent Kinetic Energy scheme associated with a mass flux scheme for the boundary layer  (Bazile et al. , 2011). The clouds and the micro-physics use 4 prognostic variables such as cloud water, cloud ice, rain and snow  (Lopez, 2002; Bouteloup et al.
Integrated Water Vapour If we now consider the evolution of IWV over the same period (Fig. 3 ), we note a slight positive change from 15 to 22 March 2011 (from 0. 3 to 0. 6 kg m -2 ), followed by an abrupt increase on 25 March of 1. 0 kg m -2 in less than 24 hours. After a 2-day plateau at 1. 4 kg m -2 in HAMSTRAD and radiosonde data, IWV decreases slowly until the end of the period. Neverthless, on 5 April, the atmosphere is the driest of the period reaching 0. 1 kg m -2 within few hours. All the data sets (HAMSTRAD, radiosonde and AROME) behave consistently during this period. HAMSTRAD and the radiosonde data do not exhibit any bias, whilst AROME outputs tend to show a much wetter atmosphere compared to HAMSTRAD and the radiosondes of about 0. 2 kg m -2 , except during the warm and wet period (25-26 March) when the bias is even greater reaching 0.
4-0. 5 kg m -2. Consistently with the time evolution of IWV, the evolution of absolute humidity in the planetary boundary layer (Fig. 4 ) and in the free troposphere (Fig. 5 ) shows an abrupt increase on 25 March and a net decrease on 5 April, but with lots of differences within all the datasets and the altitude layers considered. Prior to the warm and wet episode of 25-26 March, HAMSTRAD measurements from 4 to 100 m (Fig. 5 ) are systematically much wetter than both the radiosondes and the AROME outputs by 0. 1-0. 2 g m -3 and 0. 4-0. 5 g m -3 , respectively. It is a very well documented bias already presented in several works (e. g. Ricaud et al. , 2014a and 2015)  that HAMSTRAD measures a wetter atmosphere below 2 km and drier above, than any other data sets (radiosonde, in situ, space-borne, and meteorological analyses).
Episode 2 on 4-5 March 2013 The second episode is much shorter than the first one since it lasts only two days from 4 to 5 March 2013. It relies on the same datasets as presented in section 3 except that the analyses are from the meteorological operational model ARPEGE that routinely delivers since December 2011 every 6 hours (00:00, 06:00, 12:00, and 18:00 UTC) meteorological fields at the vicinity of the Dome C station.
Temperature The temperature anomaly over the 2-day period is represented in the Figure  10  as measured by HAMSTRAD and as calculated by ARPEGE. From the surface to about 2 km altitude, they both show a warm period on 4 March followed by a cold period on 5 March, with a transition propagating in the HAMSTRAD data up to 4 km altitude, probably due to the vertical resolution of the microwave radiometer measurements. Above this altitude, a cold period is followed by a warm period in the two datasets. Although the HAMSTRAD data are noisier than the ARPEGE data, the maxima and minima are consistently observed and.
Water Vapour The water vapour anomaly over the 2-day period is represented in the Figure  11  as measured by HAMSTRAD and as calculated by ARPEGE. From the surface to about 4-5 km altitude, they both show a wet period on 4 March followed by a dry period on 5 March. As for temperature, the HAMSTRAD H 2 O data are noisier than the ARPEGE data, but the maxima and minima are consistently observed and calculated in the lowermost troposphere around 12:00-18:00 UTC on 4 March and around 18:00-22:00 UTC on 5 March, respectively.
physics of ARPEGE and a state-of-the-art scheme to represent the snow pattern taking into account the roughness length. The ARPEGE global-scale meteorological model analyses gave the state of the atmosphere and relevant prognostics (ice precipitation, temperature and water vapour budget) on 4 and 5 March 2013. Two distinct periods are highlighted by all the datasets: the warm and wet periods (24-26 March 2011 and 4 March 2013) and the cold and dry periods (5 April 2011 and 5 March 2013). Although the time evolution of temperature in the planetary boundary layer and in the free troposphere is consistent within all the data sets, AROME in 2011 tends to model a warmer atmosphere during these two specific events at 4 m. The time evolution of absolute humidity is also consistent within all the data sets with some known wet bias in HAMSTRAD compared to radiosondes in the planetary boundary layer, and with some systematic wet bias Atmos.
Acknowledgment The present research project  HAMSTRAD  programme (910) has been performed at the Dome C station and was supported by the  French Polar Intitute, Institut polaire français Paul-Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016-815,2016 Manuscript under review for journal Atmos. Chem. Phys. Published: 15 September 2016 c Author(s) 2016. CC-BY 3. 0 License. Emile Victor (IPEV), the  Institut National des Sciences de l'Univers (INSU) / Centre National de la Recherche Scientifique (CNRS) ,  Météo-France  and the  Centre National d'Etudes Spatiales (CNES). The permanently manned Concordia station is jointly operated by  IPEV  and the  Italian Programma Nazionale Ricerche in Antartide (PNRA). We would like to thank all the winterover personels who worked at Dome C on the different projects: HAMSTRAD,  Routine Meteorological Observations  (RMO), aerosol Lidar and BSRN.
, lll,  D20305 , doi: 10. 1029/2005jD006976 , 2006. Tomasi, C. , Petkov, B. , Benedetti, E. , Valenziano, L. and Vitale, V. : Analysis of a 4 year radiosonde dataset at Dome C for characterizing temperature and moisture conditions of the Antarctic atmosphere,  J. Geophys. Res. ,  116 ,  D15304 , doi: 10. 1029/2011JD015803 ,2011. Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016-815,2016 Manuscript under review for journal Atmos. Chem. Phys. Published: 15 September 2016 c Author(s)  2016. CC-BY 3. 0 License.
As a moderate lifetime (several weeks to several months) compound emitted by incomplete combustion processes, CO represents a powerful pollution tracer able to provide information on long-range transport at the hemispheric scale. Characterising CO in troposphere may thus help to investigate the variability and trend affecting O 3. Section 2 presents the MOZAIC-IAGOS data set and describes the treatment applied to vertical profile data. The vertical distribution, the seasonal variations, the trends of O 3 and CO and the changes affecting the O 3 seasonal cycle are analysed in Sect. 3. The main results and conclusions are summarised in Sect. 4.
MOZAIC-IAGOS data set In the framework of the MOZAIC-IAGOS programme (http: //www. iagos. org), O 3 and CO measurements (among other parameters) have been carried out by commercial aircraft along various flight routes in the world (most of them from or to European airports) since 1994  (O 3  ) and 2002 (CO), respectively  (Marenco et al. , 1998; Petzold et al. , 2015). Until October 2014 (date of the last MOZAIC aircraft flight), both O 3 and CO have been measured using the same instruments in all aircraft, thus ensuring the data set consistency during most of the period. Ozone measurements were carried out using a dual-beam UV-absorption monitor (time resolution of 4 s) with an accuracy/precision estimated at about ±2 ppbv/±2 %  (Thouret et al. , 1998). Carbon monoxide was measured by an improved infrared filter correlation instrument (time resolution of 30 s) with an accuracy/precision estimated at ±5 ppbv/±5 %  (Nédélec et al.
, 2003). As MOZAIC aircraft have been retired from service, the European partners involved have been preparing the technical successor for the framework of the European Commis-sion (EC)-funded IAGOS programme  (Petzold et al. , 2015)  for years. A new concept of aircraft system and instruments has been developed and installed on Airbus long-range aircraft (A340 or A330), starting from July 2011. Seven aircraft are actually in operation with IAGOS systems. The new IA-GOS instrumentation for O 3 and CO is extensively described in  Nédélec et al. (2015). The O 3 and CO measurements are based on the same technology used for MOZAIC, with the same estimated accuracy and the same data quality control. Nearly 4 years overlap of "historical" MOZAIC and "new" IAGOS instrumentation allowed us to prove that the new IA-GOS systems provide the same data quality  (Nédélec et al.
, 2015) , which is especially important for trends analysis. Note also that several studies have investigated the consistency of the MOZAIC-IAGOS O 3 data set with other types of in situ data (e. g. surface stations, ozonesonde)  (Logan et al. , 2012; Staufer et al. , 2013 Staufer et al. , , 2014;; Tanimoto et al. , 2015; Zbinden et al. , 2013). Focusing on O 3 changes in Europe,  Logan et al. (2012)  showed a reasonable agreement between aircraft and alpine sites but noticed the absence of O 3 increase in 1994-1998 in the sonde data set (contrary to the two other types of data). Focusing on the pure tropospheric profiles,  Zbinden et al. (2013)  found a mean difference between MOZAIC-IAGOS and sondes of -2 % in Germany, -8 % in eastern United Sates and +1 % over Japan. Tanimoto et al. (2015)  obtained similar results, with differences between aircraft and sondes data around ±2 % throughout the whole troposphere in Belgium, Germany and Japan and ±5 % at Hong Kong.
The MOZAIC-IAGOS data at Munich were found to compare reasonably well with the surface observations at Hohenpeissenberg (slope of 0. 97, correlation of 0. 77). The present study will focus on Frankfurt Airport where the longest (from 1994 to 2012) and densest (18 598 flights) record is available. In order to fill a large data gap in 2005, this data set is combined to the data from the airport  Munich (2734 Munich ( flights, mostly between 2002 Munich ( and 2005)) , approximately 300 km south-east of Frankfurt, as done in several previous studies  (Logan et al. , 2012; Zbinden et al. , 2006 Zbinden et al. , , 2013)). Note also that no measurements are available during part of 2010 due to instrumental problems. An illustration of the data set density by month and year is shown in Figs. S1 and S2 of the Supplement.
The pressure at the DT (p PV=2 ) plus 15 hPa defined the top of the UT applied here. Tropospheric vertical profiles are selected according to several criteria. i. Distance from the airport: take-offs and landings do not exactly correspond to vertical profiles as aircraft travel some distance before reaching their cruise altitude. In order to limit the uncertainties induced by a potential horizontal heterogeneity, a maximum distance of 400 km from the airport is fixed for the vertical profile data selection. In practice, such a distance is sufficient for sampling the entire vertical profile in most flights and remains reasonable considering the fact that the O 3 vertical variability is expected to be higher than the horizontal one. A sensitivity test with a distance threshold of 800 km leads to differences of mean O 3 mixing ratio in the UT below 3 % at the seasonal and annual scale.
2. 2 and 3. 1. 2, the data below 1 km were skipped in order to reduce the impact of the local emissions from both the neighbouring agglomeration and the other aircraft -on tarmac and/or during the take-off/landing phases (in case the MOZAIC-IAGOS aircraft closely follows other aircraft). Indeed, many studies have shown that airport activities can impact the air quality at the local scale (e. g. Hu et al. , 2009; Pison and Menut, 2004; Yu et al. , 2004). It is worth mentioning that in a standard landing take-off cycle -comprising the approach, the taxi (plane on the tarmac), the take-off (acceleration phase on the tarmac) and the climb up to a standard atmospheric boundary layer of 915 m  (Kesgin, 2006)  -most of the CO emissions (85-95 %) occur on the tarmac during the taxi phase  (Kurniawan and Khardi, 2011).
However, even above 1 km in the LT, one cannot exclude any influence of these emissions or the emissions of the neighbouring agglomeration. To assess the spatial representativeness of the MOZAIC-IAGOS data in the LT and MT more precisely, a comparison is made with the CO mixing ratios measured at the World Meteorology Organization (WMO) Global Atmosphere Watch (GAW) surface stations (see Sect. S1 of the Supplement for details). Only the stations located between 45 and 55 • N (i. e. ±5 • from the latitude of Frankfurt) and with at least 80 % data capture for the period 2002-2012 (based on the monthly time series) are retained, which gives a set of 10 stations. The annual mean CO mixing ratio measured by MOZAIC-IAGOS aircraft in the LT (143 ppb) is in the lower range of the zonal average 155 ± 28 ppb observed among the GAW surface stations.
When considering only the stations above 1000 m (i. e. three stations, all located in Europe), the zonal average is reduced to 145 ± 19 ppb, which is very close to the mean CO observed in the LT. In the MT, the annual mean CO mixing ratio is 115 ppb, thus lower than the CO mixing ratios at the ground regardless of the station, but the difference for the highest mountain station Jungfraujoch (3580 m elevation) is very small (-7 %). Additionally, the annual MOZAIC-IAGOS CO data in both the LT and MT are strongly correlated with the CO observed at the ground (R between 0. 61 and 0. 94 at all stations except one at which R = 0. 41). Therefore, the comparison between the MOZAIC-IAGOS CO data set at Frankfurt/Munich and the GAW data set at the same latitude shows good consistency, both in terms of mean annual CO mixing ratios and interannual variations.
Discussion The previous analysis confirms that the ozone seasonal pattern in central/western Europe has been changing, at least since the mid-1990s, moving toward a lower amplitude and an earlier O 3 maximum. It is worth noting that the MOZAIC-IAGOS observations above Frankfurt/Munich represent the densest data set of O 3 vertical profiles worldwide. The vertical profile observations provide unique data, allowing us to show that this seasonal change of the phase in the O 3 maximum to earlier days in the year extends above the surface at Frankfurt/Munich. The magnitude of this shift in maximum is statistically significant through the mid-troposphere. This may highlight that the O 3 seasonal pattern behaves differently over the Northern Hemispheric continents (Europe, North America, Asia). Indeed, the FLEXPART-derived PES clearly shows that the air masses sampled by MOZAIC-IAGOS aircraft in the different tropospheric layers originate from different regions (see Fig.
Summary and conclusions Extensive databases of O 3 and CO vertical profiles above worldwide airports have been available from the MOZAIC-IAGOS programme since 1994 and 2002, respectively. In this study, we investigate the climatology, variations and trends of O 3 and CO mixing ratios above the German airports Frankfurt and Munich, which combined represent the densest and longest MOZAIC-IAGOS data set worldwide. We focus on the troposphere, with each vertical profile subdivided in three tropospheric layers: the lower, middle and upper troposphere (LT, MT and UT). The UT is defined relative to the dynamical tropopause, based on the potential vorticity extracted from ECMWF meteorological data. The main results are given below (all trends are given with uncertainties at a 95 % confidence level).
Data availability No new measurements were made for this review article. All ozone and carbon monoxide data sets mentioned in the text were obtained from the existing MOZAIC-IAGOS database, freely available on http://www. iagos. fr (IAGOS, 2016)  or via the AERIS web site http://www. aeris-data. fr (AERIS, 2016). The Supplement related to this article is available online at doi:10. 5194/acp-16-15147-2016-supplement.
Acknowledgements. The authors acknowledge the strong support of the  European Commission, Airbus , and the airlines (Lufthansa,  Air France ,  Austrian ,  Air Namibia ,  Cathay Pacific , Iberia and China Airlines so far) which have carried the MOZAIC or IAGOS equipment and undertaken maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IA-GOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. The MOZAIC-IAGOS database is supported by  AERIS  ( CNES  and  INSU-CNRS ). We are very thankful to the referees for their numerous recommendations that greatly helped us to improve this paper. Edited by: T. Röckmann Reviewed by: two anonymous referees.