dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, degassing was calculated with the DIC data from the three sampled groundwaters. DOC export (mmol m -2 day -1 ) DIC export (mmol m -2 day -1 ) Degassing (mmol m a Mean carbon export weighted by surface, assuming that the Bilos piezometer is representative of the wet Landes, that piezometer 2 is representative of the dry Landes and that piezometer 3 is representative of the mesophyllous Landes using the relative surface area of each type of Landes. b Carbon exports from first-to second-order streams calculated from the drainage of first-order streams (mm day -1 ) and the mean concentrations of DOC and DIC in first-order streams (mmol m -3 ). to DIC, the DOC concentrations in first-order streams were of the same order of magnitude as in piezometer 2 (dry Landes) and significantly lower than in the two other piezometers (wet to mesophyllous Landes), in particular during periods of high flow (Table  4 ; Fig.
Water mass balance and the role of groundwater in the hydrological carbon export Our hydrological dataset monitored continuously during 18 months allows us to separate the water budget in four terms on the monthly timescale (Table  1 ; Figs. 2c-3 ). The water budget at the Bilos plot was primarily impacted by precipitation and evapotranspiration (Table  1 ; Fig. 2c ). The transfer of precipitation to rivers involves temporary water storage in groundwater  (Alley et al. , 2002; Oki and Kanae, 2006). However, the lag time between precipitation and groundwater storage was short at our study site, as attested to by the significant correlation between these two parameters (Table  2 ). Thus, when precipitation is high (during early winter and high-flow periods), water infiltration in the sandy podzols is faster than water capture by vegetation.
Acknowledgements. This research is part of the  CNP-Leyre  project funded by the  Cluster of Excellence COTE  at the  Université de Bordeaux  ( ANR-10-LABX-45 ). We thank  Luiz Carlos Cotovicz Junior ,  Katixa Lajaunie-Salla ,  Baptiste Voltz ,  Gwenaëlle Chaillou  and  Damien Buquet  ( EPOC Bordeaux ) for their assistance in the field. We thank  Pierre Anshutz  ( EPOC, Bordeaux ),  Alain Mollier  and  Christian Morel  ( ISPA INRA ) for their involvement in the  CNP-Leyre  project, and  Céline Charbonnier  for alkalinity titrations in the laboratory. Pierre Trichet  ( ISPA INRA ) provided SOC data at the Bilos site. Edited by: Nobuhito Ohte Reviewed by:  Masanori Katsuyama  and one anonymous referee.
Data availability. Bilos site (FR-BIL) is a station as part of the ICOS project (https://www. icos-ri. eu/). Eddy covariance, meteorological and water table depth data are measured continuously at the Bilos pine plot. These data are available upon request to Denis Loustau (denis. loustau@inra. fr). River discharge data are measured continuously by the French water survey agency (DIREN) and are available at http://www. hydro. eaufrance. fr. Carbon raw data are stored on data servers and repositories and available upon request to the corresponding author. The Supplement related to this article is available online at https://doi. org/10. 5194/bg-15-669-2018-supplement.
10 (  1983)  and  Kessler (1969). The snow-ice part of MAR is derived from the snowpack model Crocus  (Brun et al. , 1992). This of at least 20 m over the permanent ice area) and 7 levels for the soil in the tundra area. The snowpack initialization is described in  Fettweis et al. (2005). MAR has often been validated against in situ observations, e. g. , in  Fettweis (2007) ;  Fettweis et al. (2013 Fettweis et al. ( , 2017)). Lateral boundary conditions can be provided either by reanalysis datasets (such as ERA-interim or NCEP) to reconstruct the recent GrIS climate (1900-2015)  (Fettweis et al. , 2017)  or by GCMs (e. g. ,  Fettweis et al. , 2013). In this study, the initial topography of the GrIS as well as the surface types (ocean, tundra and permanent ice) are derived from  Bamber et al.
Experiment terminology We force the PDD model with monthly temperature and precipitation fields from NorESM and MAR respectively, and refer to the resulting SMBs as NorESM-PDD and MAR-PDD respectively. MAR has a full surface energy balance (SEB) model implemented and its derived SMB is refered to as MAR-SEB. Additionally, we force the intermediate complexity SMB model, BESSI, with daily NorESM and MAR temperature, precipitation and the downward shortwave radiation, and call its output NorESM-BESSI and MAR-BESSI, respectively. An overview of the experimental design is shown in Fig. 1. For lack of observational data with a comprehensive coverage, we use the most complex model, MAR-SEB, as our reference SMB model. model parameters which fullfill this goal we choose the set which showed the best fit of refreeze (total amount and RMS error).
Pre-industrial climate The pre-industrial annual mean NorESM and MAR temperatures are compared with the observations in Fig. 5  (top row). The observations are taken from a collection of shallow ice core records and coastal weather station data compiled by  Faber (2016). precipitation. This overestimation is visible due to the fact that most scatter points are above the gray 1:1 diagonal, indicating a too high model value. However, it is important to note that observations from ice cores represent accumulation (i. e. precipitation minus snow drift, sublimation, and similar processes) rather than precipitation, which can partly explain an overestimation at the ice core locations. The MAR precipitation shows less spread and is closer to the observations than NorESM. The precipitation pattern of NorESM is related to its coarse representation of the topography.
Furthermore, the spatial SMB pattern does not change significantly between 130 and 125 ka in our simulations, i. e. , conclusions drawn for 130 ka are also true for 125 ka. The comparison of different SMB models requires a common ice sheet mask which is always a compromise. Vernon et al. (2013)  show that approximately a third of the intermodel SMB variation between four different regional climate models is due to ice mask variations at low altitude (models forced with 1960-2008 reanalysis data over Greenland). Resolution-dependent ice sheet mask differences between NorESM-and MAR-derived SMBs are important here. Due to the larger NorESM grid cells, the NorESM ice mask extends beyond the common MAR ice mask (not shown), and as a result the NorESM ablation zone is partly cut off when using the common MAR ice mask.
Acknowledgements. The research leading to these results has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement  610055  as part of the ice2ice project. We would like to thank all authors cited in Fig. 3 and 4 for sharing their Eemian Greenland ice sheet results. We would like to thank  Anne-Katrine Faber  for providing the shallow ice core data she compiled during her PhD. Furthermore, we would like to thank  Tobias Zolles  for discussions of the BESSI results. PML's contribution was supported by the  RISES  project of the  Centre for Climate Dynamics at the Bjerknes Centre for Climate Research.
The seasonal cycles generally show a summertime maximum in O 3 and a springtime maximum in CO in the UT, in contrast to the systematic springtime maximum in O 3 and the quasi-absence of a seasonal cycle of CO in the LS. This study highlights some regional variabilities in the UT, notably (i) a west-east difference of O 3 in boreal summer with up to 15 ppb more O 3 over central Russia compared with northeast America, (ii) a systematic west-east gradient of CO from 60 to 140 • E, especially noticeable in spring and summer with about 5 ppb by 10 degrees longitude, (iii) a broad spring/summer maximum of CO over northeast Asia, and (iv) a spring maximum of O 3 over western North America. Thanks to almost 20 years of O 3 and 12 years of CO measurements, the IAGOS database is a unique data set to derive trends in the UTLS at northern midlatitudes.
Trends in both gases are also important for assessing changes in atmospheric radiative forcing. Furthermore, both observed trends and climatologies (i. e. time-averaged data) are used as reference values in order to evaluate the ability of models to reproduce the past atmospheric composition and thus to forecast future changes. Consequently, assessing the mean distribution and the long-term evolution of O 3 and CO is within the focus of several research programs dealing with the understanding of atmospheric composition regarding air quality and climate issues. The present study is based on the measurements of these two trace gases, available in one single data set: the IA-GOS database (http://www. iagos. org). In the framework of the IAGOS program (In-service Aircraft for a Global Observing System;  Petzold et al.
, 2015) , the measurements on board commercial aircraft at cruise levels (9-12 km above sea level) provide accurate sampling with a high frequency in the UTLS, particularly over northern midlatitudes. With a monitoring period reaching 19 years for O 3 and 12 years for CO in this area, the time series based on the IAGOS data set can provide helpful information for shorter aircraft campaigns, allowing the quantification of background values as an interannually variable signal. Also, with a dense geographical coverage spreading over the midlatitudinal zonal band (except the Pacific Ocean in so far as regular measurements only started in 2012), spatial distributions seen by IA-GOS are a source of complementary information for local observations, quantifying the spatial variability at synoptic and intercontinental scales.
Compared to commercial aircraft, research aircraft can fly at higher altitudes. Such a study has taken the most advantage of this, which has made tropopause-referenced tracer profiles and tracer-tracer correlations possible and has highlighted further the transport and mixing processes in a wider vertical range. However, the IAGOS program using commercial aircraft as a multi-species measurement platform enables a better characterization of interregional differences, given its higher spatio-temporal resolution. One of the objectives of the present study is to emphasize the use of IAGOS as a complementary database to the above-mentioned ones, allowing further investigations on composition and trends in O 3 and CO in the UTLS (8-12 km) in the Northern Hemisphere. Concerning carbon monoxide, previous studies using satellite measurements have shown a significant decadal decrease in the CO column at the global scale, with a higher sensitivity in the mid-troposphere.
s. l. show a significant positive trend  (Oltmans et al. , 2013) , indicating that the increase probably occurs throughout the free troposphere. Free-and uppertropospheric O 3 trends derived from various instruments are gathered in the reviews of  Cooper et al. (2014)  and  Monks et al. (2015). Amongst the in situ observations, significant positive trends since 1971 are reported from ozonesonde data above western Europe and Japan and also from aircraft data in the upper troposphere above the northeastern USA, the North Atlantic Ocean, Europe, the Middle East, southern China, and Japan. More precisely, during the 1990-2010 decades, ozonesonde measurements above North America and the North Pacific Ocean showed positive trends. Thouret et al. (2006)  highlighted significant positive trends for O 3 mixing ratios above the eastern United States, the North Atlantic, and Europe from 1994 to 2003 with the IAGOS data set, both in the upper troposphere and in the lower stratosphere.
The IAGOS time series is now 10 years longer, and the present study brings crucial information to the interpretation of the previous trends. Whereas the bulk of these studies shows an increase in O 3 and a decrease in CO in the free troposphere, there is no available study based on in situ measurements only in the UTLS on a decadal timescale, with a dense temporal and geographical sampling at the hemispheric scale. The objective of the present study is to examine climatologies for O 3 and CO derived from the whole set of IAGOS measurements at cruise levels, as well as the trends in these two trace gases at a regional scale in the northern midlatitudes. It is both an update and an extension of  Thouret et al. (2006) , with a broader climatology and longer time series to derive trends and a more complete data set with the addition of CO measurements.
Today, the IAGOS database allows such an analysis using regular and high-resolution measurements in the UTLS, spanning 19 years for O 3 and 12 years for CO. This paper aims at providing the most complete picture of the O 3 and CO distributions at a hemispheric scale based on in situ measurements. We investigate regional differences in terms of seasonal cycles and trends over the northern midlatitudes. In Sect. 2 we describe the IAGOS program, the selected UTLS definition, and the methodology used to analyse the data. In Sect. 3 we present the climatologies, time series and analysis of the subsequent trends.
IAGOS data set The data used in this study are provided by the IAGOS European research infrastructure (www. iagos. org; Petzold et al. , 2015). IAGOS consists in automatic measurements of several trace gases and aerosols on board commercial aircraft, aiming at a better understanding of the chemical composition of the atmosphere at the global scale. IAGOS has partly taken over the MOZAIC program  (Marenco et al. , 1998). The measurements of ozone and water vapour started in August 1994  (Thouret et al. , 1998; Helten et al. , 1998). The measurements of CO started in December 2001  (Nédélec et al. , 2003). Routine IAGOS-Core measurements started in July 2011, with a new concept of aircraft system (e. g. near-real-time data transmission) and instruments (e. g. combined ozone and CO in one single analyser, as detailed in  Nédélec et al.
, 2015). Respectively, ozone and carbon monoxide have been monitored with dual-beam UV and IR absorption photometers. Measurements are affected by an uncertainty range of ±2 % ±2 ppb for ozone  (Thouret et al. , 1998)  and ±5 % ±5 ppb for CO  (Nédélec et al. , 2003) , with a temporal resolution of 4 and 30 s. Although the technology remained the same, the consistency between MOZAIC and IAGOS-Core data sets raised questions, and the overlapping period  (2011) (2012) (2013) (2014)  has been used to give the users the assurance that MOZAIC-IAGOS is a unique data set (referred to as IAGOS database hereafter). Indeed, it is of particular importance to make sure that the long-term evolutions of O 3 and CO distributions would not be biased by this technical change. A deep and thorough comparison between MOZAIC and IAGOS, based on several hundred vertical profiles over Frankfurt, has been summarized in  Nédélec et al.
(2015). The consistency between the two data sets has been demonstrated, and hence, the results in the present study can be considered independent of the transition from MOZAIC to IAGOS-Core. Thus, the recent study by  Petetin et al. (2016) , as well as the Tropospheric Ozone Assessment Report  (Gaudel and Cooper, 2018)  analysis commonly known as TOAR, makes use of the MOZAIC-IAGOS database as a seamless one. The period of the present study spreads from the start of the MOZAIC program until December 2013, since the data from the following years are not fully calibrated yet.
With a thickness as great as 30 hPa, the TPL provides a clear partitioning between the characteristics of the UT and the LS. Thus, the analysis focuses on the composition and trends in these two distinct layers. Note also that a comparison between the dynamical tropopause estimate based on the PV fields and the chemical tropopause estimate based on O 3 vertical profiles considering all IAGOS measurements near Frankfurt (where IAGOS data are the most numerous) have shown errors but a negligible systematic bias  (Petetin et al. , 2016). The PV values have been derived using the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis since 2000, with a 1 • × 1 • resolution, and interpolated along the aircraft trajectories. As for other ancillary data developed for the IAGOS database (www.
The selected size of the cells and thresholds in the amount of data ensure a good representativeness of the time period, without filtering out too high a proportion of data. We perform a subcontinental-scaled analysis in order to take the zonal variability into account. Indeed, averages made in continental-sized regions or in zonal bands are not fitted for IAGOS data because of the high geographical variability in the measurements. In eight regions of interest (see Fig. 1 ), monthly time series are calculated by averaging all corresponding instantaneous observations (not by averaging the 5 • × 5 • binned data). These monthly averages are calculated only when (i) the amount of available data points in a month exceeds 300 and (ii) the first and last measurements are separated by a 7-day period at least.
These mean seasonal cycles in all regions are used to extract deseasonalized monthly anomalies from the original monthly time series. Based on the same population of data points as used for calculating the mean values, other useful metrics like the 5th and 95th percentiles of the O 3 and CO mixing ratios are also calculated, as well as the O 3 /CO ratio. Based on these monthly anomalies, the trends are computed using non-parametric Mann-Kendall analysis combined with a Theil-Sen slope estimate  (Sen, 1968). Calculations are performed with the OpenAir R package dedicated to atmospheric sciences  (Carslaw and Ropkins, 2012). The Theil-Sen slope estimate corresponds to the median of the slopes defined by each pair of points. In this paper, all trend uncertainties are given at a 95 % confidence interval (i.
e. with a statistical significance above 2σ ). The linear regression is based on the hypothesis that every point is independent of its neighbours. As a consequence, a positive (negative) autocorrelation can lead to an overestimation (underestimation) of the regression slope. In the aim of reducing such kinds of bias, the method implemented in the OpenAir package applies a correcting factor to one half of the confidence interval depending on the autocorrelation. More details are available in the OpenAir Manual (https://cran. r-project. org/web/ packages/openair/openair. pdf). The statistics of IAGOS data are summarized in Table  1  for each region and each layer. Northwest America is the least sampled region ( 60 % of available months and 1400 flights in each layer) with a shorter time coverage per month ( 18 days per month): consequently, a mean value computed in this region is less likely to be a representative estimation of the monthly mean volume mixing ratio (VMR).
, 2007) , whereas the other maxima originate from biomass burning. Most of these maxima are consistent between the MLS and IAGOS data sets. One exception may be northern India (25-30 • N cells) where IAGOS does not clearly highlight a summertime maximum. However, available IAGOS data in this region and season are too scarce to draw a strong conclusion, likely because IAGOS aircraft often fly below the UT lower boundary (P 2 pvu + 75 hPa) in the 25-30 • N zonal band that becomes subtropical in summer, with a tropopause typically above the 150 hPa altitude level. O 3 maxima are also shown by MLS above subtropical Brazil and southern subtropical Africa during September-November. Last, the climatological means they derived reached their geographical minima all along the equatorial Pacific Ocean (15 • S-15 • N), which is consistent with the overall minima observed by IAGOS at both sides of the equatorial Pacific Ocean.
g. Madonna et al. , 2014; Nédélec et al. , 2005)  and summertime Asian convection  (Huang et al. , 2016). A peak is found in June in the TPL, too, and in the LS to a lesser extent. High summertime CO mixing ratios are also observed over eastern Asia by MLS observations at 215 hPa  (Huang et al. , 2016) , slightly above the TPL. Mean upper-tropospheric CO in Siberia shows a levelling off from February until June and peaks in July, like the 95th percentile. This last feature is consistent with the Global Fire Emissions Database (GFED3: van der Werf et al. , 2010) and the Global Fire Assimilation System (GFASv1. 0: Kaiser et al. , 2012)  inventories for CO emissions from Asian boreal forest fires  (Andela et al. , 2013). Since the peak is noticeable in the TPL too, it suggests a non-negligible impact of pyroconvection  (Tereszchuk et al.
Trends: comparison and characterization In this section, annual and seasonal trends of O 3 and CO are investigated in the different regions (see Sect. 2. 2. 2 for details on the calculations of trends), from August 1994 until December 2013 for O 3 and from December 2001 until December 2013 for CO. For a more complete description of the data set, the corresponding time series for both species are given for the three layers in Figs. B1-B6. As shown in these figures, no monthly data are available during most of 2010, due to an interruption of measurements. Most regions have a good sampling efficiency, with about 70-97 % of available data (see Table  1 ). Specifically, the lower sampling frequency in northwest America (top-left panel) allows relatively few available monthly data. For O 3 , the sampling focuses on the period 2003-2009.
(2015). Responses in tropospheric ozone at the midlatitudes to the El Niño Southern Oscillation (ENSO) have been identified in  Wespes et al. (2017)  with a 4-month or 6-month time lag. Further modelling studies are needed to assess the link between the chemical composition of the upper troposphere and the ENSO. It is worth noting that besides the IAGOS database, in situ observations are very sparse in the UTLS and their time coverage is usually too short for determining long-term trends. This considerably limits our ability to compare these results to the literature. As mentioned in Sect. 1, the IA-GOS measurements during 1994-2003 showed significant positive trends for ozone mixing ratios above the eastern United States, the North Atlantic, and Europe in the UT (respectively, +0. 99 ± 0.
Summary and conclusions In the framework of the European research infrastructure IA-GOS, in situ measurements of O 3 and CO are performed in the UTLS since 1994 and 2002 respectively. Thanks to its high frequency of measurements over such long periods, IAGOS provides the most representative in situ data set in the UTLS. In the present study, we used the IAGOS data available until 2013 to establish a new semi-global climatology and to investigate the seasonal variability and trends in both O 3 (19 years of data) and CO (12 years of data). Results are presented in three distinct layers, namely the upper troposphere (UT), tropopause layer (TPL), and lower stratosphere (LS). We also conducted a detailed interregional comparison of O 3 , CO, and the O 3 /CO ratio in eight regions of interest at northern midlatitudes (northwest America, the eastern United States, the North Atlantic, Europe, the Western Mediterranean basin, the Middle East, Siberia, northeast Asia).
Our study does not support this second option since all mean O 3 trends in the LS were found to be statistically insignificant over the studied period, although the insignificance of our trends may be due to the interannual variability. Finally, attributing the trends of O 3 in the UT to one leading process remains difficult and requires further investigations with global models. The IAGOS data set has highlighted significant interregional and interannual variabilities, which can provide helpful information for shorter and/or localized measurement campaigns. Its good vertical accuracy in the UTLS makes it a useful data set for evaluating the ability of current chemistrytransport and chemistry-climate models to reproduce the characteristics of the chemical composition in this layer, including climatologies and trends.