Water vapor (H2O) is of fundamental importance in the climate system of our Earth. As the dominant greenhouse gas it accounts for about 60 % of the natural greenhouse effect (Kiehl and Trenberth, 1997; Harries et al., 2008). The Earth's energy budget is closely linked to the global water cycle, which effectively redistributes energy by latent heat transport (Stephens et al., 2012; Wild et al., 2013). However, relevant processes determining the interaction between atmospheric humidity, circulation, and climate are still not completely understood (e.g., cloud feedback processes). A major remaining challenge in climate modeling is accurately representing the response of the hydrological cycle and general circulation patterns to climate change (Bengtsson et al., 2014; Bony et al., 2015).

Large-scale circulation patterns at the northern midlatitudes are dominated by prevailing westerly winds. Extratropical cyclones and deep convective systems facilitate transport of water vapor from moisture sources to higher altitudes and latitudes. Along these major transport pathways, other trace gas signatures and pollution plumes can travel over long distances from source to receptor regions (e.g., Stohl, 2001; Stohl et al., 2002). Transported species of predominantly natural origin include, among others, volcanic ash (e.g., Trickl et al., 2013), biomass burning aerosols (e.g., Forster et al., 2001; Wotawa et al., 2001; Damoah et al., 2004; Fromm et al., 2010; Trickl et al., 2015), pollen (e.g., Jochner et al., 2015), mineral dust (e.g., Husar, 2004; Papayannis et al., 2008; Trickl et al., 2011; Israelevich et al., 2012), and stratospheric ozone (e.g., Beekmann et al., 1997; Škerlak et al., 2014). Relevant anthropogenic tracers are, e.g., aerosols, carbon monoxide, ozone, and ozone precursors. Such transport events impact climate, air quality, and human health in receptor regions and are highly relevant for agreements on emission regulations (Holloway et al., 2003; TF-HTAP, 2010).

Research on long-range transport to central Europe has a long history at high-altitude observatories, such as Zugspitze and Jungfraujoch, which offer a unique opportunity to monitor free-tropospheric conditions. Transport studies using lidar and in situ measurements combined with transport modeling have previously identified three main long-range-transport patterns to central Europe. First, intercontinental transport from North America effectively carries anthropogenic emissions within 3–10 days to the European middle troposphere after lifting by warm conveyor belts (WCBs; Stohl and Trickl, 1999; Trickl et al., 2003). Second, intercontinental transport from northern Africa imports Saharan mineral dust into Europe in 5–15 events per year (Papayannis et al., 2008; Flentje et al., 2015). Third, stratosphere–troposphere transport injects dry, ozone-rich air into the European troposphere typically after descending 3–15 days from the lowermost stratosphere (Stohl et al., 2003; Trickl et al., 2010, 2014, 2015, 2016).

Valuable information on tropospheric moisture pathways (and associated transport of other tracers) is provided by measurements of water vapor and its isotopes (Strong et al., 2007; González et al., 2016; Schneider et al., 2016). In this context, two stable water vapor isotopes are of particular interest: H2O (1H216O) and HDO (1H2H16O) with an average abundance of 99.7 and 0.03 %, respectively. Variations in the D / H ratio of water vapor are expressed in terms of δD, i.e., as relative deviation from a reference HDO / H2O ratio standard (Craig, 1961; see Sect. 2). The isotopic composition of atmospheric water vapor is modified during phase transitions (evaporation, condensation, and sublimation) due to fractionation processes caused by isotopic mass differences (Dansgaard, 1964). After evaporation from the ocean surfaces, which represents the main source of water vapor, air masses are transported to regions of lower temperatures, where condensation or sublimation leads to dehydration and depletion in the heavier isotope HDO. Resulting large-scale isotope effects (Worden et al., 2007; Sutanto et al., 2015) include increasing depletion in HDO (decreasing δD) with higher latitudes (latitude effect), with higher altitudes (altitude effect), and with increasing distance from oceans (continental effect).

Only recently have tropospheric water vapor isotope data sets become available from satellite remote-sensing instruments (e.g., Worden et al., 2006; Schneider and Hase, 2011; Lacour et al., 2012; Boesch et al., 2013; Frankenberg et al., 2013; Sutanto et al., 2015) and from ground-based Fourier transform infrared (FTIR) spectrometers (Barthlott et al., 2017; Schneider et al., 2016) operated within the Network for the Detection of Atmospheric Composition Change (NDACC; www.ndacc.org). Progress achieved within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water; Schneider et al., 2012) now offers retrieval methods for tropospheric H2O and δD profiles from mid-infrared FTIR measurements.

So far, research on long-range transport to central Europe has mainly been based on investigations or field campaigns of special transport events combining observations of conventional tracers (such as ozone, aerosols, and humidity) at few sites. With the advance of water vapor isotope remote sensing, a promising new transport tracer (i.e., consistent {H2O, δD} pairs) has become available for globally distributed operational FTIR sites. This data set can be exploited to gain a more comprehensive picture of atmospheric moisture transport. These transport processes can be associated with the dispersion of anthropogenic (or natural) emissions, which is of particular interest for emission regulation policies and human health issues (e.g., air pollution, pollen distribution). Additionally, analysis of {H2O, δD} pairs will yield further insight into the coupling of the hydrological cycle with general circulation patterns, urgently needed to improve our understanding of climate change.

The goal of this study is to evaluate new possibilities in transport research provided by long-term observations of consistent {H2O, δD} pairs at an FTIR site which is representative of typical midlatitude conditions. We present an update of the water vapor time series obtained at Zugspitze (Sussmann et al., 2009) including water vapor isotope information. Based on this data set, transport pathways to the central European free troposphere are identified using backward-trajectory analysis. The main task is to identify distinct signatures in {H2O, δD} pairs for long-range-transport patterns and to combine the results with conventional transport tracer observations. This paper is structured as follows: Section 2 describes FTIR measurements and the retrieval of water vapor and its isotopes. The resulting long-term {H2O, δD} time series at Zugspitze is presented in Sect. 3 along with a discussion on trends and seasonal cycles. Section 4.1 gives results on moisture pathways to central Europe related to δD outliers. {H2O, δD} signatures of long-range-transport events are identified using trajectory classification (Sect. 4.2) and analysis of lidar and in situ measurements (Sect. 4.3). Finally, Sect. 5 summarizes results and draws final conclusions.

The long-term time series of water vapor and its isotopes (2005–2015) presented in this study is retrieved from ground-based solar absorption FTIR measurements obtained at Zugspitze (47.42∘ N, 10.98∘ E, 2964 m a.s.l.). This high-altitude observatory is mostly located above the moist boundary layer or just below its upper edge (Carnuth et al., 2002). Sampled air masses are representative of free-tropospheric background conditions over central Europe. Within the framework of NDACC, a Bruker IFS 125HR spectrometer is operated at Zugspitze (Sussmann and Schäfer, 1997). Mid-infrared solar absorption spectra are recorded with a typical spectral resolution of 0.005 cm-1 (according to a maximum optical path difference of 175 cm) and an integration time of 7 min (averaging six individual scans). These high-resolution spectra provide information on a large number of trace gases, including water vapor and its isotopes (Sussmann et al., 2009; Schneider et al., 2013).

The isotopic composition of atmospheric water vapor with respect to HDO is typically expressed in terms of δD, defined as relative deviation of the HDO / H2O ratio (volume mixing ratios VMRHDO and VMRH2O) from the ratio in Standard Mean Ocean Water (RSMOW=3.1152×10-4; Craig, 1961), i.e., δD=VMRHDO/VMRH2ORSMOW-1×1000‰. Additionally, columnar water vapor and δD values are applied as follows: column-based δD (δDcol) is calculated using the total column ratio in Eq. (1), and integrated water vapor (IWV) is thereafter given in units of millimeters (i.e., a water column of 1 mm corresponds to an atmospheric column density of 3.345 × 10+21 molecules cm-2, which can be derived considering the Avogadro constant and the molar mass as well as the density of water).

The retrieval of water vapor isotopes from mid-infrared FTIR spectra is very demanding due to the high variability of atmospheric water vapor compared with relatively small δD variations that result from the strong correlation of HDO and H2O abundances. Retrieval strategies for tropospheric {H2O, δD} pairs from ground-based FTIR spectra were developed within the project MUSICA (Schneider et al., 2012, 2016; Barthlott et al., 2017). The latest retrieval version (v2015) is applied here, which comprises – briefly summarized – a logarithmic-scale retrieval with an interspecies constraint between HDO and H2O, a non-Voigt line-shape model, and a priori profiles from LMDZ-iso simulations (isotopic version of the model Laboratoire de Météorologie Dynamique-Zoom; Risi et al., 2012; globally and annually averaged H2O and HDO profiles). Two data products are available: (i) optimal estimation of water vapor for maximum vertical resolution of H2O profiles (type 1) and (ii) quasi-optimal estimation of {H2O, δD} pairs derived by a posteriori data processing for consistent vertical sensitivity of H2O and δD profiles (type 2). Error estimation for the type 2 product reveals a precision below 2 % for integrated water vapor and below 30 ‰ for column-based δD (Schneider et al., 2012).

Applying this {H2O, δD} pair retrieval to Zugspitze FTIR spectra results in characteristic averaging kernels (depicted in Fig. 1) for the humidity-proxy state, i.e., 1/3 × (ln[H216O] + ln[H218O] + ln[HD16O]). Furthermore, the vertical sensitivity is shown, which is defined as the sum of the row elements of the averaging kernel matrix. A typical measurement on 29 October 2009 is chosen to represent the mean state of the Zugspitze time series (IWV = 3.8 mm, degrees of freedom for signal (DOFS) = 1.6 for the type 2 product). Optimally estimated H2O profiles (type 1 product) derived from Zugspitze data provide an average DOFS of 2.8 (standard deviation (SD) = 0.2) and are sensitive up to 12 km altitude (vertical sensitivity larger than 0.75) as shown in Fig. 1a. In the following, we use the type 2 data product (Fig. 1b), which provides consistent {H2O, δD} profiles with vertical sensitivity up to 9 km (SD = 1 km) and maximum sensitivity at 5 km a.s.l. altitude (SD = 0.5 km). On average, a DOFS of 1.6 (SD = 0.2) is obtained for H2O and δD data (type 2) derived from Zugspitze spectra. The vertical resolution amounts to 2–3 km in the lower troposphere and 4–5 km in the middle–upper troposphere, as derived from the full width at half maximum of the row kernels. Retrieval quality selection is implemented by means of a threshold in the root-mean-square (RMS) residuals of the spectral fit, which is chosen to eliminate 5 % of all spectra with the highest RMS (see Sussmann et al., 2009). The full Zugspitze time series yields a mean normalized RMS of 0.26 %. Additionally, the sum of DOFS for all retrieved isotopes (H2O, HDO, and H218O) is required to exceed a value of 4.0. To compute daily means, only days with more than one measurement are considered.

Time series of daily mean integrated water vapor and column-based δD above Zugspitze are presented in Fig. 2 for the period 2005–2015. This data set comprises 1154 daily means derived from 10184 FTIR spectra after filtering by the quality selection criteria described above, with, on average, nine spectra per measurement day and 105 measurement days per year. Seasonal cycles are determined by fitting a third-order Fourier series to the time series. This fitted intra-annual model is subtracted from the time series for the purpose of deseasonalization. The multiannual mean of the deseasonalized IWV time series amounts to 4.4 ± 0.1 mm, which reflects dry conditions at the high-altitude Zugspitze site. Throughout this manuscript, uncertainties of mean values are given as a range of ±2 SE (standard error); i.e., SE of the mean = SD / n, with sample size n (except for the discussion in Sect. 4.3, where also ranges of ±1 SE are considered). The multiannual mean of deseasonalized column-based δD amounts to -311.3±3.8 ‰. This relatively low value corresponds to strong HDO depletion at high altitudes according to the altitude effect (dehydration with decreasing temperature). The multiannual mean δDcol value at Zugspitze is slightly less depleted than observations at the nearby but even higher Jungfraujoch station (46.5∘ N, 8.0∘ E, 3580 m a.s.l.) with a multiannual mean δDcol of -330 ‰ (Schneider et al., 2012).

A short discussion is given on long-term trends derived from the daily mean IWV and δDcol time series (2005–2015). For column-based δD, we infer a statistically insignificant trend of 0.8 [-3.1, 4.7] % per decade (relative to the overall mean, uncertainty given as 95 % confidence interval). For IWV, a weak positive, and likewise insignificant, trend of 2.4 [-5.8, 10.6] % per decade (relative to overall IWV mean) is derived. These linear trends are determined by fitting a linear trend model by least-squares fit to the deseasonalized daily mean time series, and uncertainties are determined via bootstrap resampling of the residuals (Gardiner et al., 2008). Over the same time period, a significant positive temperature trend of 1.3 [0.5, 2.1] K per decade is observed at Zugspitze (derived from daily means of in situ temperature measurements coincident with FTIR spectra within a period of ±30 min; source: Deutscher Wetterdienst, available at ftp://ftp-cdc.dwd.de/pub/CDC/observations_germany/climate/hourly). Assuming constant relative humidity (RH) and following the Clausius–Clapeyron equation (Schneider et al., 2010), the observed temperature increase translates to a positive IWV trend of 9.2 [3.7, 14.7] % per decade. This IWV trend deduced from the temperature increase in consideration of its confidence interval would be formally consistent with the most probable IWV trend observed. The assumption of constant RH is valid on large spatial scales, but RH slightly decreased over specific land regions (Hartmann et al., 2013), which would explain the relatively large calculated IWV trend compared to observations.

Strong seasonal cycles of free-tropospheric water vapor and δD are observed above Zugspitze (Fig. 3a). Multiannual monthly means are shown for data at 5 km a.s.l., where the maximum in vertical sensitivity of the {H2O, δD} pair product is located (see Fig. 1b). These data are representative for an altitude region of 3–7 km, which is the full width at half maximum of the corresponding averaging kernel row. The seasonal cycle amplitude (relative difference of maximum and minimum monthly mean) amounts to 140 % for H2O and 50 % for δD relative to the overall mean. Both seasonal cycles show maxima in summer (July) and minima in winter (January). The δD maximum in summer results from more frequent ascending motions in summer with associated transport of less-HDO-depleted air masses from lower altitudes and latitudes (Risi et al., 2012). Stronger HDO depletion in winter (δD minimum) is caused by a stronger continental temperature gradient in winter and associated stronger dehydration during air mass transport from the Atlantic Ocean. Additionally, transport from eastern Europe occurs more frequently during anticyclonic conditions in winter importing strongly depleted continental air masses (Christner, 2015). Monthly frequency distributions of single H2O measurements are generally right-skewed, and variability is larger in summer (June–August) than in winter. This might be explained by extreme moistening events due to more frequent convection in summer and mixing with boundary layer air. For free-tropospheric δD measurements, monthly frequency distributions show a tendency to being moderately left-skewed (e.g., January, May, and June), which might point to episodic influence by strongly HDO-depleted air masses (e.g., originating in the upper troposphere or lower stratosphere). In the {H2O, δD} distribution plot (Fig. 3b), it becomes clear that the isotopic composition of atmospheric water vapor significantly changes over the course of a year: in spring it is less depleted in HDO than in autumn. This is mainly caused by seasonal variations in sea surface temperature of the Atlantic Ocean (i.e., the major moisture source) as derived from Rayleigh model simulations (see Fig. 5). An additional contribution is possibly due to increased mixing in spring compared to autumn. These seasonal variations in the water vapor isotopic composition above Zugspitze imply that δD observations provide information complementary to the H2O data (e.g., Risi et al., 2012; Schneider et al., 2012).

In this section, we presented a unique decadal time series of water vapor and δD derived from FTIR measurements at Zugspitze representative of central European background conditions. In the following, these δD observations are used as a tracer of atmospheric transport to study its behavior in relation to different transport pathways to central Europe.

In this study, we presented a decadal time series of water vapor and δD above Zugspitze (2005–2015) derived from mid-infrared FTIR measurements within the NDACC framework, which are representative of central European background conditions. The Zugspitze {H2O, δD} data product provides an average DOFS of 1.6 for consistent H2O and δD profiles with a maximum vertical sensitivity in the free troposphere (around 5 km a.s.l.). For the time period 2005–2015, we found no statistically significant trend of integrated water vapor and column-based δD. However, the weakly positive (insignificant) IWV trend can be reconciled with the significant temperature increase at Zugspitze over this time period, assuming constant relative humidity. Both IWV and δDcol exhibit strong seasonal cycles with summer maxima and winter minima. In the {H2O, δD} distribution plot, it becomes obvious that water vapor isotope data provide additional information not contained in H2O observations alone.

Consistent {H2O, δD} observations are a valuable proxy for atmospheric transport pathways. We demonstrated the potential of this new transport tracer by analyzing a compilation of backward trajectories to the free troposphere above Zugspitze (5 km a.s.l.). Distinct moisture pathways to the central European free troposphere were identified for days of extraordinarily high or low column-based δD observations at Zugspitze. While low-δD observations are predominantly associated with descending dry air masses from higher latitudes, high-δD data are mostly related to ascending moist air masses from lower latitudes.

Significantly different {H2O, δD} signatures were found for three main long-range-transport pathways to central Europe, i.e., stratospheric intrusions, intercontinental transport from North America, and intercontinental transport from northern Africa. We identified Zugspitze FTIR measurements influenced by these transport patterns by means of backward-trajectory classification. The corresponding free-tropospheric VMRH2O distributions exhibit mean values (±2 SE) of 1.2 [1.1, 1.3] × 103 ppmv (STI), 2.4 [2.2, 2.6] × 103 ppmv (TUS), and 2.8 [2.6, 2.9] × 103 ppmv (TNA). Mean free-tropospheric δD values are -384 [-397, -372] ‰ (STI), -315 [-326, -303] ‰ (TUS), and -251 [-257, -246] ‰ (TNA). The finding of distinct {H2O, δD} fingerprints was validated for verified STI and TNA events, deduced by data filtering using 2013–2015 lidar and in situ measurements at Zugspitze and nearby Garmisch. Mean values of IWV and δDcol for these TNA (STI) events are significantly different with respect to the rest of the time series considering uncertainties of ±2 SE (±1 SE). Deep stratospheric intrusions to Zugspitze (data filtering using 2005–2015 in situ humidity and beryllium-7 data) also reveal a significantly lower mean value of lower-tropospheric δD (3–5 km a.s.l.) than the full time series considering uncertainties of ±2 SE.

These results confirm the import of dry and strongly HDO-depleted air masses expected for STI events, which originate in the extremely dry and HDO-depleted lowermost stratosphere. STI events generally agree with Rayleigh simulations but may be influenced by mixing processes of dry lower-stratospheric air with free-tropospheric air masses. In contrast, TNA air masses originate in the moist boundary layer and are associated with dry convection and import of relatively moist and less-HDO-depleted air masses to central Europe. TUS air masses also have their origin in the boundary layer, and their humidity depends on the surface temperature of their oceanic or continental source region. However, WCB updrafts during TUS events may cause variable degrees of air mass dehydration and HDO depletion. Overall, {H2O, δD} observations add new information to previous long-range-transport studies using lidar and in situ measurements of conventional transport tracers (i.e., humidity, ozone, beryllium-7, and aerosols), which offer the possibility to identify transport events of relevant tracers actually reaching northern Alpine stations.

In future work, backward-trajectory classification for identifying STI events could be improved, e.g., by application of a dynamical tropopause definition. {H2O, δD} observations could also be combined with FTIR data of other relevant trace species, such as ozone or carbon monoxide, for more reliable detection of transport events. Furthermore, {H2O, δD} profile information provided by FTIR measurements (approximately two tropospheric partial columns) could be combined with lidar or radiosonde humidity profiles with high vertical resolution, especially for studying thin filaments of stratospheric air intrusions. Regarding long-term trends, water vapor trends could be estimated for different transport categories to investigate to what extent changing transport patterns contribute to observed water vapor trends. Of particular importance would be research on the connection between lower-stratospheric humidity trends, trends in the frequency of stratosphere–troposphere transport events, and tropospheric water vapor trends.

Finally, combining {H2O, δD} pair measurements from all globally distributed NDACC sites with observations from in situ and lidar networks and operational transport modeling could provide a unique new database for studying the impact of atmospheric transport with respect to climate, air quality, and human health. {H2O, δD} data could serve as an indicator for the presence of pronounced transport events at Zugspitze to initiate case studies using observations of the respective relevant tracers (e.g., humidity, aerosols, ozone, pollen). On demand, further FTIR sites located along simulated transport trajectories could be included. Particular focus has to be put on deepening our knowledge of the coupling of changing transport patterns in the global circulation, changes in the intensity of the hydrological cycle, and climate change.