Temporal evolution of stable water isotopologues in cloud droplets during HCCT-2010

Introduction Conclusions References

−3 which is of similar slope but with higher deuterium excess (d-excess) than Central European Meteoric Water Lines.While seasonality was reflected in decreasing δ values towards the colder season, d-excess of cloud samples was an indicator of air mass origin: polar air masses had a higher dexcess than Mediterranean air masses.The variations in δ values during one cloud event could either result from changes in meteorological conditions during condensation or from variations in δ values of the water vapor feeding the cloud.To test which of both aspect dominated during the measured cloud events, we modeled the variation in δ values in cloud water using a closed box model.We could show that the variation in δ values of two cloud events was mainly due to changes in local temperature conditions.For the other eleven cloud events the variation was most likely caused by changes in the isotopic composition of the advected and entrained vapor.Frontal passages led to the highest gradients both in δ 2 H (≈6 per hour) and δ 18 O (≈0.6 per hour) during two of the latter cloud events.Moreover, a detailed trajectory analysis for the two longest cloud events revealed that variations in the entrained vapor were most likely related to rain out or changes in relative humidity and temperature at the moisture source region or both.This study illustrates the sensitivity of stable isotope composition of cloud water to changes in large scale air mass properties.

Introduction
Stable water isotopologues ( 1 H 2 18 O and 1 H 2 H 16 O) are naturally available indicators of atmospheric processes involved in water vapor transport and phase changes on different time scales.By measuring stable water isotope ratios of ice cores, information on past climate can be retrieved (e.g.Dansgaard et al., 1993;Petit et al., 1999;Vimeux et al., 2001;Jouzel et al., 2007).On the time scale of days to years, stable water isotopologues analysis improved our understanding of the hydrological cycle by measuring their spatial and temporal distribution in precipitation all over the globe (e.g.Rozanski et al., 1993;Gat, 1996Gat, , 2000)).In ecology, stable water isotopologues have been used to assess soil evaporation, plant transpiration, drought effects on vegetation and water sources for plants (e.g.Dawson, 1998;Yakir and Sternberg, 2000;Farquhar et al., 2007).For these fields of application, a detailed mechanistic understanding of the isotopic signal in atmospheric water vapor and its changes during transport and precipitation formation is essential.(Majoube, 1971;Horita and Wesolowski, 1994;Wen et al., 2008).For evaporation at relative humidity below 100 %, the evaporating molecules need to pass a saturated transition layer above the water surface.Consequently, the stable water isotope ratios during evaporation under unsaturated conditions result from a combination of an equilibrium fracionation depleted in 18 O during evaporation at lower relative humidities than it would be for evaporation at 100 % relative humidity if 2 H is kept constant (Cappa, 2003).
Measurements of both hydrogen and oxygen isotope ratios are thus powerful tools to assess the atmospheric water cycle.So far, focus has been set on the isotopic variations in precipitation, for example in the framework of the Global Network of Isotopes in Precipitation (GNIP; Craig, 1961;Dansgaard, 1964;Merlivat and Jouzel, 1979;Rozanski et al., 1993;Araguas-Araguas et al., 1996).As the natural abundance of the heavy water isotopologues is small (0.2005 % for 1 H 2 18 O and 0.0155 % for 1 H 2 H 16 O; Risi, 2009), their isotope ratios are commonly expressed using the δ-notation: where R sample is the isotope ratio of the heavy isotope to the lighter isotope in the sample, and R V-SMOW is the ratio of Vienna Standard Mean Ocean Water which serves as a standard for water isotopologues (Coplen, 2011).The Global Meteoric Water Line (GMWL: δ 2 H = 8 × δ 18 O + 10 × 10 −3 ) is wellestablished as the average relation between δ 18 O and δ 2 H in worldwide precipitation (Craig, 1961).The slope of the GMWL arises from equilibrium fractionation, whereas the intercept (also known as deuterium excess d GMWL = δ 2 H − 8 × δ 18 O) results from kinetic fractionation (Craig, 1961).In colder regions (high latitude and/or altitude) as well as at large distances from the coast, the rain was found to be more depleted in heavy isotopologues than in warmer and coastal regions: The water vapor which forms by evaporation at low temperatures is depleted in heavy isotopologues, resulting in rain with a relatively low δ value, referred to as depleted in 2 H and 18 O and sometimes as isotopically "light" rain.Inland regions receive air masses with water vapor that is more depleted in heavy isotopologues due to the prior rain out of the air mass, a process that can be modeled using Rayleigh distillation (Gat, 1996).Consequently, the rain in such regions was also found to be more depleted in heavy isotopologues than in warmer and coastal regions. in rain has been controversial.On the one hand, the d-excess in rain is supposed to deliver information on the climatic conditions in the vapor source region: the d-excess decreases with increasing relative humidity by 0.43 % −1 and decreases by 0.4 • C −1 with decreasing temperature during evaporation from the ocean (Craig and Gordon, 1965;Merlivat and Jouzel, 1979).On the other hand, below-cloud evaporation of falling rain droplets decreases the d-excess in rain and increases the d-excess in water vapor (Rank and Papesch, 2005;Froehlich et al., 2008), overwriting the initial signature of the moisture source region.Therefore, addressing the question of atmospheric water vapor transport via stable water isotopologues in precipitation is difficult.However, measuring d-excess in cloud droplets could indeed reveal insights into the initial isotopic signature of the moisture source region, as cloud droplets did not experience any additional kinetic fractionation as rain droplets do.So far, studies that measured stable water isotope ratios in cloud droplets were conducted in ground touching clouds, which sometimes also had a larger vertical extent (Scholl et al., 2011).A cloud in contact with the ground is called fog from the meteorological point of view and therefore most authors addressed this as isotopologues in fog.As an advected cloud that intercepts with a mountaintop or slope does not change its microphysical behavior, we will use the terms cloud and fog interchangeably for a cloud touching the ground.Isotopologues in fog water have been used in combination with rain to study water fluxes through the ecosystem (first proposed by Ingraham and Matthews, 1988) as well as to assess the hydrological budget (e.g.Schmid et al., 2010a).For this purpose, fog and rain were sampled at monthly intervals (Dawson, 1998;Ingraham and Mark, 2000;Corbin et al., 2005;Scholl et al., 2007;Fischer and Still, 2007;Scholl et al., 2011) or event wise (Feild and Dawson, 1998;Corbin et al., 2005;Schmid et al., 2010a,b;Scholl et al., 2011).In most studies, fog water was found to be enriched in heavy isotopologues in comparison to rain water.Fog often condensed from local vapor which was more enriched in heavy water isotopologues while rain formed in the upper regions of the cloud, from water vapor which was typically more depleted in heavy water isotopologues (Scholl et al., 2011) water isotopic signatures could be related to water vapor source (Scholl et al., 2007).Moreover, Spiegel et al. (2012) experimentally confirmed that fog droplets do not differ in isotopic composition with respect to droplet size.As most of the studies did not resolve the temporal trends during a single cloud event, it remains unclear, whether the isotopic composition of cloud water stays constant over time or is subject to large changes that have not been identified yet.This potentially leads to misinterpretation of cumulative monthly cloud samples.
In this work, we present the first water isotopic data set resolving in time the δ value variation of individual cloud events during the Hill Cap Cloud Thuringia 2010 (HCCT-2010) campaign in Thuringia in September and October 2010.The primary goal of the present study is to deliver the first δ 2 H and δ 18 O values in cloud droplets measured in Europe and to link them to air mass origin, seasonality as well as measured δ values in monthly precipitation.Second, a classification of the measured events is proposed, in which two possible mechanisms are identified to explain the observed δ values in cloud water: 1) condensation-driven changes in isotopic composition of the cloud droplets and 2) changes in the signature of the water vapor feeding the cloud.Third, we assess how stable water isotopologues in cloud water can be used as a tool to contribute to a better understanding of atmospheric water vapor transport.

Collection and measurement of cloud water samples
Cloud water samples of three droplet size fractions (4 µm to 16 µm, 16 µm to 22 µm and > 22 µm in diameter) were collected using a three-stage Caltech Active Strand Cloudwater Collector (CASCC), similar to the two-size fraction CASCC presented by Demoz et al. (1996).The CASCC was installed on a 20 m tall measurement tower at the site.Cloud collectors of this type have been used previously for cloud water sampling for isotope ratio analysis (Michna et al., 2007;Scholl et al., 2011) and tests revealed that no significant fractionation occurs during sampling (Spiegel et al., 2012).We collected a total of 41 cloud samples, each consisting of one vial per sampling stage of the CASCC, distributed over 13, non raining cloud events with temperatures above 0 • C such that liquid clouds only were sampled (Table 1).The sampling interval of each cloud sample varied between 1 and 3 h, and each event consisted of n samples (with n between 1 and 8, depending on the duration of the event).The samples were analyzed for δ 2 H and δ 18 O using isotope ratio mass spectrome-Introduction

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Full peak-jump method) over all measurements.For details on collection and sampling techniques, we refer to Spiegel et al. (2012) and references therein.For the analysis in this work, we use the volume weighted mean values of the three stages from the CASCC and in order to distinguish the collected cloud samples from the calculated vapor samples we use c as subscript: where V i is the collected volume and δ i the measured δ value of stage number i (i = 1,2,3).The measurement error is calculated as: where ∆δ i represents the standard error from the three IRMS measurements performed per vial.

Auxiliary measurements and tools for data interpretation
For the box model (Sect.2.4) as well as for the data interpretation (Sect.3), the following additional measurements and tools were employed: 1. Liquid water content measurements: As input for the model presented in

HYSPLIT backwards trajectories:
To interpret the isotopic composition of cloud water based on air mass history, 96-h hourly backward trajectories were calculated with the HYSPLIT model (Draxler andHess, 1997, 1998;Draxler, 2003) initiated 500 m above ground level (= single run).In addition, an ensemble run was performed consisting of 26 additional runs with 26 starting points differing ±250 m in vertical and ± one meteorological grid point in horizontal dimension from the starting point of the single run.The spatial resolution of the HYSPLIT trajectories is 1 • which is about 71.5 km at 50 • N, and the temporal resolution is 1 h.Archived data from the global data assimilation system (GDAS) have been used for trajectory calculation.

Cumulative rain analysis of the single HYSPLIT trajectories:
To address how much water rained out of the measured air masses (Sect.3.2.3),we first summed up the amount of rain that formed along each trajectory as calculated by HYSPLIT (Rain tr ).Then we retrieved the cumulative rain (Rain cu ) per cloud sample, which is the total amount of rain that this air mass passing during the sampling interval had formed before reaching Schm ücke, by accumulating Rain tr of these trajectories that started during the sampling interval of each cloud sample.E.g., for a cloud sample collected from 12:15-14:15, the cumulative rain is calculated as: Rain cu = 0.75 × Rain tr (13:00) + Rain tr (14:00) + 0.25 × Rain tr (15:00).( 4)

Synoptic weather charts:
We used synoptic charts from the German Weather Service (DWD) with a time resolution of 6 h for the air mass classification and identification of frontal passages (for details see Tilgner et al., 2012).
7. Moisture source diagnostic: For the two longest cloud events lasting for 15 h each a detailed moisture source diagnostic was performed as described in Sodemann et al. ( 2008) and applied in Pfahl and Wernli (2008).Hourly wind analysis data of the regional model COSMO (Steppeler et al., 2003) were used to compute kinematic three-dimensional backward trajectories (Wernli and Davies, 1997).Five horizontal starting points were selected in a cross arrangement with the location of the Schm ücke hilltop in the center of the cross.The four points defining the cross edges were shifted by 0.2 degree in every direction from the Schm ücke hilltop.Nineteen vertical levels were selected as starting points for the trajectories.In the lowest part of the troposphere trajectories started at every identified as uptakes.To account for the uncertainty in the boundary layer height parametrization as well as for the fact that most uptakes of humidity occur at the edge of the boundary layer and the free atmosphere, the boundary layer height was scaled by a factor 1.5 as in Sodemann et al. (2008).Each uptake location was weighted according to its contribution to the final humidity of the trajectory.
The assignment of the weights to each uptake was done in a mass consistent way: if a decrease in specific humidity (i.e.rain out) occurred after an uptake the weights of the previous uptakes were discounted.The final contribution of each trajectory was weighted by its final specific humidity.The meteorological conditions at the uptake points were averaged over the uptake region using the weights determined from the specific humidity contribution.

Model description
When an air parcel is cooled below its dew point (T d ), cloud droplets form by condensation on preexisting cloud condensation nuclei.With both phases (condensate and vapor) abundant, the isotope ratios of the condensate R c and the vapor phase R v are described by the equilibrium fractionation coefficient α (for the phase change from liquid to vapor as a function of temperature) as condensation can be considered as an equilibrium fractionation from the isotopic point of view: Assuming the cloud droplets in immediate isotope equilibrium with the surrounding vapor (Spiegel et al., 2012), the change in the isotope ratio of the vapor phase in response to phase change and to changes in the surrounding conditions (such as temperature) can be modeled for an air parcel (=box) using the following equation (Gedzelman, 1988), applied for 2 H and 18 O, separately: where X v is mole fraction of the vapor phase (moles in the vapor phase divided by the moles of condensed and vapor phase).A is the fraction of the condensate remaining in the volume of interest (later on referred to as "box", see Fig. 1b for illustration).Two limiting cases of Eq. ( 7) are defined by A. For A = 1 the box model is considered to be closed: the condensate formed completely remains in the box and is in thermodynamic and isotopic equilibrium with the surrounding water vapor.In this case changes in the isotopic composition of vapor and condensate depend on temperature (α = α(T )) and liquid water content (as X v (T , LWC) also depends on LWC).Closed system cloud models have been suggested as an adequate description of a non raining cloud (Jouzel, 1986) and used for studies on the formation of hail (Facy et al., 1963;Knight et al., 1981).By setting A = 0, the model represents an open system (also known as Rayleigh condensation) where all the condensate (precipitation) is removed immediately.The Rayleigh distillation equation has been used to model such systems involving the progressive depletion in heavy water isotopologues in precipitation along an air mass trajectory (e.g.Dansgaard, 1964).Moreover, Jouzel (1979) showed that the difference in R v between the two limiting cases is negligible as long as X v remains above 0.9.
To evaluate the extent condensation controls the variation of measured δ values for the cloud samples, we first used the model (= model run A, in Fig. 1a and c) to obtain the δ value of the water vapor at the dew point (δ v,dew ).We then simulated the condensation process by running the model from the dew point to the actual temperature (= model run B, in Fig. 1a and c) and calculated the isotopic signature of the cloud droplets (δ c,mod ) based on a certain isotopic signature of the water vapor at the dew point.In a last step, we compared the modeled to the measured δ values.As we basically sampled non raining clouds and as R v was above 0.94 for all cloud events we used the box model in the closed version for all model runs.
In detail: for model run A, the box model was first initialized with the measurements.For this purpose, we calculated the mean values taken over the sampling time of each cloud sample, of the temperature T sm , the liquid water content (LWC) and the dew point (T d ) of the Schm ücke cloud.We calculated the dew point from the local mixing Introduction

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Full From these values, the initial box model variables were deduced (framed in red in Fig. 1): α 0 = α(T sm ) was calculated using the equations of Criss (1999, p.103).R v0 (which is the vapor isotope ratio inside the cloud in equilibrium with the condensate) was calculated from the measured δ c values using Eqs.( 6) and (1) (Fig. 1 item 1).X v0 was calculated based on the equilibrium water vapor pressure (vapor phase) and the LWC (condensed phase).Second, we repeatedly ran the model for every cloud sample from the measured temperature T sm to the dew point T d , changing X v from X v0 to 1 and T from T sm to T d .By doing so, we calculated the δ v value for each measurement point for the moment when the condensation started (Fig. 1 item 2).We refer to these values as δ v,dew .
For run B, we used the model a second time, from the dew point (taken as initial condition) to the actual measurement temperature T sm (and X v = 1 to X v = X v0 ).For every cloud event we performed n (= number of samples per event) different model runs B, each starting from a different δ v,dew as received from model run A (Fig. 1 item 3).This yielded n 2 calculated δ values for the cloud droplets to be compared to the measurements (Fig. 1 item 4).We categorized the temporal evolution of the measured δ value as condensation-driven if the following criteria were fulfilled: 1.The modeled and the measured values were equal within the measurement error (δ c ∈ [δ c,mod ± ∆δ c,mod ] with ∆δ c,mod = 2 × ∆δ c ).
2. Criteria 1 was fulfilled for two consecutive measurements.

Results and discussion
For the discussion of the collected data we start from a general and campaign-based context (Sect.In this section, we describe the measured isotopic signal of the cloud samples in general and compare it to δ values found in precipitation and other fog measurements (Sect.3.1.1).In Sect.3.1.2,we discuss the d-excess and its link to air mass origin.

Classification of the observations into the framework of isotopic measurements in precipitation and comparison to other fog measurements
δ c values varied from −77 to −15 (δ 2 c H) and from −12.1 to −3.9 (δ 18 c O, Fig. 2), during the whole campaign.The temperature range was 0.4 to 10.6 • C at Schm ücke (Table 1).Highest δ c values were measured at the beginning of the campaign (Sep.14th), and the lowest δ c values were measured at the end of the campaign (24th October).This fits into the seasonal trend observed in rain (e.g.Dansgaard, 1964) and in water vapor (Jacob and Sonntag, 1991) characterized by decreasing δ values towards the colder season.Interestingly, δ c values sometimes differed significantly for cloud events with similar local temperature (Fig. 2a and Table 1).As droplets in a hill capture cloud are uniformly in equilibrium with local conditions (Spiegel et al., 2012), differences among cloud events with similar temperatures are strong evidence for differing isotope signatures of the water vapor feeding the cloud.
On the other hand, the air mass origin was only a poor indicator of the δ c values as cloud water collected during the same week (seasonality can be neglected in this case) did not differ significantly for different air mass origins (Fig. 2b).The air mass origin is strongly connected to potential rain out processes: The longer the traveling distance of an air mass, the higher the probability that its moisture has been affected by Rayleigh distillation rain out processes.So we could only expect differences in isotopic composition if the traveling distance was sufficiently different for different air mass sources as, for example, it was the case in the study presented by Rank and Papesch (2005).Due Figures

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Full to the very central European measurement location, traveling distances above the continent are comparable for different air mass types, leading to similar δ c values, which was what we actually measured.Most of our measured δ c values fell into the δ range of previous fog studies (from −71 to +13 for δ 2 H and −10.4 to 2.7 for δ 18 O in Scholl et al., 2007), except for a few, which were more depleted in 2 H and 18 O (Fig. 2).This is not surprising as most of the other measurements were performed at lower latitudes or in coastal areas where due to the nearby ocean source more enriched δ values are expected (Scholl et al., 2011).For a continental site, such as Schm ücke, Rayleigh distillation by rain out is more pronounced than for a coastal measurement site.
The Cloud Water Line for our measurements CWL (δ 2 c H = 7.8 × δ 18 c O + 13.1 × 10 −3 ) was calculated by orthogonal regression, R 2 =0.96.All our measured data lay above the GMWL.This was not expected a priori: First, other authors also found values below the GMWL (Scholl et al., 2011).Second, Local Meteoric Water Lines (LMWL) in central Europe have been found to be similar to the GMWL (Schoch-Fischer et al., 1983).This was also the case for the LMWL of the closest GNIP station, Wasserkuppe Rhoen (LMWL Wk : δ 2 c H = 8 × δ 18 c O + 9.8 × 10 −3 , 50 • 30 N/ 9 • 57 E, 921 m a.s.l.; 60.2 km west of the measurement station), which was deduced by orthogonal linear regression from δ values in precipitation from a data set collected between 1978 and 2007 based on monthly samples.It is probable that the lower d-excess of the precipitation (LMWL Wk ) resulted from below-cloud evaporation of falling rain droplets.Decreases of the d-excess caused by below-cloud evaporation have been shown to be in the range of 1 to 4 for stations in Austria at a similar altitude during September and October, analyzing a 20-yr precipitation data set (precipitation-weighted monthly averages; Froehlich et al., 2008).Consequently, we assume that the difference between the precipitation LMWL Wk and the CWL at Schm ücke are likely to result from below-cloud evaporation which decreases the d-excess of the LMWL Wk with respect to the CWL.
In brief, our measured δ c values were in a range expected for this site.Fig. 3) as compared to European air moisture d-excess of 7 to 11 (Gat et al., 2003) and stayed rather constant during most of the cloud events, except for events 1 and 10, which is discussed in more detail in Sect.3.2.3.While air masses originating in polar regions (such as Scherhag (1948) weather types mPa, mP, cPa and cP) had a high d-excess (15 to 20 ), air masses arriving from the Mediterranean (mTs) had a lower d-excess (10 to 14 ).
Observations of d-excess in rain showed that d-excess is not necessarily a good tracer for the air mass origin (Rank and Papesch, 2005).The d-excess in water vapor decreases with increasing relative humidity during water vapor formation via evaporation.As the d-excess does not change during condensation, d-excess in cloud water still "remembers" the meteorological conditions during water vapor uptake of the air mass.However, the d-excess in rain is additionally influenced by below-cloud evaporation, suppressing the relative humidity memory of the cloud water d-excess during rainfall.
The finding that polar air masses are associated with higher deuterium excess than Mediterranean air masses is not intuitive.Polar air masses are characterized by low specific humidity due to the lower saturation vapor pressure at lower temperatures.Traveling towards the south, polar air masses warm up which simultaneously reduces their relative humidity.Additional water uptake of polar air masses above the sea or continent is thus likely to happen at locations with low local relative humidities.This produces an increased d-excess, which is what we actually measured at Schm ücke.Consequently, it is likely that the moisture source region for the polar air masses differed from their air mass origin.The Mediterranean air masses in this study exclusively originated from the Western Mediterranean Sea, where d-excess values of around 10 were reported in earlier studies (Gat et al., 2003).Temperatures in this region are generally higher than at Schm ücke in autumn.The air mass thus cools down during transit Introduction

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Full which limits the potential contribution from additional evaporative uptake of water vapor.Consequently, the Mediterranean air masses are more likely to keep their characteristic d-excess.So from the cloud samples measured in this study, we infer that it is very likely that the d-excess in cloud water is strongly influenced by the air mass origin and the water vapor sources connected to these air masses.

Temporal evolution of δ values during different cloud events
We showed that seasonality was reflected in the δ c values (Sect.3.1.1)and air mass origin in the d-excess (Sect.3.1.2).In this section we focus on the temporal evolution of the δ c values during each cloud event separately.The δ 2 c H and δ 18 c O values did not show any common trends in the evolution of the different cloud events.While the δ c values decreased for events 3, 5 and 9 (F, , and in Fig. 2), they increased for events 1, 7 and 12 ( , ♦, and 6) or first increased and then decreased (events 6 , 10 and 11 ×).However, analyzing the data on a diurnal scale, we observed a nocturnal trend (Fig. 2) for at least six of the cloud events with highest δ c values during nighttime and lowest during daytime (Sect.3.2.3).Furthermore, events 7 (♦) and 12 (6) clearly differed from the other events with strongly increasing δ c values with gradients of 0.6 per hour for δ This question will be addressed in the next section by using the box model presented in Sect.2.4 with measured temperature and vapor fraction as variables, and constant δ values of the water vapor feeding the cloud.

The contribution of condensation fractionation
The modeled isotopic dew point composition of the vapor (δ v,dew , a result from model runs A) showed a similar diel pattern as the δ c values of the condensed phase (Fig. 4a  and c).Moreover, modeled monthly averages of δ v,dew values for September and October were slightly more negative than the δ values measured in the water vapor of an 8-yr data set (Fig. 4b and d), taken on a 1 to 2 day basis in Heidelberg, located 227 km upwind of the Schm ücke site (Jacob and Sonntag, 1991).This could result from the higher altitude of the measurement site or the uncertainty of the modeled δ v,dew values.However, the δ values and their seasonal pattern were similar enough to assume that our modeled δ v,dew values are reasonable and can be used as an input for model runs B.More generally, these results suggest as well that the δ values of water vapor can be estimated from cloud water samples.
Based on model runs B, the criterion for local condensation during the observation interval (Sect.2.4) was fulfilled for part of the measurement period of event 6 (filled red in Fig. 4a and c) and 9 ( ).We therefore consider the temporal evolution of the δ c values of these events to be basically determined by the local condensation process.The criterion was also met for two cloud samples of event 1 ( ) and event 10 ( ).
So, the temporal evolution of the isotopic signal of these two events was most likely affected mainly by an advected signal of the water vapor, and only for a short time by the condensation process.Moreover, cloud samples 1 and 2 of event 5 (red in Fig. 4a and c) met the local condensation criterion, additional cloud samples of event 9 ( ) and 10 ( ) for δ 18 O and finally the first cloud sample of event 6 for δ 2 H ( ).
As condensation is supposed to be reflected in both δ 2 H and δ 18 O values, these data points were not classified as condensation driven.However, we cannot rule out that there might be some condensation contribution to the latter events as well.For the rest 15156 Introduction

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Full of the events model results suggest the temporal pattern in δ c values to be caused by the temporal pattern in the δ values of water vapor feeding the cloud rather than by condensation processes.Interestingly, the two "condensation" cloud events (6 and 9) differ from the others in terms of overflow dynamics of the Schm ücke mountain ridge as derived from the Froude number F (Fig. 4e).Event 6 ( ) was categorized as stagnant area (F =2.33), and event 9 ( ) was categorized as stagnant flow (F =1.75).Hence, there was either a poor or no overflow over Schm ücke for both events.This means that the air mass feeding the cloud most likely did not flow up the hill but it rather reached the measurement site on the same height.Consequently, these air masses might have been more decoupled from local sources, leading to a rather constant isotopic composition of the water vapor feeding the cloud (considering this air mass well mixed).This suggests that stable water isotope ratio measurements in combination with a box model are a valid tool to complement meteorological sounding launches and tracer measurements to better assess whether cloud events can be qualified for the interpretation of Lagrangian type aerosol-cloud interaction experiment, where good overflow conditions are essential: A good agreement of the box model output produced by model run A and B with the measured data then corresponds to a rather poor overflow, while a disagreement of the modeled and measured time course in δ 2 c H and δ 18 c O indicates a better overflow over the mountain ridge.We strongly recommend this to be further evaluated with future experiments.

Frontal passages
Events 7 (♦) and 12 (6) were both characterized by a rather high mean d-excess (17.8 ± 0.4 for 7 and 19.5 ± 0.6 for 12, Fig. 3) and the highest gradients both in for 7 and 0.49 per hour for 12; Fig. 2).Both events were associated with a frontal passage.In the case of event 7 a weak warm front passed during the cloud event, and a cold front preceded the cloud event 12 (Tilgner et al., 2012) In what follows we therefore explain these results and discuss how they fit into the findings on front passages based on cumulative cloud samples and measurements of δ values of water vapor.
As both clouds formed from Greenlandic air masses the elevated d-excess of both events most likely resulted from the air mass origin (Sect.3.1.2).The d-excess values also remained rather constant during the events which suggests that the meteorological conditions at the moisture source region did not vary much during the event.Similar observations of elevated d-excess during the passage of a cold front have been measured by Wen et al. (2008) in water vapor in Beijing.Water vapor originating from evaporation sites with low relative humidities may be the common feature explaining this similarity in d-excess observations during the passage of a cold front.
Cold fronts have been associated with more negative δ values in cumulative cloud samples (Scholl et al., 2007) as well as in water vapor (White and Gedzelman, 1984;Wen et al., 2008).This agrees with our data: the cold front carried 2 H-and 18 O-depleted water vapor causing the low δ c values at the beginning of event 12. Continuous measurements of isotope ratios in water vapor also revealed that after the passage of a cold front, the δ values increased again (Lee et al., 2005).It is likely that we caught this increase of δ values with the clouds of event 12 (Fig. 2, 6).
Warm sectors have been associated with more positive δ values of the water vapor (White and Gedzelman, 1984).We therefore expect an increase in δ c values during the transition from the colder air in front of the warm front to the warm sector terminating the warm front.The DWD synoptic charts (Sect.2.3) do not allow to determine the exact time of the frontal passage, as the time resolution of the synoptic charts is 6 h which is longer than the duration of cloud event 7.However, the δ c values indicate the warm front passage: The δ c values of event 7 (Fig. 2, ♦) increased from the first cloud sample (which was similar to the ones from the previous cloud event (6, ) nine hours before) to the most positive δ c values measured in October.In summary, clouds associated with frontal passages were characterized by a strong increase in δ values and high d-excess, which is in agreement with what has been found for water vapor.However, further investigations of frontal passages are needed to compare the different changes in isotope ratios and effects of air mass origins, as frontal passages are linked to air mass changes and thus are important for air mass origin studies.In addition, understanding variability in isotope ratios is required to interpret monthly mean values of cloud water which have been used for hydrological and ecological studies (Scholl et al., 2007).

Diel patterns: local water vapor sources versus moisture source region
The δ c values of the cloud samples revealed a diel (nocturnal) pattern with highest values at night and lowest values during daytime (Fig. 2).This was especially pronounced in the two longest cloud events (1 and 10, both lasting 15 h) in δ Similar diel patterns -as well as peak to peak differences -have been measured for vapor phase δ values during dry conditions (below ≈80 % relative humidity) in populated areas (Lee et al., 2006;Wen et al., 2010) as well as above ecosystems (Lai et al., 2006;Welp et al., 2008).Different explanations have been given.Entrainment of 2 Hand 18 O-depleted vapor from the free atmosphere into the convective boundary layer at midday has been suggested (Wen et al., 2010).A combination of both atmospheric signal and biospheric signal (evapotranspiration) has been discussed as well (Lai et al., 2006;Welp et al., 2008).evaporation and transpiration to the total moisture content of this air mass was small.As fog droplets equilibrate quickly with the advected vapor (Spiegel et al., 2012) and as we could show that condensation only had a minor impact on the temporal evolution of these two events, the temporal pattern in δ c values that we measured in the cloud water was probably advected by the air mass.According to HYSPLIT ensemble trajectories analysis, the air mass feeding the cloud spent around 64 h (85 h) over land before reaching Schm ücke, for event 1 and 10, respectively (van Pinxteren et al., 2010).So for both events, significant uptakes over land could have occurred and we hypothesize the following processes that may have contributed to changes in isotopic composition of the fog droplets at Schm ücke (Fig. 5): 1.The variations in the δ values of the advected and entrained vapor results from processes associated with air parcel history such as rain out.
2. Changes in relative humidity and temperature in the moisture source region (influencing transpiration and evaporation) cause changes in the δ values of the advected vapor.
3. The temporal evolution of the isotopic composition of these events was determined by geographical changes in moisture source region with distinct evaporative conditions (relative humidity, temperature or isotopic signature of soil water).
These processes were evaluated by a HYSPLIT rain out analysis (1) as well as by applying the moisture source diagnostic (2 and 3) as described in Sect.2.3.Note that the rain out analysis has some degree of uncertainty as only a single trajectory was used.The rain out analysis showed strong rain out for the air parcels associated with the more depleted cloud water samples at the beginning of event 10 (Fig. 5b, column 1).A similar trend was observed for event 1, for which the rain out effect is smaller in amplitude.Hence, for both events, rain out prior to the arrival at Schm üecke (1) would support the measured time trends.The moisture source diagnostic revealed minor geographical changes in the moisture source region for both cloud events, so effect 3 can be ruled out.Event 1 is char-Introduction

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Conclusions
We presented the first study resolving the temporal variation of δ2 H and δ 18 O values of cloud droplets during single cloud events.The samples were taken during the HCCT-2010 campaign on Schm ücke in central Germany.We focused on attempts to explain the temporal evolution of the isotopic signature on both event-and campaign scales.
Our major conclusions are the following: 1. Seasonality was reflected in δ Discussion Paper | Discussion Paper | Discussion Paper | Abstract In this work, we present the first study resolving the temporal evolution of δ 2 H and δ 18 O values in cloud droplets during the course of 13 different cloud events.The cloud events were probed on a 937 m high mountain chain in Germany in the framework of the Hill Cap Cloud Thuringia 2010 campaign (HCCT-2010) in September and October 2010.δ values of cloud droplets ranged from −77 to −15 (δ 2 H) and from −12.1 to −3.9 (δ 18 O) over the whole campaign.The cloud water line of the measured δ values was δ 2 H = 7.8×δ 18 O+13×10 during the transition from water to vapor and a kinetic fractionation during diffusion through the saturated transition layer.As the diffusivity D decreases with increasing mass (D1 H 2 18 O < D1 H 2 H 16 O ), the water vapor above the saturated transition layer is more Discussion Paper | Discussion Paper | Discussion Paper | Interpretation of the deuterium excess (d-excess) Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Sect. 2.4, we use the liquid water content (LWC) of the cloud which was measured close to the tower by a PVM (Particulate Volume Monitor).The PVM measured the LWC by means of light scattering.A detailed description of the PVM measurement setup can be found inGerber (1991) andArends et al. (1994).2. Meteorological measurements: The temperature and air pressure measurements needed to initialize the box model (see Sect. 2.4 for details) were obtained from the weather station (Vantage pro) which was mounted on the tower at Schm ückeDiscussion Paper | Discussion Paper | Discussion Paper |

dimensionless
Froude number was deduced to characterize the overflow over the Discussion Paper | Discussion Paper | Discussion Paper | ) where g is the gravitational acceleration [m s −2 ], θ the potential temperature [K], dθ dz is the vertical gradient of the potential temperature [K m −1 ], h m is the barrier height [m], and U mean wind speed [m s −1 ].For Froude numbers below 1, an model level (1 to 10), higher up only every second level (10 to 20) and every fifth level (20 to 40).In total 96 trajectories were computed five days backward in time every hour during the selected events.Several meteorological variables including specific humidity, 2 m temperature, 2 m dew point temperature, skin temperature, and boundary layer height, were interpolated along the backward trajectories.Moisture uptake locations were identified by changes in specific humidity along the trajectories.Only increases in specific humidity in the boundary layer were Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ratio w, which itself was deduced from the local temperature, air pressure and LWC.
3.1), and then zoom in from the campaign scale to the event scale by focusing on the temporal evolution of individual cloud events (Sect.3.2).
Discussion Paper | Discussion Paper | Discussion Paper | of the Schm ücke cloud samples was rather high (10 to 20 Discussion Paper | Discussion Paper | Discussion Paper | Sect. 3.2.2).Independent of whether the cloud that we measured formed at the Schm ücke or had already formed further away, the forced uplift of the air mass at Schm ücke continuously fed the cloud with water vapor.As cloud droplets quickly achieve isotopic equilibrium with their surrounding vapor we suggest two different reasons for the observed variations in δ c values during the cloud events: 1.The observed variation results from changes in temperature and condensed fraction, changing the δ values during condensation.2. The observed variation is a consequence of changes in the δ values in the water vapor feeding the cloud.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | have been published yet resolving the isotopic composition of cloud water right after the passage of a cold front or during the passage of a warm front.
Discussion Paper | Discussion Paper | Discussion Paper | : during event 1 ( ) the δ c values increased and during event 10 ( ) it first increased and then decreased.The peak-to-peak difference was 14.4 , 1.2 , and 4.4 for event 1 and 8 , 0.5 , and 3.8 for event 10 in δ 2 c H, δ 18 c O and d-excess, respectively.
Changes in isotopic composition of an air mass are either related to changes in temperature or rain out or by incorporation of new vapor through entrainment, transpiration or evaporation.The air mass measured at Schm ücke most likely had a high relative humidity prior to condensing to a cloud.Hence, we assume that the contribution of local 15159 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ity for event 10.Further measurements of water vapor and cloud water during different meteorological conditions are needed to quantitatively assess the importance of the mechanisms of local transpiration, boundary layer dynamics and mixing of advected free atmospheric water vapor into the boundary layer.

Fig. 2 .Fig. 4 .
Fig. 2. Diurnal evolution (shown from 10:00 am to 10:00 am next day) of the δ 2 c H and δ 18 c O values (including measurement uncertainty Eq. 3) for all measured cloud events during HCCT-2010: The different cloud events are represented with different symbols as summarized in Table 1.Different colors represent (a) the temperature range and (b) the predominant airmass during the cloud event as classified by Tilgner et al. (2012).

Fig. 5 .
Fig. 5. Panel (a): Suggested effects causing the δ c variation during cloud events 1 and 10: 1 rain out along the trajectory, 2 meteorological changes in the source moisture region or 3 change of source moisture region.Panel (b): Illustration of mechanism 1, 2 and 3 for event 1 and 10.First column: cumulative rain along the HYSPLIT single trajectory, for every measurement point as described in Sect.2.3.Second column: geographical visualization of the moisture source region deduced by the moisture source diagnostics Sect.2.3.Third column: temporal evolution of the relative humidity (blue) and temperature (red) at the moisture source region for the cloud sampling intervals indicated in black and gray.