Introduction
The hydrological cycle of the atmosphere is usually investigated by water
vapor concentration measurements. A new dimension is opened by the analysis
of H2O stable isotope ratios, which are modified by H2O
phase changes during evaporation, cloud formation, and in-cloud physics. The
main reason for this fractionation is the difference between vapor pressures
of stable isotopologues such as H216O and HD16O,
which results in a preferential condensation of HD16O. In
consequence, not only specific humidity and dew point temperature but also
the isotope concentration ratio
RD=[HD16O]/[H216O] – commonly referred to as
δD=RD/RD,VSMOW-1 with RD,VSMOW=0.00031152
– decrease in a cooling and raining air mass. The resulting relation
between condensation temperature and isotope ratios of water vapor or
precipitation is the basis for a variety of applications. Water isotopes in
ice cores are used for the high-resolution reconstruction of
paleotemperatures . The
temperature-induced gradient of isotope ratios with altitude makes water
isotopes in precipitation a proxy for paleotopography
. Hydrological studies exploit the
altitude dependence and a seasonality of isotope ratios in precipitation to
investigate groundwater formation .
Isotope fractionation during evaporation at the ground level
post-depositionally modifies the isotope ratio of water at the surface and in
the upper soil layers . Because the various isotope
applications rely on a close relation between the isotope ratio of
precipitation and the isotope ratio of water at the surface,
post-depositional effects increase the uncertainty of the isotope
applications. Most problematic in that context are systematic
post-depositional changes of isotope ratios into one direction, which most
likely affect water reservoirs with long exposure to the atmosphere such as
water intercepted in the snowpack.
Several studies report post-depositional enrichment of heavy isotopes in the
snowpack. Such enrichment was observed over a wide range of temperatures and
even at skin temperatures far below the freezing point.
observed enhanced δD values in depth-hoar layers in South Pole firn.
To explain the observations, the authors suggested non-fractionating
sublimation with part of the sublimated vapor fractionating recondensing and
part of the sublimated vapor escaping the snow layer.
analyzed surface layer snow from Switzerland (2450 m a.s.l.) during an
8-day fair weather period with air temperatures between about -5 and
0 ∘C. They observed a continuous increase of δD values in
the aging surface layer snow, which they attributed to fractionating
evaporation or sublimation of snow. report a similar
experiment from the Chilean Andes (5536 m a.s.l.) with air temperatures
between about -12 and -5 ∘C. They observed an increase of
δD values in the aging surface layer snow as well. They attributed
this increase to kinetic fractionation during sublimation, caused by smaller
coefficients of diffusion of the heavier isotopes. Based on a seasonal
increase of δD values in a snowpack in northern Norway
(∼ 900 m a.s.l.), suggested fractionating evaporation of meltwater
and subsequent recrystallization of residual meltwater. Consistent with this,
report an altitude-dependent seasonal modification of
δD values in an alpine snowpack, which they attributed to
fractionating evaporation of meltwater during ablation.
A clear assignment of such observations to a specific type of interaction
between snowpack and atmosphere or to certain meteorological conditions is
difficult, as continuous time series of high-resolution isotope profiles of
the snowpack are hard to obtain. Furthermore, processes such as meltwater
percolation, diffusion, sublimation, and deposition within the snowpack
additionally modify the profiles of isotope ratios, making an interpretation
even more challenging. The current understanding of fractionation during the
evaporation of snow is therefore highly uncertain and stretches from
non-fractionating layer-by-layer sublimation of snow without any modification
of isotope ratios in the snowpack
to systematic enrichment of heavy isotopes in the snow in
consequence of fractionating evaporation of meltwater .
This uncertainty complicates interpretation of isotope records
from snow-covered regions, as the contribution of the various potential
post-depositional effects may be different during climatologically different
time periods. In addition, this uncertainty limits the isotope modeling of
atmospheric moisture sources in snow-covered regions, as isotope-enabled
models in general only consider one of the types of isotope fractionation for
the sublimation of snow e.g.,.
In this context, continuous observations of isotope ratios of low-level water
vapor may provide new insights by offering the opportunity to investigate
fractionation during evaporation from the snowpack from a complementary point
of view. A case study by investigates local variations of
δD values in water vapor subsequent to a winter storm. Based on
observations on a research tower, the study partitioned the different surface
fluxes by assuming local evapotranspiration to consist of non-fractionating
sublimation and fractionating evaporation of meltwater.
A promising way to extend the investigation of isotope fractionation during
evaporation of snow to the remote moisture source regions is the combination
of isotope observations with Lagrangian isotope modeling. A number of earlier
studies applied Lagrangian isotope modeling along idealized climatological
trajectories or along individual back
trajectories
.
As these studies mainly focused on polar or marine regions, the applied
Lagrangian isotope models are not optimized to simulate continental
evapotranspiration in the middle latitudes or evaporation of snow or
meltwater at temperatures close to the freezing point.
In this paper, we present a time series with 17 months of continuous
measurements of δD in low-level water vapor in central Europe. The
measurements cover two winters, which were marked by a number of cold snaps
and related snowfall. By combining the δD observations during the cold
snaps with a new Lagrangian isotope model, we investigate isotope
fractionation at snow-covered moisture sources.
In the following, we present the Lagrangian isotope model
(Sect. ) and characterize the uncertainty of our δD
measurements (Sect. ). In Sect. , we
relate variations of the δD to air temperature and specific moisture
source regions during cold snaps. In Sect. , δD values
during cold snaps are analyzed with respect to isotope fractionation during
surface evaporation in snow-covered regions.
Lagrangian moisture diagnostics
Back trajectories
Isotope ratios to be analyzed in this paper were measured at a site near
Karlsruhe in central Europe (49.10∘ N, 8.44∘ E;
110.4 m a.s.l.). Kinematic 5-day back trajectories from the site were
calculated with the Hybrid Single Particle Lagrangian Integrated Trajectory
model 4.0 with a time resolution of 1 h.
Three-dimensional wind fields for trajectory calculation were derived from
the Global Data Assimilation System (GDAS) of the National Centers for
Environmental Prediction (NCEP) .
The GDAS data (V1.5) are available for every 3 h at a 1×1∘
horizontal resolution and on 23 sigma pressure levels between 1000 and
20 hPa. To account for uncertainty of the back trajectories, we used
trajectory ensembles. Each ensemble consists of nine trajectories, starting
30 m above ground level at the measurement site as well as 50 km N, NE, E,
SE, S, SW, W, and NW from the site. Back trajectories were calculated for
every 3 h of measurement time and were initialized at 00:00, 03:00, 06:00,
09:00, 12:00, 15:00, 18:00, and 21:00 UTC.
Lagrangian diagnostic of moisture sources
In order to identify major source regions of low-level water vapor in
Karlsruhe, we applied a Lagrangian source region analysis similar to the
method described by . The method traces air parcels
along kinematic 5-day back trajectories and analyzes changes of specific
humidity (q) in time intervals of 1 h. Such changes are possible due to
the formation of precipitation (P), evaporation from the ground (E),
evaporation of falling rain, air mass mixing due to convection or small-scale
turbulence, diffusion, and numerical errors. In addition, using GDAS wind
fields with 3-hourly time resolution for the calculation of back
trajectories – which is a coarse time resolution compared to the GDAS time
steps on the order of minutes – may cause deviations between the HYSPLIT
back trajectories and the exact trajectories that air parcels followed in the
GDAS. This, in turn, may result in artificial changes of q along the
HYSPLIT back trajectories. For instance, HYSPLIT trajectories not fully
capturing diurnal vertical movement in consequence of thermal expansion of
the atmospheric boundary layer (ABL) in the GDAS may show artificial diurnal
changes of q. Like and , we assume
P and E to be the dominant processes and ignore the other effects. To
avoid an overestimation of the formation of precipitation and moisture uptake
in consequence of potentially artificial diurnal variations of q along the
HYSPLIT trajectories, we smoothed q along the back trajectories with a
24 h rectangle function. Using these simplifications, the change of specific
humidity (Δq) per time step (Δt) is
Δq/Δt=(E-P)/Δt.
Assuming further that either E or P are dominating , the
net change Δq/Δt per time step is attributed to only E or
to P. Corresponding to these assumptions, an identified decrease of
specific humidity is attributed to the formation of precipitation. In cases
of a positive increment of specific humidity, the method assumes moisture
uptake from evaporation at ground level. In this case, the contribution of
surface evaporation (fm) in a time interval m to total specific humidity
at the end of this time interval (qm) is
fm=Δqmqm.
The formation of precipitation in a later time interval does not affect the
fm calculated for the earlier time interval. In contrast to that, further
moisture uptake in a later time interval n reduces the relative
contribution of surface evaporation in the earlier time interval m to the
moisture at the end of n. In this case, the contribution from earlier
moisture uptake to qn is recalculated according to
fm′=fm⋅qn-Δqnqn.
In cases of moisture uptake above the ABL an
attribution to surface evaporation is not directly evident. A maximum
altitude for consideration of moisture uptake might therefore be appropriate.
However, relate moisture uptake at higher levels for
trajectories starting from Rietholzbach in northern Switzerland to the
outflow of shallow convection. As moisture in Karlsruhe and Rietholzbach
originates from similar source regions, we also do not assume a maximum
altitude for the consideration of surface evaporation.
Lagrangian isotope model
A Lagrangian isotope model was developed to serve as a benchmark for our
δD measurements. Like the Lagrangian moisture source diagnostic, the
model runs along kinematic 5-day back trajectories and attributes changes
of specific humidity (q) to the formation of precipitation or moisture
uptake from surface evaporation. Analogous to the moisture source diagnostic,
the model does not apply a maximum altitude for moisture uptake, smoothes q
along the trajectories with a 24 h rectangle function, and uses the same
trajectory ensembles. From each trajectory ensemble, nine modeled δD
values for Karlsruhe are obtained, which are combined to one average value by
weighting the nine values with q at the arrival.
Dehydration
A decrease of specific humidity in a time interval indicates the formation of
precipitation. Because of preferential fractionation of D into the liquid
phase, the formation of precipitation results in a decreasing isotope
concentration ratio (RD=[HD16O]/[H216O]) in an
air mass. Assuming immediate rainout of the condensate, we simulate the
change of isotope ratios of the residual water vapor according to the
Rayleigh distillation model . Based on a fractionation
factor αD(T), this Rayleigh model calculates changes of RD for
infinitesimal changes of specific humidity q:
dlnRD=[αD(T)-1]⋅dlnq.
Specific humidity and air temperatures along the back trajectories were
derived from the identical GDAS data set used for the calculation of the back
trajectories. Under equilibrium conditions αD only depends on
the temperature (T) of an air parcel and increases from about 1.082 to
1.240 between +20 and -40 ∘C. For T>=0 ∘C we use a
parameterization of αD over liquid water .
For T<0 ∘C, we assume enhanced fractionation over ice and use the
parameterization of . A reduction of the fractionation
factor αD(T) in the case of supersaturation in ice clouds
by a factor on the order of 0.964 to 1
between -40 and 0 ∘C was considered following ,
with a supersaturation parameter λ of 0.004 according to
.
Moistening
In the case that specific humidity increases in a time interval
[t1, t2], we assume a permeable air parcel which
takes up moisture Δq by turbulent mixing. We attribute that moisture
to evaporation at ground level with the isotope ratio RD,E, which
was lofted via small-scale turbulence or convection to the trajectory level.
To calculate the corresponding change of RD of moisture in the
tracked air parcel, we apply the following mixing equations:
q(t2)=q(t1)+Δq,RD(t2)=RD(t1)⋅q(t1)+Δq⋅RD,E(t1)q(t1)+Δq.
If the entrained moisture from evaporation was transported via small-scale
turbulence to the trajectory altitude, mixing with air from below the
trajectory level is likely. However, applying the above equations, changes of
RD and q in consequence of mixing with low-level air masses are ignored.
We therefore implicitly assume that the air masses below the trajectory level
experienced a similar transport and precipitation history as the tracked air
parcel. RD and q of the tracked air parcel are not affected by mixing
with that air from below, only the by freshly evaporated moisture.
Depending on the type of ground and skin temperature (Tskin), we
calculate RD,E assuming evaporation from the ocean
(RD,E_ocean), continental evapotranspiration (RD,E_ET),
evaporation of melted snow (RD,E_snowevap.), or sublimation of
snow (RD,E_snowsubl.).
For evaporation from the ocean, we assume vapor pressure fractionation over
liquid water with αD(Tskin) according to the
parameterization of and Tskin at the trajectory
position. To account for additional kinetic fractionation during evaporation
on the order of αD,kin= 1.002–1.007
due to different coefficients of diffusion of
the different water isotopologues, we increased the αD by the
factor αD,kin=1.005. Please note that αD,kin
is much smaller than the vapor pressure fractionation factor
αD. Therefore, considering a dependence of
αD,kin on environmental conditions is less important for
RD than for other frequently used isotope ratios such as
[H218O] / [H216O]. Given the above assumptions, the
isotope ratio of evaporated moisture RD,E_ocean only depends on
the surface temperature and the isotope ratio of sea surface water
(RD,ocean) at the trajectory position:
RD,E_ocean=RD,oceanαD(Tskin).
We derived RD,E_ocean from observations of δD and
δ18O of ocean surface water collected in the Global Seawater
Oxygen-18 Database . Since little data with the δD
of ocean surface water exist, we calculated a median
δD/δ18O ratio of 6.56 from
, , , , , , ,
and and used this ratio to calculate δD from
δ18O data. The δ18O of ocean surface water along the back
trajectories was derived from the spatial 1×1∘ interpolation
(V1.1) of the Global Seawater Oxygen-18 Database by
(Fig. a), which we linearly interpolated to the locations
along the trajectories. To derive skin temperatures representative of
conditions during maximum evaporation (Tskin), we weighted skin
temperatures along the trajectories (Tskin,unweighted) with
positive surface latent heat flux in time intervals of ±2 h
(Fig. ). If there were less than 12 trajectory points (time
resolution of 1 h) with significant latent heat flux above
2 Wm-2 in an interval, it was extended for ±1 h until it
contained 12 data points. Tskin,unweighted and accumulated surface
latent heat fluxes along the back trajectories were derived from a reduced
GDAS data set with the same horizontal resolution of 1×1∘ as
it was used for the calculation of the back trajectories but with data only
every 6 h. The data were interpolated linearly in space and time to the
locations along the trajectories. The accumulated surface latent heat flux
from the GDAS was divided by 6 to account for the hourly resolution of the
trajectories.
(a) δD of ocean surface water derived from the
interpolation (V1.1) of the Global Seawater Oxygen-18 Database by
, assuming a constant factor of 6.56 between δD
and δ18O in ocean surface water. (b) Climatological
δD in precipitation in winter (DJF) from the Regionalized
Cluster-based Water Isotope Prediction (RCWIP), which in turn is based on
observations of the Global Network of Isotopes in Precipitation (GNIP).
(c) Same as in (b) but for summer (JJA).
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Over the continent, evapotranspiration consists of evaporation from the bare
soil, transpiration of plants, and evaporation from canopy interception. As a
first simplification we ignore canopy interception; i.e., we consider
condensation with subsequent complete re-evaporation as a neutral process
with respect to q and RD. Moisture from the two other sources strongly
differs in isotopic composition.
Evaporation from the bare soil is accompanied by isotope fractionation
. To calculate the isotope ratio of moisture evaporated from
the bare soil (RD,E_soilevap.), we assume vapor pressure
fractionation over liquid with αD(Tskin) according to
the parameterization of . To account for kinetic
fractionation during evaporation from the soil on the order of
αD,kin= 1.017–1.025 , we increased
αD by the factor 1.021. We further assume the isotope ratios
of soil water to be the same as the isotope ratios of precipitation
(RD,prec.):
RD,E_soilevap.=RD,prec.αD(Tskin).
(a) Probability distributions of continental GDAS skin
temperatures (Tskin,unweighted) (blue) and of skin temperatures
weighted with the accumulated hourly latent heat flux at ground level within
±12 h (Tskin) (green). The occurrence of low temperatures
is reduced as a consequence of the weighting. A peak around 0 ∘C
becomes more clearly visible. (b) Illustration of the weighting
algorithm for one exemplary back trajectory (arrival in Karlsruhe 4 May 2012, 21:00 UTC).
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Observations of RD,prec. have been collected in the Global Network
of Isotopes in Precipitation (GNIP) of the IAEA since the
1960s. We used climatological monthly means of RD,prec. of the
Regional Cluster-based Water Isotope Prediction (RCWIP) ,
which provides a spatial interpolation of the GNIP data. RCWIP data are
available with a horizontal resolution of 0.17×0.17∘
(Fig. b, c) and were linearly interpolated to the locations
along the trajectories. Because soil water in central Europe is generally
frequently recharged by precipitation, we ignore systematic enrichment of
HD16O in the uppermost soil layer caused by continuous
fractionating evaporation from the soil. For instance, measurements of
precipitation amount at the measurement site in Karlsruhe indicate recharge
of soil water around the site by more than 1 mm precipitation per day on
average every 2.9 days. The assumption regarding enrichment is supported by
findings of , who observed insignificant systematic deviations
of isotope ratios of water within the upper 15 cm of the soil from isotope
ratios in precipitation at several sites in France, Germany, and the Czech
Republic.
In contrast to bare soil evaporation, plants take up soil water from the soil
and transpire that water completely, and therefore there is no fractionation into
the atmosphere. Still some fractionation is possible on short timescales due
to asynchronous accumulation and the release of HD16O and
H216O in leaves . However, this process averages
out over a day and is therefore ignored for
modeling isotope ratios along the 5-day back trajectories. To calculate the
isotope ratio of moisture originating from plant transpiration, we assume
RD,E_transp.=RD,prec..
The isotope ratio of total evapotranspiration (RD,E_ET) depends
on the fraction of plant transpiration (FT) on total evapotranspiration:
RD,E_ET=RD,E_soilevap.⋅(1-FT)+RD,E_transp.⋅FT=RD,prec.αD(Tskin)⋅(1-FT)+RD,prec.⋅FT.
FT varies with region and on seasonal, synoptic, and diurnal timescales. For
modeling we ignore these variations and use a constant fraction of
transpiration. Based on , , and
we assume an average FT in Europe of 0.7.
Whenever we observe moisture uptake at continental skin temperatures below
0 ∘C, we ignore transpiration of plants (FT =0) and attribute the
moisture to the evaporation of melted snow or ice. In this case, we again
assume equilibrium fractionation over liquid. We further assume the isotope
ratio of snow to be the same as the climatological monthly means of the
RCWIP:
RD,E_snowevap.=RD,prec.αD(Tskin).
In Sect. we investigate a possible role of snow sublimation.
In this case we define a skin temperature Tsubl,max. Below that
temperature we assume complete layer-by-layer sublimation of snow without
isotope fractionation:
RD,E_snowsubl.=RD,prec..
Initialization
To initialize RD of air masses originating from altitudes
below 2 km above ground level, we assume isotope ratios (RD,ini)
in a convectively well-mixed ABL, where water
vapor and ocean surface water or soil water are in isotopic equilibrium:
RD,ini,< 2km=RD,ocean|prec.αD(Tskin).
Illustration of the isotope modeling for one exemplary back
trajectory (arrival in Karlsruhe on 18 March 2012, 00:00 UTC).
(a) Altitude of the back trajectory (black) and terrain height (gray/blue). The
isotope model was initialized at 80∘ N in the marine boundary layer
(MBL). In a low-pressure system near Iceland the tracked air parcel ascended
to an altitude of 3200 m. During the last 3 days of transport to
Karlsruhe the air parcel was sinking to the sampling altitude.
(b) After initialization in the MBL, specific humidity q (light blue and green
colored line) and δD values (thick black line) of the tracked air
parcel were slightly decreasing due to the formation of precipitation
(dashed light blue lines) within the first day. More pronounced formation of
precipitation, in consequence of lofting in a low-pressure system near
Iceland, resulted in a second decrease of q and the modeled δD value
dropped accordingly. Due to moisture uptake (green lines) related to a
descent of the air parcel in the subsequent days, q and the δD value
increased until the air parcel reached Karlsruhe. Thin black curves
illustrate the modeled δD for different initializations of δD
(Sect. ). The dependence on the initialization
decreases with the amount of moisture uptake along the trajectories and is
only low in Karlsruhe.
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For initialization at surface temperatures above 0 ∘C, we
calculate fractionation factors according to the parameterization of
. At skin temperatures below 0 ∘C we assume
fractionation over ice and apply the parameterization of
.
For air masses originating from an altitude (h) higher than 2 km above
ground level we assume a linear decrease of RD,ini from boundary
layer ratios at an altitude of 2 km to RD,10km of 0.45
at an altitude of 10 km:
RD,ini,> 2km=RD,ini,< 2km-(RD,ini,< 2km-RD,10km)h-2km10km-2km.
The dependence of modeled isotope ratios from initialization decreases with
moisture uptake along the back trajectories (Fig. ). The
uncertainty of RD,ini is especially strong at high altitudes, where
air masses are strongly dehydrated and have a long history of isotope
fractionation. Because air masses from high altitudes take up a lot of
humidity during descent and transport to Karlsruhe, the uncertainty of
RD from initialization is strongly reduced in Karlsruhe. Back
trajectories corresponding to smaller moisture uptake typically originate
from the ABL. In Karlsruhe RD of these back trajectories depends
more strongly on RD,ini, which is much better
defined within the ABL. Considering typical atmospheric moisture residence
times in the range of 4–8 days , using
10-day back trajectories instead of 5-day back trajectories should almost
eliminate model uncertainty of the δD at Karlsruhe from uncertainty of
RD,ini. However, such long trajectories easily cover distances of
10 000 km, which makes the modeled δD sensitive to potentially very
different conditions in distant regions. This, in turn, makes uncertainty
assessment of the modeled δD more complex. Using 5-day back
trajectories is therefore a trade-off between a reasonably small sensitivity
of the modeled δD at Karlsruhe on RD,ini and concentrating
the analysis to the North Atlantic and Eurasia. Whenever it is necessary for
the interpretation of our results, we assess uncertainty of the modeled
δD from the model initialization by changing RD,ini in
different model runs.
Measurements
Isotope water vapor measurements
The concentrations of H216O and HD16O in low-level water
vapor were measured for 17 months on a research campus 12 km north of
Karlsruhe in southwestern Germany (49.10∘ N, 8.44∘ E;
110.4 m a.s.l.).
For the continuous measurements we used a Picarro water isotopologue analyzer
L2120-i, which analyzed the ambient water vapor with a sampling rate of
0.6 Hz. The measurement technique is based on cavity ring-down spectroscopy,
where the beam of a tunable diode laser is directed through a cavity, filled
with the air to be analyzed. Based on the ring-down time of the laser light
intensity, absorption spectra are measured between 7183.5 and
7184 cm-1. A characterization of two similar analyzers (L1115-i
and L2130-i) can be found in . Please note that the
isotopologue analyzer also measures concentrations of the water isotope
H218O. As the isotope concentration ratio
R18O=[H218O]/[H216O] is more sensitive to
kinetic fractionation than RD, it would be essential to more
detailed consider kinetic fractionation during evaporation for modeling
R18O with the Lagrangian isotope model. Uncertainty of the
modeled R18O related to uncertainty of the kinetic
fractionation factor does not allow deeper insight from analysis of
RD and R18O than from analysis of RD
alone. Thus, we do not use the H218O measurements in this paper.
The Picarro water isotopologue analyzer was located on the sixth floor of the
Institute of Meteorology and Climate Research – Atmospheric Trace Gases and
Remote Sensing of the Karlsruhe Institute of Technology. A downward-facing
inlet funnel was installed 1 m above the edge of the roof, which corresponds
to an altitude above ground level of 28 m. The connection to the inlet was
established with a 6 m long tube with a diameter of 6.4 mm. To reduce wall
effects, we permanently flushed the inlet line with
30 standard L min-1 of air and used tubing made of electropolished
stainless steel. To avoid condensation, the wall temperature of the 5 m of
tubing inside the building was regulated to 22 ∘C. Saturation
humidity corresponding to this temperature is above the analyzer limit of
14.9 gkg-1. On 5 days in August 2012, humidity slightly exceeded
that analyzer limit. Corresponding measurements were removed from the time
series.
(a) Deviation of individual calibration measurements with
the Picarro water isotopologue analyzer from isotope ratios of the liquid
standards. Each point represents a calibration of 1 h. Dots: Standard 1;
crosses: Standard 2. (b) Humidity dependence of the δD
measurements. Long-term drift depicted in (a) was removed
in (b) by subtracting the 5-week running average of calibrations.
Red regression lines were calculated for both standards simultaneously by
subtracting the mean difference between both standards. ± gives the
difference between the slopes calculated for the first and second half of the
measurement period.
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We report isotope measurements in the δ-notation, which normalizes
isotope ratios to a standard scale, defined by the Vienna standard mean ocean
water (VSMOW: δD=0 ‰) and standard light Antarctic
precipitation (SLAP: δD=-428.0 ‰) . For
the automated calibration of the analyzer we applied a Picarro standard
delivery module (A0101), which allows the alternating injection of two
different water standards into a Picarro vaporizer (A0211). In this vaporizer
the liquid standards immediately evaporate in a constant flow of dry
synthetic air (140 ∘C, 0.3 standard L min-1 dry air flow with
1.2 mgkg-1 residual humidity). A two-point calibration was done
for 2 h every 10 h at δD of -62.1 and -142.2 ‰.
Instrumental drift during the 17 months was below 3 ‰
(Fig. a). The total accuracy of our δD measurements due
to uncertainty of calibrations and instrumental drift between two
calibrations was 0.98 ‰. The 0.6 Hz precision of the measurements
was below 1 ‰ and can be ignored for 10 min averages shown in this
paper.
Based on the two-point calibrations, we applied linear stretching to the
measurements. Sixty-three percent of our observations are within the range of
isotope ratios covered by the two standards. To approve linearity of the
applied correction for isotope ratios below that range, we performed repeated
calibrations with a third standard (δD=-245.3 ‰) in
the 2 years subsequent to the campaign. We found additional uncertainty of
δD at -245.3 ‰ due to a slight nonlinearity of the applied
correction to be smaller than 0.3 ‰, which is in agreement with the
more detailed characterization of .
To identify a potential humidity dependence of the isotope ratio
measurements, we generated three humidity levels between 1.8 and
13.7 gkg-1 during each calibration. We found the humidity
dependence of the instrument to be smaller than the uncertainty of individual
calibrations. Therefore, we only applied the average humidity dependence
found using all calibrations of
-0.021 ‰ (g kg-1)-1 to the data set
(Fig. b).
Meteorological data at the measurement site
Observations of specific humidity at the measurement site were derived from
the Picarro isotope analyzer. For calibration of the Picarro humidity
measurements we used observations of a VPT6 Thygan dew point mirror
hygrometer (Meteolabor, Switzerland), which was mounted on a meteorological
tower 30 m above ground level 900 m in the west-southwest. Since the
topography at the measurement site is flat for some kilometers in all
directions, we assume the tower observations to be representative for the
measurement site. The dew point hygrometer performed a measurement of 1 min
every 10 min and has an uncertainty of ±0.1 K. Ten-minute averages of
specific humidity derived from the Picarro Analyzer and observations of the
dew point hygrometer show a correlation coefficient R of 0.9913. For the
calibration of the Picarro humidity observations we applied the mean linear
regression between hygrometer data and Picarro measurements of 1.13, which
varied by 1 % between the first and second half of the measurement
period.
The amount of precipitation was measured at the meteorological tower at
ground level with a time resolution of 10 min.
Analysis of seasonal and synoptic variations
In this section, we present the measurements from Karlsruhe, covering the
time period from January 2012 to May 2013. For this time period, we identify
specific circulation regimes related to cold snaps in Karlsruhe. Subsequent
to this, we examine the capability of the Lagrangian isotope model of
reproducing corresponding variations of δD.
Source regions of moisture (q) 30 m above ground level in
Karlsruhe (black star) based on 5-day back trajectories for the time period
January 2012 to May 2013. The color code indicates the contribution of
different source regions to q in Karlsruhe in % per km2.
Integration over the whole map gives the total identified humidity of
47 %.
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Continental temperatures and zonal circulation
According to 5-day back trajectories, 85 % of the sampled low-level air
masses at Karlsruhe originated from altitudes below 2 km above ground level.
Consequently, most of the air masses were exposed to continuous moisture
uptake from surface evaporation during transport to Karlsruhe. For
identification of major source regions of the water vapor in Karlsruhe we
applied the Lagrangian moisture source diagnostic described in
Sect. . Based on this analysis, we found the North
Atlantic to be the most important moisture source (Fig. ),
from where westerlies transported the tracked air masses to the measurement
site. In addition to the predominantly westerly moisture transport,
inversions of zonal circulation in winter occasionally led to easterly
moisture transport. Because of the finite length of the 5-day back
trajectories, the total identified humidity is lower than 100 %. When humidity is smoothed along the back trajectories for 24 h, the total
identified humidity accounts for 47 %. If smoothing specific humidity for
12 h, diurnal and sub-diurnal variations in humidity are interpreted as the
formation of precipitation and moisture uptake, which increases the total
identified humidity to 63 %. Both numbers are in reasonable agreement
with more extended studies implying global atmospheric moisture residence
times in the range of 4–8 days .
Measurements in Karlsruhe from January 2012 to May 2013 30 m above
ground level (10 min averages). Black: 5-day back trajectories originate
from the west; gray: 5-day back trajectories originate from the east.
(a) δD of water vapor. Gaps in the time series are caused by
instrumental issues with analyzer and calibration device. (b) Air
temperature (T). (c) Specific humidity (q).
![]()
Air temperatures, specific humidity, and δD in Karlsruhe followed
similar seasonal patterns (Fig. ). In winter (December,
January, February), air temperatures 30 m above ground level (T) were on
average 2 ∘C. Towards summer (June, July, August), T increased to
on average 20 ∘C. Higher T in summer corresponds to a higher
saturation vapor pressure. That allows the transport of marine air to
Karlsruhe with less condensation in summer than in winter. Consequently
specific humidity (q) in Karlsruhe rose from 6 gkg-1 (DJF) to
14.8 gkg-1 (JJA). δD changed from -162 ‰ (DJF)
to -109 ‰ (JJA) and thereby consistently with T and q implies
a lower degree of condensation and rainout in summer than in winter.
The gray color in Fig. identifies circulation regimes with
easterly moisture transport (25 % of data). Such regimes predominantly
occurred in winter and resulted in the transport of continental air masses to
Karlsruhe. The corresponding air masses usually were marked by especially low
T and q, which led to pronounced cold snaps in Karlsruhe. Consistent with
the low T and q, the air masses during cold snaps showed an especially
low δD value.
Both findings – the seasonality of δD and the especially low
δD in cold, continental air masses from the east – are in good
agreement with the well-known “continental effect”. That effect describes a
decrease of δD in precipitation over continents with distance to the
coast (Fig. b), caused by the relation between δD and
degree of rainout. Since the δD of rain depends on the δD of
the water vapor it is formed from, it is reasonable to find a similar
continental effect imprinted to water vapor as well.
Comparison of measured and modeled <inline-formula><mml:math id="M279" display="inline"><mml:mi mathvariant="italic">δ</mml:mi></mml:math></inline-formula>D
The Lagrangian isotope model described in Sect. is
able to reproduce the observed slow seasonal variation of δD, as well
as the strong and relatively fast variations of δD due to circulation
regimes with predominantly easterly moisture transport in winter
(Fig. a, b). The mean difference between modeled and observed
δD (ΔδD) is -4.1 ‰. The correlation
coefficient R of modeled and observed δD is 0.82. Thereby, the
correlation calculated for different seasons strongly differs from the
overall correlation. For summer R is only -0.03 due to the small
variability of δD in this season. For winter R is 0.87.
Figure c shows the scatter plot of measured and modeled
δD. Furthermore, the figure illustrates the impact of the formation of
precipitation and surface evaporation on the modeled δD. If the
formation of precipitation is ignored in the model, the overall correlation
with the observations is still 0.79. The main reason for this is the relation
of observations with low δD values to easterly moisture transport.
Corresponding back trajectories are initialized with relatively low δD
values according to GNIP observations in the respective continental source
regions. This means that we only ignore the formation of precipitation in the
5 days covered by the back trajectories, but we implicitly consider the
formation of precipitation which determined the isotope ratios in
precipitation in the moisture source regions. However, due to surface
evaporation with relatively high δD values, the ΔδD in
this scenario is +23.9 ‰. If one considers the formation of
precipitation but ignores the surface evaporation, the overall R is reduced
to 0.62. The corresponding ΔδD is -34.8 ‰. So
consideration of both processes, surface evaporation and the formation of
precipitation, is essential for reproducing the observed δD.
(a) Time series of measured (blue) and modeled (red)
δD of water vapor in Karlsruhe. (b) Enlarged section
of (a), which demonstrates the capability of the model of capturing
the high variability of δD in winter. (c) Measured versus
modeled δD. The 3-hourly available modeled δD is compared
to the temporally closest 10 min average of the δD observations.
Gray: reference run, surface evaporation (E), and the formation of
precipitation (P) are considered (R=0.82, ΔδD =-4.1 ‰); green: only E is considered and P is ignored
(R=0.79, ΔδD =+23.9 ‰); magenta: only P is
considered and E is ignored (R=0.62, ΔδD =-34.8 ‰);
black: 1 : 1 line.
![]()
With respect to surface evaporation the air masses from the west and from the
east contain very different types of information. The air masses from the
west are exposed to moisture uptake from the ocean and to continental
evapotranspiration at relatively warm temperatures. Therefore they contain
information about isotope fractionation during evaporation from the ocean,
evaporation from warm land surfaces, and plant transpiration. This makes the
modeled δD of westerly air masses especially sensitive to simplifying
model assumptions regarding the δD of moisture from continental
evapotranspiration at warm skin temperatures. Increasing for instance the
fraction of plant transpiration on total evapotranspiration from 0.7 to 0.8
in the model increases the mean modeled δD from summer by
2.0 ‰. Assuming δD values of soil water which are
systematically increased by +10 ‰ increases the mean modeled
δD from summer by +2.4 ‰. In contrast to the westerly air
masses, the easterly air masses during cold snaps are sensitive to isotope
fractionation during surface evaporation at temperatures where snow and
meltwater exist. For these easterly air masses assumptions regarding
evapotranspiration at warm skin temperatures only have a small impact.
An interesting feature in Fig. a are spikes of low δD
values (blue), which are not reproduced by the model (red). We frequently
observed such spikes from spring to autumn. Potential processes causing the
spikes are the evaporation of rain below the cloud base or isotope exchange
between falling raindrops and the low-level water vapor. An impact of these
sub-cloud processes on the δD of low-level water vapor is demonstrated
for individual weather fronts by and
and is complementarily supported by observations of isotope ratios in
precipitation . As isotope
processes below clouds are not represented in the Lagrangian isotope model,
the observations related to sub-cloud processes cannot be further
investigated by means of this model. However, a relevant role of sub-cloud
processes for our observations of δD spikes is supported by the
strongly increased probability of precipitation during the spikes. For
respective observations with δD values smaller than expected from the
standard deviation between observed and modeled δD (23.5 ‰),
the probability to observe precipitation in Karlsruhe within ±3 h was
44 %, whereas for the other observations this probability was only
24 %. For the investigation of δD during cold snaps in winter
sub-cloud processes can be ignored, because the interaction between falling
precipitation and water vapor is strongly suppressed in cases of solid
precipitation.
<inline-formula><mml:math id="M336" display="inline"><mml:mi mathvariant="italic">δ</mml:mi></mml:math></inline-formula>D during cold snaps
The good agreement of modeled and measured δD in winter (R=0.87)
underlines the strong potential of the Lagrangian isotope model for analyzing
isotope processes in the remote moisture source regions during cold snaps. In
this section, we select respective observations and investigate isotope
fractionation during sublimation or evaporation at ground level.
Sublimation of snow or snowmelt evaporation?
Evaporation below 0 ∘C was historically often considered as
non-fractionating layer-by-layer sublimation of snow and ice
because of a low coefficient
of self-diffusion of water molecules in ice. However, this assumption ignores
snowmelt and fractionation during evaporation from the liquid phase, as
implied by , , and . Both
assumptions result in very different δD of moisture from evaporation.
In the case of non-fractionating sublimation, this δD equals the
δD of the snow. In the case of fractionating evaporation, the
δD value of moisture from evaporation is about 90 ‰ lower.
Our trajectory model provides an opportunity to test both formulations and to
assess which one is more reasonable for central Europe.
(a) Lagrangian source region analysis of low-level water
vapor in Karlsruhe for the observed cold snaps. The mean identified fraction
of moisture along the 5-day back trajectories is 48 %. (b) Mean
snow depth during the cold snaps based on GDAS data.
![]()
According to the Lagrangian moisture source diagnostic, only 5 % of the
humidity analyzed in Karlsruhe from January 2012 to May 2013 originated from
surface evaporation at locations with a GDAS-based snow depth greater than
0.5 cm. For this reason, we used data fulfilling three selection criteria
for the further investigation of isotope fractionation during evaporation
from snow-covered surfaces. First, we excluded observations which are
strongly affected by evapotranspiration at skin temperatures
(Tskin) above the freezing point. For this purpose, we identified
moisture uptake at Tskin>0 ∘C by means of the moisture
source diagnostic and excluded air masses with a respective contribution
above 2 %. For air masses meeting the first criterion the median
contribution of moisture evaporated at Tskin<0 ∘C is
28 %. The air masses with the smallest moisture uptake within the last
5 days are least sensitive to the δD of moisture from surface
evaporation and therefore do not allows us to robustly evaluate the model
description of isotope fractionation during the sublimation of snow and the
evaporation of meltwater. For a meaningful interpretation, we therefore only
used the half of data with a contribution from surface evaporation at
Tskin<0 higher than 28 %. Only 2 % of the corresponding air
masses originated from altitudes higher than 2000 m above ground level.
Since uncertainty of model initialization is especially high for these air
masses, we finally excluded the 2 % of air masses originating from high
altitudes.
Average differences between modeled and measured δD of the
174 selected data points (ΔδD), data points of group “cold”
(ΔδDcold), and data points of group “warm” (ΔδDwarm) from different model runs (M). ± states the
statistical uncertainty of the averages (root mean square error divided by
the square root of the number of observations). Values of particular interest
are printed in bold type.
Name of
Description of model run
ΔδD
ΔδDcold
ΔδDwarm
model run
MMW
fractionating evaporation of meltwater; (reference run)
-18.6±1.5 ‰
-26.9±1.8 ‰
-10.4±2.1 ‰
MMW+,snow
fractionating evaporation of meltwater; δD values of
-9.4±1.5 ‰
-17.5±1.9 ‰
-1.3±2.1 ‰
snow increased by 11.5 ‰
MMW+,ini
fractionating evaporation of meltwater; δD values at
-13.0±1.5 ‰
-21.7±1.9 ‰
-4.4±2.1‰
initialization increased by 11.8 ‰
MMW+,upt.36 h
fractionating evaporation of meltwater; reduced moisture
-16.3±1.6 ‰
-25.5±1.8 ‰
-7.1±2.2 ‰
uptake in consequence of smoothing q along the trajectories
with a 36 h broad rectangle kernel (instead of 24 h)
MMW+++
fractionating evaporation of meltwater; simultaneous
-0.9±1.6 ‰
-10.5±1.8 ‰
+8.6±2.2 ‰
occurrence of the three assumptions above
MS
non-fractionating sublimation of snow
+26.9±1.6 ‰
+24.8±2.6 ‰
+29.0±2.0 ‰
MS-,snow
non-fractionating sublimation of snow; δD values of
+17.1±1.6 ‰
+14.8±2.5 ‰
+19.4±2.0 ‰
snow decreased by 11.5 ‰
MS-,ini
non-fractionating sublimation of snow; δD values at
+25.2±1.7 ‰
+23.3±2.6 ‰
+27.2±2.0 ‰
initialization decreased by 3.6 ‰
MS-,upt.12 h
non-fractionating sublimation of snow; increased moisture
+26.9±1.6 ‰
+24.8±2.5 ‰
+29.1±2.0 ‰
uptake in consequence of smoothing q along the trajectories
with a 12 h broad rectangle kernel (instead of 24 h)
MS- - -
non-fractionating sublimation of snow; simultaneous
+16.0±1.6 ‰
+13.9±2.4 ‰
+18.2±2.0 ‰
occurrence of the three assumptions above
Some 174 of the 3-hourly modeled data points meet the three selection
criteria. They belong to 38 different days and were used for further
interpretation. Respective air masses mainly originated from the east
(Fig. a). The mean fraction of moisture identified for these
air masses by the moisture source diagnostic is 48 %. When
smoothing humidity along the back trajectories for 12 h instead of 24 h, the mean
identified fraction is 68 %. The GDAS data indicate the existence of
snow on the ground at 96 % of locations along the selected back
trajectories (Fig. b) with a median snow depth of
1.8±0.9 cm (± gives the interquartile range). The median
Tskin at the trajectory positions 5 days back was
-11.7 ± 5.1 ∘C. During transport to Karlsruhe the
Tskin rose on average by 6.0±3.3 K. A decrease of relative
humidity due to warming of the air masses was partially compensated by
moisture uptake and a corresponding increase of specific humidity by on
average 52±35 %.
Assuming equilibrium fractionation during evaporation of meltwater at
Tskin<0 ∘C in the reference run (MMW,
Table ), the model underestimates the selected δD
values by on average ΔδD=-18.6±1.5 ‰
(± gives the statistical uncertainty of the mean). Assuming
non-fractionating sublimation at Tskin<0 ∘C in a further
model run (MS, Table ) results in δD values
that are on average ΔδD=+26.9±1.6 ‰ above
the observations.
Considering the relatively high δD values of moisture from
non-fractionating sublimation and the about 90 ‰ lower δD
values in the case of fractionating evaporation, the difference of mean
δD between both model runs is qualitatively reasonable. To judge
whether
one run provides more realistic results, we tested if one of the two
scenarios could be brought into agreement with the observations when
considering uncertainty of model assumptions.
As the most important source of uncertainty for modeling the δD in
Karlsruhe during cold snaps we consider variability of the δD of
surface layer snow in the moisture source regions that is not fully captured
by the model. This variability may systematically affect (1) the assumed
δD of moisture from surface evaporation as well as (2) the δD
at the model initialization. In addition, (3) the amount of identified
moisture uptake needs to be accurate to reliably simulate the impact of
surface evaporation on the δD of water vapor.
In the following, we estimate uncertainty of respective model assumptions.
Subsequent to this, we vary the model assumptions in different model runs to
assess corresponding systematic uncertainty of the modeled δD.
The δD of moisture from sublimation or evaporation of meltwater
depends on the δD of the surface layer snow, which we assume to be
equal to the δD of precipitation. The δD of precipitation in an
individual year may systematically differ from the used climatological
monthly means of the RCWIP.
To assess typical interannual variability of δD of precipitation in
winter, we used data from 134 European and Russian GNIP stations from the
midlatitudes between 8.4 and 50∘ E with observations from at least
3 years. For each of the stations we calculated the mean δD in the
different winters (November, December, January, and February) and the
standard deviation of the winter averages. The mean of standard deviations of
the different stations was 11.5 ‰, which we assume to reflect the
mean interannual variability of δD in precipitation in the moisture
source regions related to cold snaps.
Because there is a general relation between surface air temperatures and
δD of precipitation in central Europe
, winter months from years with especially
high air temperatures and a potentially strong contribution from liquid
precipitation are related to relatively high δD values. Since we want
to estimate the δD of snow, data from winters with especially low
temperatures and a strong contribution from solid precipitation with low
δD values are likely to be most representative. During these winters
the δD of precipitation is probably closest to δD of the RCWIP
minus the 11.5 ‰.
In contrast to this, post-depositional fractionation processes may increase
the δD values of surface layer snow on the order of +10 to
+20 ‰ during periods with small accumulation rates
, suggesting a scenario where the δD
values of surface layer snow are higher than the δD from the RCWIP.
Please note that this scenario is not likely for a seasonal snowpack during
melt season since ablation may uncover old snow from colder winter months,
causing changes of the δD of surface layer snow of -50 ‰
. Given the relatively small snow depths in the
investigated moisture source region (Fig. b), we ignore the
uncovering of older snow layers and only consider a potential
post-depositional increase of the δD of surface layer snow.
To test whether the too high modeled δD values from the scenario of
sublimation (MS) can be significantly reduced when considering
systematic uncertainty of the δD of surface layer snow regarding
interannual variability of the δD of snowfall, we performed one model
run (MS-,snow), in which we shifted the δD of snow by
-11.5 ‰. To test whether the too low modeled δD values from
the scenario of evaporation of meltwater (MMW) can be increased by
considering a post-depositional increase of the δD of surface layer
snow, we performed one further model run (MMW+,snow), in which we
shifted the δD of surface layer snow by the same absolute value of
+11.5 ‰. The mean difference between modeled and observed
δD (ΔδD) of the different model runs is listed in
Table .
During cold snaps in Karlsruhe, on average 48 % of humidity could be
attributed to moisture uptake along the 5-day back trajectories. The other
side of this argument is that the δD of 52 % of humidity in
Karlsruhe is determined by the initialization of isotope ratios.
For initialization, we assume δD in a well-mixed ABL and isotopic equilibrium between water vapor and the climatological
monthly δD of precipitation. This assumption is in agreement with one
of the rare extended, simultaneous time series of δD in water vapor
and precipitation, conducted 45 km NNE from our site . This
study shows monthly averages of δD in precipitation and water vapor at
ground level for the years 1981–1988. The average deviation of the δD
of water vapor to isotopic equilibrium with precipitation in November,
December, January, and February was +4.1±7.7 ‰ (± states
the standard deviation calculated from the winter averages of the different
years). Ignoring the interannually varying deviation between 4.1-7.7=-3.6 ‰ and 4.1+7.7=+11.8 ‰ from the isotopic
equilibrium may systematically bias δD at the model initialization.
To test how much the values of ΔδD for MS and
MMW can be reduced by considering the uncertainty of δD at
the model initialization, we performed two further model runs. For the
scenario of sublimation we performed a model run in which we shifted
δD at the initialization for -3.6 ‰ (MS-,ini).
For the scenario of evaporation of meltwater we shifted δD at the
initialization for +11.8 ‰ (MMW+,ini).
Potentially artificial diurnal variations of q along the back
trajectories could result in an overestimation of the formation of
precipitation and moisture uptake. To avoid such an overestimation, diurnal
variations of q were suppressed by smoothing q along the back
trajectories with a 24 h broad rectangle function. Arbitrarily choosing a
width of 24 h may smooth out real sub-diurnal and diurnal variations of q
and thereby may result in an underestimation of the amount of moisture
uptake. To assess the potential impact of the smoothing on the modeled
δD, we changed the width of the applied rectangle kernel to 12 h in
MS-,upt.12 h and to 36 h in MMW+,upt.36 h.
To finally assess the minimum possible values of ΔδD in the case
of superposition of the three sources of uncertainty discussed above, we
combined the assumptions of MS-,snow, MS-,ini, and
MS-,upt.12 h in the model run MS- - - and
MMW+,snow, MMW+,ini, and MMW+,upt.36 h in the
model run MMW+++.
Table summarizes ΔδD for the different model
runs. None of the model runs considering only one source of uncertainty is
able to reduce ΔδD for the scenarios of sublimation or
evaporation of meltwater to values close to 0. Even for
MS- - -, which simultaneously assumes all the uncertainties
of model assumptions in the scenario of sublimation, ΔδD is
+16.0 ‰. The discussed uncertainty terms are therefore not able to
bring model and observations into agreement with each other when only
considering non-fractionating sublimation. This implies that fractionating
evaporation of meltwater played a significant role during our observations.
For MMW+++, which simultaneously assumes all the uncertainties of
model assumptions in the scenario of evaporation of meltwater, the absolute
value of ΔδD is reduced to 0.9 ‰. Considering the
statistical uncertainty of ΔδD of 1.6 ‰, the average
modeled and measured δD of the 174 selected air masses may therefore
be brought into rough agreement with each other when assuming fractionating
evaporation of meltwater. However, this requires superposition of the
different uncertainty terms.
In order to refine this result, we split the selected observations into two
groups of equal size according to the predominant skin temperature during
moisture uptake (Tskin,predom.). For this purpose, we weighted skin
temperatures along the individual ensembles of back trajectories with
moisture uptake identified by the Lagrangian moisture source diagnostic. The
median Tskin,predom. of the 174 selected trajectory ensembles is
-6.92 ∘C. We attributed data points to a group “cold” if
Tskin,predom. of the respective trajectory ensemble is below
-6.92 ∘C. For Tskin,predom. above -6.92 ∘C we
attributed data points to a group “warm”. Please note that also points of
group “warm” have a Tskin,predom. below 0 ∘C according
to our selection criteria. Due to interannual variability of
Tskin,predom., data of the two groups are not randomly distributed
in time. Seventy-seven percent of group “cold” corresponds to an especially
pronounced cold snap in February/March 2012, whereas 85 % of group
“warm” belongs to cold snaps between October 2012 and February 2013.
Figure shows two-dimensional probability distributions of the
selected modeled and measured δD. Blue denotes data from group
“cold” and red denotes data from group “warm”. Under the assumption of
non-fractionating sublimation (MS), modeled δD values of
both groups are significantly higher than the observed values
(Fig. a). The overestimation of modeled δD values is
especially strong in the regime with higher Tskin, where snowmelt
and fractionating evaporation would be expected. Figure b
shows the respective probability distributions under the assumption of snowmelt and fractionating evaporation of meltwater (MMW). Under this
assumption, the modeled δD of group “warm” is close to the
observations. However, modeled δD values of group “cold” are now far
too low.
Two-dimensional probability distributions of measured and modeled
δD of low-level water vapor in Karlsruhe for selected cold snap
events. Red: group “warm”; blue: group “cold”. Probabilities were
calculated for a 20 ‰ × 20 ‰ δD grid,
smoothed with a 20 ‰ broad rectangle kernel, and finally
interpolated to a 1 ‰ × 1 ‰ grid. Probabilities
are normalized to 1 at the maximum; contours show probability levels of
0.8, 0.7, 0.6, 0.45, 0.35. (a) The model assumes sublimation of
snow (no isotope fractionation) in the case of moisture uptake and skin
temperature (Tskin) below 0 ∘C. (b) The model
assumes evaporation of melted snow (equilibrium isotope fractionation) in the
case of moisture uptake and Tskin<0 ∘C. (c) The
model assumes sublimation of snow in the case of moisture uptake and
Tskin<-7.7 ∘C. In the case of moisture uptake and
-7.7 ∘C <Tskin<0 ∘C the model assumes
evaporation of melted snow.
![]()
Table lists the mean differences between modeled and
measured δD for the two groups (ΔδDcold,
ΔδDwarm). As δD of the individual groups
may deviate more from the observations than the mean δD of all 174
selected air masses, analyzing ΔδDcold and
ΔδDwarm allows drawing less ambiguous conclusions
than analysis of ΔδD. For MS,- - - the value
of ΔδDwarm (18.2 ‰) is larger than the
value of ΔδD of all selected data (16.0 ‰), which
underlines the importance of fractionating evaporation for reproducing the
observations. For MMW,+++ the absolute value of ΔδDcold (10.5 ‰) is larger than the absolute value of
ΔδD (0.9 ‰). So even MMW,+++, in which we
simultaneously assumed all the uncertainties of model assumptions in the
scenario of fractionating evaporation of meltwater, does not allow reproducing
the observations of group “cold”. This, in turn, implies significant
non-fractionating sublimation during our observations.
Comparison of δD observations with δD of the Lagrangian isotope
model therefore implies a relevant role of both types of isotope
fractionation in central Europe.
Mean difference between modeled and observed δD of water
vapor at Karlsruhe for selected cold snap events (ΔδD). Each
cross represents the ΔδD obtained from one model configuration
with specific model assumptions regarding the Tsubl,max, the
δD of snow, the δD at the model initialization, and the amount
of moisture uptake. The thick black line connects the ΔδD from
16 model configurations with Tsubl,max between -15 and
0 ∘C and the model assumptions of
MS_MW,Tsubl,max and illustrates the increase of the
modeled δD values with increasing maximum skin temperature allowing
non-fractionating sublimation. The gray shaded area depicts possible
systematic changes of the ΔδD in the case of a systematic
deviation of the δD of snow from the RCWIP climatology (light blue), a
systematically changed δD at the model initialization (yellow), a
systematically changed amount of moisture uptake (green), and superposition
of the different assumptions (thin black lines). The thin dashed black line
corresponds to the model configuration
MS_MW,+++,Tsubl,max, which does not allow reproducing
the observations in group “warm” and is therefore not considered in the
gray shaded area of uncertainty. In magenta, optimal Tsubl,max for
best agreement of model and observation (dot) and uncertainty of the optimal
Tsubl,max (error bar) due to uncertainty of model assumptions are shown.
![]()
Temperature-dependent types of fractionation
To simultaneously bring into agreement modeled and observed δD of data
corresponding to group “cold” and group “warm”, we suggest the existence
of two regimes of Tskin with predominant non-fractionating
sublimation in the colder regime and predominant fractionating evaporation of
meltwater in the warmer regime.
For the characterization of these two regimes we assume a maximum temperature
for non-fractionating sublimation in the model: Tsubl,max. For
Tskin<Tsubl,max, we assume non-fractionating sublimation.
In the case of Tsubl,max<Tskin<0 ∘C, we assume
equilibrium fractionation during the evaporation of meltwater.
To assess Tsubl,max for optimal agreement between modeled and
observed δD, we performed 16 model runs with a different
Tsubl,max in each run (-15 to 0 ∘C in steps of 1 K).
We refer to these runs as MS_MW,Tsubl,max. The thick
black line in Fig. shows the mean differences between modeled
and observed δD of the 174 selected data points (ΔδD)
from the 16 different MS_MW,Tsubl,max. Modeled
δD values are highest in MS_MW,0 ∘C, which assumes
non-fractionating sublimation of snow for all Tskin<0 ∘C
and is therefore identical with MS. Out of the 16 model runs
MS_MW,-15∘C gives the lowest δD values.
The δD values from MS_MW,-15∘C are close
to δD from MMW, as most Tskin along the back
trajectories were above -15 ∘C, which means that almost no
sublimation below Tsubl,max is considered.
MS_MW,-29∘C gives the same results as
MMW, because there was no moisture uptake identified for
Tskin<-29 ∘C.
The agreement between observed and modeled δD from the
MS_MW,Tsubl,max is best for a Tsubl,max of
-7.7 ∘C. For this Tsubl,max the mean ΔδD
of all selected data points is 0 and also δD of group “cold” as well
as the δD of group “warm” is approximately reproduced by the model.
Figure c shows the respective two-dimensional probability
distributions.
The statistical uncertainty of this optimal Tsubl,max due to
scatter between modeled and observed δD is 0.7 ∘C. Further
uncertainty is introduced to the determined optimal Tsubl,max by
the assumptions of the Lagrangian isotope model, which can systematically
change the mean modeled δD and the optimal Tsubl,max. To
assess this uncertainty of the optimal Tsubl,max, we performed
16⋅8=128 additional model runs (Fig. , thin lines)
corresponding to 16 different Tsubl,max from -15 to
0 ∘C and 8 different model configurations with the same assumptions
about a changed δD of snow, a changed δD at the model
initialization, a different amount of moisture uptake, and superposition of
the three effects as in the above uncertainty assessment for the MS
and the MMW. Analogous to the uncertainty assessment for the
MS and the MMW, we refer to the model runs as
MS_MW,+,snow,Tsubl,max,
MS_MW,+,ini,Tsubl,max,
MS_MW,+,upt.36 h,Tsubl,max,
MS_MW,+++,Tsubl,max,
MS_MW,-,snow,Tsubl,max,
MS_MW,-,ini,Tsubl,max,
MS_MW,-,upt.12 h,Tsubl,max, and
MS_MW,- - -,Tsubl,max.
The model runs
with assumptions related to higher modeled δD values (dashed thin
lines) result in a lower optimal Tsubl,max and model runs with
assumptions related to lower modeled δD values (solid thin lines)
result in a higher optimal Tsubl,max.
Thin black lines in Fig. depict the maximum possible shift of
the average modeled δD in Karlsruhe in the case of superposition of
the examined model assumptions. The solid thin black line reflects a maximum
unfavorable superposition of assumptions related to lower modeled δD
values (MS_MW,- - -,Tsubl,max). The
MS_MW,- - -,Tsubl,max therefore allow to
assess the upper bound of -3.6 ∘C of the Tsubl,max for
optimal agreement between model and observation. The lower bound of the
optimal Tsubl,max, which is derived from the
MS_MW,+++,Tsubl,max, is -15 ∘C (thin dashed
line). Here again, individual analysis of data from the groups “cold” and
“warm” allows us to refine the result. For MMW,+++ ΔδDwarm is +8.6 ‰ (Table ). Since
MMW,+++ assumes fractionating evaporation of meltwater for all
Tskin<0 ∘C, it marks the lower boundary of δD
values from the MS_MW,+++,Tsubl,max. The corresponding
set of assumptions does therefore not allow reproducing the observations in
group “warm” even when assuming a very low Tsubl,max. Assuming
maximum unfavorable superposition of the uncertainties of model assumptions
in the MS_MW,+++,Tsubl,max is therefore too
conservative for the uncertainty assessment of the optimal
Tsubl,max. For this reason, we assess the lower bound of the
optimal Tsubl,max by means of the
MS_MW,+,snow,Tsubl,max, which only consider one
uncertainty term and just allow the reproduction of the observed δD in
group “warm”. From the MS_MW,+,snow,Tsubl,max we
derive a better confined lower bound of the optimal Tsubl,max of
-11.2 ∘C.
So the uncertainty of model assumptions translates into an uncertainty range
of Tsubl,max for optimal agreement of model and observation from
-11.2 to -3.6 ∘C. Together with the statistical uncertainty
of 0.7 ∘C, the total uncertainty range of Tsubl,max
sums up to -11.9 to -2.9 ∘C.
Conclusions
In this paper, we investigated isotope fractionation during surface
evaporation in snow-covered regions. For this purpose, we combined 17 months
of measurements of δD in low-level water vapor in central Europe with
a new Lagrangian isotope model.
By means of this approach, we identified two regimes of GDAS skin
temperatures (Tskin) below the freezing point with significantly
different deviation between modeled and observed δD. To resolve this
difference, we suggest two regimes of Tskin with different types
of predominant isotope fractionation. Based on sensitivity tests with the
Lagrangian isotope model, we found that the colder regime is described best
by non-fractionating sublimation of snow. The warmer regime is described best
by fractionating evaporation of meltwater.
We determined a Tsubl,max separating both regimes of
Tskin by optimizing the agreement between modeled and observed
δD. For a Tsubl,max of -7.7 ∘C this
agreement is best. Uncertainty related to assumptions of the isotope model
corresponds to a range of uncertainty of Tsubl,max from -11.9
to -2.9 ∘C.
The finding of a cold temperature regime with a small impact of fractionation
during sublimation at ground level does not contradict earlier studies on
snow which indicate fractionating interaction between the surface layer snow
and atmospheric water vapor, even in cases of temperatures far below the
freezing point . These studies document
systematic post-depositional increases of isotope ratios in the snowpack,
which imply processes such as slight kinetic fractionation during sublimation
in consequence of different coefficients of diffusion of the different water
isotopes or fractionating vapor deposition. Given the uncertainty of the
Lagrangian isotope model, these small effects would not be detectable by our
approach. Nevertheless, these effects might result in significant
post-depositional modifications of isotope ratios in the snowpack on
timescales longer than the 5 days covered by the trajectories.
For GDAS skin temperatures between Tsubl,max and 0 ∘C
our results imply significant fractionating evaporation of meltwater. The
identification of a fractionating “meltwater regime” is consistent with
earlier observations of isotope ratios in snow, which point to fractionating
evaporation during ablation at temperatures close below the freezing point
. Since snow samples give an
integrated signal over long time periods, a detailed attribution of these
observations to certain meteorological conditions is difficult. Complementary
to the studies on snow, the method presented here allows the
post-depositional isotope fractionation to be attributed to meteorological
conditions with GDAS skin temperatures between Tsubl,max and
0 ∘C.
The determined Tsubl,max refers to a GDAS skin temperature that
was weighted with positive latent heat flux at ground level. For this reason
Tsubl,max is representative for GDAS skin temperatures during the
day, when evaporation is strongest. However, it should be kept in mind that
due to the coarse resolution of 1×1∘ of the GDAS data, much
spatial variability of the skin temperature is smoothed out. The meaning of
the Tsubl,max derived in this study is therefore an average
temperature in a 1×1∘ grid cell above which the evaporation
of meltwater exceeds the amount of moisture from sublimation. A way to derive
a more physical temperature separating the regimes of sublimation and
meltwater evaporation could be using data with a higher horizontal
resolution. This would not necessarily improve accuracy with respect to the
back trajectories' positions but locations with enhanced skin temperatures
and especially high amounts of surface evaporation would be more
realistically represented, presumably resulting in higher evaporation
weighted skin temperatures and a higher Tsubl,max.
In addition, higher horizontal resolution would allow to better account for
spatial heterogeneity of the δD of surface layer snow in mountainous
regions, for instance by also weighting the δD of the snow with the
amount of surface evaporation. In this context please note that systematic
uncertainty regarding the δD of surface layer snow turned out to be
the main limitation for determining Tsubl,max with the approach
presented here. For this reason regular analysis of the δD of surface
layer snow samples, for instance at selected GNIP stations, would be a very
desirable and efficient measure to reduce uncertainty of
Tsubl,max.
Our results show that surface evaporation in the two identified regimes of
skin temperature has a strong impact on the δD of low-level water
vapor in central Europe. For isotope applications based on relations between
δD and temperature, the consideration of the different types of
isotope fractionation in both regimes is therefore of great interest. For
instance, seasonal ablation in coastal regions of Greenland might
systematically affect the relation between the δD of water vapor and
dew point temperature over Greenland. Because such a seasonality may be
different in climatologically different time periods, it may introduce
uncertainty to temperature reconstructions from Greenland ice cores.
Furthermore, fractionating evaporation of meltwater will increase the
δD value of the residual meltwater and in the case of
recrystallization, the δD value of the snowpack. Ignoring
fractionating evaporation therefore introduces uncertainty to a variety of
isotope applications from reconstructions of paleotemperatures and
paleotopography to studies on the formation of groundwater. The specification
of a temperature regime with enhanced fractionation during evaporation may
therefore help to identify, investigate, and reduce biases inherent to these
applications.