Longitudinal δ18Opw and δDpw
gradients
To explain the change in the slopes of the longitudinal winter δ18Opw and δDpw gradients (Fig. 4), the measured
temperatures and the amount of precipitation at the GNIP stations are
evaluated. The GNIP station based continental temperature and precipitation
gradients for the different wNAOi classes show that the slopes of the
longitudinal winter temperature gradients become steeper for lower wNAOi
values and are always negative (i.e. average winter temperatures are always
lower in the east), but no equivalent relationship is observed for the
slopes of the winter precipitation gradients (Fig. 5). (The following
conclusions are not hampered if the two maritime sites Valencia Observatory (station no.
1) and Wallingford (station no. 2) (Fig. 1) are omitted from the
compilation of all continental stations (Fig. S1 in the Supplement).) The temperature
relationships suggest that while the winter air temperature clearly becomes
colder from west to east, a higher average air temperature gradient between
western and eastern Europe occurs in more negative wNAOi winters.
Furthermore, the intercept of the linear regression is progressively smaller
for more negative wNAOi modes, suggesting general cooler conditions in
central Europe during negative wNAOi modes. This is consistent with the
general relationship between the wNAOi and winter air temperatures for
central Europe (e.g. Hurrell, 1995; Comas-Bru and McDermott, 2013).
Curiously, for the most negative wNAOi class, the slope of the temperature
gradient does not follow the general trend and has a lower value, comparable
to more positive winter wNAOi modes, suggesting a smaller temperature
difference between western and eastern Europe under these conditions. The
reason for this change is unclear and possibly reflects a relationship
between the cyclone variability and the NAO (Gulev et al., 2001), which may
increase the frequency of incursions of cold easterly winds into western
Europe during very negative wNAO modes.
Based on observational data only, these four panels illustrate the
slope of the continental gradient for (a) temperature and (c) precipitation
as a function of the class of wNAOi that is calculated from GNIP station
datasets. Panels (b) and (d) shows, respectively, the intercepts of the linear regression for
the continental temperature and precipitation gradients versus the wNAOi. Black symbols indicate the results for the 6-month winter
period (October–March); grey symbols denote results for the 3-month
(December–February) winter period. The slopes for temperature and
precipitation show no relationship with the wNAOi if all six classes are
analysed (p > 0.1). However, omitting the most negative wNAOi
class yields a significant linear correlation of 0.71 and 0.67 (p < 0.01) for the 6- and 3-month averages, respectively. While there is no
significant relationship between the intercept of the precipitation
gradients with the wNAOi (p > 0.1), the intercept of the air
temperature gradients shows a clear trend, with lower temperatures
associated with lower wNAOi values.
Differences in δ18Opw and δDpw
(6-month (6 m) and 3-month (3 m) winter periods) between the western- (Valencia,
Ireland) and easternmost station (Kraków, Poland) for all wNAOi classes
based on continental GNIP station datasets. The median wNAOi for classes I
to VI, respectively, are 1.77, 1.18, 0.40, -0.46, -1.17 and -2.23. The
empirical estimates for the observed W–E difference of δ18Opw and
δDpw presented here are based on linear regressions of the
observed trends (Fig. 4). The calculated δ18Opw and δDpw values are based on the temperature difference between Valencia
(Ireland) and Kraków (Poland) estimated from the linear regression of the
continental GNIP station temperature datasets and temperature sensitivities
for δ18OP and δDP from Dansgaard (1964).
Deviation (bold numbers) means the difference between observed and
calculated δ18Opw and δDpw values. The italic
numbers state the deviation relative to the observed difference given in percentage.
W–E difference for δ18Opw
W–E difference for δDpw
Observed
Calculated
Deviation
Observed
Calculated
Deviation
wNAOi class
6 m
3 m
6 m
3 m
6 m
3 m
6 m
3 m
6 m
3 m
6 m
3 m
‰
‰
‰
‰
‰
%
‰
%
‰
‰
‰
‰
‰
%
‰
%
I: 1.77
4.78
6.02
3.75
4.98
1.03
22.5
1.04
17.3
42.62
52.10
32.80
43.56
9.82
23.0
8.54
16.4
II: 1.18
4.94
5.14
3.28
3.98
1.66
33.6
1.16
22.6
44.06
46.19
28.72
34.84
15.34
34.8
11.35
25.8
III: 0.40
5.73
6.32
4.00
5.54
1.73
30.2
0.79
12.5
49.37
51.68
35.01
48.44
14.36
29.1
3.24
6.3
IV: -0.46
6.66
7.89
4.18
5.58
2.48
37.2
2.31
29.3
56.93
65.10
36.58
48.81
20.35
35.7
16.29
25.0
V: -1.17
6.22
7.38
4.44
6.55
1.79
28.8
0.83
11.2
53.44
61.11
38.82
57.31
14.62
27.4
3.80
6.2
VI: -2.23
6.54
7.56
3.37
5.05
3.17
48.5
2.51
33.2
54.61
64.56
29.46
44.21
25.14
46.0
20.35
31.5
The observed temperature slopes can be used to calculate the expected air-temperature-driven difference in δ18Opw and δDpw between the western- and easternmost GNIP stations using
theoretical temperature sensitivities for δ18OP and
δDP (e.g. from Dansgaard, 1964). The theoretical changes
in δ18Opw and δDpw between the western- and
easternmost GNIP stations (the longitudinal difference between the Valencia
(Observatory) and Kraków (Wola Justowska) is 30.1∘) were
calculated as follows. First, the temperature difference between these two
stations was calculated from the temperature slope for the different wNAOi
classes. For example, for the highest wNAOi class, the slope of the observed
temperature gradient is -0.19 and -0.26 K ∘ E-1 for
the 6-month and 3-month winter period, resulting in a temperature difference
of 5.86 and 7.78 K, respectively. Hence, the average winter temperature at
Valencia Observatory from October to March (December to February) is about
5.86 K (7.78 K) warmer compared to Kraków Wola Justowska for the highest
wNAO class. The effect of this eastward temperature decrease can be
converted into an expected δ18Opw and δDpw
difference between the two stations. For this estimate we apply an
approximate estimation of the sensitivity of δ18OP and
δDP to temperature changes based on theoretically derived
values by Dansgaard (1964), assuming a Rayleigh-type moist adiabatic
condensation (vapour–liquid) process. For the sensitivity, the average value
for a cooling from an initial temperature of 0 to
-20 ∘C is used, which is 0.64 ‰ K-1 for δ18OP and 5.6 ‰ K-1 for δDP
(Dansgaard, 1964). Hence, for the highest wNAO class, the observed
temperature difference would cause a calculated difference of 3.75 (6 months) and 4.98 ‰ (3 months) for
δ18Opw. δ18Opw values are therefore
expected to be 3.75 and 4.98 ‰
lighter at the easternmost station compared to the westernmost station for
the 3-winter-month and 6-winter-month averages, respectively.
These theoretically calculated values are now compared with the observed
differences derived from the slopes of the linear regression of the GNIP
δ18Opw datasets (Fig. 4). For the highest wNAO class,
the slope is -0.16 and 0.20 ‰ ∘ E-1 for the 6-month and 3-month winter
period. This results in an observed difference of 4.78
(6 months) and 6.02 ‰ (3 months). Importantly, the
observed differences (longitudinal gradients) are much larger than those
calculated using the air-temperature-driven Dansgaard-type model described
above. The results of these calculations for δ18Opw and
δDpw, based on the observed temperature slopes and for all
wNAO classes are listed in Table 1.
The most important result of this simple exercise is that the observed
differences in δ18Opw and δDpw between the
western- and easternmost GNIP stations are larger than can be accounted for
by a simple air-temperature-driven Rayleigh distillation model alone (Table 1). Repeating the calculations using the sensitivity of
δ18Opw and δDpw for the vapour–ice phase change
(snow; 0.73 for δ18OP and 6.2 ‰ K-1 for δDP; Dansgaard, 1964) instead
results in calculated differences that are still too small to explain the
observed differences in δ18Opw and δDpw
between the western- and easternmost stations (not shown). This strongly
indicates that the observed longitudinal variations in winter air
temperatures alone are simply insufficient to account for the observed
difference between δ18Opw and δDpw in western and eastern Europe and that additional processes must be
considered.
Rozanski et al. (1982) pointed out that the precipitation history of
air masses is an important control on observed longitudinal δDP
gradients. Precipitation history can be expressed as a numerical value f
(fraction of remaining moisture). Hence, f depends on the balance between
the amount of precipitation (P) that has already occurred along the
longitudinal gradient and the initial amount of precipitable water in the
atmosphere (Q0). However, only weak relationships are found between the
precipitation gradients calculated from the GNIP station precipitation
datasets and the wNAOi classes, suggesting that rainfall gradients between
western and eastern Europe are fairly constant, within the range of
uncertainty (Fig. 5c and d). Furthermore, the intercept of the linear
regression of the precipitation data shows no dependence on the class of
the wNAOi. This is consistent with the findings of Baldini et al. (2008), who showed that precipitation data from continental GNIP stations
have no systematic correlation to the wNAOi (as opposed to temperature). As
discussed below, this points to differences in the initial amounts of
precipitable water (Q0) as a possible control on isotope-gradient-w–NAOi
relationships (Fig. 4), over and above those attributable to air
temperature gradients alone.
(a) Correlation map between the wNAOi and the amount of
precipitable water (PW) for the months December to March based on NCEP/NCER
reanalysis data for the period 1948-2016 and the results of the longitudinal
gradient (b) intercept; (c) slope) for grid cells where continental stations
are located (closed circles). Open triangles show the location of Alpine
stations; open squares indicate the position of Mediterranean-influenced
stations.
To better constrain the governing physical mechanisms of the precipitation
history f and their first-order effects on δ18Opw and
δDpw, a simple one-box Rayleigh-type model for the atmosphere
was used (Dansgaard, 1964; Eriksson, 1965):
xR=xR0⋅fα-1x=xR0QQ0xα-1=xR01-PQ0xα-1.
Equation (1) is the classic Rayleigh distillation model (Rayleigh,
1902; see also Mook, 2006 for detail) that describes the evolution of an
isotope ratio R (the subscript x is a placeholder for x= 2 (2H) or
18 (18O), i.e. for 18R=18O / 16O and
2R=2H / 1H) as a function of the precipitation history f.
This is a function of the amount of initial (Q0) and remaining (Q)
precipitable water in the atmosphere; Q is therefore the amount of
precipitable water after a specific amount of precipitation P
(Q0-Q= P) has formed. R0 describes the initial isotope ratio.
α is the equilibrium liquid-water isotope fractionation factor that
depends only on temperature; the subscript x denotes, as above, the related
isotope system. Because the slope of the longitudinal precipitation gradient
does not change systematically with the wNAOi class, P does not change with
the class of the wNAOi and has, therefore, a negligible effect on δ18Opw and δDpw. Hence, the slope of the longitudinal
gradient of δ18Opw and δDpw in central Europe
is driven only by the temperature-dependent isotope fractionation factor
α and by the initial amount of precipitable water Q0 in the
atmosphere. Although the classic Rayleigh-type model, adopted here, is unable
to fully capture all atmospheric processes (e.g. mixing of atmospheric
moisture with different isotope signatures and/or origins), it is
nonetheless a useful first approximation to explain the deviations between
the calculated (temperature effect) and observed differences in δ18Opw and δDpw between the western- and easternmost
stations. Therefore, our observations of the dependence of the longitudinal
δ18Opw and δDpw gradient on the class of
the wNAOi can be explained if, additionally, the amount of precipitable water
over central Europe is lower for more negative wNAOi values. As demonstrated
previously in a comparison of very positive (wNAOi > 1) and
negative (wNAOi < -1) wNAOi values, the maximum amount of
precipitable water in the atmosphere is shifted southward for very negative
wNAOi (Trigo et al., 2002). This shift in the amount of precipitable
water is associated with changing air temperature patterns, and smaller
amounts of precipitable water are associated with cooler continental air
temperatures. Accordingly, our simple model shows that the atmosphere in
central Europe contains less (more) atmospheric moisture during more
negative (positive) wNAOi states, and this is independently supported by
analysis of the amount of precipitable water in the atmosphere from an
NCEP/NCER reanalysis dataset (see below). Hence, atmospheric moisture
δ18O and δD values are likely to be more sensitive to
the rainout history during more negative wNAOi modes because f must change
at a higher rate if Q0 is smaller and P is held constant (Eq. 1). This
effect is also confirmed by a multi-box exercise that assumes a
Rayleigh-type condensation process, mimicking the longitudinal gradients.
This multi-box exercise shows that δ18OP becomes
progressively depleted in dependence on Q0 from west to east across
Europe, with steeper gradients (higher slopes) for smaller Q0 (Fig. S2).
The wNAOi–precipitable-water relationship described by Trigo et al. (2002) is also evident in the NCEP/NCER reanalysis dataset (and the
ECHAM5-wiso simulations; see Supplement; Fig. 6a). The
NCEP/NCER reanalysis dataset covers the period from 1948 to 2016 and is
analysed for the winter months December to March; it has a spatial
resolution of 2.5∘ × 2.5∘. Precipitable water shows a
positive correlation with wNAOi values in central and northern Europe (where
all continental stations are located) and a negative correlation over the
Mediterranean (including Iberia, the Balkans and Turkey; Fig. 6).
(Interestingly, the boundary between the area of positive and negative
correlation lies within the Alpine region, though the coarse resolution does
not allow any detailed conclusion for individual Alpine stations.) This
indicates that during positive wNAO modes, the amount of precipitable water
increases over central Europe and decreases during negative wNAO modes. This
finding is also confirmed by the amount of precipitable water of the
analysed NCEP/NCER reanalysis grid cells (Fig. 6b and c). The results of
the regression analysis of the longitudinal gradient of the precipitable
water shows that the slope of the precipitable water along the longitudinal
gradient is rather independent (within the range of uncertainties, although
a slight trend is visible indicating a shallower slope for more negative
NAOi classes) of the wNAOi class (Fig. 6c), while the intercept clearly
decreases for smaller wNAOi classes (Fig. 6b). Compared with the amount of
precipitable water for the highest wNAOi class, the atmosphere contains only
about 88.4 % for the lowest wNAOi class for the 6-month winter period, for
example. As a result, the precipitation history f becomes more sensitive to
the rainout history along the longitudinal gradient for lower wNAOi classes.
To summarize, the dependence of the observed longitudinal δ18Opw
and δDpw gradient on the class of wNAOi in the winter season
results from two processes: (i) the changing continental temperature
gradients via the temperature-dependent isotope fraction during
condensation, which exerts the strongest influence on the δ18Opw and δDpw gradient, and (ii) the dependence of
the amount of precipitable water or in general of the precipitation history over central Europe on the wNAOi mode, which becomes important for more
negative wNAOi classes. The latter mechanism is in agreement with recent
findings of Aggarwal et al. (2012), who showed that, generally, more
negative δ18OP values are associated with lower moisture
residence times, where the moisture residence time is defined as the ratio
between the amount of precipitable water and the precipitation
(Aggarwal et al., 2012; Trenberth, 1998), and with conclusions of Rozanski et al. (1982), who analysed summer and winter European δDP longitudinal gradients.
The four panels show the rate of change of (a) δ18Opw, (b) δDpw, (c) temperature (T)
and (d) precipitation (P) as a function of altitude for different wNAOi classes.
(The lowest wNAOi class was not analysed because data of only four stations
(out of 17) are available.) All Alpine stations are included. None of the
correlations are statistically significant (p > 0.1). Black
symbols indicate the results of the 6-month winter period; grey symbols
denote the 3-month winter period.
Alpine stations
By comparison with the low-altitude stations, the Alpine stations reveal
more complex wNAOi–δ18Opw and –δDpw patterns
(Fig. 3). North of the Alpine divide, all stations show similar δ18Opw– and δDpw–wNAOi class relationships as at
the Garmisch-Partenkirchen GNIP station (Fig. 2). The only exception to
this relationship is the δ18Opw datasets for
Thonon-les-Bains, whose δ18Opw datasets have only a weak
relationship with the wNAOi. The relationship between the δ18Opw-δDpw values and wNAOi from stations at and
south of the Alpine divide, including Grimsel (no. 18; western Alps);
Längenfeld (no. 21), Obergurgl (no. 23), Patscherkofel (no. 27; all
central Alps); Böckstein (no. 30), St. Peter (no. 31), Villacher Alpe
(no. 32) and Graz (no. 33; eastern Alps) is more complex compared to the
stations north of the Alpine divide (see Sect. 3.2 for details).
The δ18Opw, δDpw, temperature and
precipitation datasets were also grouped into six wNAOi classes according to wNAOi as previously for the non-Alpine stations (Sect. 4.1). A
detailed analysis of the δ18Opw and δDpw
values of all Alpine stations shows that the median δ18Opw
values become more negative for higher altitudes, irrespective of the wNAOi
class. This observation is well known as the “altitude effect”
(Dansgaard, 1954; Schürch et al., 2003). However, there is no
obvious relationship (p > 0.1 for all datasets) between the class
of the wNAOi and the altitude effect for the Alpine stations (Fig. 7a and b). On average, the altitude effect is
-0.32 ‰ 100 m-1 (6-month average) and -0.30 ‰ 100 m-1 (3-month average)
for δ18Opw and -2.55 ‰ 100 m-1 (6-month
average) and -2.28 ‰ 100 m-1 (3-month average) for
δDpw (Fig. 7a and b). Furthermore, the recorded air
temperature at the Alpine stations changes on average by about -0.58 and
-0.56 K 100 m-1 for the 6-month and 3-month average, respectively, independent
of the wNAOi class (p > 0.1 for all datasets; Fig. 7c). The mean
values of the temperature–altitude relationship correspond approximately to
the moist adiabatic lapse rate. Hence, a strong relationship between δ18Opw and δDpw values and air temperature is
observed in the Alpine stations (e.g. Schürch et al., 2003).
No relationship between rainfall amount and δ18Opw was
observed (Fig. 7d).
Because there is no relationship between the lapse rate and/or
precipitation amount with the wNAOi class, we conclude that the observed
relationships between δ18Opw and δDpw and the
class of the wNAOi for our selection of Alpine stations are (i) caused by
different air mass origins linked to wNAOi states and (ii) downstream
effects of the varying central European continental effect that causes more
negative δ18Opw and δDpw values for more
negative wNAOi classes. The weak or absent relationships for stations at the
Alpine divide can be caused by air masses of different origins (e.g.
variable influences of the Mediterranean-sourced moisture; Kaiser
et al., 2001). However, detailed back trajectories of rainfall events for
the entire Alpine region would be required to further evaluate this
explanation. Our analysis indicates that the Alpine divide exerts an
important influence on the winter hydrological cycle in the region, with
precipitation north of the Alps sourced by atmospheric moisture originating
from central Europe. In winter, this residual atmospheric moisture is
already depleted in 18O and 2H when it reaches the northern part
of the Alps, reflecting the ambient winter mode of the wNAOi and thereby
determining the degree of the depletion in 18O and 2H in north
Alpine winter precipitation.
To complete the above conclusion on the mixing of atmospheric moisture for
stations at the Alpine divide, the δ18Opw and δDpw of circum-Mediterranean stations were also analysed for their
dependence on the wNAOi (a discussion of the NAO relationships between
δ18Opw and δDpw and temperature and
precipitation can be found in the Supplement). These stations
are Avignon (34; southwest of the Alps), Locarno (35; south of Grimsel),
Genoa (Setri; south of the Alps; 36) and Zagreb (37; southeast of the
Alps; Fig. 1). For the 3-month δ18Opw and δDpw, only data from Avignon and Zagreb show a strong
relationship with the wNAOi (about 1 ‰/wNAOi unit for
δ18Opw; Fig. 3a and c). For the 6-month δ18Opw and δDpw data,
only the δ18Opw dataset from Locarno and the δ18Opw and
δDpw datasets from Zagreb show a relationship with wNAOi. The
NAO relationships of δ18Opw and δDpw for the
3-month winter period from the Mediterranean stations Locarno and Genoa
(Setri) show that the NAO fingerprint, which is observed for Alpine stations
north of the Alpine divide, is not transferred to these two stations. The
situation might change for δ18Opw from Locarno for the
6-month winter period where a relationship with the wNAOi is observed. The
stronger relationship for this winter period could be caused by an increase
in precipitation that results from air masses from central Europe. For
Zagreb, it is difficult to explain the observed relationships between
δ18Opw and δDpw because the closest Alpine
stations show no relationship with the wNAOi. To further investigate the
mechanisms that control the δ18Opw and δDpw
datasets in Avignon and Zagreb, the origin of the air masses in dependence
on the wNAOi needs to be investigated further using isotope-enabled regional
climate models to better constrain the effect of local temperature and
precipitation on δ18Opw and δDpw. In summary,
the variable wNAO relationship of δ18Opw and δDpw datasets from the investigated Mediterranean stations support the
observation that the Alpine divide represents an important boundary region
for the oxygen and hydrogen isotope system of Alpine precipitation.