In this section we discuss the vapor source annual cycle and statistical
relationships between the isotopic composition of precipitation, vapor source
region, and the variables ΔT‾cool, T‾d, and mtn
that characterize the relationship of vapor source and transport to the
isotope values measured at Barrow, AK.
Vapor source region annual cycle
The vapor source regions for precipitation at Barrow changed seasonally
(Fig. ). Vapor fueling winter (December, January, and
February) precipitation originated furthest south, typically in the Gulf of
Alaska, and, for most winter events, trajectories crossed the Alaskan and
Brooks ranges. In spring (March, April, and May) the vapor for roughly half the
precipitation events came from the North Pacific and traveled over the
mountain ranges, as in winter. The vapor for the remaining precipitation
events generally came from the southwest of Barrow, from the Bering Strait
and Chukchi Sea. Vapor source regions for summer (June, July, and August)
precipitation were the most northerly, typically the Chukchi Sea or Bering
Strait. Synoptic systems moving counterclockwise around the Arctic Ocean
characterized summer air parcel transport. In fall (September, October, and
November), vapor also came from the Chukchi and Beaufort seas, but with air
parcel transport from the east to Barrow, the reverse of the spring and
summer parcel transport patterns. The Gulf of Alaska provided vapor for a few
fall events, with air parcel transport over the Brooks and/or Alaskan
mountain ranges, as in winter.
In association with the latitudinal variation in the vapor source region, the
temperature difference along the trajectory ΔT‾cool and vapor
source dew point T‾d also varied (Fig. ).
The mean latitude of the vapor source region, V‾Lat, and ΔT‾cool varied inversely, with more cooling being associated with
lower V‾Lat, i.e., greater meridional transport. For any given
season T‾d was warmer in the south and cooler in the north. There
are also seasonal differences; at any latitude T‾d was warmer in
summer and cooler in winter.
(a) Covarying behavior of mean vapor source region latitude,
V‾Lat, and mean air parcel cooling during transport,
ΔT‾cool. (b) Covariation of the mean vapor source
region latitude, V‾Lat, and dew point,
T‾d. Both ΔT‾cool and
T‾d influence the δ2H of precipitation at
Barrow, AK. Lines are best fits; scatter from them is due, in part, to
seasonal variation in latitudinal temperature gradients and vapor source
conditions.
The migration of the mean latitude of the vapor source region can be tied to
the seasonal cycling of solar insolation in the Northern Hemisphere via two
mechanisms. Decreased solar insolation during winter drives expansion of the
northern polar circulation cell, which increases sea ice cover, and cold
temperatures and snow cover prevent evapotranspiration. Both sea ice cover,
which diminishes the vapor contributions of the Arctic Ocean, and inhibited
evapotranspiration allow for enhanced representation of southerly vapor
sources. Increased summer insolation drives poleward contraction of the
circulation cell and diminishes sea ice coverage, and warmer temperatures favor
evapotranspiration such that the average vapor source area migrates north.
documented similar vapor source migration over a much
larger scale, in association with the annual north–south migration of
circulation cells.
There is evidence for prior millennium-scale shifts in the southern extent of
the polar circulation cell . Aspects of the link between
seasonal variability in general circulation and seasonal vapor source cycling
may be generalizable to interannual and even millennial timescales. This is
relevant to modern changes in the hydrologic cycle as
suggest that a poleward displacement of circulation cells is already
occurring due to recent climate change. Additionally, changes in the isotopic
composition of precipitation resulting from systematic vapor source
migrations associated with changing climate may allow for interpretation of
long-term isotopic records in terms of changes in atmospheric circulation,
including but not limited to the precipitation site temperature.
The influence of vapor source on precipitation δ2H
The local meteoric water line (with 95 % confidence intervals) is
δ2H=7.78(±0.12)δ18O+7.18(±2.61).
Figure shows that the measured δ2H values
of the 70 precipitation events fall between -280 and
-50 ‰, with a pattern of summer enrichment and winter
depletion that follows the well-established annual cycle for mid- and high
latitudes . Figure
also shows the interannual, seasonal, and event-scale variability captured by
the dataset, where the spline captures 65 % of the annual and interannual
variance. The average annual cycle of the precipitation δ2H is
strong; the spline fit explains 60 % of variance in the data. The mean
latitude of the vapor source exhibits a weak seasonal pattern, where the
spline explains 19 % of the variance. The seasonal cycles of δ2H
and vapor source latitude are in phase, as shown in Fig. ,
though the inter-event variability in both variables can be as large as the
seasonal variability.
Measured δ2H in precipitation at Barrow, AK, exhibits
variability on interannual, annual, and event time scales. The spline fit,
which highlights seasonal variations, explains 65 % of variance in the data
with a root mean squared error of 39.7 ‰. Of the three timescales,
annual variability shows the greatest amplitude, though variability among
events is also substantial. Maximum enrichment corresponds roughly to the
warmest months (June, July, and August), and maximum depletion corresponds
roughly to the coldest months (December, January, and February).
The phase relationship between δ2H and the north–south migration of
the vapor source region occurs because the vapor source region governs three
critical metrics that affect the δ2H of precipitation: (1) the
temperature difference between vapor source region and precipitation site,
quantified by air parcel cooling ΔT‾cool; (2) the moisture
source conditions, quantified in this work by T‾d; and (3) the mean
air parcel transport path. A linear combination of ΔT‾cool,
T‾d, and mtn statistically represents the event-scale variation
in δ2H with an R2 value of 0.54 (p<0.001).
Table contains the partial regression slopes (β),
p-values, and the unique variance explained by each variable. Below we
discuss the physical mechanisms that may explain the influence of each of
these metrics on δ2H.
Measured δ2H of Barrow precipitation and mean latitude of
the vapor source both exhibit an annual cycle and are in phase. The circles
depict raw data, while curves are spline fits to the data. The spline fits
have R2 values of 0.60 and 0.19 for the δ2H and
V‾Lat, respectively. For both datasets, the variability
exhibited among events is of the same order of magnitude as the seasonal
variability.
In contrast with previous assumptions that local (precipitation site) surface
temperature alone is a metric for Rayleigh distillation
e.g.,, our study relates δ2H to ΔT‾cool, T‾d, and mtn. Using these metrics instead of
local surface temperature allows us to circumvent two restrictive
assumptions. First, we do not assume that δ2H has a spatially and
temporally stationary relationship to local temperature.
demonstrated that this assumption does not hold.
Rather, because meridional temperature gradients are an important driver of
the isotope temperature sensitivity , when the meridional
temperature gradient fluctuates, a quantity that ΔT‾cool
captures, the sensitivity of δ2H to local temperature also
fluctuates. As demonstrated by Fig. , the presence of
mountains along the vapor transport path will deplete the isotope ratio of
the precipitation relative to a uniform altitude transport, all other
meteorological conditions being equivalent. The second restriction associated
with using local surface temperature as a metric of Rayleigh distillation is
the assumption that vapor for all precipitation events comes from a single,
homogeneous source. It requires that the δ2H of the water vapor,
and thus the initial condensate, is constant in space and time. However,
global measurements from the Tropospheric Emission Spectrometer
indicate that the vapor in the planetary boundary layer
over the ocean varies with space and season, confirming previous land and
ship measurements e.g.,.
Likewise, our results indicate that vapor may come from a heterogeneous
source region or variety of source regions (Fig. ) and
the initial condensate, based on the evaporation conditions, should be
expected to vary. The effect of a meteorologically heterogeneous source
region(s) is captured by T‾d.
To demonstrate the effect of the air parcel transport path, the residual
δ2H of Barrow precipitation is plotted at the vapor source. The
residual δ2H is determined by subtracting the spline shown in
Fig. from the δ2H of each precipitation event.
The vapor source locations, which have 1 ∘ by
1 ∘ resolution, are smoothed for clarity. Vapor from the
Bering Strait or Chukchi Sea tends to produce precipitation that is enriched
relative to the average. Likewise, vapor from the Gulf of Alaska tends to
produce precipitation that is depleted relative to the average. This
variation in vapor source reflects a difference in transport path. Vapor
originating from the Gulf of Alaska must rise to cross over the Alaska Range,
inducing orographic precipitation and isotopic depletion relative to air
masses that do not encounter orographic obstacles.
As expected, ΔT‾cool accounts for the largest proportion of
variance in δ2H (28.7 %) among the explanatory variables. Our
multiple regression yields a sensitivity of -3.51 ‰∘C-1 for δ2H with respect to ΔT‾cool (Table ). Because Rayleigh distillation is
considered the main source of spatial variation in δ2H, comparison
with the sensitivities calculated from a simple Rayleigh model contextualize
our result. In such a model, a saturated air parcel with specified
temperature and vapor δ2H is cooled iteratively in 1 ∘C
steps. At each temperature step, the condensation amount, remaining vapor,
precipitation δ2H and vapor δ2H are calculated. No
re-evaporation or non-equilibrium conditions are considered. We determined
condensation in this air parcel for both adiabatic decompression and isobaric
radiative cooling using equilibrium isotope fractionation factors from
. Because the association between precipitation
δ2H and ΔT‾cool during a Rayleigh process
varies , the sensitivity range for moist adiabatic cooling
from 10 to -15 ∘C, with a lapse rate
of -6.5 ∘Ckm-1, ranges between
-3.46 and
-5.45 ‰∘C-1, while moist isobaric
radiative cooling across the same temperature range yields sensitivities from
-5.47 to
-7.88 ‰∘C-1. The sensitivity
exhibited by our data is just above the low end of the range determined for
moist adiabatic cooling and was substantially lower than the range using
isobaric cooling. The similarity between our data and the moist adiabatic
model results suggest that moist adiabatic cooling was likely the dominant
mechanism for precipitation during air parcel transport to Barrow, although
scatter in the δ2H data could also be due to variable contributions
of radiative cooling. The relatively low observed sensitivity relative to
both theoretical sensitivities may be explained by additions of vapor to air
parcels during poleward meridional transport, which were not considered by
our back trajectory scheme, but are supported by the two-stream isentropic
vapor source transport model .
Response variable: δ2H. Variation in
δ2H is explained by a multiple linear regression (R2=0.54)
of air parcel cooling during transport (ΔT‾cool),
moisture source conditions (T‾d), and orographic
obstacles in the vapor transport path (mtn). Values of β are the partial
coefficients of the regression, and SE is the standard error. The variance
estimate for each explanatory variable is calculated as the square of the
semi-partial correlation for that variable with δ2H. The variances
reported do not sum to the total variance explained because the explanatory
variables are not perfectly orthogonal.
Independent variable
β (±SE)
p-value
Variance
(slope units)
estimate
Intercept
-95.33 (8.62)
< 0.001
ΔT‾cool (‰ ∘C-1)
-3.51 (0.55)
< 0.001
0.287
T‾d (‰ ∘C-1)
3.23 (0.83)
< 0.001
0.105
mtn (‰ when mtn = 1)
-32.11 (11.04)
0.0049
0.059
Our multiple linear regression attributes a substantial fraction of the
variance in δ2H to variations in T‾d at 2 m
(10.5 %, Table ). T‾d at 2 m is used to
indicate conditions at the vapor source and is preferred to the classical
variables Tss, h, and the humidity hss above the laminar layer
(e.g., at 2 m), defined relative to Tss. We prefer Td at
2 m because it is directly measurable and integrates three processes
that determine the isotopic ratio of the first condensate at the lifted
condensation level, where Rayleigh distillation begins.
The first process that determines the isotopic ratio of the first condensate
is the isotopic flux of evaporation from the sea surface. The classical model
by estimates the evaporative flux primarily using the
sea surface temperature, Tss, the humidity hss above the laminar
layer (e.g., at 2 m), defined relative to Tss, and δ2H
above the laminar layer. Td and hss are related through the
specific humidity and exhibit a correlation coefficient of 0.67 in our
dataset. Likewise, Td and Tss are related on monthly and longer
timescales and exhibit a correlation coefficient of 0.46. The second
process, described below, relates Td to δ2H above the laminar
layer. Because Td is related to hss, Tss, and δ2H
above the laminar layer, it is a good proxy for the isotopic flux of
evaporation.
The second process is mixing of moist air near the ocean surface with drier,
isotopically depleted descending air . Mixing within the
planetary boundary layer results in strong humidity and temperature gradients
near the sea surface (well below 2 m), with more uniform specific
humidity and isotopic ratios in the PBL above 2 m. The values Td
and δ2H at 2 m reflect the relative proportion of the dry
and isotopically depleted air in the PBL, so they are positively correlated.
Therefore, Td at 2 m is also a proxy for the isotopic ratio of
the air in the PBL.
The third process is condensation at the LCL. The temperature of the air mass
at the LCL determines the amount of isotopic fractionation and thus the
isotopic ratio of the first condensate. Of the vapor source variables,
Td at 2 m, neither Tss nor hss, is strongly related to the
condensation temperature at the LCL. On an event scale, Td at
2 m and TLCL are correlated with a coefficient of 0.71.
Because the three processes that determine the isotope ratio of the first
condensate before Rayleigh distillation are either directly or indirectly
related to Td at 2 m, and Td at 2 m is directly
measurable, we consider Td a better indicator for the source conditions
than either Tss or hss. It is difficult, however, to theoretically
assess the sensitivity of precipitation δ2H to variations in source
Td at 2 m, because this would require quantification of the
theoretical relationship of Td to δ2H through each of the
three processes and their combinations. We report here the first empirical
sensitivity of 3.23‰ ∘C-1 (Table 1) for
δ2H relative to Td at 2 m. For Tss between 0 and
25 ∘C, equilibrium fractionation yields sensitivities
between 1.1 and 1.6 ‰∘C-1 . However, condensation at the LCL
likely offsets most of the fractionation that occurred during evaporation at
the sea surface. Consequently, the observed sensitivity likely reflects the
fraction of vapor contributed by dry, isotopically depleted air that mixes in
the PBL. Mixing with the dry air causes a decrease in Td, which affects
the δ2H of the PBL in two ways: (1) making the PBL air dry and
isotopically depleted, and (2) isotopically depleting the evaporative flux by
enhancing kinetic fractionation (low relative humidity makes evaporative flux
isotopically depleted and low isotopic ratios of ambient air makes it
enriched, but the former often out competes the latter; ). Both
mechanisms produce a positive association between δ2H and Td,
consistent with the sign of our observed partial coefficient
(Table ).
Upon leaving the vapor source region, the isotopic composition of vapor
depends on the trajectory taken. To reach Barrow, AK, air parcels originating
in the Gulf of Alaska must cross the Alaska and/or Brooks ranges, whereas air
parcels from the Bering Strait or Chukchi Sea do not have to cross high
topography. Our work shows that transport across mountain ranges resulted in
significant δ2H depletion in Barrow precipitation. Transport of
vapor over mountain ranges occurred more frequently during cold months, when
the Gulf of Alaska and North Pacific were the dominant vapor source regions.
Since the vapor source location in winter is governed by the expansion of the
polar circulation cell, the projected northward displacement of subtropical
highs and the polar front in a warming climate may be
associated with less vapor transported over the Alaskan and/or Brooks ranges
during fall, winter, and spring. Fewer events traveling over the Alaskan
and/or Brooks ranges would correspond to a pronounced enrichment in measured
δ2H at Barrow during cold months.
To study the importance of T‾d and mtn as explanatory variables
with respect to cooling during transport (ΔT‾cool), we
divided our data into subgroups, those with ΔT‾cool < 7 ∘C (corresponding to short trajectories) and those with
ΔT‾cool > 7 ∘C (corresponding to
long trajectories) and recalculated the statistics. Table
summarizes the results and Fig. shows the standard
deviation of T‾d by category. The breakpoint of
7 ∘C was chosen by testing different breakpoints and
finding one that maximized the statistical power of the short trajectory
regression while preserving the strong relationship between δ2H and
T‾d. For the small ΔT‾cool subgroup, T‾d
explains almost half the variance in δ2H (R2=0.43), whereas,
for the large ΔT‾cool subgroup, T‾d explains very
little variance (R2=0.007). This difference implies enhanced isotopic
modification over long trajectories. In contrast, the δ2H values of
the small ΔT‾cool subgroup are not well explained by the
Boolean variable mtn (R2 = 0.03), whereas mtn explains about
one-fifth of the variability of the large ΔT‾cool subgroup
(R2=0.18). For the small ΔT‾cool subgroup, ΔT‾cool R2=0.02. For the large ΔT‾cool
subgroup, ΔT‾cool explained a quarter (R2=0.22) of the
variance in δ2H.
Three simple linear regressions against δ2H,
where β is the regression coefficient and SE is the standard error.
Source conditions parameterized by T‾d explain most
variation in δ2H for small ΔT‾cool,
while topographic highs below the trajectory (mtn) explain substantial
variation for large ΔT‾cool. ΔT‾cool explains variability significantly only for the
long transport subgroup (ΔT‾cool > 7∘C).
Independent
ΔT‾cool < 7 ∘C
ΔT‾cool > 7 ∘C
variable (slope units)
β (±SE)
p-value
R2
β (±SE)
p-value
R2
Intercept (‰)
-111.4 (8.6)
< 0.001
-115.1 (18.9)
< 0.001
ΔT‾cool (‰ ∘C-1)
-1.68 (2.1)
0.428
0.03
-3.44 (0.97)
< 0.001
0.22
Intercept (‰)
-104.9 (6.75)
< 0.001
-176.5 (8.4)
< 0.001
T‾d (‰ ∘C-1)
2.89 (0.74)
< 0.001
0.43
1.04 (1.8)
0.58
0.007
Intercept (‰)
-115.2 (9.1)
< 0.001
-147.6 (11.9)
< 0.001
mtn (‰ when mtn=1)
-16.6 (24.6)
0.51
0.02
-49.8 (15.5)
0.0025
0.18
Because the events with smallest ΔT‾cool tended to occur in
summer, the strong relationship between T‾d and δ2H
indicates that precipitation δ2H in summer predominantly reflects
variability in source conditions. The strong relationship between mtn and
the variation in δ2H for large ΔT‾cool indicates
that precipitation δ2H in winter predominantly reflects whether
most air parcels crossed the Alaska and/or Brooks mountain ranges. Notably,
ΔT‾cool could significantly predict δ2H for long
trajectory events, and it explained less variance than expected, given the
emphasis on Rayleigh distillation in explaining spatial variation in
precipitation stable isotopes.
Among the simple regressions, almost half the variance in δ2H for
events with ΔT‾cool < 7 ∘C was
explained by T‾d. This is a notable result, as the isotope
composition of the initial vapor is not emphasized to the same degree as
Rayleigh distillation in isotope hydrology. There are two reasons why
T‾d may explain so much variance for short trajectory events.
First, storm events with minimal cooling during air parcel transport
typically originated close to Barrow in the Arctic Ocean. A smaller vapor
source area predicts less variation in Td among air parcels: a more
homogeneous source. We quantify this effect by examining the distribution of
intra-event T‾d standard deviations (σT‾d) for the
short and long trajectory event subsets (Fig. ). Short
trajectory (ΔT‾cool < 7 ∘C) events
had a median σT‾d of 2.79 ∘C, which was
less than the long trajectory (ΔT‾cool > 7 ∘C) median σT‾d of
4.68 ∘C. Less variability among air parcels in the short
trajectory subset allowed the among-event relationship of δ2H to
T‾d to emerge. In addition, some of the variability in measured
precipitation δ2H may be caused by processes occurring during
transport, such as radiative cooling, air mass mixing, and different degrees
of mountain-induced rainout. The opportunity for these effects to impact the
precipitation isotope value increases with increasing transport distance,
obscuring the relationship of the precipitation δ2H to the
δ2H of the initial vapor at the source and therefore to
T‾d.
Distribution of standard deviations (σ) of
T‾d for events with ΔT‾cool
< 7 ∘C (short trajectories) and ΔT‾cool > 7 ∘C (long
trajectories). Colors indicate seasons. In general, small ΔT‾cool was associated with small σT‾d. The variation in standard deviation is related to
season, where warmer months tend to have a smaller σT‾d and cooler months tend to have a larger σT‾d.
The three chosen variables explain just over half (54 %) the variance of
δ2H. This is not surprising, considering that many other mechanisms
can also influence the δ2H of the vapor and precipitation. These
mechanisms include (but are not limited to) condensation temperature,
supersaturation in the mixed-phase cloud, sub-cloud dryness, phase of
precipitation, precipitation intensity, evapotranspiration of land sources,
and the amount of sea ice at the vapor source. The effects of several of
these factors, including condensation temperature, sub-cloud dryness, sea ice
concentration at the vapor source, and phase of precipitation (rain vs.
snow), were tested as additional explanatory variables in the multiple
regression, but yielded statistically insignificant results with little to no
additional variance explained. Clearly, compared with the three chosen
variables, the effects of these variables are relatively minor, such that the
statistical power is not sufficient to reveal their significance.
The influence of vapor source on deuterium excess
Deuterium excess (d-excess, or d) of precipitation is often used to
investigate source region conditions, such as Tss and h, that affect
evaporation . Empirical studies have linked marine
boundary layer vapor deuterium excess (d=δ2H-δ18O) to
Tss and h or hss . These results agree qualitatively or semi-quantitatively with
theoretical predictions . However, in order for source
vapor d values to be preserved in precipitation, d must be conserved
through condensation and post-condensation processes. This assumption may not
be realistic for several reasons. First, even simple equilibrium Rayleigh
distillation does not yield constant d values in
precipitation . Second, non-equilibrium processes
associated with snow formation may substantially alter
d . Third, evaporation or sublimation under the cloud
base and/or at the snow surface tends to decrease d .
While studies indicate that d in vapor contains vapor source
information , direct
comparison of precipitation d to vapor source conditions via Lagrangian
back trajectory vapor source estimation has produced complicated results. For
example, found that while the d of precipitation
contains identifiable source information, it “does not directly translate
into the source region T‾ss”. In a study of vapor sources for
precipitation in Antarctica, noted that the classical
interpretation of measured d would predict that the highest average d
found at Dome Argus would correspond to the warmest (most northerly) vapor
sources. However, precipitation at Dome Argus was linked to southerly
(cooler) vapor sources. The authors suggested that the high d value was due to
the vapor pressure deficit of dry air blowing off sea ice.
Likewise, attributed the significant correlation between
high d and source relative humidity (h) for precipitation collected at
four northeast USA locations during Superstorm Sandy to oceanic evaporation
into a dry continental air mass that was entrained into the superstorm.
Our study reveals a relatively more conclusive relationship between vapor
source and event-scale precipitation d, as summarized by four simple
regressions against h2m, hss, Tss, and Td shown in
Table . Though d is not significantly predicted by
h‾2m (p=0.86), it is significantly predicted by h‾ss (p<0.001, R2=0.34), with a slope of -0.4 ‰%-1. This value is consistent with the -0.4 to
-0.6‰ %-1 range reported in the literature for vapor . T‾ss is also a significant predictor (p=0.0023) though the variance explained is 12 % and the sign of the
coefficient is negative, opposite to expectations. If d is regressed
against both T‾ss and h‾ss, the multiple regression is
significant (p<0.001, not shown in Table ) and
explains 36 % of variance, most of which is due to the strong
relationship with hss. The vapor source region dew point, T‾d,
significantly predicts d (p<0.001) and explains a nontrivial
portion of the variance (R2=0.24) with a negative slope
(-0.53 ‰∘C-1). This is an interesting
result with respect to the utility of Td, a measurable quantity, and is
consistent with our earlier argument that Td is strongly related to
hss. Both variables provide a better representation of source conditions
than Tss and/or h2m. A low value of hss or Td corresponds
to a strong influence of descending dry air within the PBL, which enhances
kinetic isotopic fractionation and produces a high value of d. This
mechanism explains the negative correlation between d and Td and is
expected for the relationship between d and hss. Alternatively, the
vapor in descending air may have a high value of d , or both
mechanisms may contribute to this result.
Explanation of deuterium excess (d) using simple
regressions against various metrics that characterize source conditions.
β is the regression coefficient, and SE is the standard error. We
show results from simple linear regressions with four different independent
variables: evaporation site relative humidity (h‾2m),
evaporation site relative humidity relative to sea surface temperature
(h‾ss), sea surface temperature
(T‾ss), and 2 m dew point
(T‾d).
Independent variable
β (± SE)
p-value
R2
(slope units)
h‾2m (‰ %-1)
0.027 (0.157)
0.86
0.0
h‾ss (‰ %-1)
-0.395 (0.067)
< 0.001
0.34
T‾ss (‰ ∘C-1)
-1.17 (0.37)
0.0023
0.12
T‾d (‰ ∘C-1)
-0.56 (0.13)
< 0.001
0.22
Our dataset also shows systematic seasonal variations in d.
Figure shows that d cycles annually, with the maximum
occurring in October or November and lagging the annual maximum of
δ2H by 2–3 months (or ∼ 90∘). This phase
relationship explains the lack of linear association between d and
T‾ss and h‾2m because the two latter variables are both
in phase with δ2H. Systematic seasonal variations in precipitation
d occur in the Northern Hemisphere , particularly in
the Arctic .
These studies suggest that the conditions producing d variation have
systematic annual variations in their magnitude and relative importance.
Annual maxima and minima in deuterium excess, d, lag those of
δ2H by 2–3 months, such that the maxima are in fall and minima in
spring.