Earth and Planetary Science Letters 231 (2005) 87 – 96
www.elsevier.com/locate/epsl
On the temperature correlation of y18O in modern precipitation
Matthew J. Kohna,*, Jeffrey M. Welkerb
b
a
Department of Geological Sciences, University of South Carolina, Columbia, SC 29208, United States
Biology Department and Environment and Natural Resources Institute, University of Alaska, Anchorage, AK 99510, United States
Received 8 March 2004; received in revised form 3 December 2004; accepted 10 December 2004
Available online 20 January 2005
Editor: E. Boyle
Abstract
Reevaluation of modern precipitation, temperature, and isotope data permits reconciliation of previous disparate values for
the correlation between y18O of modern precipitation and surface temperature. Past analysis has used the mean surface
temperature over the time interval of sample collection (e.g., mean weekly, monthly, or annual temperature) to calculate
temperature coefficients, and different approaches at mid-latitudes yield different temperature coefficients (Dy18O/DT): spatial
correlations among geographically distinct sites yield ~0.55x/K; seasonal variations at single sites yield 0.2–0.4x/K; and 12
month running averages yield 0.5–1x/K. However, there are systematic differences in temperature during precipitation events
vs. time-averaged surface temperature means. Correction for this bias using hourly weather and monthly isotope data from U.S.
sites yields a single value of ~0.55x/K for all three approaches. Revised temperature coefficients based on surface observations
are also commensurate with coefficients obtained using cloud base temperatures and with theoretical distillation models (0.5–
0.7x/K). These coefficients provide a consistent basis for validation of general circulation models that incorporate stable
isotopes of precipitation, and for comparison to independent estimators of the isotopic response to climate change.
D 2004 Elsevier B.V. All rights reserved.
Keywords: stable isotopes; precipitation; paleoclimate; GCM; 0-18/0-16; meteoric water
1. Introduction
The stable isotopes of oxygen and hydrogen (y18O
and yD) in modern precipitation have long been known
to correlate with temperature, as a result of temperature-dependent distillation processes in the atmosphere [1,2]. The slope of precipitation y18O or yD vs. T
* Corresponding author. Tel.: +1 803 777 5565; fax: +1 803
777 6610.
E-mail address: [email protected] (M.J. Kohn).
0012-821X/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.epsl.2004.12.004
(i.e., the apparent temperature coefficient, Dy18O/DT or
DyD/DT) is important because it has been and
continues to be used to calculate some paleotemperature changes (e.g., [3,4]), and because it is a basis for
validating the accuracy of general circulation models
(GCMs) that incorporate stable isotopes of precipitation (e.g., [5–8]). Indeed, such applications are a major
justification for the International Atomic Energy
Agency-World Meteorological Organization’s maintenance of the Global Network of Isotopes in Precipitation (GNIP-http://www.isohis.org) for over 40 years.
88
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
Quite simply, if we cannot understand the isotopic
systematics of modern precipitation, there is little hope
of using precipitation-linked stable isotope proxies or
models to investigate past environmental change.
Unfortunately, all previous studies have used mean
air temperature at the surface over the time interval of a
precipitation sample (i.e., mean weekly, monthly, or
annual temperature) as the basis for estimating modern
temperature coefficients and for validating GCMs.
Here, we show that such mean temperatures are
systematically biased compared to both cloud temperatures and surface temperatures during precipitation
events. Use of surface temperatures during precipitation events, rather than the mean temperature of the
overall time interval, reconciles apparent disparities of
modern correlations. This provides for more accurate
comparison with models of isotopes in precipitation,
and clearer delineation of paleoclimate vs. modern
climate isotope–temperature correlations.
2. Temperature correlations
Several different approaches, which produce differing results, have been used to evaluate temperature
correlations of stable isotopes in precipitation: (1)
bSpatial correlationQ matches mean annual temperature (MAT) with mean annual isotope composition at
different sites that have different MATs and compositions [2]. (2) bTemporal correlationQ uses a specific
site’s seasonal variation in mean monthly temperatures (MMTs) and isotope compositions [9–11]. (3)
bTime-series correlationQ compares 12-month running
averages of mean monthly temperature and composition over several decades’ observations [9,10]. These
different approaches commonly yield statistically
different temperature coefficients, which for midlatitudes are ~0.55x/K [12], ~0.2–0.4x/K [9–11],
and 0.5–1x/K [9,10] respectively, i.e., differences of
up to a factor of 5 in retrieved values.
A fourth approach (bpaleoclimate correlationQ)
matches paleoclimate records of isotope compositional change with independent estimators of temperature change (e.g., [13]). The paleoclimate-correlation
commonly yields a weaker temperature coefficient
than the spatial or time-series correlations—a point
which has led some to propose that the modern
temporal correlation, with its minimal slope, should
be used for paleoclimate applications ([14,15]; see
also discussion in [6]). In this regard, we believe it is
important to conceptually separate paleoclimate vs.
modern correlations. Certainly temperature has a firstorder impact on stable isotope compositions in
precipitation, due to distillation effects. However, as
has been extensively discussed in the modeling
community (e.g., [5–8,16,17]), compositions also
depend on numerous other factors, for example vapor
sources and source temperatures, air mass trajectories
and fallout histories, recycling of water from the
landscape, etc. Consequently, there is not necessarily a
causal dependence implied by the temperature coefficients obtained from correlation plots, nor is a
temperature coefficient that is derived from modern
observations necessarily applicable to any paleoclimate interpretations. Although we view our reinterpreted, modern temperature coefficients as critical for
framing the discussion of temperature correlations in
modern precipitation, they are largely independent of
temperature coefficients derived from paleoclimate
correlations, and consequently cannot be substituted
for them. If modern correlations have any effect on
paleoclimate interpretations, it is more likely through
their potential influence on GCM validation schemes.
3. Methods
For illustration, we mainly focused on monthly
isotope data from mid- to high-latitude sites from the
United States because (a) isotope data worldwide are
commonly collected at monthly resolution (http://
www.isohis.org), (b) stable isotopes of precipitation at
low-latitude sites show a very weak correlation with
temperature [10], (c) hourly weather observations are
readily available for many U.S. sites, and (d) at least
two U.S. sites provided cloud temperatures on an
event-by-event basis [18]. Use of weekly isotope data
[19] yields similar conclusions as monthly data. We
additionally restricted analysis to oxygen isotopes, but
the extremely strong correlation between oxygen and
hydrogen isotopes in precipitation [2,10,20] ensures
applicability to yD. For convenience we use the term
bprecipitation temperatureQ to refer to surface air
temperature during a precipitation event, although
direct measurement of the temperature of the precipitation itself (rain, snow, etc.) is not ordinarily made.
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
Mean weekly, monthly, and annual temperatures
(MWT, MMT, and MAT) were calculated from daily
means over the appropriate time interval. Mean
weekly, monthly and annual precipitation temperatures (MWPT, MMPT, and MAPT) were calculated
for an interval by weighting temperatures by precipitation amount (as is done analogously when calculating mean weighted isotope compositions), e.g. for
MMPT:
RPi Ti
RPi
4. Results
3
150
Waco, TX
MAT=18.15
MAPT=15.82
50
MMP (cm)
100
75
MAT=7.67
MAPT=5.64
125
0
-3
150
Flagstaff, AZ
-6
100
-3
75
50
-6
25
50
0
MMP (cm)
100
75
Oc
t
No
v
De
c
Jul
Au
g
Se
p
b
Ap
r
Ma
y
Jun
Ma
Jan
Fe
125
6
3
150
Fairbanks, AK
MMPT-MMT (˚C)
MAT=9.83
MAPT=13.15
0
9
150
Chicago, IL
r
-9
0
9
MMPT-MMT (˚C)
25
Oc
t
No
v
De
c
Jul
Au
g
Se
p
Jan
Fe
b
Ma
r
Ap
r
Ma
y
Jun
-9
MAT=1.23
MAPT=2.34
100
3
75
50
0
Oc
t
No
v
De
c
Jul
Au
g
Se
p
Jan
-3
b
Ma
r
Ap
r
Ma
y
Jun
25
Fe
Oc
t
No
v
De
c
Jul
Au
g
Se
p
Jan
Fe
b
Ma
r
Ap
r
Ma
y
Jun
0
125
6
25
-3
125
0
MMP (cm)
3
MMPTs are systematically warmer than MMTs in
the winter, and cooler in the summer (Fig. 1; Table 1).
MMP (cm)
where Ti and P i are temperature and precipitation
amount on an hourly basis as summed over one
MMPT-MMT (˚C)
month’s time. Because data are extremely scattered
both in temperature and composition, reduced major
axis regressions are most appropriate for estimating
temperature coefficients. Linear regressions yield
similar albeit somewhat smaller temperature coefficients, and do not alter our basic conclusions
regarding the comparability of modern spatial-,
temporal-, and time-series-correlations.
MMPT-MMT (˚C)
MMPT ¼
89
0
Fig. 1. Plot of the difference between MMPT and MMT (squares) vs. month, and precipitation amount (bars) vs. month for 4 stations at different
latitudes and with disparate precipitation patterns. Winter MMPTs are generally warmer than MMTs, and summer MMPTs are generally cooler
than MMTs. These data imply that past calculated temporal correlations [10,11], which were based on MMTs, had too large a summer vs. winter
temperature difference, biasing calculated temperature correlations to anomalously low values.
90
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
Table 1
Temperature and precipitation characteristics of select sites in the United States
Station
MMT
MMPT
Dslope
(Latitude, longitude)
(Jan/Jul)
(Jan/Jul)
(%)
Fairbanks/Bethel, AK (N64849V, W147852V)
International Falls, MN (N48834V, W93823V)
Chicago, IL (N41847V, W87845V)
Coshocton/Mansfield, OH (N40849V, W82831V)
Flagstaff, AZ (N35808V, W 111840V)
Santa Maria, CA (N34854V, W121815V)
Waco, TX (N31837V, W97813V)
Miami, FL (N25848V, W80816V)
23.6/17.0
14.0/19.4
4.6/23.2
2.5/22.6
1.4/18.5
10.8/17.0
6.8/27.9
19.6/28.0
14.6/14.1
9.0/18.4
1.3/21.5
1.6/18.7
0.9/16.0
9.2/9.2
7.7/24.1
20.2/25.2
40
20
20
40
20
N100
30
70
MAT
MAPT
MAy18O
(x)
2.6
3.6
9.8
10.4
7.7
14.1
18.2
24.2
3.9
10.2
13.2
11.6
5.6
9.3
15.8
23.7
12.0
6.2
7.4
8.0
5.8
4.0
MMT (Jan/Jul) and MMPT (Jan/Jul) are the mean monthly temperature and mean monthly precipitation temperature for January and July.
Dslope is the change in temporal slope based on Jan/Jul temperatures. MAT, MAPT, and MAy18O are the mean annual temperature,
precipitation temperature and (weighted) y18O. All temperatures are in 8C. Compositions were not measured for International Falls or Miami;
their temperature and precipitation characteristics were considered only for geographic completeness.
Warmer winter MMPTs result because cloud cover
reduces radiative cooling during longer winter nights
and because warmer air holds more moisture, so that
warmer events can contribute more precipitation. A
steeper slope at lower temperatures is augmented by
the increased isotope fractionation between water
vapor and precipitation with decreasing temperature,
particularly for ice and snow (e.g., [21–23]). Cooler
summer MMPTs result from evaporative cooling
during rainfall, plus (negative) advective heat transport during heavy precipitation events. Use of daily
weather data for calculating precipitation temperatures generally yields results intermediate between
MMPTs (hourly basis) and MMTs, suggesting that
fine-scale weather observations are needed for
accuracy. MAPTs are also offset from MATs, mainly
because of seasonal differences in the amount of
precipitation. For example Chicago and Fairbanks
receive somewhat more summer precipitation, leading to MAPTNMAT, whereas Flagstaff and Santa
Maria receive more winter precipitation, leading to
MAPTbMAT (Table 1).
With respect to calculated temperature coefficients,
global spatial correlations are unlikely to be very
strongly influenced by use of MAPTs rather than
MATs. Although any one site will certainly have
precipitation seasonality, affecting its MAPT, a
latitudinal mean over the entire Earth should generally
average out that variation. Thus, we expect that
previously calculated spatial correlations are robust,
although without hourly weather data for most sites in
GNIP, we cannot evaluate this hypothesis directly. For
the few stations in the United States with long-term
composition and hourly weather data (Table 1), the
spatial correlation using MAPTs is 0.59F0.12 x/K,
indistinguishable from the global correlation for midlatitudes based on MATs (~0.55x/K; [12]). In
contrast, systematic differences between MMTs and
MMPTs for all sites imply that temporal slopes should
be steeper than traditionally calculated. Indeed, data
for Chicago show the expected increase in slope, from
0.46F0.02x/K, to 0.55F0.03x/K (Table 2; Fig. 2).
This increase in slope is also evident in data for which
cloud base temperatures (Winnemucca, NV), and
Table 2
Temporal correlation and correlation coefficients of composition vs. temperature
Station
Dy18O/DTF1r, r 2
Dy18O/DTF1r, r 2
Dy18O/DTF1r, r 2
(MMT or MWT)
(MMPT or MWPT)
(CT)
Chicago (monthly)
Winnemucca (weekly)
Cedar City (weekly)
0.46F0.02, 0.53
0.44F0.07, 0.54
0.50F0.06, 0.62
0.55F0.03, 0.57
0.67F0.11, 0.50
0.62F0.08, 0.57
0.78F0.13, 0.47
0.56F0.06, 0.67
Regressions are based on mean monthly or weekly temperature (MMT or MWT), mean monthly or weekly precipitation temperature (MMPT or
MWPT), and cloud temperature (CT). Temperatures at cloud base for Winnemucca were directly determined, whereas cloud temperatures at 3.1
km elevation were assumed for Cedar City. Reduced major axis regressions were used and values are in x/K.
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
5
Chicago
δ18O (precipitation,‰, V-SMOW)
δ18O (precipitation,‰, V-SMOW)
5
91
0
-5
-10
-15
/K
6‰
0.4
-20
r2=0.53
-25
-15 -10
-5
0
5
10
15
20
MMT (˚C)
25
Chicago
0
-5
-10
-15
/K
-20
5‰
0.5
r2=0.57
-25
-15 -10
-5
0
5
10
15
20
25
MMPT (˚C)
Fig. 2. y18O of precipitation vs. MMT and MMPT for Chicago. Use of MMPTs increases slopes, and reconciles temporal correlation with the
mean spatial correlation for this latitude (~0.55x/K; [12]). Isotope compositions that were suggestive of evaporative enrichment during rainfall
(y18O values above zero and/or anomalous Deuterium-excess) were omitted, although use of all data does not significantly change regression
statistics.
cloud temperature at height (Cedar City, UT) were
determined [18]. Presumably regressions of y18O vs.
cloud temperature (CT) are most accurate, because
CTs are more likely a better representation of the
actual temperature of the precipitation. To compare
the use of MWTs vs. MWPTs, we first registered data
on a weekly basis, and for Cedar City restricted
consideration to a single class of storm tracks with the
most observations [18]. As expected intuitively, the
MWPT regressions yield temperature coefficients
closest to the CT regressions (Table 2), suggesting
that use of MWPTs rather than MWT is more accurate
for calculating temporal correlations.
We also evaluated the use of MAPTs for calculating time-series-correlations (Fig. 3). Chicago was
chosen for analysis because there is no pronounced
seasonal variability to precipitation amounts (Fig. 1),
and there are a large number of monthly isotope
measurements (Fig. 2). For specific months that
lacked isotopic data (b5%), we substituted monthly
mean compositions as determined from the average of
other years. The 12-month running averages of MAT
and MAPT (Fig. 3a) are generally correlated, but
show much larger amplitudes for MAPT than for
MAT—this is a product of precipitation seasonality.
For example, an anomalously dry summer yields an
anomalously low, mean annual y18O value (i.e., a
large negative Dy18O relative to the average) because
more precipitation is concentrated in the winter. This
also directly decreases corresponding MAPT (large
negative DMAPT), because it is dependent on
precipitation amounts, for example as observed for
Santa Maria and Flagstaff, which normally have low
summer precipitation. However, because MAT is
based on average temperature, it is less affected
(small DMAT), and the calculated Dy18O/DT coefficient is therefore larger. An anomalously dry winter
season has an analogous but opposite effect (large
positive Dy18O and DMAPT, but small DMAT).
Consequently, time-series correlations that use MAT
will always yield a larger apparent temperature
coefficient (Fig. 3b) than for MAPT (Fig. 3c). It is
especially noteworthy that the time-series correlation
of y18O vs. MAPT for Chicago (0.57F0.03x/K) is
indistinguishable from (a) the global spatial correlation using MAT (~0.55x/K for mid-latitudes; [12]),
(b) the U.S. spatial correlation using MAPT
(0.59F0.12x/K) and (c) the MAPT temporal correlation for Chicago (0.55F0.03x/K).
In general, use of mean surface temperatures will
always underestimate temporal correlations and overestimate time-series correlations. In contrast, use of
temperatures during precipitation events yields indistinguishable temperature coefficients for temporal,
92
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
16
14
MAδ18O(‰, V-SMOW)
MAPT
13
12
11
10
MAT
9
-6
-8
MAδ18O(‰, V-SMOW)
/K
.08
‰
1.1
8±
0
-4
-5
-6
-7
-8
-9
10
12
14
MAT (˚C)
16
75
d
-3
-4
-5
-6
-7
-8
/K
3‰
-9
r2=0.05
-10
8
19
19
-2
c
-3
70
65
75
19
70
19
19
-2
MAδ18O(‰, V-SMOW)
-4
-10
65
8
b
19
MAT, MAPT (˚C)
15
-2
a
Chicago, IL
-10
7
0.5
8
.0
±0
r2=0.25
10
12
14
16
MAPT (˚C)
Fig. 3. (a) 12-month running averages of MAT and MAPT for Chicago. Time-series of MAPT shows larger amplitude than MAT, due to seasonal
variation in precipitation amounts, implying that MAT-based time-series overestimate slope of temperature correlations. (b) 12-month running
average of y18O for Chicago. (c) Plot of mean annual y18O (MAy18O) vs. MAT implies an extremely high slope for the temperature correlation.
(d) Plot of MAy18O vs. MAPT yields a shallower slope that is indistinguishable from the spatial and temporal (MMPT) correlations.
spatial, and time-series correlations. The common
temperature coefficient determined for Chicago,
~0.55x/K is also consistent with theoretical distillation models (0.5–0.7x/K; [2]), whereas the temporal and time-series slopes that use MMTs and MATs
are not.
5. Implications for paleoclimate studies
We reiterate the strong emphasis of many workers
that modern temperature coefficients may have little
relevance for quantitative interpretation of isotopic
records of climate change because of the multitude of
factors that influence stable isotopes in precipitation.
There are important effects on modern compositions
from initial vapor sources and recycling from the
landscape, air mass trajectories and fallout histories,
micrometeorological processes (e.g., evaporation during descent and kinetic fractionations during condensation), etc., but these are either too site-specific
for generalization (e.g., [18]), or have already been
discussed elsewhere (e.g., [24]). This includes the
possibility of evaporative y18O enrichment of precip-
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
itation below the cloud base, which in principle can be
monitored either via Deuterium-excess or the 17O
anomaly [25]. Instead of reiterating those arguments
and treatments for modern precipitation, we prefer to
discuss possible pitfalls in applications where modern
temperature coefficients are used or compared to
paleoclimate temperature coefficients.
One potential issue is the validation of GCMs
that incorporate isotopes in precipitation. For
obvious reasons, validation schemes are principally
based on correspondence between predicted values
for a modern model vs. modern measurements of
mean temperature, precipitation amount, isotope
composition, etc. For example one might directly
compare mean predicted monthly temperature and
isotope composition with observed values for a
specific site, or plot y18O vs. temperature for
spatially disparate sites to see whether predicted
and measured coefficients correspond [5–8]. This
approach fundamentally assumes that a mean
temperature and mean isotope composition are in
fact comparable—yet they cannot be, because they
are not temporally equivalent. Consequently there
may be important, but overlooked biases. For
example, the composition of January precipitation
in Chicago (~14x) occurs at a MMPT of 1.3
8C, but would be validated against a MMT of 4.6
8C. Any modeled MMPT other than 1.3 8C
would be incorrect, but unless MMPT were
calculated, this error would go undetected. Some
sites show differences in MMT vs. MMPT up to 10
K for some months, and seasonal precipitation may
impart differences in MAT vs. MAPT in excess of
5 K (Table 1). Because precipitation temperatures
are not reported in the GCM literature, the
magnitude of any modeling errors is as yet
unknown. However we note that a possible 5–10
K difference in temperature far exceeds modeling
bnoiseQ of approximately a few degrees [5–8]. We
reiterate that on a global scale there may not be
much difference in retrieved coefficients for spatial
correlations if MAPTs are substituted for MATs,
supporting this specific validation of global models
[5–8]. However this conclusion does not extend to
regional models where there is a stronger potential
for systematic precipitation seasonality. It is important to recognize that GCM models already monitor
(simulated) surface temperature during precipitation
93
with fine temporal resolution, so revision of
validation approaches should be straightforward.
A second area of concern is the comparison of
paleoclimate vs. modern correlations, and attempts to
reconcile them by focusing on the smaller coefficient
for the modern temporal correlation. This is particularly relevant for interpreting the stable isotope
response to climate change in Greenland. As summarized for the central Greenland ice cores [26],
independent estimators of temperature change and
measured isotopic shifts have yielded a small temperature-coefficient for glacial–interglacial cycles
(~0.3x/K for 10–100 Ka), but a higher coefficient
for the more recent record (~0.5x/K for the last few
hundred years). This last value is strikingly similar to
the temporal correlation calculated for modern snowpits in Greenland (~0.5x/K; [27,28]), and is clearly
different from the modern spatial correlation (~0.7x/
K; [2]). However, as we have shown for other sites,
the modern temporal and spatial correlations may well
be reconcilable simply by using precipitation temperatures rather than mean temperatures.
For Summit, Greenland, we were able to test this
hypothesis, via unpublished hourly meteorological
observations that were generously provided by Dr.
K. Steffen and the Greenland Climate Network (GCNet; [29]; Table 3). This test is only approximate for
several reasons. Most importantly, although GC-Net
does measure air temperature, it does not directly
measure amount of precipitation. Instead, as a proxy
for precipitation amount, we used surface height as
monitored 4 ways—2 sensors dedicated to measuring
surface height, and the measured heights of the
temperature sensors. Because of wind scouring and
blowing snow, surface levels can vary erratically, and
to account for this we averaged surface height
observations with a 5-h window. We chose a
relatively small window because daily data for North
America yield different coefficients from hourly data.
Compaction also tends to decrease surface levels, so
we discarded any negative changes to surface levels.
This choice likely overestimates precipitation
amounts. Other possible sources of bias include: a
small data base (~50 months of data); thermal
inversions, which are common in polar regions, but
not taken into account by surface measurements; and
variations in snow water content, which was not
measured.
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
Table 3
Temperature and precipitation characteristics of Summit, Greenland
Month
MMT
(8C)
Apparent
precipitation
MMPT
MMPT-MMT
(8C)
(8C)
33.6
39.5
38.7
32.8
23.1
14.9
12.8
14.1
21.4
32.6
31.9
32.6
5.8
3.1
0.4
0.8
0.5
1.5
0.8
0.8
0.8
0.1
2.7
5.0
(cm)
January
February
March
April
May
June
July
August
September
October
November
December
39.4
42.6
39.1
32.0
23.6
16.4
13.7
14.9
22.2
32.5
34.6
37.6
19
26
83
61
46
33
39
52
54
49
46
28
Data span January 2000 through May 2004. The MAT and MAPT
are approximately 29.5 and 27.8, respectively. Precipitation
amounts are quite approximate, and are uncorrected for snow water
content, compaction, blowing snow, etc. See text for discussion.
9
150
Summit,
Greenland
MAT=-29.5
MAPT=-27.8
125
100
3
75
50
MMP (cm)
6
0
25
Oc
t
No
v
De
c
Jul
Au
g
Se
p
0
b
Ma
r
Ap
r
Ma
y
Jun
Jan
-3
Fe
Although approximate, the GC-Net data (Table 3;
Fig. 4) do suggest similar behavior for Greenland as
for other sites (Fig. 1), in that winter MMPTs are ~ 5
K warmer than MMTs. Because the total range of
MMT’s for Summit is about 30 K (Table 3), this
would increase the temporal correlation determined
from snow pits by ~20% (to ~0.6x/K). Considering
that Fairbanks, where precipitation is directly measured, shows a much more pronounced winter effect
(Fig. 1), the correction for Greenland may be underestimated. Regardless, the increase in slope helps to
reconcile Greenland’s temporal and spatial correlations, and further underscores the disparity between
modern and paleoclimate temperature correlations, as
first identified in the deeper portions of the cores, and
as emphasized by modelers.
As a final point, it might be argued that bprecipitation temperatureQ is not useful, firstly because it
combines two climate parameters (precipitation
amount and temperature), and secondly because the
paleoclimate community and public principally focus
on changes in mean temperature. While mean
temperature may indeed be of more interest, it is an
unfortunate fact that the mean temperature over a
time interval has little to do with precipitation,
because most of the time there is no precipitation,
and precipitation amounts are rarely distributed
MMPT-MMT (˚C)
94
Fig. 4. Plot of the difference between MMPT and MMT (squares)
vs. month and precipitation amount (bars) vs. month for Summit,
Greenland, showing similar pattern to mid-latitude sites (Fig. 1).
Because precipitation amounts were not directly measured, MMPT
and MAPT values are approximate.
uniformly throughout a particular time interval.
Although upon first consideration one might assume
that precipitation temperatures and mean temperatures are not significantly different, that assumption
is clearly false for nearly all time spans we have
considered (seasonal to decadal), and for nearly all
regions. That is, if isotope compositions do encode
temperature, then they must encode temperatures
during precipitation events, not mean temperatures.
Insofar as events contributing more precipitation
usually bcountQ more in an isotopic record, there is
a direct dependence of isotope composition on
precipitation amount, and this propagates to all
paleoclimate proxies whose isotopic compositions
are linked to precipitation. For isotope records to be
interpreted quantitatively in terms of changes to
mean temperature, the correspondence between precipitation temperature and mean temperature must
first be evaluated, either through modern records or
through GCMs, where mean temperature and precipitation temperature are independently known or
calculated.
Acknowledgements
We thank the K-12 science classes of the Oregon
Network of Isotopes in Precipitation (ORNIP) for
M.J. Kohn, J.M. Welker / Earth and Planetary Science Letters 231 (2005) 87–96
alerting us to the importance of event-based data
analysis, and K. Steffen for his generous and timely
provision of unpublished observations for Summit,
Greenland. T. Edwards and an anonymous reviewer
are thanked for their helpful comments. This material
is based upon work supported by the National Science
Foundation under Grant Nos. EAR 0304181 (to MJK)
and ESH 0196475 (to JMW).
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