Reconstruction of a Temperature Time Series for Linnédalen, Svalbard
Daniel P. Lane
Atmospheric Science, College of Agriculture and Life Sciences
Spring 2005
Abstract
A temperature time series has been reconstructed for the period from 1974 to
2004 at Linnédalen, Svalbard (78°N latitude, Norway). A method for extending the time
series back to the early 20th Century is also presented. This reconstruction is important as
a surrogate instrumental record in a region of low observation density and high observation value. As an important site of paleoclimate research, Linnédalen’s meteorological
record is essential to calibrating a multi-century record of regional climate change. Lake
Linné, a glacial lake situated in the valley of Linnédalen, provides researchers with a
high-resolution sediment record of climate change during the late Holocene. To better
interpret this record, limited local meteorological data from the Valley have been coupled
with longer regional weather records to develop an extended meteorological record for
the location. The reconstruction and methods used are presented and assessed and recommendations for further work are provided. This project is part of the Svalbard Research Experience for Undergraduates (REU) program sponsored by the National Science
Foundation.
Introduction
Global climate models indicate
that warming associated with rising atmospheric concentrations of greenhouse
gases will have its greatest magnitude in
the high latitudes (Overpeck, et. al.
1997). Projected temperature increases
for the interior Arctic are as large as 4°C
by the middle of this century with most
of that warming occurring during the
winter months. This is more than twice
the mean projected warming for the
globe (IPCC 2001). Due to the sensitivity of the Arctic, much research has focused on monitoring high latitude climate with the goal of detecting the re-
gionally amplified signals of global climate change. Successful detection of
these signals will require 1) a sufficient
data collection network to assemble
modern meteorological observations and
2) an adequate knowledge of the preanthropogenic modes of variability in
Arctic climate (Johannessen, et. al.
2004). Neither is currently available. In
spite of evidence for Arctic environmental change during the twentieth century
(Overpeck, et. al. 1997; Przybylak
2003), a lack of comprehensive observational records from the region has made
it difficult to separate internal variability
from externally-forced trends (Moritz,
et. al. 2002). Moreover, the variability
1
of high latitude climate is one the greatest of any region in the world on interannual and longer time scales (Przybylak
2003).
Therefore, detecting climate
change in the Arctic will likely require a
longer data record than at latitudes
where the climate is less variable. Analysis of the observational records that are
available has elucidated some of the
characteristics of decadal and interdecadal variability (Polykov and Johnson
2000), while paleoclimate proxy records
have been employed to understand variations on longer time scales (Overpeck,
et. al. 1997). A more complete picture
of Arctic climate change will require further efforts to assemble and analyze both
modern meteorological data as well as
paleoclimate data. This paper contributes to both of those initiatives by presenting a reconstructed temperature time
series for Linnédalen, a glacial lake valley in western Svalbard.
Svalbard is a mountainous archipelago that lies north of Norway and
about 1000 km from the North Pole.
The islands lie at the climatological fulcrum between the northernmost advance
of the warm Gulf Stream current and the
southernmost extent of the wintertime
pack ice (Capelotti 2000). Settlement of
Svalbard was motivated by coal prospecting interests in the early part of the
20th Century, and meteorological records
exist at Longyearbyen (the territory’s
largest settlement) for most of the period
since then. Other records are also available from Isfjord Radio and Ny-Alesund.
Linnédalen (78°N, 13°E) lies a few kilometers inland from Isfjord Radio on
Svalbard’s west coast, about 50 km to
the west of Longyearbyen and about 150
km to the south of Ny-Alesund. The
valley harbors Svalbard’s largest lake—
Linnévatnet. The lake contains a highresolution record of Holocene climate in
the glacial silt sediments that line its bottom. For this reason, Linnévatnet has
been the focus of extensive investigation
by geologists and paleoclimatolgists for
several decades. As part of an ongoing
effort to collect and calibrate sediment
cores from this basin, meteorological
data have been collected from the valley.
This limited instrumental record and the
longer observational record from Longyearbyen and other regional stations
have been coupled using a regression
technique to produce an extended record
for the valley. The resulting reconstruction serves as a new observational record
for the high Arctic and will allow for a
meaningful interpretation of a new proxy
record of several centuries of climate in
the region.
Data and Methods
Local Observation Network
During July 2003, three temperature logger stations and an automated
weather station were established in Linnédalen. The loggers collected synchronous temperature readings every half
hour while the weather station recorded
temperature, barometric pressure, wind
speed and direction, rainfall, solar insolation, and soil temperature at the same
interval. A map of the local observation
network is shown (Fig. 1).
2
Figure 1. A map of Linnédalen showing the local area of observation.
Data from these instruments was
downloaded during the following summer. Two of the temperature loggers
provided a continuous record, but one
was destroyed by an avalanche during
the spring melt. The weather station
gathered data on all variables until
March 2003 when it was silenced by an
icing event or an altercation with a reindeer. Fortunately, the weather station
was equipped with an auxiliary temperature logger that provided a continuous
record for the weather station location
for the entire observation period. The
local temperature data were converted to
daily averages by calculating the mean
of the daily maximum and minimum
temperature at the weather station site.
A time series of daily average temperatures was then reconstructed for Linnédalen for the period during which regional observations are available but local observations are absent.
Regional Observation Network
To produce the best reconstruction possible, adequate predictor data
must be selected. The regional observation stations (Figure 2) were evaluated
based on their proximity to Linnédalen,
the length and continuity of their record
and their preliminary Pearson correlation
coefficient with the local temperature
record. Individually, all regional stations as predictors produced regressions
accounting for at least 96.7% of the variation in Linnédalen temperature. Longyearbyen (LYR) has by far the longest
period of record, but the daily data available to the author only extend back to
1975. Ny-Alesund (NYS) has the longest available record (back to 1974), but
the smallest R2 value (96.7%). Isfjord
Radio (ISF) has the largest R2 value but
the shortest available period of record
(back to 1996). Given the strengths and
weaknesses of each dataset, the stations
were used to create multiple linear regressions for each period of available
observations. Local and regional data
for the overlapping observational period
for all stations (11 Jul 2003 through 8 Jul
2004) was used to develop each regression model.
Data Reconstruction Methods
Data reconstruction is essential to
the study of climatology. Gaps in daily
datasets can hinder the analysis and application of meteorological data, and for
this reason reconstruction of missing data from existing instrumental records is a
common practice. Though these methods are usually applied to datasets with
scattered missing daily values, reconstructions have also been made for longer periods.
Klingbjer and Moberg
(2003) coupled regional instrumental
records with local daily temperature rec-
3
ords found in a journal to construct a two
hundred year temperature record for
Tornedalen in northern Sweden. Similarly, regression reconstructions have
been used to evaluate anomalously warm
19th century summer temperature records
from Scandinavia (Moberg, et. al. 2003).
There are three classes of data
reconstructions that use instrumental
records. Intrastation techniques use the
data from the local station that frames
the period of missing record. Averaging
yesterday’s and tomorrow’s temperature
to infer today’s temperature is an example of one type of intrastation reconstruction. These often produce the least
accurate reconstructions of the three
methods. Secondly, interstation reconstructions use climatological departures
at neighboring stations to infer local departures and thus local, missing temperature values. Finally, regression-based
reconstructions use statistical transfer
functions between local and regional stations. This third type is generally the
most accurate (DeGaetano, et. al 1995).
Figure 2. Map of regional instrumental record and chart of predictor correlation with Linnédalen temperature. Red dots indicate stations with data unavailable to the author and blue dots indicate stations with
available data.
The period of record from Linnédalen
frames no missing period of data (intrastation methods not possible) and is not
of sufficient length to construct a local
climatology (interstation methods not
possible). The nature of the dataset necessitates a regression-based method
that, fortuitously, is often the most accurate approach. That said, high linear
correlation coefficients between Lin4
nédalen temperature and the temperature
recorded at regional stations have already been demonstrated (see Figure 2).
Given that high degree of correspondence, focus was placed on the development of multiple linear regressions describing the relationship between local
and regional temperature observations.
Different multiple linear regressions
were then used to reconstruct Linnédalen
temperature depending upon which appropriate regional datasets were available for a particular period. The resulting
time series (Aug 1974 – Jul 2004) was
then pieced together. A simple linear
regression that uses Longyearbyen temperature to predict Linnédalen temperature was also constructed so that, once
the longer regional record becomes
available, it will be possible to reconstruct the local record back to 1911. In
every case, residuals were analyzed in
order to frame the limitations of each
regression and provide some caveats regarding its use.
(April – September) and differ more
widely during the polar night (October –
March). LYR was generally warmer
than LIN during the daytime months and
colder than LIN during the nighttime
months whereas this relationship is reversed for ISF and NYS which are both
generally warmer in winter than LIN due
to their more maritime climate. LIN is
located several kilometers inland and
behind a ridge while NYS and ISF are
situated directly on Svalbard’s west
coast where the warm Spitzbergen Current keeps the ocean surface unfrozen
during the winter. LYR has a more interior location on Svalbard and thus has a
more continental climate with colder
winters. Figures 3 and 4 demonstrate
that temporal and spatial temperature
variability is greater during the polar
night. For this reason, regressions will
have a better fit during the summer
months than during the winter months.
Multiple Linear Regression for Linnédalen Temperature: 1996 to 2004
Results
Due to the temporal limitations
imposed by the record from Linnédalen,
regression models were developed using
the mutually available temperature data
from the local station at Linnédalen
(LIN) and the three regional stations
(LYR, NYS and ISF) for the period from
11 July 2003 to 8 July 2004. The time
series of average daily temperature for
this period (Figure 3) show a close correspondence among the locations.
The time series of the temperature difference between the local (LIN)
station and the regional stations (Figure
4) reveal that the values match more
closely during the polar daytime months
Three regional datasets exist for
the period from September 1996 through
July 2004. A regression was produced
using forward selection and all three stations as predictors. This gave the highest apparent R2 (99.3%) value; however,
discarding the NYS data for this period
(Figure 5) resulted in more normally distributed residuals (Figure 6 and 7) with a
minor reduction in R2 (99.2%). This
second regression, which uses ISF and
LYR as predictors, was therefore chosen
to reconstruct LIN temperatures for this
time period. Figure 8 demonstrates the
heteroscedasticity of the residuals, that
is,
variance
is
not
5
Local and Regional Avg. Daily Temperature
15
10
5
Temp (C)
-10
-15
-20
-25
-30
-35
Time
LIN Temp
LYR Temp
ISF Temp
NYS Temp
Figure 3. A time series of local and regional daily temperature from 11 Jul 2003 to 8 Jul 2004.
Temperature Departures (Regional - LIN)
8
6
4
1/
20
03
7/
25
/2
00
3
8/
8/
20
03
8/
22
/2
00
3
9/
5/
20
03
9/
19
/2
00
3
10
/3
/2
00
10
3
/1
7/
20
03
10
/3
1/
20
03
11
/1
4/
20
03
11
/2
8/
20
03
12
/1
2/
20
03
12
/2
6/
20
03
1/
9/
20
04
1/
23
/2
00
4
2/
6/
20
04
2/
20
/2
00
4
3/
5/
20
04
3/
19
/2
00
4
4/
2/
20
04
4/
16
/2
00
4
4/
30
/2
00
4
5/
14
/2
00
4
5/
28
/2
00
4
6/
11
/2
00
4
6/
25
/2
00
4
0
7/
1
Del T (C)
2
-2
-4
-6
-8
-10
Date
LYR - LIN
NYS - LIN
ISF - LIN
Figure 4. A time series of the temperature differences between the local and regional stations.
constant but tends to increase with decreasing temperature. Again, seasonality
provides a physical mechanism for this.
The highest spatial and temporal vari-
ance in temperature occurs during the
polar night along with the coldest temperatures of the year. Unfortunately, this
means that the regression will overcon-
6
Ju
n04
Ap
r-0
4
M
ay
-0
4
M
ay
-0
4
Ju
n04
Ap
r-0
4
Ap
r-0
4
ar
-0
4
ar
-0
4
M
M
Fe
b04
Fe
b04
Ja
n04
Ja
n04
ec
-0
3
ec
-0
3
D
ov
-0
3
D
ct
-0
3
ov
-0
3
N
N
ct
-0
3
O
ct
-0
3
O
O
Se
p03
Se
p03
Au
g03
-5
Ju
l-0
Ju
l-0
3
3
Au
g03
0
fidently reconstruct relatively cold temperatures and will underconfidently reconstruct relatively warm temperatures
(the large R2 is an average measure of fit
throughout the year). Note also that the
residuals of the regression are significantly serially correlated (DurbinWatson
=
0.95).
Figure 5. Multiple linear regression using LYR
and ISF as predictors for LIN temperature for
1996 to 2004 period.
Figure 8. The residuals demonstrate decreasing
(non-constant) variance.
Multiple Linear Regression for Linnédalen Temperature: 1975 to 1996
Figure 6. Histogram of the residuals for 1996 to
2004 model.
Two regional datasets exist for
the period from August 1975 through
September 1996. By forward selection,
a regression (Figure 9) using both stations as predictors produced an apparent
R2 value of 98.5%. This regression uses
LYR and NYS as predictors and was
chosen to reconstruct LIN temperatures
for this time period. Figures 10 and 11
show a reasonable degree of normality in
the regression’s residuals while Figure
12 demonstrates the non-constant variance of the residuals. Note also that the
residuals of this regression are significantly serially correlated (DurbinWatson = 1.24)
Figure 7. Normal probability plot of the residuals
for 1996 to 2004 model.
7
Figure 9. Multiple linear regression using LYR
and NYS as predictors for LIN temperature for
1975 to 1996 period.
Figure 12. The residuals demonstrate decreasing
(non-constant) variance.
Simple Linear Regression for Linnédalen
Temperature: 1974 to 1975
Figure 10. Histogram of the residuals for 1975
to 1996 model.
One regional dataset is available
for the period from August 1974 through
August 1975. A simple linear regression
(Figure 13) using NYS temperature as a
predictor produced an apparent R2 value
of 96.7%. NYS provides the only data
available to reconstruct LIN temperature
during this period. Figures 14 and 15
show a reasonable degree of normality in
the regression’s residuals while Figure
16 demonstrates the non-constant variance of the residuals. Note also that the
residuals of this regression are significantly serially correlated (DurbinWatson = 1.45)
Figure 11. Normal probability plot of the residuals for 1975 to 1996 model.
Figure 13. LIN temperature versus NYS temperature.
8
Figure 14. Histogram of the residuals for 1974
to 1975 model.
Figure 15. Normal probability plot of the residuals for 1974 to 1975 model.
Figure 16. The residuals demonstrate decreasing
(non-constant) variance.
Simple Linear Regression for Linnédalen
Temperature: Prior to 1974
The dataset from LYR extends
back to 1911, but only the LYR record
after 1975 is available to the author. A
simple linear regression is presented so
that once the longer dataset is obtained
the Linnédalen record can be recon-
structed back to 1911. Daily temperatures for LIN were plotted against those
at LYR and a simple linear regression
was constructed with an apparent R2
value of 97.7% (Figure 17). This first
regression is produced by using all the
available temperature data for one fit
(not segregated) and is dubbed the annual model. A histogram of the residuals
for this model (Figure 19) shows that
they are normally distributed and centered at zero indicating that a linear
model appropriately describes the relationship. Time series of the residuals
(Figure 22) confirm that the largest
magnitude residuals are present during
the winter months. Since this regression
will ultimately be used to reconstruct a
time series for LIN temperature for most
of the 20th Century, every effort was
made to qualify the fit of the model. A
power transformation was attempted on
the LIN data, but this overcorrected the
variance of the residuals and skewed
them significantly to the right. The data
were then segregated by season and two
different regressions were fit in order to
get a better estimate of the true R2 value.
A second regression model was
fit with the influence of seasonality in
mind. It is referred to as the seasonal
model. At LIN and LYR, the sun is
completely below or above the horizon
for 24 hours a day for eight months of
the year. The other four months (roughly March, April, September, and October) are transition periods during which
the sun spends some time above and below the horizon each day. To construct
a seasonal model, two separate regressions were fit (Figure 18) for the period
from 21 March to 20 September (day)
and for the period from 21 September to
20 March (night). Thus, the transition
periods were each halved and categorized into either day or night observa-
9
tions. The histograms of the residuals
for both the annual and seasonal models
(Figures 19 and 20) show little difference in the center and distribution of the
residuals. However, the residuals are
significantly serially correlated (annual
model Durbin-Watson = 1.18). The R2
value should be revised downward toward its lower bound in each model
(which would occur at night when the
error in the model is highest). For day
and night regressions, the R2 values were
96.9% and 95.4% respectively—both
smaller than that of the annual model but
still indicative of good regressions. Dichotomizing the data reveals that the R2
value of 97.7% for the single regression
is overstated due to the unmet assumption of constant variance. By breaking
the data into day and night, two datasets
with more constant (though nonconstant) variance are produced. The
resulting regressions have smaller, more
realistic R2 values.
Understanding the quality of fit
for this model is particularly important if
it is to be used to reconstruct more than
sixty years of data. Also, this simple
linear regression is more likely subject to
seasonal influences. In the case of the
multiple regressions, a more continental
station (LYR) is coupled with a coastal
station (NYS or ISF) to predict the temperature at an intermediate location
(LIN). Thus, the seasonal differences
between continental and coastal climates
are stabilized in the case of two predictors. This stability is lacking in the single regression model so this further necessitated the division of the data into
polar day and polar night. As it turns out,
the residuals for the seasonal model are
essentially the same as those for the annual model.
Therefore, this tworegression seasonal model is not used to
reconstruct the local record because it
does not provide much improvement
over the single regression. It only qualifies
the
fit
of
the
Annual Linear Model: LIN Temp vs. LYR Temp
Seasonal Linear Model: LIN Temp vs. LYR Temp
15
15
y = 0.9053x + 0.0315
10
y = 0.8892x + 0.0405
R2 = 0.9773
10
0
-25
-20
-15
-10
-5
0
-5
LIN Te mp (C)
-30
-10
5
10
15
LIN Tem p (C)
0
-35
R2 = 0.969
5
5
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
-5
y = 0.9148x + 0.1607
-10
R2 = 0.9543
-15
-15
-20
-20
-25
-30
-25
-35
-30
LYR Temp (C)
-35
LYR Temp (C)
Day
Night
Linear (Night)
Linear (Day)
Figures 17 & 18. The annual and seasonal model regressions.
Figures 19 & 20. Histograms of the annual and seasonal models respectively. Both reveal similar patterns
of normally distributed residuals.
10
annual model.
Reconstructed Temperature Time Series
For Linnédalen Temperature: 1974 –
2004
The available regional instrumental record was used to calculate Linnédalen average daily temperature for
the period from August 1974 to July
2004 (Figure 21) using the above regressions developed for the time periods in
which appropriate predictor data was
available. Rather than the 1996 to 2004
regression (which uses ISF data), the
regression developed for the period from
1975 to 1996 was used to reconstruct
local temperature for the period from 5
February 2002 to 20 June 2002 due to
missing data at ISF.
Discussion
Limitations of the Reconstruction
Though all of the regression
models produced large R2 values, these
were likely overestimations of the fit in
each case. Inferences regarding the fit of
a regression require three assumptions
about the residuals: that they be normally distributed, display constant variance,
and that they are independent (not serially correlated). The histograms of the
residuals above demonstrate a fair degree of normality in each case; however,
the latter two assumptions are not met in
any case. A time series of the residuals
for each regression is shown (Figure 22)
for the period of local instrumental record. Upon qualitative assessment, it is
apparent that variance is not constant
and is higher in the winter, and that the
residuals are not independent and are
perhaps most serially correlated in the
summer months. This means that the fit
of each regression is not as good as the
regression parameters would suggest. A
table summary of each regression is
shown
Reconstructed Linnédalen Temperature
20.0
10.0
0.0
Temperature (C)
01.08.1974
31.07.1978
30.07.1982
29.07.1986
28.07.1990
27.07.1994
26.07.1998
25.07.2002
-10.0
-20.0
-30.0
-40.0
-50.0
Time
Figure 21. The reconstructed temperature dataset for Linnédalen from 1974 to 2004.
11
Residuals for Regressions (LIN - LIN Calc)
10.0
8.0
6.0
2.0
n04
Ju
-0
4
n04
Ju
-0
4
ay
M
r- 0
4
r- 0
4
ay
M
Ap
Ap
-0
4
r- 0
4
ar
Ap
M
04
-0
4
ar
M
04
Fe
b-
n04
Fe
b-
Ja
3
n04
Ja
3
-0
D
ec
3
-0
D
ec
3
-0
-0
N
ov
-0
3
N
ov
-0
3
ct
ct
O
ct
O
O
3
-0
3
3
p0
Se
3
p0
Se
3
g0
Au
g0
Ju
Au
Ju
-2.0
l-0
3
0.0
l-0
3
Residual (C)
4.0
-4.0
-6.0
-8.0
Time
LIN(ISF,LYR)
LIN(NYS,LYR)
LIN(NYS)
LIN(LYR)
Figure 22. A time series of the residuals for each regression for the period from 8 Jul 2003 to 11 Jul 2004.
Period of Reconstruction
Regression
R-Squared
DurbinValue
Watson Value
3 Sep 96 - 4 Feb 02,
21 Jun 02 - 8 Jul 04
LIN = -0.281 + (0.867*ISF) +
(0.201*LYR)
99.2%
0.95
1 Aug 75 - 2 Sep 96,
5 Feb 02 - 20 Jun 02
LIN = 0.226 + (0.551*LYR) +
(0.408*NYS)
98.5%
1.24
1 Aug 74 - 1 Aug 75
LIN = 0.366 + (1.01*NYS)
96.7%
1.45
Prior to 1 Aug 74
LIN = 0.0315 + (0.9053*LYR)
97.7%
1.18
Table 1. Summary of each period of record, reconstruction and the regression used. Values reflecting the
fit of each model and the autocorrelation of their residuals are also reported.
(Table 1). The Durbin-Watson statistic
is included for each regression and suggests the relative autocorrelation of the
residuals in each case (the smaller the
value, the higher the autocorrelation,
though the lowermost critical value is
slightly smaller for the regressions with
two predictors).
Multiple Linear Regressions and Overfitting
Overfitting is a concern when
adding predictors to a linear regression.
The two multiple regressions presented
above were constructed through the forward selection of physically relevant
predictors. The decision to add a second
predictor variable was motivated by geography in each case rather than by an
arbitrary stopping value of R2. When
dealing with simple linear regressions
with R2 values of greater than 95%, a
R2 of 5% (a typical criterion for pre-
12
dictor addition) is impossible. As previously mentioned, each multiple linear
regression coupled a coastal station with
LYR (a more continental station) in order to account for both influences at
LIN. ISF is located less than 100 m
from the coast and the ocean remains
open throughout the year. Due to the
moderating effect of the ocean, the relationship between the temperatures at ISF
and LIN cannot be precisely linear. The
difference in temperature between the
two stations is, in fact, influenced by the
temperature itself (Figure 23). Adding
LYR as a more continental predictor
compensates
residuals for the days with the largest
(top 10%, N=37) positive (p) regional
pressure gradient values (pressure at
NYS – pressure at LYR), negative (n)
pressure gradient values, and smallest
magnitude (z, close to zero) pressure
gradient values. Similarly, Figure 25
shows boxplots of residuals by regional
temperature gradient.
Figure 24. Boxplots of residuals for LIN(LYR)
by regional pressure gradient.
Figure 23. Plot of LIN temp versus temperature
gradient between LIN and ISF reveals role of
maritime influence.
for this while maintaining the superior
R2 value of ISF as a predictor. The logic
is the same for the LIN(LYR,NYS) regression which couples a coastal and an
interior station.
Residuals and Other Meteorological
Factors
The data were analyzed to evaluate the effects of regional pressure and
temperature gradients, local wind speed,
direction and solar insolation on the
magnitude and sign of residuals for the
LIN(LYR) simple linear regression
model. Figure 24 shows boxplots of the
Figure 25. Boxplots of residuals for LIN(LYR)
by regional temperature gradient.
T-tests for the residuals associated with the positive and negative pressure and temperature gradients gave P
values of 0.0002 and 0.0000 respectively. The significance of these values is
somewhat misleading, however, since
large positive/negative gradients tend to
be clustered in time. Thus, the data are
partially serially correlated. The physical explanation for the differences in the
pressure gradient boxplots relates to regional flow and local geometry. When
13
the pressure is much higher at NYS than
at LYR, the flow will generally be out of
the north. This represents an up-valley
flow at Linnédalen and hence adiabatic
cooling at the site of the weather station.
This causes the valley to be cooler than
expected and makes LIN(LYR) – LIN
more positive. The opposite is true
when the pressure at LYR is much higher than the pressure at NYS. The physical explanation for the difference in residuals due to temperature gradient is
rooted in seasonality. NYS is more
coastal and LYR is more interior. Positive gradient values will tend to occur in
the winter months while negative values
will tend to occur in the summer. Thus,
the differences in the residuals are only a
reflection of the seasonal bias of the
model. Similar analyses for wind direction and speed and solar insolation were
inconclusive due to a lack of adequate
data (the weather station stopped recording these values in March 2004). Once
more data is available, further analysis
of these variables will be possible.
Anomalies and the Short Period of Local
Record
There is always a risk of selecting an unrepresentative sample of data
when attempting to infer the attributes of
a large dataset from a subset of that dataset. In this case, one year of daily
temperature data was available to infer
the relationship between regional and
local temperature over several decades.
The problem of sampling is complicated
by the large interannual variability exhibited in the record (see Figure 21).
Any meteorological anomalies during
the period of local record that would affect the relationship between temperatures at different stations would cause
additional errors in the regression. The
regressions developed here may produce
good results and small residuals for the
period of model development but may
produce poor results for other years.
Figure 27 shows standard anomalies of
barometric pressure and temperature at
LYR for the period of local record. The
anomalies were calculated from 14-day
climatological (1975 – 2004) pressure
and temperature means and standard deviations at LYR and the curves shown in
Figure 27 are themselves 5-day averages
(smoothed) of those anomalies. Due to
the averaging and high correlation between local and regional conditions, it is
reasonable to assume these anomalies
are essentially the same at Linnédalen as
well. The average standard pressure
anomaly (Z = 0.13) and temperature
anomaly (Z = 0.22) for the year were
both positive but not very large. Since
the regressions perform better with higher temperatures, this may reflect another
reason to suspect their overconfidence.
Implications for Paleoclimate Reconstruction
More importantly for paleoclimatic reconstruction, the spring of 2004
appeared to be quite warm. This coincides with the period of the spring melt
when there is a large flux of sediment
into the Lake. Thus, an unusually warm
spring during the period of observation
could impact the calibration and interpretation of the sediment record that was
underway during this field season. The
warmth of this past field season may also have influenced the ablation of the
up-valley glacier. The effects on sedimentation (Figure 26) and ablation will
be clarified by forthcoming research
pursued by other members of the Svalbard REU project. Standard anomaly
analysis may also have implications for
14
the study of glacier health in the region.
Peaks and troughs in the anomaly curves
seem to be correlated to one another. It
appears that large dips and large peaks in
pressure and temperature often coincide;
however, large peaks and large dips also
overlap as well. These combinations
could be related to different air masses
or storm systems (i.e. warm and wet,
cold and dry, warm and dry, etc.) that
would have different implications for
glacier growth and ablation.
other factors as well. For example, a
year could have a very cold average
temperature but a change in the temporal
distribution of precipitation (a dry winter
with little snow and a wet summer with
lots of rain) would lead to a negative
glacier mass balance for the year. In this
way, anomaly analysis could be used to
infer the seasonal storminess and temperature trends that would have implications for glacial changes. Meanwhile,
close attention to temperature anomalies
during the spring melt would inform the
study of sediment deposition into Lake
Linné.
Standard anomalies like these are
available and have been calculated for
LYR by the author back to 1975. With
access to LYR data going back to 1911,
regional anomalies could be calculated
for most of the 20th Century.
Figure 26.
Thin-section
of a sediment
core retrieved
from Lake
Linné during
the 2004 field
season.
Glacier health is not only sensitive to average temperature but to many
Standard Anomalies at LYR
2.5
2
1.5
0.5
Jun-04
Jun-04
May-04
May-04
Apr-04
Apr-04
Apr-04
Mar-04
Mar-04
Feb-04
Feb-04
Jan-04
Jan-04
Dec-03
Dec-03
Nov-03
Oct-03
Nov-03
Oct-03
Oct-03
Sep-03
Sep-03
Aug-03
Aug-03
-0.5
Jul-03
0
Jul-03
Std. Anom (Z)
1
-1
-1.5
-2
Time
Pressure Anom.
Temp Anom.
Figure 27. Standard anomalies of pressure and temperature at LYR for the period of local record.
15
Figure 28. 20th Century instrumental record from Longyearbyen. Courtesy Ole Humlum, UNIS
Trends in the Regional and Reconstructed Local Record
The long-term record from LYR (Figure
28) reveals low variability in summertime temperatures with the exception of
a slight warming trend during the 1990s.
Wintertime temperatures, however,
demonstrate very large interannual variability. Figure 28 suggests winter warming at the beginning and end of the 20th
Century with a period of cooler winters
during the 1960s and 1970s. A running
30-day average of reconstructed Linnédalen temperature (Figure 29) is consistent with the seasonality of interannual variability discussed above. High
within-season variability is also apparent
during the winter, while variability within the summer is minimal. The reconstruction is also consistent with the
warmer/cooler cycling of winter temperatures exhibited in the regional record
since the 1980s.
16
30-Day Running Avg of Reconstructed LIN Temp
10.0
5.0
0.0
01.08.1974
31.07.1978
30.07.1982
29.07.1986
28.07.1990
27.07.1994
26.07.1998
25.07.2002
Temp (C)
-5.0
-10.0
-15.0
-20.0
-25.0
-30.0
Time
Figure 29. Smoothed curve of reconstructed temperature record for LIN.
High wintertime interannual variability is related to Svalbard’s position
on the southernmost extent of the wintertime pack ice. Small changes in the location of the ice/ocean boundary from
year to year lead to large changes in the
regional meteorological conditions from
year to year. Identification of trends
from the limited reconstructed record is
merely qualitative. A longer reconstruction, like that available from a paleoclimatic study, is required to quantify climate change in the region.
Conclusion
A time series of daily average
temperatures has been reconstructed for
the period from 1974 to 2004 at Linnédalen, Svalbard. Caveats regarding
the accuracy of this reconstruction have
been demonstrated. Value is assigned to
this reconstruction due to the problem of
monitoring climate change, the sensitivity of the Arctic to such change, the relative lack of meteorological data from the
Arctic and the importance of Linnédalen
as a field site of paleoclimatic research.
As part of a larger project, this paper reports on the meteorological component
of a broader framework of goals, namely
to calibrate a multi-century climate record. However, this study can also inform the objectives of the project for the
upcoming field season and beyond. To
improve the regression models, it is suggested that the regression be refitted after the next field season using additional
data from the August 2004 to July 2005
period. This will help reduce the sampling error inherent in using one annual
cycle of data at a site with high interannual variability. Verification of the regressions developed here is also suggested. This can be achieved by comparing the local temperature time series
retrieved during the next field season to
the time series predicted by running regional data through the regression for the
same time period. Verification could
provide some insight into how well the
pre-instrumental record is being repro-
17
duced for Linnédalen. Further analysis
of standard anomalies is also recommended due to their importance in understanding annual sediment flux into
the Lake and glacial health in the region.
The local observation network, a system
of temperature loggers at different altitudes and locations up the valley, would
be conducive for microclimate studies.
With a spatial scale of a couple kilometers and a temporal scale of half an hour,
an in-depth study of the local dynamics
of Linnédalen’s meteorology might be a
worthwhile project. More extensive meteorological data (wind speed, direction,
etc.) will also be available should the
weather station go unperturbed until the
next field season. This will enable new
layers of complexity for those studying
meteorology in Linnédalen. For example, adiabatic and upslope/downslope
local temperature changes could be assessed from the lapse rate, wind speed
and direction. Many additional projects
will undoubtedly be spurred by the doubling of data anticipated after the next
field season. In light of this new evidence, the reconstruction, conclusions
and suggestions put forth here will likely
be reexamined and modified. However,
a line of reasoning and method of interrogating the data for a larger understanding has been established. This will provide groundwork for the study of meteorology through the Svalbard REU project and, in turn, advance the study of
climate change in the high arctic.
Acknowledgements
I would like to thank the National Science Foundation’s Svalbard REU Program and its principle investigators Al
Werner of Mount Holyoke College and
Steve Roof of Hampshire College for
their organization of the program and
support during the field season. I would
like to thank my faculty advisor Art
DeGaetano of Cornell University for his
guidance and support during the analysis
of the data and the writing of this paper.
I thank Mark Wysocki and Dan Wilks of
Cornell University for their thoughtful
review of this document. I thank Ole
Humlum of the University of Norway
Center on Svalbard (UNIS) and the University of Oslo for providing regional
climate data for the Svalbard area. I
thank the Cornell Presidential Research
Scholars Program for financial provisions and guidance throughout my undergraduate career.
References
Capelotti, P. J., 2000: The Svalbard Archipelago:
American Military and Political Geographies of
Spitzbergen and Other Norwegian Polar
18
Territories, 1941-1950. McFarland & Company,
Inc., Publishers.
DeGaetano, A. T., K. L. Eggleston and W. M.
Knapp, 1995: A Method to Estimate Missing
Daily Maximum and Minimum Temperature
Observations. Journal of Applied Meteorology.
34, 2, 371-380.
Intergovernmental Panel on Climate Change
(IPCC), 2001: Climate Change 2000 – Third
Assessment Report, Cambridge University Press.
Johannessen, O.M., L. Bengtsson, M.W. Miles,
S.I. Kuzmina, V.A. Semenov, G.V. Alekseev,
A.P. Nagurnyi, V.F. Zakharov, L.P. Bobylev,
L.H.
Kingbjer, P. and A. Moberg, 2003: A Composite
Monthly Temperature Record for Tornedalen in
Northern Sweden. International Journal of Climatology, 23, 1495-1494.
Moberg, A., H. Alexandersson, H. Bergstrom,
and P. D. Jones, 2003: Were Southern Swedish
Summer Temperatures Before 1860 as Warm as
Measured? International Journal of Climatology.
23, 1495-1521.
Moritz, R. E., C. M. Blitz and E. J. Steig, 2002:
Dynamics of Recent Climate Change in the Arctic. Science, 297, 1497-1502
Overpeck, J., et. al., 1997: Arctic Environmental
Change of the Last Four Centuries. Science, 278,
1251-1256.
Pettersson, K. Hasselmann, and H. P. Cattle,
2004: Arctic climate change: observed and modeled temperature and sea-ice variability. Tellus
A, 56, 4, 328-341.
Polyakov, I. V. and M. A. Johnson, 2000: Arctic
decadal and interdecadal variability. Geophysical
Research Letters, 27, No. 24, 4097-4100.
Przybylak, R., 2003: The Climate of the Arctic.
Atmospheric and Oceanographic Science Library. 26. Kluwer Academic Publishers.
19