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Climate Change Effect on Winter Temperature and Precipitation of Yellowknife, Northwest Territories, Canada from 1943 to 2011

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American Journal of Climate Change, 2013, 2, 275-283
Published Online December 2013 (http://www.scirp.org/journal/ajcc)
http://dx.doi.org/10.4236/ajcc.2013.24027
Open Access AJCC
Climate Change Effect on Winter Temperature and
Precipitation of Yellowknife, Northwest Territories,
Canada from 1943 to 2011
Janelle Laing, Jacqueline Binyamin*
Department of Geography, University of Winnipeg, Winnipeg, Canada
Email: *J.binyamin@uwinnipeg.ca
Received August 14, 2013; revised September 16, 2013; accepted October 15, 2013
Copyright © 2013 Janelle Laing, Jacqueline Binyamin. This is an open access article distributed under the Creative Commons Attri-
bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
ABSTRACT
The correlation of the Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), Pacific North American
Oscillation (PNA), Arctic Oscillation (AO), and Scandinavia (SCAND) indices with winter (DJF) temperature and pre-
cipitation for the period of 1943 to 2011 was analyzed to study climate change and variability of Yellowknife, NWT.
SOI correlated negatively with both temperature (r = 0.14) and precipitation (r = 0.06) causing colder, drier condi-
tions during La Niña and warmer, wetter conditions during El Niño. PDO was shown to have a strong positive correla-
tion with both temperature (r = 0.60) and precipitation (r = 0.33) causing warmer, wetter weather in the positive phase
and colder, drier weather in the negative phase. PNA showed the strongest positive correlation for both temperature (r =
0.69) and precipitation (r = 0.37) causing very warm and wet conditions in the positive phase and very cold and dry
conditions during the negative phase. AO correlated negatively with temperature (r = 0.04) and positively with pre-
cipitation (r = 0.24) causing colder, wetter conditions in the positive phase and warmer, drier conditions in the negative
phase. Finally SCAND was shown to have a weak negative correlation with both temperature (r = 0.10) and precipita-
tion (r = 0.18). Sunspot area showed a strong negative correlation (r = 0.30) with temperature and a very weak posi-
tive correlation (r = 0.07) with total annual precipitation. Yellowknife’s average annual temperature and precipitation
have increased by 2.5˚C and 120 mm, respectively throughout the past 69 years.
Keywords: Yellowknife; Climate Change; Climate Variability; Climate Modes; Teleconnections; ENSO; SOI; PDO;
PNA; AO; SCAND; Sunspot Area
1. Introduction
The arctic system is particularly sensitive to change, and
in light of anthropogenic climate change the arctic can be
seen as an indicator to such change. There is later freeze-
up and earlier break-up of ice on arctic rivers and lakes
[1,2], furthermore the overall extent of sea-ice is dimin-
ishing. This is uniquely important in the Arctic and Ant-
arctic due to the high albedo of snow and ice which re-
flects much of the incoming solar radiation, and a posi-
tive feedback mechanism is seen when incoming solar
radiation is increased and the extent of snow and ice is
decreased and replaced by dark water or bare soil, rock, and
vegetation which have a much lower albedos and absorb
more radiation. Increased temperature and precipitation
are seen throughout the Northern hemisphere including
Yellowknife, NWT, Canada [3].
Yellowknife is located in the subarctic at 62˚27'17"N
(latitude) and 114˚22'35"W (longitude). It is the capital
of the NWT, and is situated on the north shore of Great
Slave Lake at 206 m elevation. The station is 9 km in
distance from the Yellowknife airport. Monthly mean
winter (DJF) temperature and total precipitation values
from the Global Historical Climate Network (GHCN-V3)
during the period of 1943 to 2011 were analyzed to study
climate change and variability in Yellowknife. Data were
separated into two time periods (1943-1972 and 1973-
2011) to evaluate the influence of selected climate modes
on the station. The two time periods were chosen strate-
gically to assess to what degree increased greenhouse gas
emissions have had on the influence of climate modes.
*Corresponding author.
J. LAING, J. BINYAMIN
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276
The effects of sunspot area were also assessed and were
shown to have a strong influence on the climate of the
station.
Climate modes or teleconnections are naturally occur-
ring aspects of the quasi-chaotic atmospheric system.
Sea-surface temperatures and ocean circulation patterns
play a vital role in the creation and persistence of tele-
connections. Selected climate modes were studied to
assess their influence on the climate of Yellowknife. El
Niño Southern Oscillation (ENSO), Pacific Decadal Os-
cillation (PDO), Pacific North American Oscillation (PNA),
Arctic Oscillation (AO), and Scandinavia (SCAND) were
all shown to have varying degrees of influence on the
temperature and precipitation of Yellowknife. The strong-
est links between Yellowknife’s climate and the telecom-
nections were in the winter season (DJF), which is con-
current with climate data throughout Canada [4].
Bonsal and Shabbar [4] explained the significant rela-
tionships between the outlined teleconnection patterns
and ecosystem-related variables such as the duration of
lake and river ice, the timing of snowmelt and spring
peak stream flow, and the onset of spring. Most studies
focus on the effects of climate modes on a very broad
area [5-8] however this study examines the effects of five
climate modes on Yellowknife’s temperature and pre-
cipitation. Currently there are no studies focused exclu-
sively on our station’s climate.
Section 2 describes and discusses temperature and pre-
cipitation results and their correlation with climate modes
as well as sunspot area; and Section 3 includes a sum-
mary and conclusions.
2. Results and Discussion
2.1. Temperature
Yellowknife’s climate is extremely seasonal, experienc-
ing an annual temperature range of 39.2˚C. The average
annual temperature during the study period (1943 to
2011) was 4.7˚C, the coldest season being winter (DJF)
with an average temperature of 24.6˚C, followed by
spring (MAM) at 6.3˚C, then fall (SON) at 2.5˚C, and
finally summer (JJA) being the warmest at 14.6˚C.
Yellowknife has experienced an increase in tempera-
ture of 2.5˚C during the 69-year study period (Figure 1).
Decadal and 30 year temperature averages show a cooler
period from 1943-1972 and a warmer period from 1973-
2011 (Figures 2 and 3). This is concurrent with the
warming trend of the arctic and subarctic regions [9].
Causes for the warming can be linked to anthropogenic
forcing through increased greenhouse gas emissions, snow-
ice albedo feedback mechanisms, and the influence of
certain climate modes and sunspots [3]. The increase in
temperature has caused a variety of changes in the land-
scape including a decline in permafrost levels which are
consequently causing an increase in outflow of water into
the Mackenzie Basin [10,11].
2.2. Precipitation
Yellowknife received an average total annual precipita-
tion of 382.9 mm, receiving the greatest amount of pre-
cipitation in the fall (115.2 mm) and summer (111.6 mm),
followed by winter (68.8 mm) and by spring (53.0 mm)
with the least amount of precipitation.
Figure 1. Annual average temperature (˚C) for Yellowknife, NWT, Canada for the years 1943 to 2011.
J. LAING, J. BINYAMIN
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277
Figure 2. 30 year average for temperature (˚C) in Yellow-
knife, NWT, Canada for 1943-2011.
Figure 3. Ten year average temperature (˚C) for Yellow-
knife, NWT from 1943 to 2011.
Yellowknife has experienced an increase of 120 mm in
total annual precipitation from 1943 to 2011 (Figure 4).
Precipitation is highly dependent on temperature in this
region as the warmer air temperature holds more water
vapor and possibly leading to cloud formation and pre-
cipitation. The dependency of Yellowknife’s precipita-
tion on its temperature has shown a strong positive cor-
relation (r = 0.3, graph not shown), therefore the increase
in precipitation is in accordance with the overall increase
in temperature. Many of the same effects from the tele-
connections seen in temperature were also seen in pre-
cipitation values.
2.3. Correlation with Climate Modes
2.3.1. Southern Oscillation Index (SOI)
ENSO is characterized by the ocean-atmospheric interac-
tion in the equatorial Pacific and overlying atmosphere.
SOI defines the atmospheric anomaly and is generated by
the pressure differences between Tahiti and Darwin, Aus-
tralia. During the positive SOI index (La Niña phase),
there is an unusually shallow thermocline in the eastern
tropical pacific, strong easterly winds, and lower than
average pressure over Darwin causing updraft and higher
than normal pressure over the eastern tropical Pacific,
causing subsistence. During the negative SOI index (El
Niño phase) the effects are reversed; easterlies weaken or
reverse in direction and become westerlies. There is a
low pressure cell over the eastern tropical Pacific, in-
creasing precipitation, and the thermocline sinks to a
greater depth and a higher pressure cell over Darwin,
Australia. Secondary effects are seen in North America
and are as follows: during La Niña episodes high pres-
sure over the north Pacific pushes the Polar jet stream
further north producing colder temperatures in the north-
ern United States and north western Canada. However,
during El Niño episodes warmer temperatures are seen
throughout western Canada and northern United States
and wet cool weather over southern United States and
Northern Mexico [4].
Figure 5 shows the correlation between Yellowknife’s
average winter temperature and total winter precipitation
with winter SOI values. The correlation is negative for
both temperature (r = 0.14) and precipitation (r = 0.06).
Seasonal averages are not shown as they do not have any
significant influence on the station’s climate. SOI data
are from: http://www.cru.uea.ac.uk/cru/data/soi/soi.dat.
Temperature outliers in Figure 1 can be explained in
part by the influence of ENSO such as the annual average
temperature in 1998 at 1.04˚C, which was the warmest
annual average recorded and was also a strong El Nino
year, as well as 2010 and 1987 at 1.56˚C and 2.06˚C,
respectively, which were moderate El Nino years. How-
ever, there are other outliers that do not have the same
correlation with a moderate to strong ENSO event such
as 1983, which was a strong El Nino year, the annual
average was 4.93˚C. However Mount St. Helens located
south of Seattle, Washington erupted in May 1980 and El
Chichón located in southern Mexico erupted in April of
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Figure 4. Total annual precipitation (mm) for Yellowknife, NWT, Canada from 1943 to 2011.
=0.020
=0.001
0
20
40
60
80
100
120
140
35
30
25
20
15
10
5
0
3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0
Precipitation(mm)
Te m p e r a t u r e (C)
SouthernOscillationIndex(SOI)
Tempe rature
Precipitation
Linear
(Temperature)
Linear
(Precipitation)
r=0.06
r=0.14
Figure 5. Correlation between average winter SOI and average winter temperature (˚C) and total winter precipitation (mm)
for Yellowknife, NWT, Canada for the years 1943 to 2011.
1982, which both were possible contributors to the cool-
ing. Other significant temperature outliers do not follow
a pattern in regards to ENSO.
When the data was broken up into the following sec-
tions: 1943 to 1972 and 1973 to 2011, for the purposes of
comparing pre and post major greenhouse gas emissions,
different correlation coefficients between temperature,
precipitation and SOI were yielded. The era before in-
creased greenhouse gas emissions (1943-1972), the cor-
relation coefficient for average winter temperature values
was 0.09 and precipitation values were 0.01. However,
for the following period, 1972-2011 the correlation coef-
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279
ficient increased to 0.15 for temperature and 0.05 for
precipitation, meaning ENSO gained a stronger influence
on the temperature and precipitation in Yellowknife.
Winters in Canada following El Niño events are com-
monly linked with below normal precipitation throughout
western and central Canada and are often colder and snow-
ier than normal following La Niña events [6]. However,
Figure 5 shows that during La Niña episodes precipita-
tion in Yellowknife is slightly reduced and during El
Niño episodes precipitation in slightly increased.
2.3.2. Pacific Decadal Oscillation (PDO)
PDO events typically persist twenty to thirty years before
changing phase; effects are most visible in the North Pa-
cific and North American sectors with secondary effects
in the pacific equatorial region (Bonsal, and Shabbar,
2011). The teleconnection is similar to ENSO in spatial
positioning but much longer-lived. It is characterized by
changes in sea surface temperature, sea level pressure,
and wind patterns. The positive or warm phase is charac-
terized by warm ocean waters along the north-western
coast of North America and equatorial region and cool
sea surface temperatures in the central North Pacific.
Opposite conditions are observed in the negative or cool
phase [4].
Winter PDO values compared to average winter tem-
peratures show a very strong positive correlation (Figure
6) with r = 0.60; meaning that in the positive phase
higher temperatures were observed and in the negative
phase lower temperatures were observed. A cool PDO
event dominated from 1947-1976 and a warm event from
1977 through to approximately the mid 1990s. Figures 2
and 3 show a similar pattern with cooler temperatures
from 1943 to 1972 and warmer temperatures from 1973
onward. PDO data are from:
http://jisao.washington.edu/pdo/PDO.latest.
When the data is separated into pre- and post-increased
greenhouse gas emissions time periods, correlation coef-
ficient values differ by a reasonable amount. From 1943
to 1972, PDO strongly influences temperature (r = 0.66),
however from 1973 to 2011 the influence of PDO on the
temperature weakens as the correlation value drops to 0.36.
PDO has shown to have a strong positive correlation (r
= 0.33) on total winter precipitation (Figure 6), PDO
causes more precipitation in the positive phase and less
in the negative phase. When separated into the two time
periods, the effect of PDO differed significantly, from
1944 to 1972 it showed a very strong positive correlation
(r = 0.51), that influence weakens immensely from 1973
to 2011 as the correlation changed to weak and negative
(r = 0.07). In the earlier period, the teleconnection
caused more precipitation in the positive phase and less
in the negative phase, and in the later period the positive
phase caused slightly less precipitation and slightly more
in the negative phase. This could be due to the overlap
with other teleconnections.
=0.328
=0.111
0
20
40
60
80
100
120
140
35
30
25
20
15
10
5
0
43210123
Precipitation(mm)
Te m pe r a t u r e (ºC)
PacificDecadalOscillation(PDO)
Temper ature
Precipitation
Linear(Temperature)
Linear(Precipitation)
r=0.33
r=0.60
Figure 6. Correlation between winter PDO and average winter temperature (˚C) and total winter precipitation (mm) for Yel-
lowkniwfe, NWT, Canada from 1943 to 2011.
J. LAING, J. BINYAMIN
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2.3.3. Pacific North American Oscillation (PNA)
The PNA is one of the more influential climate modes in
the Northern hemisphere mid-latitudes and is found to be
strongly influenced by ENSO. It is characterized by 700
mb pressure height anomalies over the Aleutian Islands
and the vicinity of Hawaii. The positive phase is associ-
ated with Pacific warm episodes comparable to El Niño
and above normal pressure heights in the vicinity of Ha-
waii and western Canada causing above normal tem-
peratures, and below normal heights over the eastern US
causing below normal temperatures. The negative phase,
contrary to the positive phase is associated with Pacific
cold episodes similar to La Niña and below normal pres-
sure heights in the western Canada causing below normal
temperatures and above normal pressure heights in the
eastern US causing above normal temperatures [5].
Figure 7 shows the correlation between winter PNA,
average winter temperature and total winter precipitation
from 1951 to 2011. Temperature values showed a strong
positive correlation (r = 0.69) with PNA, causing warmer
temperatures in the positive phase and cooler tempera-
tures in the negative phase. From 1951 to 1972 the cor-
relation is at its strongest (r = 0.79), then drops to 0.6
from 1973 to 2011. PNA values in Figure 7 are from:
http://www.cpc.ncep.noaa.gov/products/precip/CWlink/p
na/norm.pna.monthly.b5001.current.ascii.table.
PNA has a strong positive correlation (r = 0.37) with
total winter precipitation throughout the study period,
producing more precipitation during the positive phase
and less precipitation during the negative phase (Figure
7). PNA has a much stronger influence on precipitation
from 1951 to 1972 (r = 0.49) than it does from 1973 to
2011 (r = 0.13).
2.3.4. Arctic Oscillation (AO)
AO also referred to as the Northern hemisphere annular
mode is characterized by sea level pressure (SLP) anoma-
lies poleward of 20˚N. In its positive phase, AO causes
strong winds to circulate around the North Pole confining
colder air. As AO enters its negative phase, winds die
down and allow cold arctic air masses to infiltrate into
the lower latitudes [7,12].
When analyzed over the duration of the 69 year study
period, AO was shown to have a very weak influence on
the temperature of Yellowknife (r = 0.04, Figure not
shown). The lack of significant correlation was surpris-
ing given the subarctic location of the station. However,
when broken up into separate time periods, its influence
is extremely variable, from 1951 to 1972 the correlation
coefficient is 0.32, meaning the positive phase causes
cooler temperatures and the negative phase causes warmer
temperatures. From 1973 to 2011, that influence weakens
(r = 0.14), still causing similar effects but to a lesser
degree.
AO has shown to have a strong positive correlation on
the total winter precipitation (r = 0.24). The influence
increased from r = 0.05 (1951-1972) in the earlier period
to r = 0.20 in the later period (1973-2011).
=0.474
=0.137
0
20
40
60
80
100
120
140
35
30
25
20
15
10
5
0
2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0
Precipitation(mm)
Te m p e r at u r e (C)
PacificNorthAmericanOscillation
(
PNA
)
Tem per atur e
Preci pitation
Linear(Temperature)
Linear(Precipitation)
r=0.37
r=0.69
Figure 7. Correlation between winter PNA and average winter temperature (˚C) and total winter precipitation for Yellow-
knife, NWT, Canada from 1951 to 2011.
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2.3.5. Scandinavia Pattern (SCAND)
SCAND is characterized by a primary pressure circula-
tion over Scandinavia, with secondary opposing pressure
cells over western Europe and eastern Russia/western
Mongolia. The positive phase is associated with positive
pressure height anomalies (higher pressure) over Scan-
dinavia and western Russia with below average precipita-
tion across Scandinavia and colder temperatures throughout
western Europe and central Russia. The negative phase
exhibits opposing temperature and precipitation effects.
The influence of SCAND has not shown to have a sig-
nificant influence on Yellowknife’s temperature, a slight
negative correlation was found (r = 0.10). Although the
influence was greater in the past than it is now, from
1951 to 1972 the correlation coefficient was equal to
0.11and from 1973-2011 that influence weakened sig-
nificantly (r = 0.001).
Throughout the duration of the study period SCAND
has shown to have a significant influence on precipitation
of Yellowknife; a strong negative correlation (r = 0.18)
was shown. Therefore SCAND causes less precipitation
during its positive phase and causes an increase in pre-
cipitation during its negative phase. SCAND showed a
significant variation of influence between the two time
periods, from a correlation coefficient of 0.30 from
1951 to 1972 to a much weaker relationship (r = 0.01)
from 1973 to 2011.
2.4. Sunspot Area
Sunspots appear as dark spots on the sun’s surface sur-
rounded by bright faculae. They are caused by magnetic
activity that inhibits convection and consequently have a
reduced temperature. Similar to climate modes, sunspots
are believed to go through cycles of diminished and en-
hanced activity [13]. The number and area of sunspots
are closely related (r = 0.98 from 1943 to 2011).
Sunspot area has shown to have a significant influence
on the temperature of Yellowknife, during the study pe-
riod the correlation coefficient was 0.30 (Figure 8)
meaning larger sunspot area causes colder temperatures
and smaller sunspot area causes warmer temperatures.
When broken up into the separate time periods, the in-
fluence remained consistent. Sunspot area data are from:
http://solarscience.msfc.nasa.gov/greenwch/sunspot_area
.txt.
The influence of sunspot area on total annual precipi-
tation showed a weak positive correlation (r = 0.06, Fig-
ure 8) for the whole period from 1943 to 2011. Extreme
variation was seen between the two time periods. The
first time period (1943 to 1972) showed a very strong
positive relationship (r = 0.43), which means more pre-
cipitation was formed by increased sunspot area. In the
later period from 1973-2011 the relationship was re-
versed; a correlation coefficient of 0.12 was yielded
=0.088
=0.003
0
100
200
300
400
500
600
8
7
6
5
4
3
2
1
0
0500100015002000250030003500
Precipitation(mm)
Te m pe r a t u r e (ºC)
SunspotArea(millionthsofahemisphere)
Temper atur e
Precipitation
Linear
(Temperature)
Linear
(Precipitation)
r=0.06
r=0.30
Figure 8. Sunspot area (millionths of a hemisphere) in relation to annual average temperature (˚C) and total annual precipi-
tation (mm) for Yellowknife, NWT, Canada from 1943 to 2011.
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282
meaning that less precipitation was caused by an in-
creased area of sunspots and more precipitation was caus-
ed by a decreased area of sunspots.
3. Summary and Conclusions
Both temperature and precipitation have increased over
the 69-year study period, temperature increased by 2.5˚C
and precipitation by 120 mm. The results from the com-
parisons between the time period of 1943 to 1972 and the
later period from 1973 to 2011 demonstrate the varying
degrees of impact of different climate modes and sunspot
area has on Yellowknife’s temperature and precipitation
over time. The mid-1970s mark an important period, many
climatic changes and phenomena are shown to begin or
accelerate during this period. The increase in both tem-
perature and precipitation can be attributed in part to the
intensification of the greenhouse effect through increased
anthropogenic greenhouse gas emissions [3,14].
ENSO events, although short lived have a weak nega-
tive correlation with winter temperature and precipitation
generating colder, drier weather during La Niña events
and warmer wetter winters during El Niño events. ENSO
gained a stronger influence on temperature over time but
its influence remained consistent over time for precipita-
tion.
The PDO has shown to be very influential in Yellow-
knife’s climate and may be a partial source of the warm-
ing that is observed. The correlation between PDO and
temperature is very strong and positive (r = 0.60) but
weakened over time (r = 0.36). In the earlier time period
PDO showed a strong positive correlation with precipita-
tion (r = 0.33) then changed to a weak negative correla-
tion (r = 0.07) in the later period. Similarly to PDO,
PNA showed a very strong positive correlation with tem-
perature (r = 0.69) but weakened over time (r = 0.60) and
showed a similar pattern with precipitation (r = 0.37) and
changed to (r = 0.13). AO was shown to have a weak
negative correlation with temperature (r = 0.04) and
when analyzed in the separate time period it showed a
decreasing influence from r = 0.32 to r = 0.14. AO
was shown to have opposite effects on precipitation yield-
ing a strong positive correlation (r = 0.24) over the dura-
tion of the study period and when separated into the ear-
lier and later periods, was shown to gain influence. Fi-
nally SCAND was shown to have a negative correlation
with temperature (r = 0.10) and precipitation (r = 0.18),
and showed a weaker influence in the later period.
The effect of sunspots on Yellowknife’s temperature
was opposite from what would normally be expected;
causing cooler temperatures with a greater sunspot area
and warmer temperatures with a smaller sunspot area.
The effect remained fairly consistent over time. However,
with increasing sunspot area more precipitation was
caused when analyzed from 1943 to 2011. The influence
of sunspot area over time showed great variability, in the
earlier period the correlation was strong and positive (r =
0.47) but in the later period the correlation was weak and
negative (r = 0.12).
The changing influence of the teleconnections on the
climate of Yellowknife cannot be attributed solely to the
increased amount of greenhouse gas emissions as there is
currently no consensus on how increases in greenhouse
gas emissions have influenced the occurrence of the out-
lined teleconnections.
Similar findings by Bonsal and Shabbar [4] showed
that teleconnections had the strongest influence on Cana-
dian climate in the cold season. Positive PDO events
were related to be warmer than normal temperature over
western and central Canada on a longer time-scale; the
opposite effects were seen during the negative phase of
PDO. Also teleconnection-precipitation relationships were
not found to be as significant as teleconnection-temper-
ature relationships.
Future work should focus on climate warming and the
decline of the extent of permafrost, which has implica-
tions that extend beyond the environmental impacts. The
people living in the communities up north can no longer
be assured that the existing structures built on terrain
formerly underlain by permafrost are still stable. Also the
influence of warmer climate on the earlier ice-break up
and later ice freeze-up on sub-arctic and arctic rivers and
lakes is very important which causes issues of trans-
portation for people traveling by ski-doo and dog sled.
These are both primary modes of transportation for nor-
thern communities as there are not extensive road net-
works.
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... It is known that any external factor affecting the ecosystem has a certain range in which its action is most effective [54]. In our case, the influence of the temperature factor is most pronounced in winter [55,56]. It was found that when estimating stem biomass growth by using the annual ring width, the greatest contribution to explaining its variability being made by summer temperature accounting for from 16% of the total dispersion [57] to 50% of the residual one [58]. ...
... With an inter-annual time step, the predominant influence of summer temperature is quite normal [60], and the model for deciduous species is possible in such cases only in relation to the summer temperature. But against the background of long-term climatic shifts for decades, the prevailing influence is acquired by winter temperatures [61], having in mind that winter temperatures in the Northern hemisphere are increased faster than summer ones during the 20th century [55,56,62,63,64]. In terms of regression analysis, a weak temporal trend of summer temperatures compared to a steep trend of winter ones means a smaller regression slope and a worse ratio of residual variance to the total variance explained by this regression. ...
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Current climate changes caused by greenhouse gas emissions alter landscape and environmental conditions, increase instability in many ecosystems, and increase the global role of forest cover. We attempted to model Quercus spp. single-tree biomass using the data from 500 sample trees distributed along the trans-Eurasian hydrothermal gradients. Today, several equations for the tree biomass involve both morphological-structural characteristics of trees and stands, and climate indicators as independent variables. The models make it possible to predict changes in biomass due to shifts in climate trends, but do not show the contribution of climate variables to the explanation of biomass variability. This variability depends on both the species of the tree and stand, and the structure of a model. The models designed show to what extent the deviation from the classical allometric model caused by the inclusion of additional independent variables, increases the contribution of climate variables to the explanation of biomass variability. The model shows the greatest contribution when it includes age, stem diameter, tree height, and their combined effect. The 3D-interpretation of the "best" model showed a propeller-shaped dependence of the components of oak tree biomass on temperatures and precipitation. The shape of this dependence is a mirror image of a similar dependence for the biomass of trees of two-needled pines and larches. This may be due to the functioning traits of leaved and coniferous species. Opposed Reviewers: Additional Information: Question Response Abstract: Current climate changes caused by greenhouse gas emissions alter landscape and environmental conditions, increase instability in many ecosystems, and increase the global role of forest cover. We attempted to model Quercus spp. single-tree biomass using the data from 500 sample trees distributed along the trans-Eurasian hydrothermal gradients. Today, several equations for the tree biomass involve both morphological-structural characteristics of trees and stands, and climate indicators as independent variables. The models make it possible to predict changes in biomass due to shifts in climate trends, but do not show the contribution of climate variables to the explanation of biomass variability. This variability depends on both the species of the tree and stand, and the structure of a model. The models designed show to what extent the deviation from the classical allometric model caused by the inclusion of additional independent variables, increases the contribution of climate variables to the explanation of biomass variability. The model shows the greatest contribution when it includes age, stem diameter, tree height, and their combined effect. The 3D-interpretation of the "best" model showed a propeller-shaped dependence of the components of oak tree biomass on temperatures and precipitation. The shape of this dependence is a mirror image of a similar dependence for the biomass of trees of two-needled pines and larches. This may be due to the functioning traits of leaved and coniferous species.
... Recently, the average winter temperature has been rising not only in Korea but also globally, and the frequency of cold waves has increased (Jeon and Cho, 2015;Laing and Binyamin, 2013). In Korea, dormancy breaking in apple trees occurs between January and early February (Kweon et al., 2013). ...
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Rapid apple decline (RAD) is a complex phenomenon affecting cultivated apple trees and particularly dwarf rootstocks on grafted young apple trees. Since its first appearance in the United States, RAD has been reported worldwide, for example in Canada, South America, Africa, and Asia. The phenomenon has also been observed in apple orchards in Korea, and it presented similar symptoms regardless of apple cultivar and cultivation period. Most previous reports have suggested that RAD may be associated with multiple factors, including plant pathogenic infections, abiotic stresses, environmental conditions, and the susceptibility of trees to cold injury during winter. However, RAD was observed to be more severe and affect more frequently apple trees on the Malling series dwarf rootstock. In this study, we reviewed the current status of RAD worldwide and surveyed biotic and abiotic factors that are potentially closely related to it in Korea.
... High-latitude regions, including Canada's north, are experiencing increased air temperatures across their landscapes (Carpino et al., 2018;Eichler et al., 2013;Laing and Binyamin, 2013;Sharma et al., 2019). Lakes comprise a significant part of this landscape, and the processes they undergo -some of which are indicated by the lake temperature -are reacting to these shifting air temperatures. ...
... Electronic copy available at: https://ssrn.com/abstract=4474059 P r e p r i n t n o t p e e r r e v i e w e d 3 44 1 Introduction 45 High-latitude regions, including Canada's north, are experiencing increased air temperatures 46 across their landscapes (Carpino et al., 2018;Eichler et al., 2013;Laing and Binyamin, 2013; 47 Sharma et al., 2019). As a significant part of this landscape, lakes, and their processes, some 48 of which are indicated by lake temperature, are responding to these changing air temperatures. ...
... В качестве исходной схемы распределения температур (изотерм) на территории Евразии мы выбрали карту средних январских температур, поскольку наиболее выраженные изменения в биоте связаны с зимней, а не c летней и не со среднегодовой температурой [109], и именно зимние температуры более чувствительны к текущим изменениям климата [11,91,95]. Соответственно, прогнозы будущего климата указывают на дальнейшее повышение температуры, в первую очередь, в холодное время года [84]. ...
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... The actual values of the biomass of 413 stands of the genus Populus (Figure 1) based on the known coordinates of the sample plots established, we superimposed on the maps of winter (January) temperatures and average annual precipitation distribution and related them with the isolines of the mentioned indices on the maps [33]. In our case, the schematic map of the isolines of mean January temperature, rather than that of the mean annual temperature, was used, as climate warming is most pronounced in the cold half of the year [34,35]. Obviously, taking the mean winter temperature as one of the independent variables, we get a more reliable dependence having the higher predictive ability. ...
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... With an inter-annual time step, the predominant influence of summer temperature is quite normal (Zubairov et al., 2018). But against the background of long-term climatic shifts for decades, the prevailing influence is acquired by winter temperatures (Morley et al., 2017), having in mind that winter temperatures in the Northern hemisphere are increased faster than summer ones during the 20th century (Emanuel et al., 1985;Folland et al., 2001;Laing and Binyamin, 2013;Felton et al., 2016). In terms of regression analysis, a weak temporal trend of summer temperatures compared to a steep trend of winter ones means a smaller regression slope and a worse ratio of residual variance to the total variance explained by this regression. ...
... Yellowknife's average annual temperature and precipitation have increased by 2.5˚C and 120 mm, respectively between 1943 and 2011 (Laing and Binyamin, 2013). In recent decades, the precipitation pattern is changing in the northwestern Canadian Shield, especially during fall, as precipitation now arrives more commonly as rain rather than snow (Spence et al., 2011), which has led to increased winter streamflow (Spence et al., 2014(Spence et al., , 2015. ...
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Human-released metals are present to some extent in soil and sediment from even the remotest areas, including the Canadian Arctic. The cumulative impact of legacy pollution, ongoing release of contaminants and climate change could lead to important modifications to metal transport and transformation processes in the environment that can affect the exposure of biota and humans to metals. Large uncertainties remain regarding the future transport of metals on the subarctic landscape, including those that can be toxic in low dose like lead and mercury. Paleoecology is a powerful tool to evaluate changes in metal pollution and recovery in lakes by providing long-term records of environmental conditions at a relatively low cost and with rapid analysis. Paleoecological records can help fill important research gaps that current monitoring approaches can’t address because of the lack of temporal perspective. In this thesis, the records of multiple environmental archives were analysed and compared to understand the changes in metal accumulation and transport that occurred over the last centuries to millennia in subarctic Canada, from the Yellowknife (Northwest Territories) and the Whitehorse Regions (Yukon). Multiple approaches to times series analysis were developed to evaluate the individual and cumulative impacts of specific sources and processes commonly affecting subarctic boreal lakes. These processes include local point-source emissions, catchment retention and transport of contaminants, and contaminants released by wildfires. This thesis provides quantification for processes that are seldom addressed in the literature so far, especially for subarctic environments. Subarctic lakes will continue receiving anthropogenic metal for years regardless of future emissions because of the impact of catchment retention. Terrestrial heavy metals retained in catchments are susceptible to remobilisation toward aquatic environments by natural processes such as land erosion, permafrost thaw and wildfires; and these processes may be enhanced by climate change. Recovery of any specific site from heavy metal pollution is also dependent on local parameters, explaining the necessity to characterise ecosystem recovery from heavy metal pollution in different types of ecosystems, including subarctic environments.
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This article was the first attempt of modeling changes in the additive component composition of 900 spruce stands’ (genus Picea sp.) biomass (t/ha), according to the trans-Eurasian hydrothermal gradients of Eurasia using the database compiled on the structure of harvest biomass. This study found that the biomass of all components increases with higher mean January temperature, regardless of mean annual precipitation, but any increase in precipitation leads to a decrease in biomass of all the components. For the first time, trans-Eurasian patterns of changes in the biomass of all components of spruce stands in the climate gradients were obtained due to the authors’ first database on the biomass of forest-forming species in Eurasia. In contrast to previously published regional and transcontinental weakly adequate or unreliable regularities that are not additive in the biomass component composition and depersonalized in the species composition, in the proposed additive models for spruce, the contribution of climate variables to the explanation of biomass variability was 9%. The importance of selecting the correct structure of the developed model is shown.
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We show that the well-known "Waldmeier effect," or anticorrelation between the peak in sunspot number of a cycle and the time from minimum to reach that peak, is not present in sunspot area data averaged in the same way. With a 13-rotation Gaussian running average applied to both sunspot number and sunspot area, we find the correlation between cycle rise time and cycle peak is r = − 0.71 for sunspot number and r = 10−4 for sunspot area. Hathaway et al. previously showed that the Waldmeier effect is much weaker (r = − 0.34) in sunspot group number. Thus the Waldmeier effect may be specific to only Wolf sunspot number.