Download Natural and forced air temperature variability in the Labrador region

Theor Appl Climatol
DOI 10.1007/s00704-014-1248-2
ORIGINAL PAPER
Natural and forced air temperature variability in the Labrador
region of Canada during the past century
Robert G. Way & Andre E. Viau
Received: 26 February 2014 / Accepted: 29 July 2014
# Springer-Verlag Wien 2014
Abstract Evaluation of Labrador air temperatures over the
past century (1881–2011) shows multi-scale climate variability and strong linkages with ocean-atmospheric modes of
variability and external forcings. The Arctic Oscillation,
Atlantic Multidecadal Oscillation, and El Nino Southern
Oscillation are shown to be the dominant seasonal and interannual drivers of regional air temperature variability for most
of the past century. Several global climate models show disagreement with observations on the rate of recent warming
which suggests that models are currently unable to reproduce
regional climate variability in Labrador air temperature. Using
a combination of empirical statistical modeling and global
climate models, we show that 33 % of the variability in annual
Labrador air temperatures over the period 1881–2011 can be
explained by natural factors alone; however, the inclusion of
anthropogenic forcing increases the explained variance to
65 %. Rapid warming over the past 17 years is shown to be
linked to both natural and anthropogenic factors with several
anomalously warm years being primarily linked to recent
anomalies in the Arctic Oscillation and North Atlantic sea
surface temperatures. Evidence is also presented that both
empirical statistical models and global climate models underestimate the regional air temperature response to ocean salinity anomalies and volcanic eruptions. These results provide
important insight into the predictability of future regional
climate impacts for the Labrador region.
R. G. Way (*) : A. E. Viau
Department of Geography, University of Ottawa, Ottawa, Ontario,
Canada K1N 6N5
e-mail: [email protected]
A. E. Viau
Laboratory for Paleoclimatology and Climatology, Department of
Geography, University of Ottawa, Ottawa, Ontario, Canada K1N
6N5
1 Introduction
The climate of the Labrador region of northeastern Canada is
characterized by low annual air temperatures and strong
coastal-continental moisture and summer air temperature gradients (Maxwell 1981). This region (Fig. 1) is strongly correlated with episodes of multi-scale climate variability (e.g.,
D’Arrigo et al. 2003; Viau and Gajewski 2009). Changes in
northwest Atlantic sea surface temperatures (e.g., Atlantic
Multidecadal Variability, AMO index) and large-scale North
Atlantic Ocean and atmospheric modes of variability (e.g.,
the Arctic Oscillation (AO) and North Atlantic Oscillation
(NAO)) have a profound impact on Labrador’s climate
(D’Arrigo et al. 2003). In addition, the region’s proximity to
the deep ocean Thermohaline Circulation, the warm Gulf
Stream, and cold Labrador Sea surface currents is also climatically important (Maxwell 1981; Banfield and Jacobs 1998).
For example, the Labrador Current transports cold Polar water
southward along the Labrador Sea coastline leading to anomalously cold summer and annual air temperatures relative
regions in western Canada and Europe at similar latitudes
(Hiljmans et al. 2005). Northern Labrador also includes the
southernmost glaciers in the eastern Canadian Arctic (Brown
et al. 2012; Way et al., Accepted), the transition between permafrost zones in eastern Canada (Brown 1979), and the southernmost Arctic tree line in Canada (Elliott and Short 1979).
Throughout much of the modern era (1957 to present),
Labrador has experienced cooling in air and ground temperatures (Allard et al. 1995; Banfield and Jacobs 1998) until a
pronounced warming period beginning in the late 1990s
(Brown et al. 2012). The recent (post-1995) warming observed in Labrador has resulted in significant negative repercussions on local communities, particularly in the Innu and
Inuit regions of northern Labrador where locals are reliant on
winter sea ice and snow for access to hunting grounds. For
example, during the anomalously warm winter of 2010,
R.G. Way, A.E. Viau
Fig. 1 Map of study area
depicting the spatial distribution
of Berkeley Earth Surface
Temperature Project air
temperature grid cells and
Environment Canada
meteorological stations
coastal Labrador residents were unable to access traditional
fishing and hunting grounds or neighboring communities due
to poor ice and snow conditions (Wolf et al. 2013). Regional
climate models suggest that these conditions will occur more
frequently due to regional warming expected to be on the
order of 3 °C by 2038–2070 (Finnis 2013). However, relevant
internal climate variability associated with North Atlantic sea
surface temperatures is not well reproduced in many global
climate models (Ruiz-Barradas et al. 2013). The vulnerability
of Labrador’s communities to environmental change and the
region’s sensitivity to major oceanic and atmospheric circulation variability make this region an ideal study area to explore
regional climate variability and its relations with both natural
and anthropogenic factors.
This study evaluates historical changes in Labrador air
temperatures (hereafter, LATs) in relation to major oceanicand-atmospheric modes of variability and external climate
forcings. Using both empirical statistical (e.g., Lean and
Rind 2008; Foster and Rahmstorf 2011; Chylek et al. 2013;
Folland et al. 2013) and global climate models (GCMs), we
provide a constraint on the magnitude of natural and forced
components of the region’s temperature history (e.g., Chylek
et al. 2014). This type of research provides regional historical
insight on changes in LATs which can be used to improve our
predictive capabilities for future climate change impacts in
this region.
2 Methods
2.1 Data
LATs were derived from the gridded Berkeley Earth Surface
Temperature (BEST; 1° by 1°) analysis product which is
publically available online (Rhode et al. 2013a, http://
berkeleyearth.org/data/). We chose the BEST dataset over
Natural and forced air temperature variability in the Labrador
other major surface air temperature data sets (e.g., Crutempv4,
Jones et al. 2012; GISS, Hansen et al. 2010) to study this
region for the following reasons: (a) the BEST data set is
quality-controlled and homogenized to compensate for erroneous measurements and station inhomogeneities (Rhode
et al. 2013a), (b) it includes a larger meteorological data
network than any other major organizations (Rhode et al.
2013b), and (c) the BEST data set provides regional air
temperature coverage for Labrador back to 1880 when many
of the weather observations were taken by Moravian missionaries in the region (Demarée and Ogilvie 2008). To provide
absolute air temperatures from the anomaly series, we extracted regional time series for each 1° grid cell in the BEST
inventory using the ncdf package in R and then re-added the
surface climatology to the anomaly series. A regional areaweighted mean was calculated using the fraction of total
land cover in each cell to compensate for coastal biases
in the meteorological network. For comparison with the
BEST regional series, we provide the regional anomalies
from the Cowtan and Way (2014) gridded product and a
composite series of (mostly coastal) meteorological stations from Environment Canada (e.g., Zhang et al.
2000) showing strong agreement (Fig. 2). Although the
early record of the BEST time series is based on very
few stations, comparing an out-of-sample air temperature database (1883–1894) collected by Wenner (1947)
for Hopedale, Labrador with the corresponding BEST grid
cell data shows strong agreement (root mean square error
(RMSE)=0.22 °C, R2 =0.95).
Fig. 2 Comparison of
reconstructed Labrador air
temperatures over the period
1881–2011 using Environment
Canada meteorological stations
(red), the 5° gridded Cowtan and
Way (2014) product, and the areaweighted regional average from
Berkeley Earth used in this study
(Rohde et al. 2013a)
Data for ocean and atmospheric modes of variability and
external climate forcings (hereafter, climate indices) were
extracted from various publically available sources
(Table 1). Due to possible contamination of the standard
AMO index (Trenberth and Shea 2006), we use an alternative
data set derived by subtracting the trend in global sea surface
temperatures (SSTs) from the North Atlantic SSTs (van
Oldenborgh et al. 2009). To compensate for the short climate
record of the Arctic Oscillation (1950–present), we derive our
own AO proxy (pAO) using a similar method to the original
AO data set (e.g., Thompson and Wallace 1998) but on a
much longer sea level pressure data set (HadSLP2; Allan
and Ansell 2006). This calculation uses the first empirical
orthogonal function (EOF) of the Northern Hemisphere sea
level pressure data extending back to 1880 AD and shows
good agreement (R2 =0.69) with the original AO index during
their overlapping period (1,363 months). Several studies have
described physical associations between several of the climate
indices used in this analysis (e.g., Dong et al. 2006;
d’Ogreville and Peltier 2007; Grossman and Klotzbach
2009) which could influence the interpretation of correlation
results or cause collinearity in empirical statistical models
(Dormann et al. 2013). However, inter-comparison of input
climate indices on monthly and seasonal timescales shows no
statistically significant relations between most variables with
the key exceptions being between NINO and the PDO, and the
pAO and NAO (winter), both expected as they describe directly related phenomena. Global circulation models (GCMs)
for the region were accessed using the KNMI climate explorer
R.G. Way, A.E. Viau
Table 1 Sources of air temperature, external forcing, and climate
variability data
Indices Time period Resolution Original reference
AMO
1880–2011
Monthly
Van Oldenborgh et al. 2009
pAO
GHG
1880–2011
1880–2011
Monthly
Annual
Allan and Ansell 2006; this Study
Hansen et al. 2011
NINO
1880–2011
Monthly
Trenberth 1997; Smith et al. 2008
NAO
1880–2011
Monthly
Hurrell 1995; Jones et al. 1997
PDO
1880–2011
Monthly
Zhang et al. 1997; Trouet and Van
Oldenborgh 2013
SSN
1880–2011
Monthly
Clette et al. 2007
VF
1880–2011
Monthly
Sato et al. 1993; Hansen et al. 2011
LATs
1880–2011
Monthly
Rhode et al. 2013a, b; this Study
Rhe Atlantic Multidecadal Oscillation (AMO), Proxy Arctic Oscillation
(pAO), Greenhouse Gas Forcing (GHG), El Nino Southern Oscillation
(NINO–NINO 3.4), North Atlantic Oscillation (NAO), Pacific Decadal
Oscillation (PDO), Sunspot Numbers (SSN), Volcanic Aerosol Forcing
(VF), and Labrador air temperatures (LATs) are included
(e.g., Trouet and Van Oldenborgh 2013) within the CMIP5
framework under the RCP4.5 climate scenario. The selected
models for comparison were GFDL-CM3 (Donner et al.
2011), HadGEM2-ES (Jones et al. 2011), and CanESM2
(Arora et al. 2011). The HadGEM2-ES and GFDL-CM3
models were selected because they perform well at reproducing changes in North Atlantic sea surface temperatures over
the past century (Ruiz-Barradas et al. 2013; Kavvada et al.
2013) while the CanESM2 model was selected because it is
the most recently developed Canadian model. The CMIP5
multi-model ensemble (n=42) (climexp.knmi.nl/) for the region was also extracted to provide a baseline of temperature
change expected due to external forcing without the regional
climate variability signal.
2.2 Correlation analysis and multivariate regression modeling
Monthly, seasonal, and annual climate indices for the major
oceanic and atmospheric modes of variability and external
climate forcings were compared to LATs over the verification
period 1900–1979 AD selected based on the lack of trend in
LATs. Cross-correlation functions (CCFs) were used to determine the magnitude and significance of correlations between
LATs and climate indices on multiple time lags (1 to 100 time
units) for monthly, seasonal, and annual periods. Coefficients
of determination (R2), adjusted R2, and statistical significance
(p value) were computed between the climatic indices, external forcings, and the LATs on monthly, seasonal, and annual
timescales to assess the strength their statistical associations
(e.g., Mann 2006). A multivariate regression modeling
approach was then used to evaluate the degree to which
modes of natural variability were able to explain changes in
seasonal and annual LATs over the past century. Climate
indices demonstrating strong statistical relations with LATs
(p value <0.05) were considered for inclusion in the ensemble
of multiple regression models. The models were selected
based on the Akaike information criterion (AIC), adjusted
R2, degrees of collinearity (variance inflation factor, see
Dormann et al. (2013)), standard errors (SEs), and R2 shrinkage during cross validation (e.g., Zuur et al. 2010). All selected models were also assessed using K-fold cross validation
(k=5, k=10) to determine the robustness of statistical associations and to evaluate R2 shrinkage (e.g., Stone 1974; Kohavi
1995). The multiple regression methodology was applied
on seasonal and annual timescales with the objective of
modeling natural variability in annual LATs associated
with climate indices. GCMs were evaluated based on
their agreement with observed LATs on an annual timescale using the meteorological year (Dec–Nov). Relative
to observations, models were assessed by comparing
reconstructed long-/short-term trends (°C/century), total
temperature range (°C), mean absolute differences (MADs),
and root mean square error (RMSE) in addition to visual
examination of time series.
3 Results and discussion
3.1 Labrador air temperature reconstruction
Over the past century, Labrador air temperatures (LATs) show
an overall warming of ~1.5 °C from 1881 to 2011 (Fig. 2).
This warming occurred at all seasons with the greatest temperature change in winter (+2.03 °C) and the least in the spring
(+0.96 °C). Gradual warming in the annual record is seen
between 1881 and 1960, a period of cooling between 1960
and 1994 followed by a rapid warming period post-1995
(7.2±0.8 °C/century). The coolest periods in the LAT
record is observed in the mid-1880s, early-1900s, and
early-1990s with the exception of one anomalously cold
year in 1972 (−22122.5 °C below the twentieth century
average). By contrast, the warmest years in the record are
mostly during the past decade and to a lesser extent during the
late-1950s (Wood et al. 2010). Since 2000, LATs in 10 of
11 years have been above the twentieth century average with
the warmest year recorded in 2010 associated with a decadal
mean temperature anomaly of +1.3 °C. Our results show
large positive and negative departures in LAT anomalies
with the greatest anomalies occurring in 2010 (+3.4 °C),
1972 (−2.5 °C), 2006 (+2.44 °C), 1884 (−2.44 °C),
1999 (+1.86 °C), and 1993 (−1.66 °C). These anomalous
years contribute to large annual air temperature variability
(standard deviation ±1 °C) and can impact local communities
due to the extreme variations in the regional distribution of
mean annual air temperatures. For example, if the conditions
Natural and forced air temperature variability in the Labrador
present in 1972 (Fig. 3) were to remain static, permafrost
would be expected to be widespread across the study area,
whereas under the 2010 conditions (Fig. 3), only a few isolated regions of permafrost would persist (e.g., Gruber 2012).
Separating natural and anthropogenic contributions to regional climate variability is therefore essential for understanding
the controlling mechanisms for these high-amplitude events
(e.g., Screen and Simmonds 2013).
3.2 Climate model performance
Comparison of state-of-the-art GCMs with observed LATs
shows that interannual LAT variability is not well reproduced
by climate models (Table 2). Although the numerical models
and observations largely agree on the magnitude of the longterm trends, the simulated temperature trends from 1995 to
2011 are highly variable with the HadGEM-ES model indicating no trend while the GFDL-CM3 and CanESM2 models
overestimate the trend by 46 and 16 %, respectively. Relative
to observations, the CMIP5 multi-model mean (MMM) has
the lowest mean absolute difference (MAD) and root mean
square error (RMSE) but also underestimates the range in
interannual variability by over half due to the averaging of
many GCM runs (Table 2). In contrast, the GFDL-CM3
model reproduces the observed range in reconstructed LATs
but has the highest MAD and RMSE statistics suggesting
excessive variability in this model. Both the CanESM2 and
HadGEM2-ES models perform better than GFDL-CM3 in
this regard but still understate the total range in our reconstructed LATs. Based on these results, we use the CMIP5
multi-model mean (MMM) to approximate the total contribution of anthropogenic forcings to recent warming. Comparing
the MMM to observations over the full (1881–2011) and short
(1995–2011) periods suggests that regional (natural) climate
variability could be responsible for some of the recent regional
warming (Table 2).
3.3 Correlation analysis
In order to estimate the impacts of natural climate variability
on LAT anomalies, we compared observations with climate
indices (listed in Table 1). Most indices are strongly
correlated on specific timescales (e.g., monthly/seasonal)
reflecting either a lack of robustness or synoptic controls on
the relation (e.g., sensitivity only during a particular season).
The strongest seasonal and annual associations between the
Fig. 3 Spatial distribution of mean annual air temperatures across the Labrador region for the coldest year in the record (1972; left panel) and the
warmest year in the record (2010; right panel)
R.G. Way, A.E. Viau
Table 2 Comparison between estimated temperature anomalies for Labrador from selected Earth system models and the CMIP5 multi-model mean with
observed LATs
Model
Trend (1881–2011)
Trend (1995–2011)
Range
MAD
RMSE
GFDL-CM3
HadGEM2-ES
CanESM2
Multi-Model Mean (n=41)
1.41±1.04
0.92±0.62
0.97±0.52
1.12±0.28
10.48±1.05 °C/century
0.44±0.46 °C/century
8.33±0.43 °C/century
4.69±0.14 °C/century
5.78
3.32
3.47
2.26
0.88
0.69
0.71
0.65
1.13
0.88
0.89
0.81
Observations
1.13±0.86 °C/century
7.16±0.80 °C/century
5.89 °C
°C/century
°C/century
°C/century
°C/century
°C
°C
°C
°C
°C
°C
°C
°C
−
°C
°C
°C
°C
−
Values are provided for long and recent air temperature trends, the total range in each dataset, and the mean absolute difference (MAD) and root mean
square error (RMSE) from observations
climate indices and our LAT anomalies were found at zero
lags. The AMO, pAO, and NINO indices all show statistically
significant relations with LAT anomalies on five of six timescales examined with each being less sensitive to a particular
season (AMO = winter, pAO = fall, NINO = spring). The
NAO was significantly correlated with LAT anomalies on four
of six timescales with no relations evident for the summer and
fall. The relation between LATs and North Atlantic indices
(AMO, AO, and NAO) is well documented and expected
based on similar associations noted throughout the North
Atlantic basin (D’Arrigo et al. 2003; Brown et al. 2012).
Volcanic forcing (VF) and LAT anomalies were statistically
related on monthly, summer, and annual timescales which
agrees with D’Arrigo et al. (2013) who discussed clear linkages between volcanism and tree rings in northern Labrador.
The PDO indices showed only weak, albeit statistically significant, relations in two seasons (summer and fall) (Table 3).
Cross validation (K=5, K=10) of seasonal and annual regression models revealed R2 shrinkage between 0.02 and 0.05 for
all indices using 20 iterations.
3.4 Assessing regression models and GCMs over the full
record
All seasonal and annual empirical statistical models were
tested for skill over the full period (1881–2011) and over the
period of recent warming (1995–2011). In general, on each
timescale examined (seasonal/annual), the explained variances
for the regression models declined over the full period relative
to the verification period (Table 3). The seasonal models’
explained variance declined by 2–12 % while the annual
models explained 5–18 % less variance. The hybrid annual
model displayed the greatest change from the verification
period to the full period (−18 %) explaining a total of ~33 %
Table 3 Regression statistics for empirical statistical models used in this study
Model variables
Period
Time
R2
AdjR2
SE
MAD
RMSE
DF
VIF
AIC
1.04
219
AMO+NINO
Fall
1900–1979
0.10
0.07
0.92
0.69
0.90
77
AMO+NINO
pAO+NINO
pAO+NINO
AMO+pAO+NINO
AMO+pAO+NINO
AMO+pAO
AMO+pAO
AMO+pAO
AMO+pAO
Multiple regression (Hybrid Nat)
Multiple regression (Hybrid Nat)
MMM+Natural
Natural only
Fall
Winter
Winter
Summer
Summer
Spring
Spring
Annual
Annual
Annual
Annual
Annual
Annual
1881–2011
1900–1979
1881–2011
1900–1979
1881–2011
1900–1979
1881–2011
1900–1979
1881–2011
1900–1979
1881–2011
1881–2011
1881–2011
0.08
0.46
0.34
0.36
0.25
0.41
0.32
0.35
0.27
0.51
0.33
0.65
0.51
0.07
0.45
0.33
0.33
0.25
0.39
0.32
0.34
0.26
0.51
0.32
0.64
0.51
1.01
1.45
1.68
0.62
0.64
1.09
1.22
0.66
0.82
0.57
0.79
0.57
0.57
0.79
1.10
1.31
0.48
0.53
0.89
1.00
0.52
0.65
0.47
0.61
0.47
0.47
1.00
1.43
1.69
0.61
0.65
1.07
1.21
0.65
0.81
0.58
0.79
0.57
0.57
129
77
129
76
128
77
129
77
128
129
1.01
1.15
1.01
1.00
1.00
379
292
511
157
261
246
427
167
326
142
314
229
229
Collected statistics include coefficient of determination (R2 ), adjusted coefficient of determination (AdjR2 ), standard error (SE), mean absolute
difference from observations (MAD), root mean square error from observations (RMSE), number of degrees of freedom (DF), variance inflation factor
(VIF), and akaike information criterion (AIC). Models are compared to observed LATs with the exception of the final row (Natural only) which is
compared to the multi-model mean residual from observations
Natural and forced air temperature variability in the Labrador
of the variance in annual LAT anomalies. Although the Hybrid
approach was able to reproduce some of the warming observed
over the more recent period (1995–2011), this natural-only
regression model was largely unable to reproduce the magnitude of post-1995 warming by a rate of seven times less than
observations (Fig. 4a). This result supports the influence of
Fig. 4 a Time series showing
observed Labrador air
temperatures (black) in
comparison with the multiple
regression natural-only hybrid
model (blue) and the CMIP5
multi-model mean used in this
study (red). b Time series
comparing the multi-model mean
residual from observed Labrador
air temperatures (red) with the
multiple regression natural-only
hybrid model temperature
prediction (blue). Inset shows
scatterplot comparing both time
series
external anthropogenic influence. In order to distinguish the
forced and unforced components of regional climate, we
subtracted the CMIP5 multi-model mean from the observed
LATs. The difference between the GCM multi-model mean
and observations shows a strong relation with the natural-only
Hybrid model of LATs (R2 =0.51) throughout the entire record.
R.G. Way, A.E. Viau
Therefore, the natural signal is embedded in the anthropogenically forced LATs (Fig. 4b). Combining the multi-model mean
(MMM) estimate of forced regional air temperature changes
with our reconstructed natural-only variability produces a historical temperature evolution (MMM+Natural) which increases the association (R2 =0.65) to the LAT observations over
both long and short periods (Fig. 5). The MMM+Natural
model also shows provide a better fit to the LAT observations
of the past decade (R2 =0.70). Our evaluation of the selected
GCMs and the MMM+Natural models suggests that the latter
offers more skill throughout the entire record with deviations
rarely exceeding ±1 °C as compared to GCMs which can show
differences from observations greater than ±1.5 °C (Fig. 6).
For the MMM+Natural model, the mean absolute difference
(0.47 °C) and root mean square error (0.57 °C) compared to
observations are significantly lower than for any of the respective GCMs (Table 2). However, despite the MMM+Natural
model’s performance, there are still notable differences from
LAT observations occurring for both individual and adjacent
years reflecting possible stochastic processes that are not well
reproduced by both the GCMs and regression models.
Moreover, climate models do not accurately reproduce the
magnitude of regional cooling in LATs following volcanic
eruptions in the mid-1880s (Krakatoa) and early-1990s
(Pinatubo). The disagreement between the selected models
and LAT observations suggest that the regional response to
volcanic aerosol loadings is still not fully understood or replicated in current GCMs nor are contributions from internal
variability (e.g., AO/NAO or AMO).
Fig. 5 Time series comparing
observed Labrador air
temperatures (black) with the
multiple regression natural-only
hybrid model temperature
prediction (blue) and the multimodel mean+natural model
prediction (red)
3.5 Anomalous years
LAT anomalies show a series of large seasonal/annual
departures corresponding to both very cold and very
warm seasons/years (Fig. 7). Several of the most prominent cold anomalies are attributable to aerosol loading
in the atmosphere from volcanic eruptions and the corresponding atmospheric response from the AO (e.g.,
1883, 1884, and 1992; Sato et al. 1993; Shindell et al.
2004; Stenchikov et al. 2002; 2006). Cold anomalies in
the year(s) following major volcanic eruptions are most
apparent in the winter suggesting that a positive AO
response to volcanism plays an important role (e.g., Shindell
et al. 2004). Extreme cooling in 1972 and the warmest year in
the record (2010) both require fuller explanations. For 1972
(−2.5 °C), cold conditions are visible in all months but strongest in winter and spring. The cold conditions coincide with
the Great Salinity Anomaly (GSA) that entered the Labrador
Sea in 1971–1972 and caused a significant reduction of
Labrador Sea surface temperatures (Belkin et al. 1998). The
mechanisms controlling the initiation and magnitude of GSAs
are still debated; however, they are generally associated with
the enhanced export of freshwater and sea ice through the
Fram Strait (Belkin et al. 1998; Belkin 2004). GSAs were also
observed to have propagated through the Labrador Sea and
west Greenland between 1971 and 1972, 1982 and 1983, and
1889 and 1990 (Belkin et al. 1998; Belkin 2004) with associated increases in Labrador Sea ice cover (Deser et al. 2000).
The LAT anomaly reconstruction also shows cold anomalies
Natural and forced air temperature variability in the Labrador
Fig. 6 Time series showing the
difference from observed
Labrador air temperatures for
three global climate models
(GFDL-CM3 [red], HadGEM2ES [green], CanESM2 [yellow])
and the multi-model mean+
natural model prediction
in 1982 and 1990 coinciding with other known GSA events;
however, the magnitude of cooling observed is muted relative
to the 1972 event.
The single warmest year (2010, +3.4 °C) coincides with
anomalously warm conditions in all seasons but especially
during the winter (+6.8 °C). Although the 2010 winter warmth
Fig. 7 Synthesis of observed Labrador air temperatures annotated with natural contributors to regional climate variability
R.G. Way, A.E. Viau
is unprecedented in our time series, it is important to note that
previous strong winter warming events also occurred in 1958
(+4.9 °C) and 1969 (+5.5 °C). Using the modeling approaches
presented above, we show that nearly half of the 2010 event is
associated with natural variability with the natural-only model
predicting 2010 as being the warmest year over its entire
record. This result is in agreement with Cohen et al. (2010)
who relate the exceptional winter warmth during 2010 in the
eastern Canadian Arctic to an unprecedented negative anomaly in the Arctic Oscillation. The data presented in this study
and others (e.g., Cohen et al. 2010) suggest that natural
climate variability (negative AO/positive AMO) played an
important role in modulating the magnitude of warming in
northeastern Canada. However, the magnitude of the 2010
anomaly would be significantly attenuated without the contributions from anthropogenic warming. These discussions do
not include linkages between extreme weather and changes in
Arctic sea ice (e.g., Francis and Vavrus 2012) which may be
an important factor in understanding recent regional warming
in Labrador. Several authors (e.g., Cohen et al. 2012; Tang
et al. 2013) have suggested that the loss of fall/winter Arctic
sea ice has the potential to increase surface evaporation and
snowfall in the Eurasian Arctic leading to an increase in the
frequency of blocking highs over the mid-latitudes and causing predominantly negative AO conditions in the following
winter. Linkages between Arctic amplification and sea ice
changes to blocking patterns are still widely debated in the
literature (e.g., Screen et al. 2013; Screen and Simmonds
2013) with many of these associations not observed in
CMIP5 models (Woollings et al. 2014). Although this subject
is yet unresolved, better understanding the links between
atmospheric circulation, Arctic amplification, and sea ice
and their influences on modes of natural climate variability
(e.g., AMO, AO, ENSO, and NAO) is integral for predicting
future climate impacts in the Labrador region.
4 Conclusion
This study shows that Labrador air temperatures (LATs) are
highly sensitive on monthly, seasonal, and annual timescales
to modes of natural oceanic and atmospheric modes of variability, external forcings, and anthropogenic forcings. We
show that the past decade (2000–2011) has been the warmest
on record and includes the 2 warmest years since 1880. By
comparing LAT anomalies with well-known modes of climate
variability, the strong seasonal and annual impacts of the
various climate indices reveal that the Atlantic Multidecadal
Oscillation, Arctic Oscillation, and El Nino Southern
Oscillation dominate interannual climate variability in this
region. Using a Hybrid approach that incorporates simple
regression modeling with GCMs, this study demonstrates that
a large proportion (~65 %) of year-to-year variations in LATs
can be reproduced over the period 1881–2011 with a skill
greater than using GCMs alone. Our results suggest that at
least ~10 % of the post-1995 warming trend can be attributed
to natural climate variability assuming that the direct response
to anthropogenic warming is properly estimated. The dynamic
nature of Labrador’s climate makes this region more difficult
to evaluate in the context of global temperature reconstructions as evidenced by pronounced cooling during parts of the
modern era (e.g., Banfield and Jacobs 1998). Complicating
the interpretation of regional variability in Labrador is the
overall difficulty in modeling North Atlantic sea surface temperature variability in CMIP3 and CMIP5 models (e.g., Stoner
et al. 2009; Ruiz-Barradas et al. 2013). The region’s sensitivity
to the North Atlantic modes of climate variability (e.g., AO
and AMO) and to changes in ocean salinity lead us to conclude that future regional warming will be characterized by
periods of acceleration and deceleration reflecting natural
variability superimposed on an overall anthropogenic
warming trend. Our study shows that natural variability associated with the AO, AMO, and El Nino alone can explain as
much as ~33 % of the interannual changes in air temperature
over the past 130 years, highlighting the difficulty of
predicting regional climate impacts in the future. Moreover,
we provide a snapshot through time where it is not known
whether these proportions may change under an increasing
anthropogenic influence in the near future. Given Labrador air
temperature’s sensitivity to ocean temperature and salinity, it
is possible that there may be additional climate impacts expected in the future associated with freshwater input into the
Labrador Sea from the Greenland Ice Sheet which has increased by 48 % over the past ~20 years (Bamber et al. 2012).
These results provide important findings that can be used
to improve regional climate modeling and will inform
decision makers on issues related to local ecosystems
and human adaptive capacity in the region. Finally, this
study concludes that the use of a hybrid approach incorporating climate indices and anthropogenic forcing is needed to
understand complex regional air temperature histories and
extreme events.
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