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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. References Allan R, Ansell T (2006) A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2) 1850–2004. 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