1. Introduction
While there is robust evidence that global temperatures are rising, there exists large spatial and temporal heterogeneities in the rates of change. This creates a challenge for climate change communication and translating climate change information into policies and decision-making (Meah 2019). The magnitude and direction of temperature changes varies with season (Cohen et al. 2012; Abatzoglou et al. 2014), latitude (Hansen et al. 2010; Gulev et al. 2021), and the time period evaluated (Rahmstorf et al. 2017). Periods of accelerated or subdued rates of warming are evident over recent decades (van Oldenborgh et al. 2009; Lewandowsky et al. 2016) as short-term climate variability underlies longer-term trends (Hansen et al. 2010; Bush et al. 2019). This includes an apparent “global warming hiatus” from 1998 to 2013, during which a slowdown in the magnitude of warming was reported (Meehl et al. 2013; Yan et al. 2016). Inconsistencies in the rates of warming over different time periods have been attributed to natural variability in ocean–atmosphere energy fluxes (Trenberth and Fasullo 2013; Hu et al. 2019; Wan et al. 2019) and associated atmospheric circulation dynamics (Johnstone and Mantua 2014; Smoliak et al. 2015; Deser et al. 2017; Yu et al. 2016; Xiao et al. 2023). This is unsurprising given the strong links between patterns of atmospheric circulation and surface climate (e.g., Bonsal et al. 1999; Stahl et al. 2006; Cassano et al. 2006; Wallace et al. 2012; Pfahl and Wernli 2012; Newton et al. 2014; Fleig et al. 2015; Vavrus et al. 2017). The growing recognition of the role of atmospheric drivers in observed climate change prompts the need to improve our understanding of how internal dynamic variability influences climate change assessments.
The global climate system is dependent upon large-scale atmospheric circulation driving the exchange of warm, cold, moist, and dry air masses between low and high latitudes. Regional and local climate anomalies in the middle and high latitudes result from interconnected circulation features, such as the location and intensity of cyclones (McCabe et al. 2001; Bengtsson et al. 2006; Shaw et al. 2016), position and strength of the polar vortex (Thompson and Wallace 1998; Overland and Wang 2019; Kolstad et al. 2010; Huang et al. 2021), and the structure of planetary waves (Cohen et al. 2014). Meridional planetary wave and jet streamflow results in slower-moving circulation systems (Francis and Vavrus 2015), leading to extreme and persistent surface weather phenomena, such as heat waves (Francis and Vavrus 2012; Tang et al. 2013; Petoukhov et al. 2013; Thomas et al. 2021; Rogers et al. 2022). Specifically, the amplitude of planetary waves is positively related to the magnitude of temperature and precipitation anomalies, while zonal flow is associated with near-average climate (Screen and Simmonds 2014). Dominant modes of these circulation patterns are represented in synoptic climatological classifications and ocean–atmosphere teleconnection indices, such as the Pacific–North American (PNA) pattern, Pacific decadal oscillation (PDO), and El Niño–Southern Oscillation (ENSO).
Numerous cold regions hydrological processes are dependent on winter climate dynamics, including snowpack and river ice development. Snowpack is critical for the seasonal redistribution of water resources, delaying release of stored water to the warm season (Barnett et al. 2005; Bonsal et al. 2019, 2020) and is the dominant mode of groundwater recharge (Hayashi 2020; Campbell and Ryan 2021). Precipitation phase, snow accumulation, and snowpack integrity are sensitive to winter temperatures (Pierce et al. 2008; Abatzoglou 2011; Morán‐Tejeda et al. 2013; Sospedra‐Alfonso et al. 2015; Rottler et al. 2021). Consequently, rising winter temperatures increase the risk of rain on snow and midwinter thaw, which may lead to flooding (Kattelmann 1997; Marks et al. 1998; Sui and Koehler 2001; McCabe et al. 2007; Beniston and Stoffel 2016), river ice break-up (Beltaos 2002; Newton et al. 2017), and alter the dynamics of groundwater recharge mechanisms (Pavlovskii et al. 2019; Negm et al. 2021). Changes to snowpack duration and dynamics increase avalanche risk (Peitzsch et al. 2021) and reduce the viability of winter outdoor recreational activities such as downhill skiing (Almonte and Stewart 2019). Rising winter temperatures also disrupt forest ecosystems through altered productivity (Grimm et al. 2013) and the spread of mountain pine beetle (Sambaraju et al. 2012; Embrey et al. 2012; Aukema et al. 2008). Across the Northern Hemisphere, the largest rates of temperature increases are found for winter months (Hansen et al. 2010; Allen et al. 2018). In Canada, the rates of winter temperature increases are nearly twice that of any other season and exceed that of annual increases (Vincent et al. 2012; O’Neil et al. 2017; Vincent et al. 2015; Zhang et al. 2019). However, there is spatial variability across Canada, with temperatures in northern and prairie regions rising faster than southern and coastal regions (Vincent et al. 2015; Bonsal et al. 2020). Among the highest rates of change are regions in western Canada, where winter temperature increases of 3.7° and 3.1°C from 1948 to 2016 were reported for British Columbia and the prairie provinces, respectively (Zhang et al. 2019).
Winter temperatures in western Canada are primarily a function of large-scale atmospheric circulation. Weak atmospheric ridges and zonal flow produce more moderate climate conditions (Quamme et al. 2010; Newton et al. 2014), while extreme high or low temperatures are linked to strong meridional flow (Overland et al. 2011; Screen and Simmonds 2014) and persistent blocking high pressure systems (Whan et al. 2016; Jeong et al. 2021). There is robust evidence indicating that a ridge of high pressure centered over western Canada is associated with above-average temperatures (Cannon et al. 2002; Romolo et al. 2006; Newton et al. 2014). Conversely, anomalously cool and extreme cold temperatures in this region are produced by Arctic outflow associated with a ridge of high pressure centered over the Pacific Ocean and adjacent trough of low pressure over western Canada (Cannon et al. 2002; Romolo et al. 2006; Quamme et al. 2010; Newton et al. 2014). Teleconnection patterns indicative of dominant circulation patterns provide easily indexed metrics to compare with surface climate (e.g., Bonsal et al. 2001; Fleming and Whitfield 2010). El Niño and positive phases of the PDO and PNA are linked to an enhanced frequency of a high pressure ridge over western Canada and anomalously warm temperatures and opposite climate anomalies during La Niña and negative phases of the PDO and PNA (Shabbar and Khandekar 1996; Bonsal et al. 2001; Stahl et al. 2006; Romolo et al. 2006; Newton et al. 2014). Changes to the frequency of dominant circulation patterns, and trends or phase shifts in teleconnection patterns, are evident over the last several decades. The PDO shifted from a negative to positive phase in the mid-1970s (Mantua et al. 1997), coinciding with a shift from predominantly negative to positive PNA (Abatzoglou 2011; Liu et al. 2021), and a shift from a lower to higher frequency and persistence of a strong ridge of high pressure over western Canada (Newton et al. 2019). Decreases in the frequency of zonal flow and a ridge of high pressure over the Pacific Ocean and adjacent trough of low pressure over western Canada, and increasing trends in a strong ridge of high pressure over western Canada were detected over 1950–2011 (Newton et al. 2014) and 1949–2012 (Newton et al. 2019).
It is intuitive that variability or changes in atmospheric circulation features influence the magnitude and direction of observed climate trends, given the strong linkages between circulation and surface climate. While anthropogenic and natural forcing both contribute to observed and projected climate changes, anthropogenic factors, including greenhouse gases, aerosols and land cover/land use changes remain the dominant driver (Bush et al. 2019; Wan et al. 2019). Recent studies found that dynamic variability accounted for approximately one-third to one-half of observed winter temperature changes in the Northern Hemisphere (Smoliak et al. 2015) and North America (Deser et al. 2016). However, these studies found considerable geographic heterogeneities in the strength of atmospheric circulation dynamics on climate changes. Vincent et al. (2015) found that removing the influence of the PDO and North Atlantic Oscillation (NAO) reduced the magnitude of mean winter temperature trends in Canada from 3.3° to 2.1°C over 1948–2012, and similar findings were reported by Wan et al. (2019). Although these teleconnection patterns are known to influence atmospheric circulation, the authors did not directly evaluate the role of dynamic variability on winter climate. This study seeks to refine the role of changes to large-scale atmospheric circulation on observed winter temperature changes in western Canada. Specifically, we adopt a synoptic classification approach to identify dominant circulation patterns influencing winter temperatures across British Columbia (BC), Alberta (AB), and Saskatchewan (SK). We then evaluate trends in winter temperatures using an approach that identifies and separates the component of temperature changes related to changes in the frequency of atmospheric circulation patterns. Results of this research provide clarity on the magnitude of winter temperature changes over this region. Additionally, highlighting the role of atmospheric dynamics on climate variability facilitates the selection of periods of records for time series analyses or baseline climate data critical for climate and hydrologic modeling studies.
The following section outlines the data and methods used in this study, including the algorithm used to differentiate thermodynamic and dynamic contributions to temperature trends. Results and discussion of the analyses are presented in section 3, including atmospheric circulation and climate variables. Conclusions of this study are provided in section 4.
2. Data and methods
Daily winter geopotential height (gph) data at 500 hPa were downloaded and extracted from the National Centers for Environmental Prediction–National Center for Atmospheric Research (Kalnay et al. 1996; https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html). The synoptic window extends from 100° to 150°W and from 40° to 65°N, which captures midtropospheric circulation over the study region and northwestern Pacific Ocean. The 500-hPa gph is aligned with previous studies incorporating a similar study region and characterizes key circulation features related to surface climate and extreme events (e.g., Romolo et al. 2006; Newton et al. 2014; Screen and Simmonds 2014; Francis and Vavrus 2015; Horton et al. 2015; Swain et al. 2016; Bonsal and Cuell 2017; Faranda et al. 2023). Major winter air mass source regions influencing climate over the study region originate over the Pacific Ocean or the Arctic Ocean and northern Canada. Daily gph data were classified using the self-organizing maps (SOM) toolbox for MATLAB (Vesanto et al. 2000). SOM classifies daily circulation patterns into dominant circulation types based on a k-means clustering algorithm while maintaining topological relationships (Kohonen 2001) and is commonly used to classify daily atmospheric classification (e.g., Cassano et al. 2006; Newton et al. 2014; Horton et al. 2015; Rogers et al. 2022) and identify important circulation characteristics (e.g., Thomas et al. 2021). That is, neighboring patterns are updated during the classification process such that it preserves relationships between these patterns. This classification scheme is ideal for continuous data such as atmospheric circulation as daily patterns are not discrete but rather are related to atmospheric circulation the previous and subsequent days. This facilitates analyses of trajectories, or preferred transitions among patterns, and persistence of atmospheric regimes (Newton et al. 2019). The average frequency (days per year) was calculated for each synoptic type, as well as the time series of annual frequencies over 1950–2020. Some studies have accounted for thermal expansion in the midtroposphere by subtracting the regional mean linear trend from the 500-hPa gph (e.g., Cattiaux et al. 2013; Cattiaux and Cassou 2013). However, the magnitude of thermal expansion is spatially heterogeneous (Christidis and Stott 2015; Horton et al. 2015; Swain et al. 2016) and trends are a function of both midtropospheric circulation changes and thermal expansion (Swain et al. 2016), suggesting that removal of the trend may not be appropriate to eliminate thermal expansion effects. Furthermore, several studies found that using detrended gph data provided no improvement over raw gph for the detection and evaluation of dynamic changes (Horton et al. 2015; Zhou et al. 2020; Zhang et al. 2022). Over this study period, trends in gph range from 0.33 to 1.07 m yr−1 with a domain-averaged linear trend of 0.70 m yr−1. Therefore, this study does not account for thermal expansion but acknowledges that it is a component of changes in midtropospheric circulation.
Mean daily air temperatures for winters (1 December–28 February) 1950–2020 were obtained from the Adjusted and Homogenized Canadian Climate Data (AHCCD) dataset (Mekis and Vincent 2011; Vincent et al. 2012) (https://open.canada.ca/data/en/dataset/d6813de6-b20a-46cc-8990-01862ae15c5f). The AHCCD dataset was developed to address inhomogeneities or nonclimate variations arising from changes to monitoring instrumentation and station relocations. Stations with more than 5% missing winter temperature data were excluded from analysis. Of the 91 stations included, 42 are located in BC, 25 in AB, and 24 in SK. The majority of stations are located in central and southern BC, AB, and SK, with station density decreasing with increasing latitude. The median latitude is 51.52°N and longitude is 115.55°W. Station elevations range from 2 to 1397 m MSL, with a median elevation of 586 m MSL. For the purposes of this research, stations are delineated by province and stations below 53°N are classified as “southern” and above are classified as “central-northern” (Fig. 1). Near-coastal temperatures in BC are modulated by the Pacific Ocean while AB and SK are strongly influenced by continental air masses. Daily mean temperatures were averaged over December–February (DJF) for each year to calculate a seasonal average. Daily temperature anomalies were calculated for each station as the departures from the long-term (1950–2020) average temperature for that day. These daily temperature anomalies were matched to each daily synoptic type and the average temperature anomaly for each type was calculated.
AHCCD climate stations located in the western Canadian provinces of British Columbia (42 stations), Alberta (25 stations), and Saskatchewan (24 stations). The region below 53°N (blue line) is classified as south, and the region above is classified as central-north.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
The thermodynamic component evaluates the changes in temperature under stationary frequencies of atmospheric circulation patterns. That is, it is assumed that the frequency of synoptic types does not change over time and that observed temperature changes are the result of thermodynamic drivers. This component was calculated as the product of average synoptic-type frequency, as a fraction of total winter days, and trend in temperature for each synoptic type. Conversely, the dynamic component evaluates changes in temperature by assuming that the mean temperature anomaly for each synoptic type remains stationary and only synoptic-type frequencies have changed over time. This component was calculated as the product of average temperature anomaly for each synoptic type and the trend in synoptic-type frequency, as a fraction of total winter days. The covariation component evaluates the interaction of temperature and trends in synoptic-type frequency. This term is generally a very small contribution to overall changes (Cassano et al. 2006; Horton et al. 2015).
Synoptic-type frequencies and temperatures associated with each synoptic type were evaluated using the Mann–Kendall (MK) nonparametric test for trend (Mann 1945; Kendall 1975) and a block bootstrapping technique in the “modifiedMK” (Patakamuri and O’Brien 2021) package using RStudio running R v4 and 2000 bootstrapped simulations. The block bootstrapping evaluates and accounts for significant autocorrelation in the time series. Significance was evaluated at p < 0.1 or better and trend slopes were estimated using Sen’s slope estimator (Sen 1968), which was tested against S = 0 at the same confidence level. To evaluate the model performance, winter (DJF) temperature trends were also calculated for each station using the MK method and were compared with trends calculated using Eq. (2).
3. Results and discussion
a. SOM classification
Daily winter (DJF) gph data were classified into 12 dominant circulation types. The 3 × 4 SOM array is topologically ordered such that nearby patterns are similar and opposite corners represent maximum variance in the dataset (Fig. 2). Synoptic types in the four corners of the SOM array occur with the greatest frequency, and patterns in the middle row occur with the lowest frequency (Table 1). These types (5–8) represent transition patterns between dominant atmospheric states. Atmospheric circulation in the midtroposphere directs surface high and low pressure systems, with lines of equal gph indicating the path of airflow (Holton 2004). Zonal flow travels from west to east roughly parallel to lines of latitude and is generally associated with faster-moving weather systems. Type 4, in the top-right corner, represents zonal flow over the Pacific Ocean and western Canada, and the movement of warm, moist air masses over the study region. Meridional flow is characterized by troughs and ridges and is associated with slower-moving or persistent surface weather (Francis and Vavrus 2012, 2015). A ridge of high pressure in the midtroposphere indicates a surface high pressure system to the right of the ridge axis, and the advection of cold northerly air over the adjacent region, beneath a midtropospheric trough of low pressure. Typically, to the left of the ridge axis is a surface low pressure system and the northward movement of warm, moist air. The strength and position of the ridge are positively related to the magnitude of surface climate anomalies (Bonsal et al. 2001; Stahl et al. 2006; Newton et al. 2014; Screen and Simmonds 2014; Newton et al. 2019). Type 1, in the top-left corner, is characterized by a strong ridge of high pressure centered over the Pacific Ocean and an adjacent trough of low pressure over western Canada, indicating the influence of cold Arctic air over the study region. When this ridge of high pressure is centered over BC–AB (types 9–12), cold air is blocked from entering the study area.
Daily winter (DJF) midtropospheric (500 hPa) geopotential heights for 1950–2020 classified using self-organizing maps.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
Synoptic-type frequency (%), in the same order as the array of circulation patterns given in Fig. 2.
b. Temperature anomalies
Daily temperature anomalies were matched to the corresponding daily synoptic type to calculate an average temperature anomaly for each type. The temperature anomaly maps for each type are given in the same order as the SOM array (Fig. 3). Although general patterns of below, above, and near average temperatures emerge, considerable spatial variability in the strength of temperature anomalies are evident within individual types. Circulation patterns along the top row (types 1–4) are associated with largely below average temperatures across the study region. Type 1 is associated with strong negative temperature anomalies in southern AB and SK and moderately below average temperatures in southeastern BC. Moderately below average temperatures in southern BC, AB, and SK occur with type 4, whereas stronger negative temperature anomalies occur in central and northern BC, AB, and SK. The middle row (types 5–8) is generally associated with near-normal or slightly above- and below-average temperatures. Stronger positive temperature anomalies are found in northern BC and AB with type 5 and in southern AB and SK with type 8. Types 9–12 along the bottom row are associated with strong positive temperature anomalies, particularly in northern BC and AB with type 9 and southern AB and SK with type 12. In southern BC, near normal temperatures are associated with type 9, and moderately positive anomalies are associated with types 10–12.
Temperature anomalies at AHCCD stations associated with synoptic types given in Fig. 2, in the same position on the array.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
The magnitude of temperature anomalies exhibits strong spatial gradients in types 1, 4, 9, and 12. This is particularly evident in types 4 and 9, for which strong gradients exist over a smaller distance. For type 9, near-normal temperatures occur in southern BC, up to 7.6°C above normal in northern BC and AB, and slightly above average in southeastern SK. A similar but opposite pattern is found with type 4, where slightly below normal temperatures are seen in southern BC and SK, and well below normal (anomalies up to −8.1°C) temperatures occur in central AB and northern AB and BC. Slightly below average temperatures are found along the coast in type 1, and anomalies of up to −7.6°C in southern Alberta are seen. For type 12, slightly above normal temperatures are found along the coast and the highest temperature anomalies are observed in southern AB and SK. These spatial gradients highlight the variable surface climate responses to patterns of atmospheric circulation, as well as climate regulation due to proximity to the Pacific Ocean.
c. Synoptic-type trends
The frequency of circulation patterns in the left column of the SOM array, type 1 (p < 0.1), type 5 (p < 0.05), and type 9 (p < 0.05), significantly increased over the study period (1950–2020) (Fig. 4). The frequency of type 10 also increased slightly, although the trend was not significant. Significant decreases in type 4 (p < 0.05) and type 8 (p < 0.05) were detected. Small, insignificant decreasing trends were found for types 3 and 12. The trends with largest magnitude in frequency were for type 4 (−0.15 days yr−1), type 9 (+0.10 days yr−1), and type 1 (+0.08 days yr−1). Over the 71-yr study period, these trends represent a loss of ∼11 winter days of zonal flow (type 4), and an increase of ∼7 and ∼6 winter days of a midtropospheric ridge of high pressure centered over the Pacific Ocean (type 9) and central BC (type 1), respectively. As noted above, types 1, 4, and 9 are associated with strong temperature anomalies and spatial gradients, suggesting a heterogeneous temperature response to increasing and decreasing trends with these patterns. For example, a decrease in type 4 and increase in type 9 is expected to decrease the frequency of very cold days and increase the frequency of very warm days in northern BC and AB. There are notable high-frequency (>25 days) winters in the time series of type 1, type 4, and type 12. For type 1 these are seen near the beginning and end of the time series, whereas for type 4 these occurred in the early decades of the study period. Type 12 occurred on 35 days in both 1983 and 1998, related to a strong El Niño pattern during those winters (Newton et al. 2014).
Synoptic-type average frequencies (red dotted line) and trends (blue solid line) from 1950 to 2020, in the same position as the array in Fig. 2. Trends were evaluated using the MK nonparametric test for trend. Significance levels are denoted by one asterisk for p < 0.10 and two asterisks for p < 0.05.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
d. Climate trends: Thermodynamic and dynamic
Trends in dominant atmospheric circulation patterns were detected for 1950–2020, including increases in a strong ridge of high pressure in the midtroposphere centered over the Pacific Ocean, coastline, and BC, and decreases in zonal flow and a ridge of high pressure centered over AB. Given the strong temperature anomalies associated with several of these patterns, changes to the frequencies of these types are expected to influence observed temperature trends. Additionally, climate gradients associated with these types suggest geographically variable surface climate responses to changing atmospheric circulation patterns, such as the spatially variable anomalies seen in types 4 and 9. Winter temperature trends were evaluated for the 91 AHCCD stations using results from the SOM classification, trend analyses, and temperature anomalies as inputs to Eq. (2). Therefore, these trend values represent the sum of thermodynamic and dynamic components. Results reveal widespread increases in DJF temperatures, with considerable geographic variability in the magnitude of trends. The greatest changes are found in northern BC and AB, where trends of up to 5.8°C over 71 years (0.08°C yr−1) were detected (Fig. 5a). Moderate trends (2.1° to 4.3°C over 71 years or 0.03 to 0.06°C yr−1) were found in most of central BC and central and southern AB and SK, and the lowest trends (1.4°C over 71 years or 0.02°C yr−1) in southern BC. There is good agreement between the temperature trends calculated using this method and trends calculated using the MK test (Fig. 5b). Results show the model generally slightly underestimates trend magnitudes in BC and southern AB and SK and overestimates trend magnitudes in central SK and central and northern AB. The magnitude of the discrepancy is low, ranging from −0.72° to 0.68°C over 71 years (from −0.0101° to 0.0096°C yr−1), the majority being within 0.21°C over 71 years (±0.003°C yr−1).
(a) Winter (DJF) temperature trends over 1950–2020 calculated as the sum of thermodynamic, dynamic, and interaction components, and (b) model error, calculated as the difference between model and MK trend results. Values represent totals change over the 71-yr period.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
Evaluating winter temperature trends using Eq. (2) facilitates the identification of thermodynamic, dynamic, and interaction components. The thermodynamic component of winter temperature changes identifies the trends in DJF temperatures without the influence of atmospheric circulation changes, which includes anthropogenic factors such as greenhouse gases and aerosols, localized effects such as changes to land cover or land use, and other natural processes (Fig. 6a). Dynamic contributions represent the component of temperature trends resulting from changes in the frequency of atmospheric circulation patterns (Fig. 6b). The interaction component is a very small contribution (from −1.3% to 0.4%) and is not shown. Results indicate that atmospheric dynamics have a larger effect on temperature trends in central and northern BC and AB where a maximum of 54% and average of 29% and 31% of observed trends are attributed to dynamic changes (Table 2). Moderate dynamic contributions (0%–20%) are found in southern AB, central and southwestern SK, and a few stations in coastal BC. Negative dynamic contributions are found at 12 stations in southern and coastal BC and 5 stations in southeastern SK. The average dynamic contribution in southern BC is −6%. In these cases, dynamic changes contribute to cooling at these stations, counteracting the thermodynamically induced temperature increases. The net effect is increasing DJF temperatures; however, model results suggest that without dynamic cooling, the observed temperature trend would have been larger.
(a) Thermodynamic and (b) dynamic contributions to winter temperature change, given as a percentage of total trend.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
Regional mean values for thermodynamic, dynamic, and interaction components and temperature trend.
When controlling for changes in atmospheric circulation, the magnitude of trends is predominantly lower than the observed trend, indicating that the dynamic variability inherent in the time series inflates the observed trend over this time period. Isolating thermodynamic trends thus facilitates the representation of changes in winter temperature due to changes in radiative fluxes, land cover changes, sea ice extent, soil moisture, and other thermodynamic factors, including the effects of anthropogenic drivers. The resulting trends are as much as 0.03°C yr−1 [2.0°C (71 yr)−1] lower than the trend values shown in Fig. 5a, which include the dynamic, or internal variability, component. The lowest magnitude trends are found in southwestern and coastal BC where trends range from 0.006°C yr−1 [0.4°C (71 yr)−1] to 0.023°C yr−1 [1.6°C (71 yr)−1] (Fig. 7). Winter temperature trends in eastern and central BC are moderate [from 0.014° to 0.043°C yr−1 or from 1.0° to 3.1°C (71 yr)−1], and high in northeastern BC [from 0.050° to 0.055°C yr−1 or from 3.5° to 3.9°C (71 yr)−1]. Temperature trends in AB are less regionally coherent, with a mix of moderate to high trends across the province and low trends at two stations in central AB [0.013° and 0.015°C yr−1 or 1.0° and 1.1°C (71 yr)−1]. Stations in southern and mountainous AB exhibit moderate trends ranging from 0.026° to 0.044°C yr−1 or from 1.9° to 3.1°C (71 yr)−1, while high magnitude trends, of up to 0.057°C yr−1 or 4.0°C (71 yr)−1 were found for several stations in central and northern AB. With the exception of one low-magnitude trend in the southwest, trends across SK are moderate to high, ranging from 0.031° to 0.058°C yr−1 or from 2.2° to 4.1°C (71 yr)−1. Patterns of winter trend magnitude are in agreement with previous studies, which found generally lower trends in southern and coastal BC, moderate to high trends across AB and SK, and trend magnitudes increasing with latitude across western Canada (Vincent et al. 2012, 2015; Wan et al. 2019; Zhang et al. 2019; Bonsal et al. 2020).
Thermodynamically driven winter (DJF) temperature trend from 1950 to 2020. Values represent the total change over 71 years.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
The spatial patterns of above-average, average, and below-average temperatures associated with dominant atmospheric circulation types are key to understanding the geographic variability in thermodynamic and dynamic contributions to winter temperature trends. This is particularly evident for circulation patterns that are associated with strong differences in the magnitude of temperature anomalies among coastal, inland, and northern portions of the study region, such as types 4 and 9. The atmospheric circulation patterns with the highest magnitude trends, types 1, 4, and 9, contribute the most to dynamic components of temperature trends for most of the study region. Type 9 increased at a rate of 0.10 days yr−1 and is associated with strong temperature anomaly gradients across the study region. This contributes to strong dynamic warming in northern BC and AB, moderately strong warming in central AB, and a slight dynamic warming in central BC and southeastern SK. In southern BC, type 9 is associated with average temperatures; therefore, increases in this circulation type minimally contribute to dynamic warming or cooling. Type 4 significantly decreased by 0.15 days yr−1, contributing to dynamic warming by reducing the frequency of circulation conducive to moderate to strong below-average temperatures. This contributes to strong dynamic warming in northern BC and central and northern AB, moderate dynamic warming in central BC, southern AB, and SK, and slight warming in southern BC. Increases of 0.08 days yr−1 in type 1 contributed to strong dynamic cooling in AB, SK, and most stations in northern BC, and moderate dynamic cooling in southern BC over this time period. Focusing on southwestern BC, types 1 and 4 have the strongest influence on dynamically driven changes, but type 9, which is associated with average temperatures, does not have an appreciable contribution to dynamic warming. Additionally, trends in types 3, 5, 8, 10, and 12 all contribute moderately to net dynamic changes in the region.
Negative dynamic contributions to observed trends in southern BC and southeastern SK are consistent with trends in atmospheric circulation patterns. Stations in southern BC with negative dynamic contributions are largely driven by the increase in type 1, which is associated with the strongest negative temperature anomalies in the region. Type 4 decreased and type 9 increased over 1950–2020. However, these two types are associated with near-average temperatures in southern BC, and therefore have little impact on observed trends. In southeastern SK, stations with a negative dynamic contribution are driven by increases in type 1, which is associated with strong negative temperature anomalies. Increases in type 9, associated with slightly above-average temperatures in southeastern SK, and in type 10 are insufficient to counteract the dynamic cooling.
Six stations were selected to demonstrate the variable effects of atmospheric circulation dynamics on winter temperature trends. The time series of mean winter temperature anomalies and trend magnitudes are shown in Fig. 8 for stations with strong, moderate, and negative dynamic components. A strong dynamic influence was detected for Grande Prairie (33% of the 4.7°C increase) located in northwestern AB, and Fort St. John (36% of the 5.6°C increase) in northeastern BC (Figs. 8a,b). The temperature anomalies associated with each synoptic type for these two stations (Table 3) suggest a strong influence of the combination of a decreasing trend in Type 4 and increase in Type 9. Saskatoon, located in central SK, and Pincher Creek, in southwestern AB, have a moderate dynamic component, at 9.2% and 9.8%, respectively (Figs. 8c,d). The winter temperature increase for Saskatoon is 3.5°C and Pincher Creek is 3.0°C over 71 years (Table 3). These two stations are strongly influenced by temperature anomalies associated with type 1 and moderately influenced by anomalies associated with type 9, both of which increased in frequency over the study period. Negative dynamic influence was detected for Vancouver in southeastern coastal BC (Fig. 8e) and Regina in south-central SK (Fig. 8f). In Vancouver the temperature increased 0.8°C, and dynamic component was −21%, while Regina saw an increase of 2.7°C, with a −3.9% dynamic contribution. This suggests that Vancouver would have warmed 1.0°C and Regina 2.8°C if not for the dynamic cooling effect. This is largely driven by the temperature anomalies associated with type 1, which counteracts warming driven by thermodynamic component and other dynamic changes, such as the slight increase in type 10, which is associated with positive temperature anomalies at these two stations.
Time series of winter (DJF) mean temperatures (dark-gray bars) and trends (red dashed lines) for stations where the dynamic component has had a (a),(b) strong; (c),(d) moderate; and (e),(f) negative influence on temperature trends.
Citation: Journal of Climate 37, 2; 10.1175/JCLI-D-23-0138.1
Temperature anomalies associated with each synoptic type, and total trend for each example station given in Fig. 8. Trends in synoptic types are also provided. Significance levels for synoptic-type trends are denoted by one asterisk for p < 0.10 and two asterisks for p < 0.05.
4. Conclusions
Natural internal variability drives fluctuations in climate over annual and multiyear to decadal time scales and can influence the magnitude and direction of trend assessments (Bush et al. 2019; Eyring et al. 2021). Moreover, it is the largest source of uncertainty in climate change projections (Abatzoglou et al. 2014). Despite this influence, the role of internal variability is often disregarded in trend analyses of historic climate. Rates of change in a given location may vary as a function of the start and end dates and duration of the time series evaluated, which can be a challenge for communicating climate change information and informing science policy decisions including adaptation strategies (Eyring et al. 2021). The variability in trend magnitudes is evident, even for relatively similar time periods and spatial locations. Climate variability can also pose a challenge for climate and hydrologic modeling. Model calibration, the magnitude of projected changes, and interpretation of model results are dependent on the selected baseline period. Controlling for dynamic variability, or identifying periods of anomalous circulation pattern frequencies, provides more certainty over a selected time series for analysis or as a baseline climatology. Results of this research identify winter temperature trends under the influence of internal variability over 1950–2020. This study walked through the process of identifying patterns of atmospheric circulation in the midtroposphere, associated winter temperature anomalies across western Canada, and calculated trends in the frequency of these circulation types. This provided an easily identifiable link between changing circulation type frequencies and surface climate responses.
Winter temperature trends for 91 AHCCD stations across BC, AB, and SK were calculated as the sum of thermodynamic, dynamic, and interaction components using methodology adapted from equations first described by Bony et al. (2004) and Emori and Brown (2005) to characterize atmospheric drivers of cloud radiative forcing and precipitation, respectively. Equations were revised to accommodate interval scale temperature data, in which zero represents a meaningful value rather than the absence of the variable. This approach uses a synoptic classification to identify dominant circulation patterns in the midtroposphere that influence surface climate in western Canada. Mean values and trends in synoptic-type frequencies and winter temperatures associated with each of these types facilitate the evaluation of temperature trends under the assumption of stationary circulation dynamics, and circulation trends under the assumption of stationary mean temperatures. Synoptic classifications have commonly been used to identify drivers of surface climate variability and results of the synoptic classification and surface climate anomalies are consistent with previous studies conducted in western Canada (e.g., Romolo et al. 2006; Stahl et al. 2006; Cuell and Bonsal 2009; Newton et al. 2014). Changes in midtropospheric circulation detected in this study are consistent with other studies in a similar region (Horton et al. 2015; Swain et al. 2016) and across the Northern Hemisphere (Screen and Simmonds 2014; Christidis and Stott 2015; Thomas et al. 2021; Rogers et al. 2022; Faranda et al. 2023), including increases in circulation patterns conducive to extreme weather events. This study focused on midtropospheric circulation, and it is recommended that further insights into dynamic drivers of change could be elucidated through analyses of circulation at other pressure levels including sea level. Future research should apply this method to evaluate winter precipitation, with a particular focus on snowpack, given the importance of snow to freshwater resources (Barnett et al. 2005; Bonsal et al. 2019, 2020). Additionally, it is recommended to apply this method to summer climate variables, particularly as they pertain to drought research in the Canadian prairie provinces.
The greatest temperature trends were found for stations in central and northern AB and BC. Model results indicate this region had the strongest dynamic influence on temperature trends. The lowest trend magnitudes were found in southern and coastal BC where the Pacific Ocean modulates coastal climate. A negative dynamic influence was detected for this region, suggesting that observed trends would be higher without the dynamic cooling effect. Results are similar in southeastern SK, where moderate increasing trends and dynamic cooling were detected. The pattern of temperature trends remained when the thermodynamic component was isolated; however, trend magnitudes were subdued. Deser et al. (2016) detected similar patterns of higher dynamic contributions to winter temperature trends in northern Canada over 1963–2012. However, they detected very low dynamic contributions in southern BC and negative contributions across the western United States. Winter temperature trend magnitudes are within a similar range to those detected by Vincent et al. (2015), Zhang et al. (2019), and Bonsal et al. (2020). Additionally, Vincent et al. (2015) and Wan et al. (2019) reported reduced temperature trends when removing the influence of the PDO and NAO, with the PDO exerting the strongest influence on temperatures in western Canada. Given the relationships between the PDO and atmospheric circulation patterns (Newton et al. 2014), it is unsurprising that changes in the PDO are expected to have similar impacts on winter temperatures in western Canada. However, the interaction effect of multiple teleconnection indices can strengthen or weaken surface climate response (Bonsal et al. 2001), leading to uncertainty in interpreting the PDO as a proxy for atmospheric circulation. Future atmospheric circulation and teleconnection regimes will affect surface climate in western Canada, and act as a dynamic drive of climate change. Models predict future enhancements to atmospheric waviness and reduced zonal flow (Peings et al. 2017; Vavrus et al. 2017; Fabiano et al. 2021) and greater variability of sea surface temperatures and associated ENSO and PDO indices (Fredriksen et al. 2020; Lee et al. 2021). However, there is considerable uncertainty in the prediction of future circulation (Peings et al. 2017; Fredriksen et al. 2020; Lee et al. 2021) and associated surface climate impacts.
There are limitations associated with the calculation of temperature trends as the sum of thermodynamic and dynamic components. Trends calculated using this method were compared with temperature trends calculated at each station using MK analyses. Stations exhibited good or excellent agreement with model results, the majority of stations within 0.21°C (71 yr)−1 of the modeled trend; however, there are geographic regions of over- or underestimation. The model slightly underestimated trend magnitudes in BC and a few stations in southwestern AB. Conversely, the model slightly overestimated trend magnitudes at most stations in AB and SK. This study separated temperature trends into three components: dynamic, thermodynamic, and the interaction between dynamic and thermodynamic components. Thus, they are treated as discrete contributing drivers. However, thermodynamically driven temperature changes can influence atmospheric circulation patterns, and there may be feedbacks that are not captured through this partitioning. This study is also limited by the aggregation of daily climate data into a seasonal mean. There is variability in climatological mean monthly temperatures and dominant atmospheric circulation patterns throughout the winter, which was not explicitly evaluated in this study. Future research would benefit from evaluating trends in temperatures and atmospheric dynamics over a finer temporal scale.
Numerous studies have identified thermodynamic and dynamic components of changes in climate and atmospheric circulation, largely with the aim of quantifying the anthropogenic influence on these variables. While some of these methods utilize reanalysis data (e.g., Christidis and Stott 2015), the majority rely on model simulations. For example, the single model initial condition large ensemble (SMILE) method uses numerous runs of a single model initialized with slightly different conditions resulting in a range of potential states used to determine the forced (anthropogenic) component (Deser et al. 2016; Lehner et al. 2020; Lehner and Deser 2023). Dynamical adjustment using circulation analogs is then used to further divide thermodynamic and dynamic components into forced (anthropogenic) and unforced (natural) drivers. That is, while the present study identifies the dynamic component of observed winter temperature changes, alternative model-based methods can identify how much of that dynamic component is driven by anthropogenic factors. However, models may underperform with respect to identifying internal variability and improvements are ongoing to reduce uncertainty in model predictions (Lehner and Deser 2023). Given these concerns, it is recommended that both observational/reanalysis and model-based methods are important when evaluating anthropogenic influences on regional climate changes.
The separation of temperature changes into thermodynamic and dynamic components provides valuable information about the drivers of observed temperature trends and the influence of dynamic variability on the magnitude and direction of these trends. Atmospheric dynamics, specifically the trends in frequency of dominant circulation patterns, have contributed to observed winter temperature trends in western Canada. The methodology used in this study represents a positive step toward improving temperature trend analyses and providing a more robust understanding of observed climate changes. Evaluating and identifying the dynamic and thermodynamic components of temperature change improves the selection of a time series for future trend analyses or a baseline period for climate and hydrologic modeling.
Acknowledgments.
The authors gratefully acknowledge the time and effort that this submission received from two anonymous reviewers. Their thoughtful comments and recommendations helped to improve this paper.
Data availability statement.
Datasets analyzed during the current study are available from the in the National Centers for Environmental Prediction–National Center for Atmospheric Research (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html), described in Kalnay et al. (1996), and the Adjusted and Homogenized Canadian Climate Data (AHCCD) dataset (https://open.canada.ca/data/en/dataset/d6813de6-b20a-46cc-8990-01862ae15c5f), described in Mekis and Vincent (2011) and Vincent et al. (2012).
REFERENCES
Abatzoglou, J. T., 2011: Influence of the PNA on declining mountain snowpack in the Western United States. Int. J. Climatol., 31, 1135–1142, https://doi.org/10.1002/joc.2137.
Abatzoglou, J. T., D. E. Rupp, and P. W. Mote, 2014: Seasonal climate variability and change in the Pacific Northwest of the United States. J. Climate, 27, 2125–2142, https://doi.org/10.1175/JCLI-D-13-00218.1.
Allen, M. R., and Coauthors, 2018: Framing and context. Global Warming of 1.5°C, V. Masson-Delmotte et al., Eds., Cambridge University Press, 616 pp., https://doi.org/10.1017/9781009157940.
Almonte, J. D., and R. E. Stewart, 2019: Precipitation transition regions over the southern Canadian Cordillera during January–April 2010 and under a pseudo-global-warming assumption. Hydrol. Earth Syst. Sci., 23, 3665–3682, https://doi.org/10.5194/hess-23-3665-2019.
Aukema, B. H., A. L. Carroll, Y. Zheng, J. Zhu, K. F. Raffa, D. R. Moore, K. Stahl, and S. W. Taylor, 2008: Movement of outbreak populations of mountain pine beetle: Influences of spatiotemporal patterns and climate. Ecography, 31, 348–358, https://doi.org/10.1111/j.0906-7590.2007.05453.x.
Barnett, T. P., J. C. Adam, and D. P. Lettenmaier, 2005: Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438, 303–309, https://doi.org/10.1038/nature04141.
Beltaos, S., 2002: Effects of climate on mid‐winter ice jams. Hydrol. Processes, 16, 789–804, https://doi.org/10.1002/hyp.370.
Bengtsson, L., K. I. Hodges, and E. Roeckner, 2006: Storm tracks and climate change. J. Climate, 19, 3518–3543, https://doi.org/10.1175/JCLI3815.1.
Beniston, M., and M. Stoffel, 2016: Rain-on-snow events, floods and climate change in the Alps: Events may increase with warming up to 4°C and decrease thereafter. Sci. Total Environ., 571, 228–236, https://doi.org/10.1016/j.scitotenv.2016.07.146.
Bonsal, B. R., and C. Cuell, 2017: Hydro-climatic variability and extremes over the Athabasca River basin: Historical trends and projected future occurrence. Can. Water Resour. J., 42, 315–335, https://doi.org/10.1080/07011784.2017.1328288.
Bonsal, B. R., X. Zhang, and W. D. Hogg, 1999: Canadian prairie growing season precipitation variability and associated atmospheric circulation. Climate Res., 11, 191–208, https://doi.org/10.3354/cr011191.
Bonsal, B. R., A. Shabbar, and K. Higuchi, 2001: Impacts of low frequency variability modes on Canadian winter temperature. Int. J. Climatol., 21, 95–108, https://doi.org/10.1002/joc.590.
Bonsal, B. R., D. L. Peters, F. Seglenieks, A. Rivera, and A. Berg, 2019: Changes in freshwater availability across Canada. Canada’s Changing Climate Report, E. Bush and D. S. Lemmen, Eds., Government of Canada, 261–342.
Bonsal, B. R., R. R. Shrestha, Y. Dibike, D. L. Peters, C. Spence, L. Mudryk, and D. Yang, 2020: Western Canadian freshwater availability: Current and future vulnerabilities. Environ. Rev., 28, 528–545, https://doi.org/10.1139/er-2020-0040.
Bony, S., J. L. Dufresne, H. Le Treut, J. J. Morcrette, and C. Senior, 2004: On dynamic and thermodynamic components of cloud changes. Climate Dyn., 22, 71–86, https://doi.org/10.1007/s00382-003-0369-6.
Bush, E., N. Gillett, E. Watson, J. Fyfe, F. Vogel, and N. Swart, 2019: Understanding observed global climate change. Canada’s Changing Climate Report, E. Bush and D. S. Lemmen, Eds., Government of Canada, 24–72.
Campbell, É. M., and M. C. Ryan, 2021: Nested recharge systems in mountain block hydrology: High-elevation snowpack generates low-elevation overwinter baseflow in a Rocky Mountain river. Water, 13, 2249, https://doi.org/10.3390/w13162249.
Cannon, A. J., P. H. Whitfield, and E. R. Lord, 2002: Synoptic map-pattern classification using recursive partitioning and principal component analysis. Mon. Wea. Rev., 130, 1187–1206, https://doi.org/10.1175/1520-0493(2002)130<1187:SMPCUR>2.0.CO;2.
Cassano, J. J., P. Uotila, and A. Lynch, 2006: Changes in synoptic weather patterns in the polar regions in the twentieth and twenty‐first centuries, Part 1: Arctic. Int. J. Climatol., 26, 1027–1049, https://doi.org/10.1002/joc.1306.
Cattiaux, J., and C. Cassou, 2013: Opposite CMIP3/CMIP5 trends in the wintertime Northern Annular Mode explained by combined local sea ice and remote tropical influences. Geophys. Res. Lett., 40, 3682–3687, https://doi.org/10.1002/grl.50643.
Cattiaux, J., H. Douville, and Y. Peings, 2013: European temperatures in CMIP5: Origins of present-day biases and future uncertainties. Climate Dyn., 41, 2889–2907, https://doi.org/10.1007/s00382-013-1731-y.
Christidis, N., and P. A. Stott, 2015: Changes in the geopotential height at 500 hPa under the influence of external climatic forcings. Geophys. Res. Lett., 42, 10 798–10 806, https://doi.org/10.1002/2015GL066669.
Cohen, J., J. C. Furtado, M. Barlow, V. A. Alexeev, and J. E. Cherry, 2012: Asymmetric seasonal temperature trends. Geophys. Res. Lett., 39, L04705, https://doi.org/10.1029/2011GL050582.
Cohen, J., and Coauthors, 2014: Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci., 7, 627–637, https://doi.org/10.1038/ngeo2234.
Cuell, C., and B. Bonsal, 2009: An assessment of climatological synoptic typing by principal component analysis and k-means clustering. Theor. Appl. Climatol., 98, 361–373, https://doi.org/10.1007/s00704-009-0119-8.
Deser, C., L. Terray, and A. S. Phillips, 2016: Forced and internal components of winter air temperature trends over North America during the past 50 years: Mechanisms and implications. J. Climate, 29, 2237–2258, https://doi.org/10.1175/JCLI-D-15-0304.1.
Deser, C., R. Guo, and F. Lehner, 2017: The relative contributions of tropical Pacific sea surface temperatures and atmospheric internal variability to the recent global warming hiatus. Geophys. Res. Lett., 44, 7945–7954, https://doi.org/10.1002/2017GL074273.
Embrey, S., J. V. Remais, and J. Hess, 2012: Climate change and ecosystem disruption: The health impacts of the North American Rocky Mountain pine beetle infestation. Amer. J. Public Health, 102, 818–827, https://doi.org/10.2105/AJPH.2011.300520.
Emori, S., and S. J. Brown, 2005: Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett., 32, 2005GL023272, https://doi.org/10.1029/2005GL023272.
Eyring, V., and Coauthors, 2021: Human influence on the climate system. Climate Change 2021: The Physical Science Basis, V. Masson-Delmotte et al., Eds., Cambridge University Press, 423–552, https://doi.org/10.1017/9781009157896.005.
Fabiano, F., V. L. Meccia, P. Davini, P. Ghinassi, and S. Corti, 2021: A regime view of future atmospheric circulation changes in northern mid-latitudes. Wea. Climate Dyn., 2, 163–180, https://doi.org/10.5194/wcd-2-163-2021.
Faranda, D., G. Messori, A. Jezequel, M. Vrac, and P. Yiou, 2023: Atmospheric circulation compounds anthropogenic warming and impacts of climate extremes in Europe. Proc. Natl. Acad. Sci. USA, 120, e2214525120, https://doi.org/10.1073/pnas.2214525120.
Fleig, A. K., L. M. Tallaksen, P. James, H. Hisdal, and K. Stahl, 2015: Attribution of European precipitation and temperature trends to changes in synoptic circulation. Hydrol. Earth Syst. Sci., 19, 3093–3107, https://doi.org/10.5194/hess-19-3093-2015.
Fleming, S. W., and P. H. Whitfield, 2010: Spatiotemporal mapping of ENSO and PDO surface meteorological signals in British Columbia, Yukon, and southeast Alaska. Atmos.–Ocean, 48, 122–131, https://doi.org/10.3137/AO1107.2010.
Francis, J. A., and S. J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in mid‐latitudes. Geophys. Res. Lett., 39, 2012GL051000, https://doi.org/10.1029/2012GL051000.
Francis, J. A., and S. J. Vavrus, 2015: Evidence for a wavier jet stream in response to rapid Arctic warming. Environ. Res. Lett., 10, 014005, https://doi.org/10.1088/1748-9326/10/1/014005.
Fredriksen, H. B., J. Berner, A. C. Subramanian, and A. Capotondi, 2020: How does El Niño–Southern Oscillation change under global warming—A first look at CMIP6. Geophys. Res. Lett., 47, e2020GL090640, https://doi.org/10.1029/2020GL090640.
Grimm, N. B., and Coauthors, 2013: The impacts of climate change on ecosystem structure and function. Front. Ecol. Environ., 11, 474–482, https://doi.org/10.1890/120282.
Gulev, S. K., and Coauthors, 2021: Changing state of the climate system. Climate Change 2021: The Physical Science Basis, V. Masson-Delmotte et al., Eds., Cambridge University Press, 287–422, https://doi.org/10.1017/9781009157896.004.
Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, https://doi.org/10.1029/2010RG000345.
Hayashi, M., 2020: Alpine hydrogeology: The critical role of groundwater in sourcing the headwaters of the world. Ground Water, 58, 498–510, https://doi.org/10.1111/gwat.12965.
Higgins, M. E., and J. J. Cassano, 2009: Impacts of reduced sea ice on winter Arctic atmospheric circulation, precipitation, and temperature. J. Geophys. Res., 114, D16107, https://doi.org/10.1029/2009JD011884.
Holton, J. R., 2004: An Introduction to Dynamic Meteorology. 4th ed. Elsevier Academic Press, Inc. 535 pp.
Horton, D. E., N. C. Johnson, D. Singh, D. L. Swain, B. Rajaratnam, and N. S. Diffenbaugh, 2015: Contribution of changes in atmospheric circulation patterns to extreme temperature trends. Nature, 522, 465–469, https://doi.org/10.1038/nature14550.
Hu, X., S. A. Sejas, M. Cai, P. C. Taylor, Y. Deng, and S. Yang, 2019: Decadal evolution of the surface energy budget during the fast warming and global warming hiatus periods in the ERA-Interim. Climate Dyn., 52, 2005–2016, https://doi.org/10.1007/s00382-018-4232-1.
Huang, J., P. Hitchcock, A. C. Maycock, C. M. McKenna, and W. Tian, 2021: Northern Hemisphere cold air outbreaks are more likely to be severe during weak polar vortex conditions. Commun. Earth Environ., 2, 147, https://doi.org/10.1038/s43247-021-00215-6.
Jeong, D. I., B. Yu, and A. J. Cannon, 2021: Links between atmospheric blocking and North American winter cold spells in two generations of Canadian Earth System Model large ensembles. Climate Dyn., 57, 2217–2231, https://doi.org/10.1007/s00382-021-05801-0.
Johnstone, J. A., and N. J. Mantua, 2014: Atmospheric controls on northeast Pacific temperature variability and change, 1900–2012. Proc. Natl. Acad. Sci. USA, 111, 14 360–14 365, https://doi.org/10.1073/pnas.1318371111.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kattelmann, R., 1997: Flooding from rain-on-snow events in the Sierra Nevada. IAHS Publ., 239, 59–65.
Kendall, M. G., 1975: Rank Correlation Methods. Charles Griffin & Co., 202 pp.
Kohonen, T., 2001: Self-Organizing Maps. 3rd ed. Springer, 505 pp.
Kolstad, E. W., T. Breiteig, and A. A. Scaife, 2010: The association between stratospheric weak polar vortex events and cold air outbreaks in the Northern Hemisphere. Quart. J. Roy. Meteor. Soc., 136, 886–893, https://doi.org/10.1002/qj.620.
Lee, J.-Y., and Coauthors, 2021: Future global climate: Scenario-based projections and near-term information. Climate Change 2021: The Physical Science Basis, V. Masson-Delmotte et al., Eds., Cambridge University Press, 553–672, https://doi.org/10.1017/9781009157896.006.
Lehner, F., and C. Deser, 2023: Origin, importance, and predictive limits of internal climate variability. Environ. Res., 2, 023001, https://doi.org/10.1088/2752-5295/accf30.
Lehner, F., C. Deser, N. Maher, J. Marotzke, E. M. Fischer, L. Brunner, R. Knutti, and E. Hawkins, 2020: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst. Dyn., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020.
Lewandowsky, S., J. S. Risbey, and N. Oreskes, 2016: The “pause” in global warming: Turning a routine fluctuation into a problem for science. Bull. Amer. Meteor. Soc., 97, 723–733, https://doi.org/10.1175/BAMS-D-14-00106.1.
Liu, Z., and Coauthors, 2021: Acceleration of western Arctic sea ice loss linked to the Pacific North American pattern. Nat. Commun., 12, 1519, https://doi.org/10.1038/s41467-021-21830-z.
Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245–259, https://doi.org/10.2307/1907187.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1080, https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
Marks, D., J. Kimball, D. Tingey, and T. Link, 1998: The sensitivity of snowmelt processes to climate conditions and forest cover during rain-on-snow: A case study of the 1996 Pacific Northwest flood. Hydrol. Processes, 12, 1569–1587, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L.
McCabe, G. J., M. P. Clark, and M. C. Serreze, 2001: Trends in Northern Hemisphere surface cyclone frequency and intensity. J. Climate, 14, 2763–2768, https://doi.org/10.1175/1520-0442(2001)014<2763:TINHSC>2.0.CO;2.
McCabe, G. J., M. P. Clark, and L. E. Hay, 2007: Rain-on-snow events in the western United States. Bull. Amer. Meteor. Soc., 88, 319–328, https://doi.org/10.1175/BAMS-88-3-319.
Meah, N., 2019: Climate uncertainty and policy making—What do policy makers want to know? Reg. Environ. Change, 19, 1611–1621, https://doi.org/10.1007/s10113-019-01492-w.
Meehl, G. A., A. Hu, J. M. Arblaster, J. Fasullo, and K. E. Trenberth, 2013: Externally forced and internally generated decadal climate variability associated with the Interdecadal Pacific Oscillation. J. Climate, 26, 7298–7310, https://doi.org/10.1175/JCLI-D-12-00548.1.
Mekis, É., and L. A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmos.–Ocean, 49, 163–177, https://doi.org/10.1080/07055900.2011.583910.
Morán‐Tejeda, E., J. I. López‐Moreno, and M. Beniston, 2013: The changing roles of temperature and precipitation on snowpack variability in Switzerland as a function of altitude. Geophys. Res. Lett., 40, 2131–2136, https://doi.org/10.1002/grl.50463.
Negm, A., P. Abdrakhimova, M. Hayashi, and K. Rasouli, 2021: Effects of climate change on depression‐focused groundwater recharge in the Canadian prairies. Vadose Zone J., 20, e20153, https://doi.org/10.1002/vzj2.20153.
Newton, B. W., T. D. Prowse, and B. R. Bonsal, 2014: Evaluating the distribution of water resources in western Canada using synoptic climatology and selected teleconnections. Part 1: Winter season. Hydrol. Processes, 28, 4219–4234, https://doi.org/10.1002/hyp.10233.
Newton, B. W., T. D. Prowse, and L. P. de Rham, 2017: Hydro-climatic drivers of mid-winter break-up of river ice in western Canada and Alaska. Hydrol. Res., 48, 945–956, https://doi.org/10.2166/nh.2016.358.
Newton, B. W., B. R. Bonsal, T. W. D. Edwards, T. D. Prowse, and G. R. McGregor, 2019: Atmospheric drivers of winter above‐freezing temperatures and associated rainfall in western Canada. Int. J. Climatol., 39, 5655–5671, https://doi.org/10.1002/joc.6178.
O’Neil, H. C. L., T. D. Prowse, B. R. Bonsal, and Y. B. Dibike, 2017: Spatial and temporal characteristics in streamflow-related hydroclimatic variables over western Canada. Part 1: 1950–2010. Hydrol. Res., 48, 915–931, https://doi.org/10.2166/nh.2016.057.
Overland, J. E., and M. Wang, 2019: Impact of the winter polar vortex on greater North America. Int. J. Climatol., 39, 5815–5821, https://doi.org/10.1002/joc.6174.
Overland, J. E., K. R. Wood, and M. Wang, 2011: Warm Arctic–cold continents: Climate impacts of the newly open Arctic sea. Polar Res., 30, 15787, https://doi.org/10.3402/polar.v30i0.15787.
Patakamuri, S. K., and N. O’Brien, 2021: Modifiedmk: Modified versions of Mann Kendall and Spearman’s rho trend tests. CRAN Repository, accessed 26 January 2021, https://CRAN.R-project.org/package=modifiedmk.
Pavlovskii, I., M. Hayashi, and D. Itenfisu, 2019: Midwinter melts in the Canadian prairies: Energy balance and hydrological effects. Hydrol. Earth Syst. Sci., 23, 1867–1883, https://doi.org/10.5194/hess-23-1867-2019.
Peings, Y., J. Cattiaux, S. Vavrus, and G. Magnusdottir, 2017: Late twenty-first-century changes in the midlatitude atmospheric circulation in the CESM large ensemble. J. Climate, 30, 5943–5960, https://doi.org/10.1175/JCLI-D-16-0340.1.
Peitzsch, E. H., G. T. Pederson, K. W. Birkeland, J. Hendrikx, and D. B. Fagre, 2021: Climate drivers of large magnitude snow avalanche years in the US northern Rocky Mountains. Sci. Rep., 11, 10032, https://doi.org/10.1038/s41598-021-89547-z.
Petoukhov, V., S. Rahmstorf, S. Petri, and H. J. Schellnhuber, 2013: Quasiresonant amplification of planetary waves and recent Northern Hemisphere weather extremes. Proc. Natl. Acad. Sci., 110, 5336–5341, https://doi.org/10.1073/pnas.122200011.
Pfahl, S., and H. Wernli, 2012: Quantifying the relevance of atmospheric blocking for co‐located temperature extremes in the Northern Hemisphere on (sub‐) daily time scales. Geophys. Res. Lett., 39, 2012GL052261, https://doi.org/10.1029/2012GL052261.
Pierce, D. W., and Coauthors, 2008: Attribution of declining western U.S. snowpack to human effects. J. Climate, 21, 6425–6444, https://doi.org/10.1175/2008JCLI2405.1.
Quamme, H. A., A. J. Cannon, D. Neilsen, J. M. Caprio, and W. G. Taylor, 2010: The potential impact of climate change on the occurrence of winter freeze events in six fruit crops grown in the Okanagan Valley. Can. J. Plant Sci., 90, 85–93, https://doi.org/10.4141/CJPS09042.
Rahmstorf, S., G. Foster, and N. Cahill, 2017: Global temperature evolution: Recent trends and some pitfalls. Environ. Res. Lett., 12, 054001, https://doi.org/10.1088/1748-9326/aa6825.
Rogers, C. D., K. Kornhuber, S. E. Perkins-Kirkpatrick, P. C. Loikith, and D. Singh, 2022: Sixfold increase in historical Northern Hemisphere concurrent large heatwaves driven by warming and changing atmospheric circulations. J. Climate, 35, 1063–1078, https://doi.org/10.1175/JCLI-D-21-0200.1.
Romolo, L., T. D. Prowse, D. Blair, B. R. Bonsal, P. Marsh, and L. W. Martz, 2006: The synoptic climate controls on hydrology in the upper reaches of the Peace River Basin. Part II: Snow ablation. Hydrol. Processes, 20, 4113–4129, https://doi.org/10.1002/hyp.6422.
Rottler, E., K. Vormoor, T. Francke, M. Warscher, U. Strasser, and A. Bronstert, 2021: Elevation-dependent compensation effects in snowmelt in the Rhine River Basin upstream gauge Basel. Hydrol. Res., 52, 536–557, https://doi.org/10.2166/nh.2021.092.
Sambaraju, K. R., A. L. Carroll, J. Zhu, K. Stahl, R. D. Moore, and B. H. Aukema, 2012: Climate change could alter the distribution of mountain pine beetle outbreaks in western Canada. Ecography, 35, 211–223, https://doi.org/10.1111/j.1600-0587.2011.06847.x.
Screen, J. A., and I. Simmonds, 2014: Amplified mid-latitude planetary waves favour particular regional weather extremes. Nat. Climate Change, 4, 704–709, https://doi.org/10.1038/nclimate2271.
Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934.
Shabbar, A., and M. Khandekar, 1996: The impact of El Niño–Southern Oscillation on the temperature field over Canada: Research note. Atmos.–Ocean, 34, 401–416, https://doi.org/10.1080/07055900.1996.9649570.
Shaw, T. A., and Coauthors, 2016: Storm track processes and the opposing influences of climate change. Nat. Geosci., 9, 656–664, https://doi.org/10.1038/ngeo2783.
Skific, N., J. A. Francis, and J. J. Cassano, 2009: Attribution of seasonal and regional changes in Arctic moisture convergence. J. Climate, 22, 5115–5134, https://doi.org/10.1175/2009JCLI2829.1.
Smoliak, B. V., J. M. Wallace, P. Lin, and Q. Fu, 2015: Dynamical adjustment of the Northern Hemisphere surface air temperature field: Methodology and application to observations. J. Climate, 28, 1613–1629, https://doi.org/10.1175/JCLI-D-14-00111.1.
Sospedra‐Alfonso, R., J. R. Melton, and W. J. Merryfield, 2015: Effects of temperature and precipitation on snowpack variability in the Central Rocky Mountains as a function of elevation. Geophys. Res. Lett., 42, 4429–4438, https://doi.org/10.1002/2015GL063898.
Stahl, K., R. D. Moore, and I. G. Mckendry, 2006: The role of synoptic‐scale circulation in the linkage between large‐scale ocean–atmosphere indices and winter surface climate in British Columbia, Canada. Int. J. Climatol., 26, 541–560, https://doi.org/10.1002/joc.1268.
Sui, J., and G. Koehler, 2001: Rain-on-snow induced flood events in southern Germany. J. Hydrol., 252, 205–220, https://doi.org/10.1016/S0022-1694(01)00460-7.
Swain, D. L., D. E. Horton, D. Singh, and N. S. Diffenbaugh, 2016: Trends in atmospheric patterns conducive to seasonal precipitation and temperature extremes in California. Sci. Adv., 2, e1501344, https://doi.org/10.1126/sciadv.1501344.
Tang, Q., X. Zhang, and J. A. Francis, 2013: Extreme summer weather in northern mid-latitudes linked to a vanishing cryosphere. Nat. Climate Change, 4, 45–50, https://doi.org/10.1038/nclimate2065.
Thomas, C., A. Voulgarakis, G. Lim, J. Haigh, and P. Nowack, 2021: An unsupervised learning approach to identifying blocking events: The case of European summer. Wea. Climate Dyn., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021.
Thompson, D. W., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1297–1300, https://doi.org/10.1029/98GL00950.
Trenberth, K. E., and J. T. Fasullo, 2013: An apparent hiatus in global warming? Earth’s Future, 1, 19–32, https://doi.org/10.1002/2013EF000165.
van Oldenborgh, G. J., S. Drijfhout, A. van Ulden, R. Haarsma, A. Sterl, C. Severijns, W. Hazeleger, and H. Dijkstra, 2009: Western Europe is warming much faster than expected. Climate Past, 5 (1), 1–12, https://doi.org/10.5194/cp-5-1-2009.
Vavrus, S. J., F. Wang, J. E. Martin, J. A. Francis, Y. Peings, and J. Cattiaux, 2017: Changes in North American atmospheric circulation and extreme weather: Influence of Arctic amplification and Northern Hemisphere snow cover. J. Climate, 30, 4317–4333, https://doi.org/10.1175/JCLI-D-16-0762.1.
Vesanto, J., J. Himberg, E. Alhoniemi, and J. Parhankangas, 2000: SOM toolbox for Matlab 5. Helsinki University of Technology Rep. A57, 60 pp., http://www.cis.hut.fi/somtoolbox/documentation/.
Vincent, L. A., X. L. Wang, E. J. Milewska, and H. Wan, 2012: A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. J. Geophys. Res., 117, D18110, https://doi.org/10.1029/2012JD017859.
Vincent, L. A., X. Zhang, R. D. Brown, Y. Feng, E. Mekis, E. J. Milewska, H. Wan, and X. L. Wang, 2015: Observed trends in Canada’s climate and influence of low-frequency variability modes. J. Climate, 28, 4545–4560, https://doi.org/10.1175/JCLI-D-14-00697.1.
Wallace, J. M., Q. Fu, B. V. Smoliak, P. Lin, and C. M. Johanson, 2012: Simulated versus observed patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Proc. Natl. Acad. Sci. USA, 109, 14 337–14 342, https://doi.org/10.1073/pnas.1204875109.
Wan, H., X. Zhang, and F. Zwiers, 2019: Human influence on Canadian temperatures. Climate Dyn., 52, 479–494, https://doi.org/10.1007/s00382-018-4145-z.
Whan, K., F. Zwiers, and J. Sillmann, 2016: The influence of atmospheric blocking on extreme winter minimum temperatures in North America. J. Climate, 29, 4361–4381, https://doi.org/10.1175/JCLI-D-15-0493.1.
Xiao, C., C. Qian, A. Huang, R. Guo, and X. Kuang, 2023: Evaluation of AMIP models from CMIP6 in simulating winter surface air temperature trends over Eurasia during 1998–2012 based on dynamical adjustment. Climate Dyn., 60, 17–31, https://doi.org/10.1007/s00382-022-06295-0.
Yamada, T. J., T. Hoshino, and A. Suzuki, 2021: Using a massive high‐resolution ensemble climate data set to examine dynamic and thermodynamic aspects of heavy precipitation change. Atmos. Sci. Lett., 22, e1065, https://doi.org/10.1002/asl.1065.
Yan, X. H., T. Boyer, K. Trenberth, T. R. Karl, S. P. Xie, V. Nieves, K. K. Tung, and D. Roemmich, 2016: The global warming hiatus: Slowdown or redistribution? Earth’s Future, 4, 472–482, https://doi.org/10.1002/2016EF000417.
Yu, B., H. Lin, Z. W. Wu, and W. J. Merryfield, 2016: Relationship between North American winter temperature and large-scale atmospheric circulation anomalies and its decadal variation. Environ. Res. Lett., 11, 074001, https://doi.org/10.1088/1748-9326/11/7/074001.
Zhang, W., V. Hari, S. S‐Y Wang, M. D. LaPlante, G. Garfin, G. Affram, and R. Kumar, 2022: Fewer troughs, not more ridges, have led to a drying trend in the Western United States. Geophys. Res. Lett., 49, e2021GL097089, https://doi.org/10.1029/2021GL097089.
Zhang, X., and Coauthors, 2019: Changes in temperature and precipitation across Canada. Canada’s Changing Climate Report, E. Bush and D.S. Lemmen, Eds., Government of Canada, 112–193.
Zhou, B., P. Zhai, and Y. Chen, 2020: Contribution of changes in synoptic‐scale circulation patterns to the past summer precipitation regime shift in eastern China. Geophys. Res. Lett., 47, e2020GL087728, https://doi.org/10.1029/2020GL087728.
