The warm Arctic-cold north american pattern in CanESM5 large ensemble simulations: Eurasian influence and uncertainty due to internal variability

Abstract

This study examines the warm Arctic-cold North American pattern (WACNA) and its connection with the warm Arctic-cold Eurasia pattern (WACE) using ERA5 reanalysis and a 50-member ensemble of historical climate simulations produced by CanESM5, the Canadian model participated in CMIP6. The results indicate that a negative WACE-like pattern typically precedes a positive WACNA pattern by one month, and the presence of a negative Asian-Bering-North American (ABNA)-like circulation pattern connects Eurasia and North America, along with the Pacific-North American (PNA)-like pattern. The negative ABNA-like pattern can be attributed to anomalous heating in southern Siberia, which is associated with the negative WACE pattern and its featured Eurasian warming. The negative PNA-like pattern is influenced by negative SST anomalies in the tropical Pacific, resembling tropical ENSO variability. Anomalous temperature advection in the lower troposphere follows the circulation anomaly, which supports the formation of WACNA. Conversely, processes with circulation anomalies of opposite sign result in a negative WACNA pattern. The tropical ENSO variability does not significantly impact the WACNA pattern and its linkage with WACE. CanESM5 simulates the WACNA pattern and WACE-WACNA connection well, with some discrepancies in the magnitude of anomalies compared to ERA5 reanalysis. The uncertainty in the simulated WACNA pattern due to internal climate variability is dominated by two modes of inter-member variability: a southeast-northwest phase shift and a local variation in amplitude.

1 Introduction

Extensive scientific research has been devoted to investigating Arctic amplification, Arctic atmospheric circulation patterns, and their teleconnections with other regions (e.g., Cohen et al. 2020; Previdi et al. 2021; Henderson et al. 2021 and references therein). Among these patterns, the warm Arctic-cold continents pattern (WACC) (Overland et al. 2011) has garnered considerable attention in recent studies, especially its distinct characteristics and implications for climate (e.g., Cohen et al. 2014; Mori et al. 2014; Kug et al. 2015; Sun et al. 2016; Blackport et al. 2019; Clark and Lee 2019; Guan et al. 2020, 2021; Park and Lee 2021; Zhong and Wu 2022; Xiu et al. 2022; Zhao et al. 2023). The pattern is characterized by warming in the Arctic and cooling in the northern midlatitude continents in boreal winter. WACC involves two counterparts based on midlatitude temperature anomalies: warm Arctic-cold Eurasia (WACE) over Eurasia (e.g., Cohen et al. 2012; Mori et al. 2014, 2019; Sorokina et al. 2016) and warm Arctic-cold North America (WACNA) over the North American (NA) sector (e.g., Lin 2015, 2018; Blackport et al. 2019; Guan et al. 2020; Yu and Lin 2022; Lin et al. 2022). Recent studies have also explored the relationship between the two counterparts on intraseasonal (Lin et al. 2022) and interannual (Yu and Lin 2022) timescales.

On interannual timescales, a negative phase of WACE tends to precede a positive phase of WACNA by approximately 25 days (Yu and Lin 2022). The WACNA pattern is driven by two primary sources: the decline in Siberian snow, which results in diabatic heating in the lower troposphere, and SST anomalies over the tropical central-eastern Pacific Ocean that resemble tropical El Niño-Southern Oscillation (ENSO) variability. The former drives a zonally oriented atmospheric teleconnection like the Asian-Bering-North American pattern (ABNA) (Yu et al. 2016, 2018), while the latter drives a tropical-extratropical teleconnection similar to the Pacific-North American pattern (PNA) (Wallace and Gutzler 1981). Both teleconnections contribute to the formation of the WACNA pattern. The WACE-WACNA linkage enhances our understanding of the evolution and formation of the WACNA pattern. However, the stability of this linkage remains unclear and will be addressed in this study. Additionally, we examine here the potential influence of tropical ENSO variability on the modulation of WACNA and its relationship with WACE, considering the demonstrated impact of ENSO on North American temperatures (e.g., Trenberth et al. 1998). These analyses not only provide further observational evidence on WACNA but also offer means for subsequent model verification. It is important to note that unlike the study of Yu and Lin (2022), which relied on daily data, this analysis reveals a strong and consistent connection between WACE and WACNA during the winter season using monthly data. This greatly simplifies the process of validating climate models by employing monthly data in large ensemble climate simulations. In addition, we perform a field significance test for results in this study, as outlined in Sect. 2, which effectively mitigates the risk of spurious or coincidental associations for grid points and provides more reliable and robust results.

Exploring simulations of the WACNA pattern and WACE-WACNA connection in climate models, as well as the uncertainty of outcomes resulting from internal climate variability, is another intriguing topic. There are three sources of uncertainty in climate simulations including model response, internal variability, and external forcing, as demonstrated in previous studies (e.g., Deser et al. 2012, 2014; Kay et al. 2015 and references therein). Climate response to anthropogenic forcing is evident globally, whereas internal climate variability redistributes heat and momentum between various components of the climate system and significantly influences climate on continental and regional scales (e.g., Deser et al. 2014; Wallace et al. 2014; Yu et al. 2020). The single model initial-condition large ensemble (SMILE) simulation method, as described below in Sect. 2, has been widely used in recent years in exploring the contribution of internal climate variability to various aspects of climate simulations. In this study, we examine the WACNA pattern in outputs from historical SMILE simulations conducted using the Canadian Earth System Model, version 5 (CanESM5). We employ CanESM5 simulations to evaluate the performance of this model in capturing the WACNA pattern and WACE-WACNA connection. We then explore the diversity of these results across large ensemble simulations due to internal climate variability. These are important aspects of model verification and will support our further study on projected changes of WACNA using CanESM5 scenario simulations.

The rest of the paper is organized as follows. Section 2 describes the reanalysis and observational data, CanESM5 historical simulations, and analysis methods employed in this study. In Sect. 3, we analyze the WACNA pattern using observational and reanalysis data, including its formation, the connection between WACE and WACNA, and the impact of ENSO on the results. In Sect. 4, we examine the WACNA pattern and the Eurasian influence on the pattern in CanESM5 historical simulations and assess the uncertainty of the results due to internal climate variability. Finally, a summary is given in Sect. 5.

2 Data and methodology

2.1 ERA5 reanalysis and observational data

The observation-based analysis is mainly based on monthly atmospheric variables extracted from the fifth generation of atmospheric reanalysis (ERA5) (Hersbach et al. 2020) developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA5 reanalysis fields we employed include surface air temperature (SAT), as well as geopotential, temperature, and wind velocities in the troposphere. These variables are analyzed on 2.5° × 2.5° grids over the period from 1960 to 2010, consistent with the later period of historical climate simulations described below. Years refer to the January dates throughout this study. In addition, we use monthly sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5) dataset (Huang et al. 2017), with a resolution of 2.0° × 2.0° over 1960–2010. The monthly Niño3.4 index over the same period from the Climate Prediction Center (CPC, http://www.cpc.ncep.noaa.gov/data/indices) is used to characterize the tropical ENSO variability. For simplicity, we will refer to these reanalysis and observation-based data as “observations” in the remainder of this paper.

2.2 CanESM5 historical simulations

Outputs from historical climate simulations conducted with CanESM5 are employed. CanESM5 is a fully coupled ocean-atmosphere-land-sea ice climate model (Swart et al. 2019 and references therein) that was developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) and participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6). It has a horizontal T63 spectral resolution of approximately 2.8° in the atmosphere and roughly 1° in the ocean. A detailed description of the model can be found on the website CanESM5-The Canadian Earth System Model version 5-Open by Default Portal (canada.ca). We analyze its single model initial-condition large ensemble historical simulations, consisting of 50 ensemble members of 165-yr integrations over the period 1850–2014. Each simulation is forced by identical historical greenhouse gas concentration, sulfate aerosols, and other observation based radiative forcings over 1850–2014 (Eyring et al. 2016; Swart et al. 2019), with slightly different initial conditions for each run in 1850. Owing to the design, differences between individual SMILE realizations are solely attributed to internally generated climate variability. The modelled variables we considered are interpolated to 2.5° × 2.5° grids using bilinear interpolation. We examine 50 historical simulations over 1960–2010 and compare the results with those obtained from observations.

2.3 Data processing and analysis methods

To analyze monthly anomalies of variables, we first calculate their differences from the 50-year monthly mean climatology over 1961–2010 after removing the secular linear trend. Empirical orthogonal function (EOF) analyses are then employed to identify dominant modes of SAT anomalies over different domains of interest. Previous studies have shown that the leading mode (EOF1) of SAT anomalies over the NA sector features the WACNA pattern (e.g., Guan et al. 2020; Yu and Lin 2022), while the second mode (EOF2) of SAT anomalies over the Eurasian sector features the WACE pattern (e.g., Mori et al. 2019). These modes are well separated from other corresponding EOFs based on the criterion of North et al. (1982). We use the first principal component (PC1) over North America as an index of the WACNA pattern and the second principal component (PC2) over Eurasia as a WACE index. An EOF analysis is also performed to capture the WACNA pattern simulated by CanESM5, using the 50 large ensemble simulations as a collective dataset. In addition, another EOF analysis is conducted to characterize the inter-member variability of the WACNA patterns in the ensemble simulation.

To quantify relationships between an index and variables of interest, correlation and regression analyses are used. The statistical significance of a correlation is determined by a Student-t test, with the effective degree of freedom estimated by considering the autocorrelation of the time series (Bretherton et al. 1999). To avoid potential over-interpretation of multiple testing results for grid points over a domain of interest, we apply the false detection rate (FDR) approach demonstrated in Wilks (2016) as a field significance test. For most variables, we consider the Northern Hemisphere (0–90° N) domain, which consists of 5328 local tests at 2.5° × 2.5° grids (as shown in Figs. 1 and 2 below) with a significance level of 5% (α_FDR = 0.05). For SST, we examine the 30° S–60° N band, which covers both tropical and northern mid-latitude regions. We use 5670 ocean grids at 2.0° × 2.0° for ERSSTv5 and 3521 ocean grids at 2.5° × 2.5° for CanESM5 model simulations to perform local tests. In addition, the power spectrum of a time series is estimated using the Parzen estimator (e.g., von Storch and Zwiers 1999).

To explore the driving mechanism of the circulation anomalies in association with the WACNA variability, we examine the PC1 associated wave activity flux anomalies in the upper troposphere. The horizontal wave activity flux ($\text{W}\text{A}\text{F}$) is computed following Takaya and Nakamura (2001) as follows:

$$WAF = \frac{1}{{2\left| {U_{g} } \right|}}\left[ {\begin{array}{*{20}c} {\overline{{u_{g} }} \left( {\mathop {\psi ^{\prime}}\nolimits_{x}^{2} - \psi ^{\prime}\psi ^{\prime}_{{xx}} } \right) + \overline{{v_{g} }} \left( {\psi ^{\prime}_{x} \psi ^{\prime}_{y} - \psi ^{\prime}\psi ^{\prime}_{{xy}} } \right)} \\ {\overline{{u_{g} }} \left( {\psi ^{\prime}_{x} \psi ^{\prime}_{y} - \psi ^{\prime}\psi ^{\prime}_{{xy}} } \right) + \overline{{v_{g} }} \left( {\mathop {\psi ^{\prime}}\nolimits_{y}^{2} - \psi ^{\prime}\psi ^{\prime}_{{yy}} } \right)} \\ \end{array} } \right]$$

(1)

where $U_{g} = (u_{g} ,v_{g} )$ and $\psi$ are horizontal geostrophic winds and stream function, respectively, which are calculated from the geopotential field. The overline and prime denote the 50-DJF (December-January-February) climatological mean over 1961–2010 and its anomaly, respectively. The subscripts represent partial derivatives. WAF tends to be parallel to the local group velocity of stationary Rossby wave, which indicates sources and sinks of wave activity and reveals the atmospheric wave dispersion (Takaya and Nakamura 2001).

The PC1 associated temperature advection anomalies are examined to aid in understanding the formation of surface temperature anomalies. The anomalous horizontal temperature advection (F_adv) in the lower troposphere, which is dominated by wind variations, can be written as,

$${\text{F}}_{{{\text{adv}}}} = - V^{ * } \cdot \nabla \bar{T}$$

(2)

where $V^{ * } = \left( {u^{ * } ,v^{ * } } \right)$ is the PC1 associated horizontal wind velocity anomaly and $\stackrel{-}{T}$ is the 50-DJF climatological mean temperature.

3 WACNA in the ERA5 reanalysis

3.1 WACNA and Eurasian influence

WACNA is identified as the leading EOF mode of monthly SAT anomalies over the NA sector (20–90° N, 150° E–40° W) for the 50 DJFs over 1961–2010. EOF1 accounts for 32.2% of total variance and is dominated by a dipole structure over the NA sector (Fig. 1, top-right). Specifically, it features a large SAT anomaly over NA, centered over the Great Plains, and another anomaly of opposite sign spreading over the central-eastern Arctic and mid-high latitude North Pacific, centered over the Chukchi-Bering Seas (CBS). Similarly, an EOF analysis of DJF monthly SAT anomalies over the Eurasian sector (20–90° N, 0–150° E) is conducted. EOF1 over the Eurasian sector accounts for 34.5% of total variance and reveals a North Atlantic Oscillation (NAO, e.g., Hurrell et al. 2003) or Arctic Oscillation (AO) (Thompson and Wallace 1998) like pattern in the Northern Hemisphere (not shown), consistent with Mori et al. (2014). EOF2, which accounts for 15.3% of the total variance, is identified as the WACE pattern (Fig. 1, top-left). WACE also shows a dipole structure, one centered on the Barents-Kara Sea (BKS) and the opposite sign in southern Siberia. These patterns resemble relevant WACC patterns over the NA and Eurasian sectors, respectively, that were previously identified in observational and climate simulation studies on intraseasonal and interannual timescales (e.g., Kug et al. 2015; Lin 2015; Blackport et al. 2019; Guan et al. 2020; Yu and Lin 2022; Lin et al. 2022) and in long-term SAT trends (e.g., Kug et al. 2015; Sigmond and Fyfe 2016; Sun et al. 2016), indicating the robustness of the two patterns.

Prior to the occurrence of the WACNA pattern, there are significant SAT anomalies in the upstream over northern Eurasia (Fig. 1, bottom panels). In particular, the SAT anomalies at one month prior to WACNA, based on November-December-January (NDJ) months and denoted as (SAT(− 1), Fig. 1, bottom-right), exhibit a notable cooling over the western Arctic region spanning from BKS to northeastern Siberia, accompanied by a marked warming in the southern flank over southern Siberia. The SAT(− 1) pattern bears a resemblance to a negative WACE pattern (Fig. 1, top-left), with a pattern correlation of − 0.61 over (20–90° N, 0–150° E), although the SAT(− 1) anomalies over eastern Asia are stronger compared to WACE. The negative WACE-like pattern is absent two months before the WACNA pattern (SAT(− 2), Fig. 1, bottom-left), based on October-November-December (OND) months. Conversely, SAT(− 2) reveals a weak positive WACE-like structure, with a pattern correlation of 0.41. In general, a negative (positive) WACE-like pattern typically precedes a positive (negative) WACNA pattern by one month, consistent with the finding of Yu and Lin (2022).

Fig. 1

(top panels) Regressions of DJF SAT anomalies onto the WACE (left) and WACNA (right) indices. The green boxes indicate the regions of (20–90° N, 0–150° E) and (20–90° N, 150° E–40° W) used for the EOF analysis to define the WACE and WACNA patterns, respectively. Contour interval is 0.5 °C. (bottom panels) Lead regressions of SAT anomalies onto the WACNA index, with the temperature anomaly leads WACNA by 2 months (left, based on OND months) and 1 month (right, based on NDJ months). Contour interval is 0.2 °C. Black dots indicate the regression is statistically significant with p values small enough to satisfy the FDR criterion of ${\alpha }_{FDR}=0.05$. Results are based on the ERA5 reanalysis over 1961–2010

To explore the formation of the WACNA pattern, Fig. 2 displays the PC1 associated large-scale atmospheric circulation anomalies. The circulation anomalies are dominated by zonally oriented ABNA-like and tropical-extratropical related PNA-like atmospheric teleconnections throughout the troposphere. This is evident from the pronounced geopotential anomalies in the middle-upper troposphere (Fig. 2, left panels) over the centres of action of ABNA (i.e., North Asia, Bering Sea and Strait, and NA) and the action centers of PNA (i.e., the vicinity of Hawaii, south of the Aleutian Islands, the NA intermountain region, and the southeastern US). The wave activity flux for stationary Rossby waves at 250-hPa (Fig. 2, top-left, vectors) is also dominated by two wave trains over the northern extratropics. One follows the PNA pattern, with large WAF fluxes originating from the subtropical North Pacific and flowing downstream across the northeastern Pacific towards NA. Another follows the ABNA teleconnection, with weak WAF fluxes originating from eastern Asia and flowing downstream across Bering Sea and Strait towards NA. In general, negative (positive) ABNA-like and PNA-like patterns are associated with a positive (negative) WACNA pattern. The ABNA-like atmospheric teleconnection connects WACE and WACNA across continents.

Fig. 2

(top-left) Regression of Φ₂₅₀ anomalies (shading in m²s⁻²) onto PC1 and the corresponding wave activity fluxes poleward of 20° N (vectors in m²s⁻², flux values less than 0.5 m²s⁻² are omitted). The vector scale is shown at the lower middle. (top-right) Regression of U₂₀₀ anomalies (contour, interval 1.0 ms⁻¹) onto PC1 superimposed on the DJF climatological mean U₂₀₀ (shading in ms⁻¹). (bottom-left) Regression of Φ₅₀₀ anomalies (interval 60 m²s⁻²) onto PC1. (bottom-right) Anomalies of horizontal temperature advection (F_avd, shading in °C day⁻¹) and winds (arrows in ms⁻¹ with the scale shown at the lower middle, anomalies less than 0.1 ms⁻¹ in both directions are omitted) at 850-hPa regressed upon PC1. Black dots indicate the regression is statistically significant with p values small enough to satisfy the FDR criterion of ${\alpha }_{FDR}=0.05$. Results are based on the ERA5 reanalysis over 1961–2010

Consistent with the circulation anomaly, two meridional tripole structures in the 200-hPa zonal wind anomalies are observed around the dateline and NA (U₂₀₀, Fig. 2, top-right). Specifically, in association with a positive WACNA, U₂₀₀ reveals a strong easterly anomaly in the central North Pacific, located to the north of the exit of the strongest subtropical jet in the western Pacific. This easterly anomaly is surrounded by westerly anomalies on its south and north sides. Relatively weak U₂₀₀ anomalies are also seen over NA, with two centers around the NA subtropical jet and a third centered over the Canadian Arctic Archipelago. In the lower troposphere, the anomalous temperature advection follows the circulation anomaly (F_adv and V at 850-hPa, Fig. 2, bottom-right). This results in warming in the mid-high latitude North Pacific and central-eastern Arctic with an action center over CBS, as well as cooling in the NA northwest, which contributes to the WACNA pattern.

Fig. 3

Lead regressions of thickness Φ_500−1000 (top, interval 60 m²s⁻²) and SST (bottom, interval 0.1 °C) anomalies onto PC1, with the anomaly leads PC1 by one month. Black dots indicate the regression is statistically significant with p values small enough to satisfy the FDR criterion of ${\alpha }_{FDR}=0.05$

The WAF analysis above indicates that the primary source of wave activity associated with the WACNA pattern can be traced back to the tropical-subtropical North Pacific Ocean and Eurasia. Figure 3 displays the PC1 associated anomalies of geopotential thickness between 500-hPa and 1000-hPa (Φ_500–1000, top) and SST (bottom), with the anomalies leading WACNA by one month marked as Φ_500–1000(− 1) and SST (− 1), respectively. Φ_500-1000 represents the average temperature of the lower troposphere. Prior to the positive WACNA pattern, Φ_500–1000(− 1) reveals a significant positive (negative) anomaly, indicating anomalous heating (cooling) in southern (northern) Siberia. Previous studies have demonstrated that a negative ABNA-like pattern can be driven by diabatic heating perturbations in the lower troposphere in southern Siberia (Yu and Lin 2022; Zhong and Wu 2023). The Siberian heating is also related to a negative WACE pattern and its featured Eurasian warming, as well as snow decline in southern Siberia. Meanwhile, SST(− 1) exhibits a pronounced negative SST anomaly over the tropical central-eastern Pacific (Fig. 3). The PC1 associated SST anomalies exhibit their highest values in the tropical central-eastern Pacific, slightly surpassing those observed in SST (− 1), when PC1 lags by two months (not shown). Hence, the negative PNA-like pattern is influenced by negative SST and deep convection anomalies in the tropical Pacific resembling tropical ENSO variability, as demonstrated in previous studies (e.g., Trenberth et al. 1998).

3.2 ENSO influence on WACNA

Fig. 4

Normalized DJF monthly time series of the PC1 (red), ENSO-removed PC1 (PC1-rENSO, blue), and Nińo3.4 (grey bar) indices over the period from 1961 to 2010. The correlation coefficients (r) between the time series are given in the upper right corner

The correlation between the PC1 and Niño3.4 indices is low (− 0.2) over 1961–2010 and statistically insignificant at the 5% level, indicating that WACNA is not simultaneously related to tropical ENSO variability. To further investigate the potential influence of ENSO on both WACNA and the relationship between WACE and WACNA, we remove the ENSO contribution from the field of interest by using linear regression to isolate the non-ENSO related variability. We construct monthly residual SAT anomalies and conduct an EOF analysis of monthly SAT residuals over the NA sector. This EOF1 explains 31.6% of total variance and is still dominated by a dipole pattern over the NA sector (not shown), similar to the original EOF1. The pattern correlation between the two is high at 0.99 over the NA sector. The corresponding principal component, denoted as PC1-rENSO, is highly like PC1 (Fig. 4), with a correlation of 0.98 between the two indices. Additionally, PC1-rENSO associated temperature and circulation anomalies (not shown) closely resemble the corresponding anomalies associated with PC1. The correlation between anomaly patterns for both indices is above 0.96 across all three fields in the Northern Hemisphere. This confirms that the WACNA pattern and the linkage between WACE and WACNA are not significantly influenced by ENSO.

4 WACNA in CanESM5 SMILE simulations

To examine the simulation of the WACNA pattern and its connection with WACE in CanESM5, we perform an EOF analysis on monthly SAT anomalies over the NA sector during DJF from 1961 to 2010, using data from all 50 historical simulations. We analyze the temperature and circulation anomalies associated with the corresponding PC1, and then explore the diversity of results by analyzing the inter-member variability among the 50 ensemble members. By examining the inter-member variability, we can assess the uncertainty of outcomes due to internal variability.

4.1 WACNA in historical simulations

Fig. 5

Regressions of SAT anomalies onto the simulated WACNA index, with the temperature anomaly leads WACNA by 0 (top, interval 0.5 °C) and 1 (bottom, interval 0.1 °C) month. The green box indicates the region (20–90° N, 150° E–40° W) for the EOF analysis. Black dots indicate the regression is statistically significant with p values small enough to satisfy the FDR criterion of ${\alpha }_{FDR}=0.05$. Results are based on CanESM5 historical simulations over 1961–2010, using the 50 large ensemble simulations as a collective dataset

The simulated EOF1, which explains 29.5% of the total SAT variances over the NA sector, exhibits the WACNA pattern simulated by CanESM5 (Fig. 5, top). This EOF1 closely resembles the EOF1 in ERA5 (Fig. 1, top-right), with a pattern correlation of 0.89 over the NA sector, indicating that CanESM5 well captures the observed WACNA pattern. However, there are slight differences in the intensity and location of the centers of action of WACNA between the observation and simulations. The action centers are slightly stronger in CanESM5 than in ERA5 and are located slightly eastward in CanESM5. Furthermore, CanESM5 also reproduces a negative WACE-like pattern upstream over northern Eurasia one month before a positive WACNA (Fig. 5, bottom), which is consistent with the ERA5 result. The correlation between the SAT(− 1) patterns in ERA5 and CanESM5 is 0.84 over the region of (20–90° N, 0–150° E). However, the SAT(− 1) anomaly is generally weaker and the pattern is smoother in CanESM5 than in ERA5, partially due to the large ensemble data used in the CanESM5 calculation. Overall, CanESM5 and ERA5 show good agreement in terms of the WACNA pattern and its connection with WACE.

The circulation anomalies associated with WACNA in CanESM5 (Fig. 6) are generally similar to those in ERA5 (Fig. 2), with differences mainly in the anomalous magnitude. In particular, the circulation anomalies in the mid-upper troposphere are dominated by two wave trains over the Northern Hemisphere: the PNA-like and ABNA-like teleconnections (Fig. 6, left panels). Despite slight differences in the Φ₅₀₀ anomalies, with CanESM5 exhibiting slightly lower values over northern Eurasia and the subtropical North Pacific and higher values in the northern North Pacific and the southeastern US, the correlation between the Φ₅₀₀ patterns in CanESM5 and ERA5 is high at 0.90 over the Northern Hemisphere. Similarly, the two meridional tripole structures in the U₂₀₀ anomaly in CanESM5 (Fig. 6, top-right) correspond well to ERA5, although the simulated anomalies are slightly weaker over the North Pacific and stronger over the southern US. In addition, the anomalous circulation and temperature advection at 850-hPa are also similar in CanESM5 and ERA5, as depicted in the bottom-right panels of Figs. 2 and 6, with slight differences in the anomalous magnitude. Overall, although there are some differences in the anomalies of ERA5 and CanESM5, the similarity of anomalous circulation and temperature advection patterns is generally high.

Fig. 6

As in Fig. 2, but for the variables regressed on the PC1 from the 50 CanESM5 historical simulations over 1961–2010

Figure 7 displays the anomalies of Φ_500−1000(− 1) and SST(− 1) associated with the PC1. Preceding the positive WACNA pattern, Φ_500−1000(− 1) shows a significant positive (negative) anomaly in southern (northern) Siberia, consistent with the ERA5 result over Eurasia. The simulated SST(− 1) pattern also bears resemblance to the observed pattern, with a negative SST anomaly in the tropical central-eastern Pacific. However, the Φ_500−1000(− 1) anomalies are weaker in CanESM5 than in ERA5, as indicated by the different contour intervals used in Figs. 3 and 7. This weakness is similar to the weak SAT(− 1) anomalies described above. Meanwhile, the SST(− 1) anomalies are much weaker in CanESM5 than in ERA5. The small SST(− 1) anomalies seen here may be associated with the weak ENSO variability observed in CanESM5 (Swart et al. 2019). The relatively weak anomalies can also be attributed in part to the application of large ensemble data in the CanESM5 calculation, as previously discussed. In addition, the similarity of the WACNA and anomalous circulation patterns in ERA5 and CanESM5, along with the weak ENSO variability in CanESM5, strengthens the argument that the WACNA pattern and its connection to WACE are not significantly impacted by ENSO.

Fig. 7

As in Fig. 3, but for the variables regressed on the PC1 from the 50 CanESM5 historical simulations, with contour intervals of 20 m²s⁻² for Φ_500−1000(− 1) and 0.02 °C for SST(− 1)

The variability of WACNA in ERA5 and CanESM5 historical simulations has been further compared. The PC1 time series from ERA5 exhibits interannual variances dominated by frequencies ranging from 3 to 7 years, with one power peak of 3.6 years that is statistically significant at the 5% level and another at 6.2 years that is insignificant at the 5% level (Fig. 8, red curves). To calculate the spectra of the CanESM5 simulations, the corresponding PC1 series from the 50 ensemble members are used. The ensemble mean spectrum shows reasonable reproducibility of the ERA5 result, with variances also weighing toward frequencies from 3 to 7 years. However, CanESM5 reveals two power peaks of 3.1 and 5.6 years that are not significant at the 5% level (Fig. 8, blue curves). Additionally, the CanESM5 simulations display a considerable spread of spectra across the ensemble members, as indicated by the spectrum between the ensemble mean plus and minus one inter-member standard deviation of the 50 spectra (Fig. 8, grey shading). Nevertheless, the inter-member spread covers most of the observed variability, especially the 6.2-year power peak in ERA5 but not the 3.6-year peak in ERA5.

Fig. 8

Power spectra of the PC1 time series for ERA5 (red) and the ensemble mean of the CanESM5 simulations (blue). The dot curve is the red-noise spectrum calculated from the lag 1 autocorrelation. The dash curve is the power spectrum of 95% confidence level. Ensemble spread, indicated by the spectrum between the CanESM5 ensemble mean plus and minus one inter-member standard deviation of the 50 spectra, is shown by grey bars

4.2 Uncertainty due to internal climate variability

In addition to the power spectrum as discussed above, the impact of internal climate variability on WACNA can be assessed by examining the inter-member standard deviation of the WACNA patterns across the ensemble members. The individual WACNA patterns are obtained by regressing SAT anomalies onto the corresponding PC1 series in the 50 members. The pattern variation is particularly pronounced in northern regions (Fig. 9, top), including norther Europe and northern Russia, extending from the Bering Strait across Alaska to northwestern Canada, eastern Canada and the northern North Atlantic.

Fig. 9

(top) Inter-member standard deviation (STD, interval 0.1 °C) of regressions of SAT anomalies onto the corresponding PC1 series from the 50 ensemble members. (middle-bottom) Regressions of individual WACNA associated SAT patterns in the 50 members (contour, interval 0.1 °C) onto the leading principal component (PC1₅₀) and second principal component (PC2₅₀) of the 50 SAT patterns. The green box indicates the region for the EOF analysis. Color shading (in °C) indicates the ensemble mean of the 50 SAT patterns. Black dots indicate the regression is statistically significant with p values small enough to satisfy the FDR criterion of ${\alpha }_{FDR}=0.05$.

The variability of the 50 WACNA patterns is dominated by two leading modes, which collectively account for 27.1% and 18.9% of the total variance, as revealed by an EOF analysis over the NA sector. The middle and bottom panels of Fig. 9 display the regressions of anomalous WACNA patterns onto the principal components, PC1₅₀ and PC2₅₀, corresponding to the two leading modes. In association with the positive phase of the PC1₅₀ index, the SAT anomaly exhibits a positive signal over northwestern NA, along with a negative signal over the southeastern US (contours in Fig. 9, middle). This suggests that the WACNA pattern shifts southeastward. On the other hand, the negative phase of the PC1₅₀ index corresponds to a northwestward movement of WACNA. By contrast, during the positive phase of the PC2₅₀ index, the SAT anomaly displays positive values over northern Eurasia, around the Bering Strait, and the southeastern US, while negative values are observed over western NA, eastern Canada and the northern North Atlantic (contours in Fig. 9, bottom). This indicates local variations of the amplitude of the WACNA pattern.

Fig. 10

Regressions of individual SAT(− 1) (contour, interval 0.03 °C, top-left), Φ_500−1000 (− 1) (interval 4 m²s⁻², bottom-left), U₂₀₀ (0) (interval 0.1 ms⁻¹, top-right), and Φ₅₀₀ (0) (interval 10 m²s⁻², bottom-right) patterns onto PC1₅₀, with the SAT(− 1) and Φ_500−1000 (− 1) anomalies lead PC1₅₀ by one month. Color shading indicates the corresponding ensemble means of SAT(− 1) in °C, Φ_500−1000 (− 1) in m²s⁻², U₂₀₀ (0) in ms⁻¹ and Φ₅₀₀ (0) in m²s⁻², respectively. Black dots indicate the regression is statistically significant with p values small enough to satisfy the FDR criterion of ${\alpha }_{FDR}=0.05$.

Figure 10 further displays the regressions of the anomalous patterns of WACNA associated SAT(− 1), Φ_500−1000 (− 1), U₂₀₀ (0), and Φ₅₀₀ (0) onto PC1₅₀. The anomalies of the Φ₅₀₀ (0) pattern show a dominant action center around (60° N, 130° W), while the anomalies of the U₂₀₀ (0) pattern exhibit a dominant tripole meridional structure along 80–120° W (Fig. 10, right panels). These suggest that the WACNA pattern propagates eastward by 40–60°. Moreover, the PC1₅₀ associated anomalies of the SAT(− 1) and Φ_500−1000 (− 1) patterns reveal significant features over Eurasia, particularly a slight eastward propagation of warming over southern Siberia (cf. contours with shading in Fig. 10, left panels). In contrast, the anomalies of the Φ₅₀₀ (0) and U₂₀₀ (0) patterns associated with the PC2₅₀ index are mainly characterized by local changes, especially the action centers along the dateline corresponding to the WACNA pattern (Fig. 11, right panels). The local variations are also evident in the anomalies of the SAT(− 1) and Φ_500−1000 (− 1) patterns, as shown in the left panels of Fig. 11.

Fig. 11

As in Fig. 10, but for the variables regressed onto PC2₅₀

Overall, the uncertainty in WACNA patterns due to internal climate variability is dominated by two modes of inter-member variability: a southeast-northwest phase shift of WACNA and a local variation of WACNA.

5 Summary

This study analyzes the WACNA pattern and WACE-WACNA linkage over the period from 1960 to 2010, using ERA5 reanalysis and CanESM5 large ensemble historical simulations. One main objective is to determine whether WACNA and the WACE-WACNA connection are affected by tropical ENSO variability. The other is to assess simulations of WACNA and the WACE-WACNA relationship in CanESM5, as well as the impact of internal climate variability on these simulations.

Based on the ERA5 reanalysis, a negative WACE-like pattern typically precedes a positive WACNA pattern by one month, with an atmospheric teleconnection resembling the ABNA pattern linking Eurasia and North America. Upstream, negative PNA-like and ABNA-like circulation patterns are observed in association with a positive WACNA pattern. Consistent with the circulation anomaly, two meridional tripole structures in the 200-hPa zonal wind anomalies appear around the dateline and NA, which correspond to changes in subtropical jets in the western Pacific and NA. In the lower troposphere, the anomalous temperature advection follows the circulation anomaly and supports the WACNA pattern. The negative ABNA-like pattern may be attributed to heating anomalies in the lower troposphere in southern Siberia, which is associated with a negative WACE pattern and its featured Eurasian warming. The negative PNA-like pattern is influenced by negative SST and deep convection anomalies in the tropical Pacific, resembling tropical ENSO variability. Conversely, processes with circulation anomalies of opposite sign could result in a negative WACNA pattern. These observational results are consistent with those in Yu and Lin (2022) for a different period over 1980–2019, indicating the robustness of the WACNA pattern and WACE-WACNA connection. In addition, the main results can be reproduced using non-ENSO related variables, suggesting that the WACNA pattern and the link between WACE and WACNA are not significantly influenced by tropical ENSO variability.

CanESM5 reasonably well simulates the WACNA pattern, although there are slight differences in the intensity and location of the centers of action of WACNA between ERA5 and CanESM5 results. The model also reproduces the WACE-WACNA connection and anomalous circulation and temperature advection patterns, with some differences mainly in the anomalous magnitude. Consistent with ERA5, the model simulations show that heating in the lower troposphere in southern Siberia and negative SST in the tropical central-eastern Pacific precede the positive WACNA pattern. However, the Φ_500−1000(− 1) and SST(− 1) anomalies are weaker in CanESM5 than in ERA5. This is partly due to the application of large ensemble data in CanESM5, which may dampen the signal. Nevertheless, the similarity of WACNA and its associated circulation patterns in ERA5 and CanESM5, along with the weak ENSO variability in CanESM5, strengthens the argument that the WACNA pattern and its connection to WACE are not significantly influenced by ENSO.

The WACNA patterns in both ERA5 and CanESM5 simulations reveal interannual variances with dominant frequencies ranging from 3 to 7 years. ERA5 exhibits a significant power peak at 3.6 years and another insignificant peak at 6.2 years. The ensemble mean spectrum from the CanESM5 simulations shows two power peaks at 3.1 and 5.6 years, although they are not significant at the 5% level. CanESM5 also displays a considerable spread of spectra across the 50 ensemble members. The inter-member spread covers most of the observed variability, especially the 6.2-year power peak in ERA5. The impact of internal climate variability on WACNA is also assessed by examining the inter-member variance in WACNA patterns across the ensemble members. The uncertainty in the WACNA pattern is dominated by two modes of inter-member variability: a southeast-northwest phase shift and a local variation of its amplitude.

The WACNA pattern and WACE-WACNA linkage contribute to our understanding of Arctic atmospheric circulation patterns and their teleconnections with other regions. The findings also have important implications for our understanding of the impact of global warming, especially Arctic amplification and its connections with mid-latitudes. The identified lag relationship between the WACNA and WACE patterns holds potential for enhancing seasonal forecasting in North America. Additionally, addressing the sources of uncertainties in the simulated WACC pattern, including model parameterizations and model structure uncertainty, would improve our understanding of the underlying mechanisms driving the WACC pattern and enhance our confidence in the projected changes under future climate scenarios.