1. Introduction
Climatological-mean jets in the troposphere can act as waveguides to facilitate the zonal propagation of Rossby waves (Branstator 1983; Manola et al. 2013; Chowdary et al. 2019; White et al. 2022). This effect arises because Rossby wave parameters, such as group velocity or wavenumber, depend on the dynamical and thermal structure of the mean flow (Matsuno 1970; Hoskins et al. 1977; Hoskins and Karoly 1981; Karoly and Hoskins 1982; Hoskins and Ambrizzi 1993; Li et al. 2015). The jet waveguide can trap low-frequency Rossby waves, produce zonally oriented chains of perturbations with a lifetime ranging from days to months, and create waveguide teleconnections (Hsu and Lin 1992; Branstator 2002; Branstator and Teng 2017; Teng and Branstator 2019). Since atmospheric waveguides constrain perturbations’ meridional dispersion, waveguide teleconnections can usually extend into far regions in the zonal direction and possibly become circumglobal, causing stagnant surface weather anomalies over broad regions (Screen and Simmonds 2014; Xu et al. 2021b).
(a) The meridional gradient of the climatological-mean summer 250-hPa potential vorticity (PV250). The isobaric potential vorticity is defined after Bluestein (1992). (b) The temporal standard deviation of the summer 250-hPa meridional wind (V250). The value in each grid point in (a) and (b) has been standardized by the area-weighted mean from 0° to 90°N at its longitude for visual convenience [see Eq. (1) for mathematical definition]. The blue box in (b) represents the region 20°W–150°E, 50°–80°N, where the PFJ waveguide teleconnections are defined. The results are based on the JRA-55 dataset and the period from 1981 to 2010.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
The waveguide teleconnections along the STJ have been investigated comprehensively, such as the circumglobal teleconnection (CGT; Branstator 2002; Ding and Wang 2005; Wei et al. 2015; Hu et al. 2017; Lin et al. 2017; Teng et al. 2019; Zhou et al. 2019, 2020; Beverley et al. 2021; Riboldi et al. 2022), the Silk Road pattern (SRP; Lu et al. 2002; Wu 2002, 2017; Enomoto et al. 2003; Kosaka et al. 2009; Chen and Huang 2012; Chen et al. 2013; Hong and Lu 2016; Wang et al. 2017; Du et al. 2020; Wang et al. 2021; Xu et al. 2022), and other new defined patterns over North Pacific (Du and Lu 2021; Wang and Tan 2021; Zhuge and Tan 2021). In contrast, although their climate influences have been noticed in many previous studies (e.g., Nakamura and Fukamachi 2004; Lin 2014; Sun et al. 2015; Park et al. 2021), the waveguide teleconnections along the PFJ themselves have received much less attention. Since the Rossby wave activities trapped along the PFJ are even stronger than that along the STJ (Fig. 1b), the waveguide teleconnections along the PFJ essentially contribute to circulation and climate variability over Eurasia. As such, a thorough understanding of the PFJ waveguide teleconnections is warranted.
Recently, the dominant summer waveguide teleconnection along the PFJ has been investigated and named the British–Baikal Corridor (BBC) pattern (XWC). It consists of four zonally oriented action centers over the west of the British Isles, the Baltic Sea, western Siberia, and Lake Baikal, respectively, and can influence the atmospheric circulation and surface climate over Eurasia significantly (Wang et al. 2019; Li et al. 2021; Jin et al. 2022; Hong et al. 2022). It is also responsible for some recent temperature extremes over Eurasia, such as the European heatwave in June 2019 (Xu et al. 2020a, 2021a) and the Siberian heatwave in June 2020 (Xu et al. 2021b). Nevertheless, XWC only investigated one example of the waveguide teleconnections along the PFJ. It remains unclear whether there are other PFJ waveguide teleconnections and, if any, what unique characteristics they have. Answering these questions is essential to understanding the PFJ waveguide teleconnections and summer circulation variability over Eurasia.
The organization of the remaining text is as follows. Section 2 describes the datasets and methods used in this study. Section 3 investigates the new waveguide teleconnection along the PFJ and identifies its interannual linkage to the SRP. Section 4 examines the stationarity of the linkage between this new teleconnection and the SRP and its implication for surface climate variability. Section 5 investigates the underlying mechanism driving the linkage based on dynamical diagnostics and simple model experiments. Section 6 discusses further issues about the linkage and some other open issues. Finally, section 7 summarizes the main findings.
2. Data and methods
Monthly mean atmospheric reanalysis data used in this study are from several sources, including the ERA-Interim dataset (Dee et al. 2011), the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis dataset (Kalnay et al. 1996), the Japanese 55-year Reanalysis (JRA-55) dataset (Kobayashi et al. 2015), the European Centre for Medium-Range Weather Forecasts (ECMWF) twentieth-century reanalysis (ERA-20C) dataset (Poli et al. 2016), and the Twentieth Century Reanalysis dataset, version 2c (20CRV2c; Compo et al. 2011). Monthly mean precipitation and surface air temperature data are from the Climatic Research Unit (CRU) high-resolution gridded datasets, version 4.04 (CRU TSv4.04) (Harris et al. 2020). The oceanic data are from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset (Rayner et al. 2003). In addition, a linear baroclinic model (LBM) based on a dry atmospheric general circulation (Watanabe and Kimoto 2000) is used in this study. The horizontal resolution is T42 with 20 σ layers in the vertical. The model includes biharmonic horizontal diffusion with an e-folding time of 6 h for the shortest wave. Details of the experiment design are introduced in section 4.
In XWC, the BBC pattern is defined as the first empirical orthogonal function (EOF1) mode of the summer mean V250 over northern Eurasia (50°–80°N, 20°W–150°E, blue box in Fig. 1b). In this study, the same domain and variable are used in EOF analysis, but higher EOF modes are considered. To keep consistency and make easy comparisons among datasets with different temporal coverage, the waveguide teleconnection pattern is defined by applying EOF analysis to the overlapping period among different datasets (i.e., 1981–2010). The teleconnection index in an extended period is obtained by projecting the teleconnection pattern onto the V250 anomalies of the whole period in each dataset. This approach has been used in Wang et al. (2017) and Xu et al. (2020b) for the SRP and the BBC pattern. This study focuses on the boreal summer mean, defined as the mean of June, July, and August. Regression and composite analyses are used in this study. The two-tailed Student’s t test is used to evaluate their statistical significance.
3. The British–Okhotsk Corridor pattern along the PFJ
Figure 2 presents the first two EOFs of V250 along the PFJ during the 1981–2010 summers. The two EOFs are well separated from the remaining modes according to the criteria of North et al. (1982). EOF1 is the BBC pattern, which has been previously documented in XWC and Xu et al. (2020b). EOF2 also features zonally oriented centers of action with alternating signs, implying its trapped nature by the PFJ waveguide. It explains about 20.8% of the total variance, which is only slightly lower than that of the BBC pattern. The first two EOFs together explain nearly 50% of the total variance, indicating their crucial role in the summer circulation variability over northern Eurasia.
The (a) first and (b) second EOF of the summer mean V250 over the region 50°–80°N, 20°W–150°E (blue box in Fig. 1b). The variance explained by each EOF is indicated in the upper right corner. The results are based on the JRA-55 dataset and the period 1981–2010.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
Figure 3a presents the regressed summer mean geopotential height at 250-hPa (Z250) onto the second principal component (PC2) and associated horizontal component of the wave activity flux derived from 1981 to 2010. In contrast to EOF1 (i.e., the BBC pattern), which has only one branch of wavelike pattern along the PFJ, EOF2 has two branches of wavelike patterns concurrently along the PFJ and STJ. The north branch has four action centers over the British Isles, western Russia, northern Siberia, and Okhotsk, respectively, roughly in quadrature with the BBC pattern. Considering the shape and location of the north branch of the wavelike pattern, we name this pattern the British–Okhotsk Corridor (BOC) pattern for convenience. The south branch of the EOF2 has three action centers over eastern Europe, the Caspian Sea, and Mongolia, respectively, projecting well onto the waveguide teleconnection along the STJ (i.e., the SRP) (e.g., Wang et al. 2017; Fig. 3b). Here, the SRP is defined as the EOF1 of V250 over the STJ (30°–130°E, 20°–50°N). The temporal correlation coefficient between the SRP index and BOC index is −0.49 during 1981–2010, exceeding the 99% confidence level. The relatively high temporal correlation and spatial similarity suggest that the BOC pattern is possibly linked to the SRP on the interannual time scale, although by definition the BOC pattern is the second leading waveguide teleconnection along the PFJ. Iwao and Takahashi (2008) documented a covaried relation between the Rossby wave activities along the PFJ and the STJ on the intraseasonal time scale. Their circulation anomalies (see their Fig. 7c) are very similar to the BOC–SRP linkage shown in Fig. 3a. These results suggest that the BOC–SRP linkage exists on the interannual time scale in addition to the intraseasonal time scale discussed in Iwao and Takahashi (2008).
(a) Summer mean 250-hPa geopotential height anomalies (Z250) [black contour; contour interval (CI) = 10 gpm] and the horizontal component of the wave activity flux (arrow; m2 s−2) associated with the EOF2 shown in Fig. 2b, obtained via a linear regression onto the corresponding PC time series (i.e., the BOC index). The overlaid 18 m s−1 purple contours indicate the climatology of the 250-hPa summer mean zonal wind (U250). (b) As in (a), but for the Silk Road pattern (SRP), which is defined as the EOF1 of the V250 over 30°–130°E, 20°–50°N. The SRP index has been multiplied by −1 for visual convenience. The results are based on the JRA-55 dataset and the period 1981–2010.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
As the BOC and BBC patterns are in quadrature with each other, one may wonder whether the two patterns represent not two independent modes but one traveling mode. To examine which possibility is more likely, we tackle this issue from the perspective of their climate influences. The assumption is that their transient nature will not be able to produce any long-lasting influence on the circulation or surface weather if they reflect one traveling wave. Figure 4 presents the summer mean V250 anomalies in 2002 and 2010 when the BBC pattern and BOC pattern are particularly amplified, respectively. In the 2002 summer, the normalized index is −2.39 for the BBC pattern, and it is only 0.13 for the BOC pattern. In the 2010 summer, the normalized index is 2.47 for the BOC pattern, and it is only −0.43 for the BBC pattern. The anomalous activities of the BBC pattern and BOC pattern have apparent footprints in the summer mean climate near the surface, such as the surface air temperature (Figs. 4b,d). This result indicates that the BOC and BBC patterns exert distinct spatial influences on climate and show no traveling features. The result is also supported by several previous studies (e.g., Xu et al. 2020a, 2021a,b). For example, Xu et al. (2020a, 2021b) showed that the BBC pattern was active for nearly the entire June 2019. The persistent stationary BBC pattern exerted a long-lasting geographically fixed influence on the surface and led to the record-breaking European heatwave in June 2019. Therefore, the BBC and BOC patterns, whose influences can appear in the monthly and even summer mean maps, do not reflect one traveling pattern. In contrast, they are two independent stationary patterns.
The summer mean 250-hPa meridional wind [V250; shading; shading interval (SI) = 2 m s−1] anomalies in (a) 2002 and (c) 2010, respectively. (b),(d) As in (a) and (c), but for the surface air temperature (shading; °C). The normalized index of the BBC pattern and BOC pattern in the two summers are given in the left upper corner.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
To further confirm the above argument, the temporal characteristics of the BBC and BOC indices are also examined. Figure 5 presents the day-to-day evolution of the BBC index and BOC index based on the composited life cycles of the two patterns. No matter which pattern is considered, its index shows a large amplitude during the peak of the pattern, but the index of the other pattern shows a much smaller amplitude in the meantime. The result holds for both patterns and their positive and negative phases (Fig. 5). An inspection of the daily evolution of the composited Z250 reveals that their action centers remain quasi-stationary during the whole life cycle for the BBC pattern (e.g., Fig. 4 in Xu et al. 2020b) and the BOC pattern (not shown). Their spatial structures primarily develop and decay in situ and do not translate into the other pattern. This result further demonstrates the independence and stationarity of the BBC pattern and BOC pattern.
Time evolution of the composited normalized BBC pattern index and BOC pattern in the (top) positive phase and (bottom) negative phase of the life cycle of the (a),(b) BBC pattern events and (c),(d) BOC pattern events.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
4. The time-varying BOC–SRP linkage in reanalysis datasets
Section 3 reveals a linkage between the BOC pattern and the SRP. A natural question is why the BOC pattern, the second leading mode along the PFJ, projects onto the SRP, the first leading mode along the STJ. There are two possible answers to this question. The first is that the BOC pattern and SRP are two indispensable parts of one unified variability mode. The second is that the BOC pattern and SRP are two independent modes, but they are coupled for specific reasons during particular periods. Similar to the approaches in Gong et al. (2018), the two possibilities are examined from the perspective of the decadal changes in the BOC–SRP linkage based on multiple reanalysis datasets.
a. The time-varying BOC–SRP linkage
Examining the possible decadal changes in the BOC–SRP relationship requires reanalysis data with longer temporal coverage. The variability of the SRP has been proved highly consistent among the ERA-Interim, JRA-55, NCEP–NCAR, ERA-20C, and 20CRV2c datasets (Wang et al. 2017), so a similar validation is applied to the BOC pattern. Table 1 shows the correlation coefficients of the BOC index among different reanalysis datasets during their overlapping periods. The BOC indices are almost identical in the ERA-Interim, JRA-55, and NCEP–NCAR datasets, with all the correlation coefficients exceeding 0.99 for the 1979–2010 and 1958–2010 periods. The BOC variability in the ERA-20C dataset is highly consistent with those in the ERA-Interim, JRA-55, and NCEP–NCAR datasets, with correlation coefficients on the order of 0.9 or higher during their overlapping periods. In contrast, the BOC variability in the 20CRV2c dataset is less consistent with those in the other datasets. This result is the same as the representation of the SRP in different datasets (Wang et al. 2017). Hence, the results based on the ERA-Interim, JRA-55, NCEP–NCAR, and ERA-20C datasets are more reliable than those based on the 20CRV2c dataset in the following analyses.
Correlation coefficients of the BOC indices among the different datasets for 1979–2010, 1958–2010, 1948–2010, and 1920–2010. All correlation coefficients exceed the 99.9% confidence level based on the two-tailed Student’s t test.
Figure 6 shows the running correlation coefficients between the BOC pattern index and SRP index with a 13-yr window in five datasets. An interesting feature is that the BOC–SRP linkage shows distinct interdecadal fluctuations. The BOC–SRP linkage was strong and significant during 1970–2000, but it was weak and insignificant during 1920–40, 1955–70, and from 2000 onward. The fluctuation is highly consistent among the five datasets after 1920. Four transition years are consistently detected around 1940, 1950, 1970, and 2000 based on Student’s t test. One transition year is detected in the 1920s in the 20CRV2c dataset alone, indicating the uncertainty of its existence. In the following, the period 1900–2010 in ERA-20C is divided into the high-correlation periods (HIGH-epoch) and low-correlation periods (LOW-epoch) according to Fig. 6d. The HIGH- and LOW-epoch are defined if the absolute value of the 13-yr running correlation coefficient exceeds and falls below 0.5, respectively. As such, the HIGH-epoch includes 35 years (1939–40, 1948–49, 1968–90, and 1993–2000), and the LOW-epoch includes 60 years (1907–38, 1941–47, 1950–64, 1966–67, 1991–92, and 2001–03). Other slightly modified thresholds were also tested, and the subsequent results and conclusions are insensitive to the choice of thresholds.
The moving correlation coefficients (blue lines) between the BOC index and SRP index with a 13-yr window in the (a) ERA-Interim, (b) JRA-55, (c) NCEP–NCAR, (d) ERA-20C, and (e) 20CRV2c datasets. Dashed red lines indicate the 90% confidence level based on a two-tailed Student’s t test. The SRP index has been multiplied by −1 for visual convenience.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
Figures 7a and 7b present the regressed Z250 upon the BOC pattern index during the HIGH- and LOW-epoch, respectively. To calculate the regression in each epoch, both the summer mean Z250 and the BOC pattern index are arranged chronically within the epoch, so their orders are consistent with each other. During the HIGH-epoch, a bifurcated wavelike pattern is observed along the PFJ and STJ (Fig. 7a), resembling Fig. 3a. The correlation coefficient between the BOC and SRP indices is −0.65, exceeding the 99% confidence level and confirming the linkage between the BOC pattern and SRP. In contrast to the bifurcated wavelike pattern during the HIGH-epoch, only one wave train is observed along the PFJ during the LOW-epoch (Fig. 7b). The correlation coefficient between the BOC index and SRP index is −0.14, suggesting the independence of the BOC pattern and SRP during the LOW-epoch. These results imply that the BOC pattern is an independent teleconnection embedded in the PFJ waveguide and that the projection of the BOC pattern onto the SRP during the HIGH-epoch may arise from the coupling between the two patterns under certain conditions.
Summer mean Z250 anomalies (black contour; CI = 5 gpm) and the horizontal component of the wave activity flux (arrow; m2 s−2) associated with the BOC index during the (a) the HIGH-epoch and (b) LOW-epoch, obtained via a linear regression overlaid by the climatology of the U250, as indicated by the 18 m s−1 purple contour. (c),(d) As in (a) and (b), but for the precipitation (shading; SI = 2 mm month−1). (e),(f) As in (a) and (b), but for the surface air temperature (shading; SI = 0.2°C). The light and dark shading in (a) and (b) and the gray and black dots in (c)–(f) indicate the 90% and 95% confidence levels based on the two-tailed Student’s t test, respectively. The results are based on the ERA-20C dataset.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
b. Influences on climate
Figures 7c–f show the surface temperature and precipitation anomalies associated with the BOC pattern during the two epochs. Corresponding to the wavelike patterns in Figs. 7a and 7b, Europe and northern Siberia feature large-scale wet and cold anomalies, and western Russia and northeastern Siberia feature large-scale dry and warm anomalies. The precipitation anomalies are located to the west of the surface temperature anomalies slightly, consistent with the constraint of the omega equation under the quasigeostrophic framework. The spatial pattern of precipitation and surface temperature anomalies over northern Eurasia are almost identical in the HIGH- and LOW-epochs, indicating the robustness of the BOC pattern itself. However, the precipitation and surface temperature anomalies associated with the BOC pattern during the HIGH-epoch can extend into much lower latitudes and cover broader domains than those during the LOW-epoch because of its coupling to the SRP. Significant precipitation and surface temperature anomalies can be observed over the Caspian Sea and Mongolia during the HIGH-epoch, in contrast to during the LOW-epoch.
The BOC pattern and SRP are two major teleconnections over Eurasia, so the establishment or collapse of the BOC–SRP linkage may affect the dominant modes of climate variability over Eurasia. Iwao and Takahashi (2006) identified a precipitation dipole between Northeast Asia and Siberia in boreal summer on the interannual time scale. The period in their analysis (i.e., 1979–2004) belongs to the HIGH-epoch (Fig. 7), and we infer that the precipitation dipole is sensitive to the interdecadal fluctuations of the BOC–SRP linkage. To test this inference, EOF analysis was applied to the summer precipitation over the same domain (70°–140°E, 30°–70°N; blue box in Fig. 8a) as that in Iwao and Takahashi (2006) for the HIGH- and LOW-epoch, respectively. In the HIGH-epoch (Fig. 8a), the EOF1 is a precipitation dipole whose loadings are over Northeast Asia and Siberia, consistent with Iwao and Takahashi (2006). In contrast, it shows a monopole pattern across the domain in the LOW-epoch (Fig. 8b). These results suggest that the time-varying BOC–SRP linkage strongly affects the dominant precipitation mode over eastern Eurasia.
(a) Summer mean precipitation anomalies (shading; SI = 2 mm month−1) associated with the EOF1 of summer mean precipitation over 70°–140°E, 30°–70°N (blue box in Fig. 8a) during the HIGH-epoch, obtained via linear regression. (b) As in (a), but during the LOW-epoch. (c) Summer mean surface air temperature anomalies (shading; SI = 0.2°C) associated with the EOF1 of summer mean surface air temperature over 10°–130°E, 30°–70°N (blue box in Fig. 8c) during the HIGH-epoch, obtained via linear regression. (d) As in (c), but during LOW-epoch. The gray and black dots indicate the 90% and 95% confidence levels based on the two-tailed Student’s t test, respectively. The results are based on the CRU TS v4.04 dataset.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
In addition to precipitation, the time-varying BOC–SRP linkage also influences the dominant temperature pattern. Deng et al. (2018) identified a wavelike pattern of extreme heat days in summer across Eurasia, with action centers over eastern Europe and northern China. The circulation anomalies associated with the pattern are very similar to those related to the BOC pattern in the HIGH-epoch. To test the interference of the BOC–SRP linkage on this pattern, Figs. 8c and 8d present EOF1 of the summer surface air temperature over Eurasia. The domain 10°–130°E, 30°–70°N (blue box in Fig. 8c) is used in the EOF analysis, consistent with that in Deng et al. (2018). In the HIGH-epoch, EOF1 shows a primary loading center over eastern Europe and a weaker one over Mongolia. The pattern quite resembles that in Deng et al. (2018), although the two studies analyzed different variables (surface air temperature vs heatwave days). In the LOW-epoch, in contrast, EOF1 shows a wavelike pattern confined to northern Eurasia. The contrasting patterns between the two EOF1s suggest that the time-varying BOC–SRP linkage also strongly affects the dominant surface air temperature mode over Eurasia.
5. Mechanism for the time-varying BOC–SRP linkage
The structure of the BOC pattern during the LOW-epoch quite resembles the north branch wave train during the HIGH-epoch, although there are slight differences in the location and magnitude of each center of action (Figs. 7a,b). Therefore, the question is why the BOC has a south branch and projects onto the SRP during the HIGH-epoch. In this section, we study the underlying mechanism by conducting idealized model experiments using the LBM.
We start by examining whether the simple model can reproduce the BOC–SRP linkage and its fluctuation seen in observation. Two experiments, named EXP-1A and EXP-1B (see Table 2 for a list of experiments), are designed for this purpose. In EXP-1A, the basic flow is set as the climatology in HIGH-epoch, and a hypothetical vorticity forcing is prescribed around 40°N, 25°W. The horizontal profile and vertical profile of the prescribed forcing are shown in Fig. 9a, which are designed to mimic the anticyclone action center above the Atlantic in the HIGH-epoch. For the same reason, the basic flow in EXP-1B is set as the climatology in LOW-epoch, and a hypothetical vorticity forcing, whose horizontal and vertical profiles are shown in Fig. 9c, is prescribed around 50°N, 35°W, to mimic the anticyclone action center over the Atlantic in the LOW-epoch. The atmospheric responses averaged from days 15 to 25 for EXP-1A and EXP-1B are shown in Figs. 9b and 9d, respectively. The responses remain almost the same if a different time window is used for the time average. For the response in EXP-1A (Fig. 9b), a well-organized wave train develops along the STJ in addition to the wave train propagated along the PFJ. The wave activity flux around the Mediterranean points southeastward to the low latitudes, indicating a strong energy outflow into the STJ. The Z250 response quite resembles that associated with the BOC pattern in the HIGH-epoch (Fig. 7a), indicating that the BOC pattern and its coupling are well reproduced in this case. Figure 9d shows the Z250 response in EXP-1B. Although the wave train propagating along the PFJ still persists as a steady response, the wave train along the STJ is remarkably reduced. The wave activity flux that orients southeastward around the Mediterranean in EXP-1A significantly weakens in EXP-1B. It indicates that the BOC–SRP linkage is not well established in this case. Therefore, although the LBM is a highly simplified linear model, it can generally reproduce the essential features of the BOC–SRP linkage found in observation. This success allows us to further investigate the mechanism of BOC–SRP linkage with the LBM.
(a) The horizontal distribution at σ = 0.23 (10−11 s−2; shading) and the vertical profile (10−11 s−2; line) at the forcing center of stationary vorticity forcing prescribed to the LBM. (b) 250-hPa geopotential height (Z250) response (gpm; shading) and wave activity flux (m2 s−2; vector) to the vorticity forcing. The climatological U250 is indicated by the purple contour. The prescribed forcing center and basic flow epoch are given at the upper left in (a). (c),(d) As in (a) and (b), but for the prescribed forcing at 50°N, 35°W. (e),(f) As in (a) and (b), but with the basic flow replaced by the LOW-epoch. (g),(h) As in (c) and (d), but with the basic flow replaced by the HIGH-epoch.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
The LBM experiment setting.
Since the forcing location and basic flow are both different between EXP-1A and EXP-1B, we conduct EXP-1C and EXP-1D to examine the sensitivity of atmospheric response in LBM to the difference in mean flow and forcing location. In EXP-1C, the forcing is set the same as EXP-1A, but the mean flow is replaced by LOW-epoch. Similarly, in EXP-1D, the forcing is set the same as EXP-1B, but the mean flow is replaced by HIGH-epoch (Table 2). The effect of forcing location can be examined by comparing the atmospheric response in EXP-1A with EXP-1D or EXP-1B with EXP-1C, and the effect of mean flow can be examined by comparing the atmospheric response in EXP-1A with EXP-1C or EXP-1B with EXP-1D. Results suggest that the mean flow plays a negligible role in the BOC–SRP linkage because the differences in the atmospheric responses between EXP-1A and EXP-1C or between EXP-1B and EXP-1D are small (Fig. 9b vs Fig. 9f, Fig. 9d vs Fig. 9h). In contrast, the forcing location can explain the differences in the atmospheric responses, as the atmospheric response shows considerable differences between EXP-1A and EXP-1D (Fig. 9b vs Fig. 9h) and between EXP-1B and EXP-1C (Fig. 9d vs Fig. 9f).
To further verify the above results, another group of LBM experiments is conducted to understand the mechanism of BOC–SRP linkage. The experiments (Group II; see Table 2) are designed similarly to Group I but focus on the action center of the BOC pattern around the prime meridian (Fig. 7). Again, we first examine whether the LBM can reproduce the observed BOC–SRP linkage. In EXP-2A, the basic flow is set as the climatology in HIGH-epoch, and the vorticity forcing is added around 0°, 55°N (Fig. 10a). In EXP-2B, the basic flow is set as the climatology in LOW-epoch, and the vorticity forcing is added around 55°N, 5°E (Fig. 10c). EXP-2A and EXP-2B can both reproduce the stationary wave activities along the PFJ (Figs. 10b,d). Although the atmospheric responses along the STJ in EXP-2A and 2B are both weaker than in EXP-1A, the cyclonic action center around central Asia is stronger in EXP-2A than in EXP-2B. The wave activity flux oriented toward the STJ around the Mediterranean is also stronger in EXP-2A than in EXP-2B. Therefore, the EXP-2A and EXP-2B can generally reproduce the main features of the BOC–SRP linkage as in the observation. Similar to Group I, we conduct EXP-2C and EXP-2D to investigate the relative importance of forcing location and mean flow in the response difference between the EXP-2A and EXP-2B. Consistent with the conclusions drawn from Group I, the forcing location primarily accounts for the response difference (Fig. 10b vs Fig. 10f, Fig. 10d vs Fig. 10h), and the basic flow plays a negligible role.
(a) The horizontal distribution at σ = 0.23 (10−11 s−2; shading) and the vertical profile (10−11 s−2; line) at the forcing center of stationary vorticity forcing prescribed to the LBM. (b) 250-hPa geopotential height (Z250) response (gpm; shading) and wave activity flux (m2 s−2; vector) to the vorticity forcing. The climatological U250 is indicated by the purple contour. The prescribed forcing center and basic flow epoch are given at the upper left in (a). (c),(d) As in (a) and (b), but for the prescribed forcing at 0°, 55°N. (e),(f) As in (a) and (b), but with the basic flow replaced by the LOW-epoch. (g),(h) As in (c) and (d), but with the basic flow replaced by the HIGH-epoch.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
The numerical experiments with the simple model LBM suggest the importance of the forcing location and the minor role of the Eurasian basic flow in determining the BOC–SRP linkage. A natural question is what determines the locations of the upstream action centers of the BOC pattern. According to XWC and Xu et al. (2020b), the teleconnections along the PFJ are primarily excited by the multiscale interactions among the mean flow, low-frequency flow variabilities, and high-frequency transients in the exit of the Atlantic jet. The climatological flow tends to be barotropically unstable due to the large horizontal gradient of basic wind in the exit region of the North Atlantic jet. Any random disturbance with a small amplitude tends to grow readily through extracting energy barotropically from the background flow. The resultant amplified disturbance can modify the transient eddy activities via changing the meridional position of the North Atlantic storm track on the one hand and be further reinforced by the feedback forcing of synoptic-scale transient eddies on the other hand. The nonlinear interactions between the mean flow, low-frequency variabilities, and high-frequency transients can eventually lead to the excitation of the teleconnection along the PFJ. To explore whether this mechanism is responsible for the location difference of the upstream action centers of the BOC pattern between HIGH-epoch and LOW-epoch, Fig. 11 presents the regressed 300-hPa geopotential height tendency induced by the high-frequency transient eddies upon the BOC index in the HIGH-epoch (Fig. 11a) and in the LOW-epoch (Fig. 11b). The geopotential height tendency induced by the high-frequency transient is defined following Eq. (4).
(a) Regressed 300-hPa geopotential height tendency induced by high-frequency transient eddies with respect to the BOC pattern during the HIGH-epoch (CI = gpm day−1). (b) As in (a), but for the LOW-epoch. The gray and black dots indicate the 90% and 95% confidence levels based on a two-tailed Student’s t test, respectively. The stars indicate the locations with maxima or minima values within the action centers. The result is based on the NCEP–NCAR dataset during 1948–2020.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
The BOC pattern is associated with significant geopotential height tendency induced by the transient eddies both in the HIGH-epoch and LOW-epoch (Fig. 11). The negative action centers of geopotential height tendency around 30°W and the positive action centers around the prime meridian both correspond well with the two action centers of the BOC pattern, regardless of the HIGH-epoch and LOW-epoch. A close examination of the geopotential height tendency induced by the high-frequency transients reveals that the geographical locations of action centers are different between the HIGH-epoch and LOW-epoch. For example, the action center above the Atlantic in the HIGH-epoch locates more southeastward than in the LOW-epoch. In contrast, the action center around the British Isles in the HIGH-epoch locates more northwestward than that in the HIGH-epoch. In addition, the location difference of geopotential height tendency induced by transients between the HIGH- and LOW-epochs is consistent with the shifted action centers of the BOC pattern (Fig. 7). The above results suggest that different locations of the wave–mean flow interactions at the exit of the Atlantic jet stream are responsible for the different exciting regions of the BOC pattern between the HIGH- and LOW-epochs.
6. Discussion
a. The possible factors responsible for different exciting locations of BOC pattern
Section 5 studies the primary mechanism for the different BOC patterns during the HIGH- and LOW-epoch. Results indicate that different exciting locations, which result from the wave–mean flow interactions that happen at different locations, are the primary cause. However, a question remains as to why the wave–mean flow interaction processes through which the BOC pattern is excited take place at different locations. To address this issue, Fig. 12a shows the difference of U250 between the HIGH-epoch and LOW-epoch. The Atlantic jet stream is significantly sharpened and extended eastward in the LOW-epoch. The different jet stream structures might alter excitation regions of the BOC pattern between the two epochs. In addition, the U250 changes might be further attributed to the underlying sea surface temperature (SST) changes. As shown in Fig. 12b, a tripolar SST difference pattern with negative anomalies in the middle latitudes and positive anomalies in the high and low latitudes can be observed over the Atlantic. Since the large-scale atmosphere circulation needs to maintain its thermal wind balance, this SST anomalies pattern might be a potential factor contributing to the zonal wind difference in the upper troposphere by affecting the baroclinicity in the low level.
(a) The difference of the U250 between the HIGH- and LOW-epochs (shading; SI = 1 m s−1) overlaid by the zonal wind climatology (contour; CI = 4 m s−1 starting from 16 m s−1). The results are based on the ERA-20C dataset. (b) The difference of the sea surface temperature (SST) between the HIGH- and LOW-epochs (shading; SI = 0.1°C). The results are based on the HadISST dataset. (c),(d) As in (a) and (b), but based on the INMCM4.0 climate model. The gray and black dots indicate the 90% and 95% confidence levels based on the two-tailed Student’s t test, respectively.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
To further verify the robustness of the result obtained from observations, we examine the output of the Institute of Numerical Mathematics Climate Model, version 4.0 (INMCM4.0; Volodin et al. 2010) that participates in phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012). The INMCM 4.0 model is one of the best CMIP5 models that can reproduce the structure of the BBC pattern, the BOC pattern, and the SRP. Here, the model outputs are from the first run of the INMCM4.0 historical simulations that participate in CMIP5. The analyzed period is 1851–2004. The spatial correlations between the simulated BBC pattern, BOC pattern, and the SRP and that in observations are all larger than 0.8, indicating the fidelity of INMCM4.0 to simulate the dynamics of waveguide teleconnections along the PFJ and STJ. Figures 13a and 13b present the spatial structure of the BOC pattern and SRP. Similar to observation, the BOC pattern is characterized by two branches of wave structure along the PFJ and STJ, and the structure along the STJ resembles the SRP. Figure 13c presents the moving correlation coefficient between the simulated BOC index and SRP index with a 13-yr window during 1851–2004. Significant interdecadal fluctuations of the BOC–SRP linkage can also be reproduced in the INMCM4.0 model. For example, the correlation is high during 1920–50 and low during 1890–1920. In addition, the simulated BOC pattern and its fluctuated linkage to the SRP during the HIGH- and LOW-epochs in INMCM4.0 are also very similar to that in observation (not shown).
(a) Summer mean Z250 (black contour, CI = 5 gpm) and the horizontal component of the wave activity flux (arrow; m2 s−2) associated with the BOC pattern in INMCM4.0 climate model, obtained via a linear regression onto the BOC index. The overlaid 16 m s−1 purple contours indicate the climatology of the U250. (b) As in (a), but for the Silk Road pattern (SRP). The SRP index has been multiplied by −1 for visual convenience. (c) The moving correlation coefficients (blue lines) between the BOC index and SRP index with a 13-yr window in the INMCM4.0 climate model. Dashed red lines indicate the 90% confidence level based on a two-tailed Student’s t test.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
Figures 12c and 12d show the U250 difference and SST difference between the HIGH-epoch (1920–50) and LOW-epoch (1890–1920), respectively, in the INMCM4.0 model. The INMCM4.0 model reproduces an Atlantic jet stream structure difference that is very similar to the observation. The Atlantic jet core is strengthened and extended more eastward in the LOW-epoch than in the HIGH-epoch. Compared with the Atlantic jet stream, however, the Asian jet stream difference simulated in the INMCM4.0 model shares less similarity with that in observation. It confirms our previous results derived from the LBM experiments that the downstream jet stream over Eurasia only plays a secondary role in affecting the propagation route of the BOC pattern. In addition, the INMCM4.0 model simulates a similar SST difference pattern between the HIGH- and LOW-epoch, as in observation, with the cold SST anomalies in the middle latitudes and warm SST anomalies in the high and low latitudes (Fig. 12d vs Fig. 12b). This result further indicates the different behaviors of the Atlantic jet stream, which is probably responsible for the BOC–SRP linkage, very likely arise from the fluctuations of underlying SST anomalies tripolar pattern over the Atlantic.
b. Role of the diabatic heating in affecting the BOC pattern
As shown in Fig. 7, the BOC pattern can exert profound influences on the surface temperature and precipitation over Eurasia. There is a possibility that the induced surface temperature anomalies and precipitation anomalies can further influence the BOC pattern by acting as a heating source in the lower or upper troposphere. To verify this possibility, we conducted another two groups of LBM experiments. In the first group of experiments (EXP-T), we prescribe heating forcing that maximizes in the lower troposphere to mimic the effect of temperature anomalies in the lower troposphere associated with the BOC pattern. In the second group of experiments (EXP-P), we prescribe heating forcing that maximizes in the middle troposphere to mimic the effect of diabatic heating caused by the precipitation. The amplitudes of all heating forcing are set to be 1 K day−1. Detailed information about the experiment settings is given in Table 2.
Figure 14 presents the horizontal distribution and vertical distribution of the prescribed forcing (left panel) and the Z250 response (right panel) averaged from day 15 to 25. The results remain almost the same if we adopt other averaging days. The Z250 response to the heating forcing in the lower troposphere is confined to the neighboring downstream region of the forcing region. The response is roughly in quadrature with the action centers of the BOC pattern. It indicates that the temperature anomalies in the lower troposphere act to strengthen or dampen the regional action centers of the BOC pattern alternatively. Figure 15 presents the Z250 response to the precipitation anomalies associated with the BOC pattern. The response is characterized by a zonally elongated action center which coincides with both the positive and negative action centers of the BOC pattern. This indicates that the precipitation anomalies also act to strengthen and dampen the regional action centers of the BOC pattern alternatively. XWC investigated the role of diabatic heating in maintaining the BBC pattern by calculating the time scales when the spatially integrated energy associated with the BBC pattern could be replenished through the diabatic heating. Their result indicates that the diabatic heating is inefficient in affecting the BBC pattern significantly compared with the barotropic and baroclinic interactions with the mean flow. In this study, the experiments with a simple model confirm the low efficiency of diabatic heating in affecting the BBC pattern or BOC pattern. Moreover, they attribute the mechanism to the local constructive or destructive role of diabatic heating, which leads to a weak net contribution as a small residual due to the cancellation.
(a) The horizontal distribution at σ = 0.95 (10−5 K s−1; shading) and the vertical profile (10−5 K s−1; line) at the forcing center of stationary diabatic heating prescribed to the LBM. The forcing is designed to mimic the effect of temperature anomalies in the lower troposphere associated with the BOC pattern. (b) Z250 response (gpm; shading) and wave activity flux (m2 s−2; vector) of the LBM to the heating forcing. The climatological U250 is indicated by the purple contour. The prescribed forcing center is given at the upper left in (a). (c),(d) As in (a) and (b), but for diabatic heating forcing centered at 55°N, 45°E. (e),(f) As in (a) and (b), but for diabatic heating forcing centered at 62°N, 90°E. The initialized basic flow is the summer climatology during 1920–2010 based on ERA-20C datasets.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
(a) The horizontal distribution at σ = 0.45 (10−5 K s−1; shading) and the vertical profile (10−5 K s−1; line) at the forcing center of stationary diabatic heating prescribed to the LBM. The forcing is designed to mimic the effect of precipitation anomalies associated with the BOC pattern. (b) Z250 response (gpm; shading) and wave activity flux (m2 s−2; vector) of the LBM to the heating forcing. The climatological U250 is indicated by the purple contour. The prescribed forcing center is given at the upper left in (a). (c),(d) As in (a) and (b), but for diabatic heating forcing centered at 58°N, 58°E. (e),(f) As in (a) and (b), but for diabatic heating forcing centered at 60°N, 100°E, respectively. The initialized basic flow is the summer climatology during 1920–2010 based on ERA-20C datasets.
Citation: Journal of Climate 35, 17; 10.1175/JCLI-D-21-0705.1
c. Implications for the interdecadal variations of the SRP
The SRP has clear interdecadal variations that explain approximately 50% of its total variance (Wang et al. 2017). Its spatial pattern features a second wave train over the northern part of Eurasia on the interdecadal time scale and leads to a larger meridional scale than that on the interannual time scale. This second northern wave train (Fig. 4a in Wang et al. 2017) quite resembles the BOC pattern shown in this study (Fig. 7a). This result implies that the large meridional scale of the interdecadal component of the SRP may result from the BOC–SRP linkage. More vigorous studies are needed to address this question.
d. Further caveats
In this study, we revealed that changes of Atlantic mean jet stream can affect the eddy forcing by modulating the mean-flow interactions, as the Atlantic jet stream itself is a key ingredient of the wave–mean flow interaction process. However, we still cannot ascertain to what extent and how the changes in the Atlantic jet stream affect the eddy forcing. Since the wave–mean flow interaction is highly nonlinear and variations of waves and mean flow are often symbiotic, in this context, the observed eddy forcing is the adjusted result of the wave–mean flow interactions, and it is difficult to unravel their contributions explicitly only based on observational diagnoses. We speculate that the shape of the Atlantic jet stream might be one of the factors responsible for the different eddy forcing, as both the observation and INMCM4 model show a sharper jet stream in the LOW-epoch. Checking the role of eddy forcing in INMCM4.0 by using the daily output might be helpful in shedding light on this issue in the future.
7. Conclusions
The summer PFJ and STJ over Eurasia are atmospheric waveguides that can trap Rossby wave activities and create waveguide teleconnections. Previous studies primarily focus on the waveguide teleconnections along the STJ and rarely discuss the waveguide teleconnections along the PFJ. As an extension to our previous work, this study investigates the second leading waveguide teleconnection along the PFJ, the British–Okhotsk Corridor (BOC) pattern. The BOC pattern consists of four zonally oriented action centers over the British Isles, western Russia, northern Siberia, and the Okhotsk Sea, respectively, and explains 20.8% of the total variance of summer circulation over northern Eurasia. Specifically, the BOC pattern has a close linkage to the SRP, which is the dominant waveguide teleconnection along the STJ. A positive phase of the BOC is often accompanied by a negative phase of the SRP on the interannual time scale. Such a linkage fluctuates on the interdecadal time scale and are highly consistent among five reanalysis datasets. The time-varying BOC–SRP linkage plays an important role in setting the dominant surface climate variability modes over Eurasia, such as the precipitation seesaw mode between Siberia and Northeast Asia (Iwao and Takahashi 2006) and the wavelike temperature mode across Eurasia (Deng et al. 2018).
Experiments based on the LBM indicate that the changed excitation location of initial action centers of the BOC pattern is the crucial factor in determining the BOC–SRP linkage. Although the mean flow does not directly affect the propagation route over Eurasia once the BOC pattern is excited, it strongly influences the excitation locations of upstream action centers of the BOC pattern by affecting the nonlinear interaction processes between the Atlantic jet stream and storm track and thus indirectly affects the BOC–SRP linkage. An anomalous SST tripolar pattern over the Atlantic possibly accounts for the atmospheric mean state changes above. The similarity between the observation and the simulations in INMCM4.0 climate model further increases the confidence of this result. The feedback of temperature anomalies and precipitation anomalies associated with the BOC pattern plays a certain role in affecting the local action centers by acting as a heating source at the lower and upper troposphere. However, their net contributions are small since their effects are canceling out.
Acknowledgments.
We appreciate the three anonymous reviewers for their insightful suggestions that helped us improve the paper. We are grateful to Dr. Michiya Hayashi of the National Institute for Environmental Studies and Dr. Zhibiao Wang of IAP for their help with the LBM experiments. PX appreciates Dr. Peili Wu of the Met Office and Dr. Hao Fu of Stanford University for their helpful suggestions. This work is supported by the National Natural Science Foundation of China (41925020, 42005057), the China Postdoctoral Science Foundation (2020M670418, 2021T140652), and the Special Research Assistant Project of the Chinese Academy of Sciences.
Data availability statement.
The ERA-Interim and ERA-20C data were downloaded at https://www.ecmwf.int/en/forecasts/datasets. The JRA-55 data were downloaded at https://jra.kishou.go.jp. The NCEP1 and 20CRV2c data were downloaded at https://psl.noaa.gov/data/gridded/. The HadISST data were downloaded at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. The CRU dataset is publicly available at https://crudata.uea.ac.uk/cru/data/hrg/.
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