1. Introduction
In winter, the anomalous surface air temperature (SAT), particularly the extremely cold weather, has an important impact on ecosystem, human health, economic, and social activities (Blunden et al. 2011). The pan–North Pacific region, mainly consisting of extratropical East Asia and North America, is one of the most frequently influenced regions by the cold extremes. Because this region is also a densely populated and economically developed area, understanding the variability of atmospheric circulation and the associated SAT anomaly in the pan–North Pacific region is crucial.
Several previous studies have explored the dominant modes of winter SAT variability over the extratropical Northern Hemisphere (Koide and Kodera 1999; Miyazaki and Yasunari 2008; Wu et al. 2009; Park et al. 2021). For example, the contribution of Arctic sea ice loss to the warm Arctic–cold Eurasian continent mode has been extensively investigated (Cohen et al. 2012; Mori et al. 2014; Woollings et al. 2014; Kug et al. 2015). The reduced westerlies favor the cold air advection (Yao et al. 2017), which leads to a colder Eurasian continent. For North America, many studies emphasize the crucial role of the Pacific–North America (PNA) mode and Arctic Oscillation/North Atlantic Oscillation (AO/NAO) during boreal winter (Rodionov and Assel 2003; Griffiths and Bradley 2007; Ning and Bradley 2016). Yu et al. (2016) and Yu and Lin (2018) proposed the Asian–Bering–North American mode, which describes the coherent changes of wintertime SAT over North Asia and North America. In addition, powerful basin-scale oceanic processes, including El Niño, Pacific decadal oscillation (PDO), Victoria mode (VM), and Atlantic multidecadal oscillation (AMO), among others, are believed to affect nearly global atmospheric circulation and SAT (Trenberth and Hurrell 1994; Wang et al. 2000; Sheppard et al. 2002; Li and Bates 2007; Linkin and Nigam 2008; Kim et al. 2014; Wang 2019).
These studies mainly focus on the forcing role of polar sea ice and basin-scale sea surface temperature anomalies (SSTa). On the other hand, the midlatitude SST frontal zone is another hotspot of air–sea interaction despite the narrow spatial scale (Chelton et al. 2004; Nakamura et al. 2004, 2008; Minobe et al. 2008, 2010; Chelton and Xie 2010; Kwon et al. 2010). The SST fronts could influence the convergence and divergence in the local air–sea boundary layer by causing differences in the release of heat and moisture across the front (Lindzen and Nigam 1987). The SST fronts can also promote (inhibit) the vertical mixing of air to accelerate (decelerate) wind speed over the warmer (cooler) side of the SST front (Wallace et al. 1989). Thus, the midlatitude SST fronts play an important role in shaping the average state of the midlatitude atmospheric circulation, including the anchoring and maintenance of the storm track and the westerly jet (Hoskins and Valdes 1990; Nakamura et al. 2004, 2008; Frankignoul et al. 2011; Taguchi et al. 2009, 2012; Kwon and Deser 2007; Kwon et al. 2010; Ma et al. 2017; Yao et al. 2018; Wang et al. 2019; Zhang et al. 2019; C. Zhang et al. 2020).
The sharpest SST frontal zone in the wintertime North Pacific is located at the confluence region of the Kuroshio Extension and the Oyashio Extension, which is called the Kuroshio and Oyashio Extension (KOE) region. A series of modeling studies has strongly suggested the significant response of the tropospheric atmosphere to the SSTa over the western boundary current. Variables such as the atmospheric horizontal divergence, vertical motion, precipitation, and cloud cover all have a good coupling correspondence with the KOE SSTa (Liu and Xie 2008; O’Neill et al. 2010; Taguchi et al. 2012; Masunaga et al. 2016, 2020; Wang and Liu 2015; Z. Zhang et al. 2020). In addition, both the observations (Kwon and Joyce 2013; O’Reilly and Czaja 2015; Révelard et al. 2016, 2018; Wills and Thompson 2018) and modeling studies (Smirnov et al. 2015; Tatebe et al. 2017; Okajima et al. 2018) have explored the possible impact of the KOE SSTa on the large-scale atmospheric circulation. Considering the importance of midlatitude air–sea interaction in climate change and extreme weather prediction, a few studies have begun to investigate the climatic effect of western boundary current (Ma et al. 2015; Yu et al. 2017, 2019; Lei et al. 2020; Che et al. 2021; Long et al. 2021; Siqueira et al. 2021). But there is still a lack of clear understanding of the specific pathway and mechanism of the interaction between the SSTa and extratropical regional climate. Therefore, this study attempts to investigate the possible link between the wintertime pan–North Pacific SAT pattern and the variation of KOE SSTa.
The remainder of the paper is organized as follows. The dataset, model experiments design, and method used are described in section 2. Section 3 shows the principal SAT modes in the pan–North Pacific region and its related oceanic variability. The atmospheric circulation and SAT responses to the KOE SSTa are illustrated in section 4. Section 5 explores the dynamic mechanism of the atmospheric response. Both the results of data reanalysis and model experiments are presented in sections 4 and 5. Finally, conclusions and further discussion are presented in section 6.
2. Data and model
a. Data
The daily, multilevel atmospheric variables are obtained from National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996), which is available with a 2.5° × 2.5° spatial resolution. The daily SAT from NCEP reanalysis used in this paper is defined by air temperature at the lowest sigma level. Observed monthly SAT anomalies are obtained from Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP), version 4, which is provided by the National Aeronautics and Space Administration with a 2° × 2° spatial resolution (Lenssen et al. 2019; GISTEMP Team 2022). The NCEP reanalysis and GISTEMP data used in this study extend from December 1949 to February 2020. Monthly skin temperature is gained from the fifth major global reanalysis produced by the European Center for Medium-Range Weather Forecasts (ECMWF) (ERA5; Hersbach et al. 2020) with a spatial resolution at 0.25° × 0.25° from December 1959 to February 2020. Monthly SAT is gained from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017), with a spatial resolution at 0.5° × 0.625°. The MERRA-2 reanalysis data are provided by the National Aeronautics and Space Administration (NASA) from December 1980 to February 2020. Monthly ground temperature is gained from the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015; Harada et al. 2016) with a spatial resolution at 1.25° × 1.25°. The JRA-55 data are provided by the Japan Meteorological Agency (JMA) from December 1958 to February 2020. The wintertime period means 90 days from 1 December of one year to 28 February of the next year. The extremely cold weather is defined by daily wintertime SAT anomalies grid by grid. If a grid SAT is lower than the threshold for at least three consecutive days, this grid point is experiencing extremely cold weather. The threshold is defined by the lower-tenth percentile of the total daily wintertime SAT anomaly sequence at each grid point.
To cover a sufficiently long period and obtain a clear structure of SSTa, two SST datasets are used in this study: monthly Hadley Centre Global SST (HadISST) (Rayner et al. 2003) with a 1° × 1° spatial resolution and daily Optimum Interpolation Sea Surface Temperature (OISST), version 2, dataset with a 0.25° × 0.25° spatial resolution (Reynolds et al. 2002). The monthly HadISST is obtained for the period between December 1949 and February 2020, while the daily OISST is obtained for the period between December 1981 and February 2020. Daily sensible and latent heat fluxes for the period 1958–2020 are gained from the WHOI objectively analyzed air–sea fluxes (OAFlux) project (Yu et al. 2008) with a 1° × 1° spatial resolution.
The monthly AO, Niño-3, and Niño-4 indices are obtained from the NOAA website (https://psl.noaa.gov/data/climateindices/list/), extending from January 1950 to December 2020. The wintertime PDO and the VM index are calculated by the principal components (PCs) time series of the first and second dominant empirical orthogonal function (EOF) mode of North Pacific (poleward of 20°N) SST variability, using HadISST data. The index of eastern Pacific (EP) El Niño and central Pacific (CP) El Niño are calculated by the method proposed by Yeh et al. (2009). A KOE front (KOEF) index proposed by Che et al. (2021) is calculated to demonstrate the variation of the KOE region. This index is defined by the regional average of the meridional SST gradient over the strongest SST frontal zone (142°–155°E, 38°–43°N) in the KOE region.
The global warming signal is defined by the ensemble mean of annual global SAT from phase5 of the Coupled Model Intercomparison Project (CMIP5) provided by Dai et al. (2015), which is designed to isolate the contribution of external forcing to global warming from the atmospheric internal variability. During the research period of this paper (1949–2019), the global warming trend is nearly linear. All reanalysis and observation data used in this study have had the part removed that linearly regressed on the global warming signal. Anomalous fields in this study are calculated by removing the long-term mean (1949–2019 for most cases) for each day/month at each grid point.
b. Model description and experiment design
The Community Earth System Model, version 2.2.1 (CESM 2.2.1; Danabasoglu et al. 2020), is a global climate model created by NCAR. Different geophysical models, including atmosphere, ocean, and land, among others, are coupled together by the version 7 coupler (Craig et al. 2012). The model component set used in this article is F2000climo, in which the Community Atmosphere Model, version 6.0 (CAM6; Gettelman et al. 2019), is coupled to the Community Land Model, version 5.0 (CLM5; Lawrence et al. 2019), and forced by prescribed SST and sea ice. CAM6 is integrated with 0.9° × 1.25° horizontal resolution, 32 vertical levels, and a time step of 30 min. The component set F2000climo includes a set of monthly climatological SST and sea ice concentration boundary fields, atmospheric initial fields, and external forcing fields. The SST boundary fields in F2000climo contain the climatological averaged SST fields from 1995 to 2005 for 12 months of the year. They are produced from monthly HadISST, version 1, and weekly OISST, version 2 (Hurrell et al. 2008).
To spin up, the model is run for 5 years under the default monthly climatological SST and sea ice concentration boundary conditions, constant atmospheric composition, and other external forcings. The first day of the sixth year is used as the initial time for the following experiments. The control (CTRL) run keeps all default external forcing and boundary conditions. For the forcing (FORC) run, the SST boundary field from March to November also keeps all default external forcing and boundary conditions. But in December–February (DJF), the SST boundary field of the FORC run is the sum of default SST climatology and fixed forcing SSTa signal. The fixed SSTa (as shown in Fig. 6e) added in the FORC run is the composite differences of wintertime SST between strong and weak KOEF winters over the KOE region. Detailed description of the fixed SSTa forcing signal can be found in section 3c. Both experiments are integrated from the same initial field for 30 years. The differences between the two experiments (the result of FORC minus the result of CTRL) represent the atmospheric response to wintertime SSTa in the KOE region.
c. Method
3. Wintertime pan–North Pacific SAT patterns and related SST signals
a. The first four pan–North Pacific SAT EOF modes
The EOF 2 (Fig. 1b) demonstrates an opposite change in SAT over East Asia and North America. The regressed Z500 field against the PC 2 has three local extrema, which are in Siberia, North America, and North Pacific. In the positive (negative) phase of EOF 2, the intensified (weakened) Siberia high pressure and East Asian trough will promote (inhibit) the invasion of cold air to the south to 40°N. EOFs 3 (Fig. 1c) and 4 (Fig. 1d) both depict the regression coefficients of SAT with dipole patterns over extratropical East Asia and North America. EOF 3 depicts an opposite change in SAT from north to south over extratropical East Asia and an opposite change from northwest to southeast over North America. The regressed Z500 field against PC 3 shows a PNA-like pattern, with a deepened Aleutian low pressure and strengthened Alaska ridge. For EOF 4 (Fig. 1d), the regression coefficients of SAT over East Asia (North America) are similar (opposite) to that in Fig. 1c, associated with the change of East Asian trough, Alaska ridge, and North Pacific westerly jet.
EOF analysis results may not be the same when using different datasets. Figure 2 compares the PC time series obtained by different reanalysis datasets. Despite the four datasets’ differences in the temporal scope and spatial resolution, the first two EOF modes obtained are almost identical. The orders of EOFs 3 and 4 in JRA-55 and ERA5 are reversed compared to the NCEP results. Nevertheless, the correlation coefficients of the PC 3 and PC 4 time series between the results calculated from NCEP and the other reanalysis data are above 0.69.
b. The related SST signals
To investigate the relevant climate phenomena in the North Pacific and the possible link between them to these four EOF modes, Fig. 3 shows the regression coefficients of Pacific SST on the normalized PC time series. In the Pacific, only the SST changes over the eastern equatorial Pacific are significantly correlated with EOF 1 (Fig. 3a), and the correlation coefficient between the EP El Niño and PC 1 is 0.27 (Table 1). The variability of EOF 1 is highly related to the change of polar vortex, with a correlation coefficient of 0.60. AO is also significantly related to PCs 2 and 3. Figure 3b shows a band of significant warm SSTa from the Kuroshio to the central North Pacific, surrounded by opposite-sign SSTa to the north of 40°N and the south of 20°N. Those SSTa are similar to the pattern regressed to the VM, and the correlation coefficient between PC 2 and the VM index is −0.41. As for EOF 3, the regressed SST field shows a clear PDO pattern and significant signal over tropical Pacific, and the correlation coefficients between PDO and EP and CP El Niño are significant at 0.62, 0.26, and 0.32, respectively. Figure 3d shows significant warm SSTa on the coast of the northwest Pacific, especially to the east of Japan, that is, the KOE region. It also presents weak VM-like and EP El Niño–like SSTa patterns, and the correlation coefficient analysis shows PC 4 is significantly correlated with the VM index and EP El Niño index. This result is not surprising, considering the impact of PDO, VM, and El Niño on the variability of the Kuroshio Extension (Qiu and Chen 2005, 2010; Taguchi et al. 2007; Ceballos et al. 2009; Di Lorenzo et al. 2010).
Correlation of multiple climate indices with the PC time series and the AKNA index. The asterisk indicates the correlation coefficient is significant at a 95% confidence level.
c. AKNA pattern and KOE SSTa
As stated in the introduction, the influence of climate signals, including AO, El Niño, PDO, and VM, on the wintertime SAT has been extensively discussed (Trenberth and Hurrell 1994; Wang et al. 2000; Sheppard et al. 2002; Rodionov and Assel 2003; Kim et al. 2014; Wang 2019). However, the correlation between the EOF 4 mode and the SSTa in the KOE region suggests that EOF 4 may play the role of being the “bridge” between extratropical climate and the SST frontal zone in the KOE region. Because of the association of EOF 4 with KOE SSTa, and SAT anomalies in East Asia and North America, this EOF 4–related SAT pattern is called the Asia–Kuroshio and Oyashio Extension–North America (AKNA) pattern herein.
Figure 5a exhibits the regression coefficients of extremely cold weather on the AKNA index. The variation of extremely cold weather shows two dipole patterns in extratropical Asia and North America, just like the anomalous SAT pattern shown in Fig. 4b. The AKNA pattern exhibits a strong (weak) impact on the extremely cold weather in the eastern coastal regions of East Asia (North America), with the local variance contribution rate (square of correlation coefficient) reaching up to 20% (5%) in the eastern coastal region of East Asia (North America). Figures 5c–e show the regression coefficients of DJF mean SST and turbulent heat flux on the AKNA index. The SST and turbulent heat flux show opposing-sign correlations with the AKNA index in most parts of the Pacific Ocean; the intensified (reduced) heat flux forced by the atmosphere cools (warms) the ocean, which emphasizes the dominant role of the atmosphere (Cayan 1992). In the KOE region, both the SST and heat flux show significant and same-sign correlations with the AKNA index (as shown in Figs. 6b,c); the warmer (colder) seawater promotes (inhibits) the release of heat flux in the positive (negative) phase of AKNA, which indicates a possible forcing role of SSTa in the KOE region (Tanimoto et al. 2003; Chelton and Xie 2010).
The KOEF index, the time series of the meridional SST gradient area average over the KOE region (142°–155°E, 38°–43°N, marked by the black dashed box in Fig. 6a), is calculated to depict the variation of the KOE region. The variation of KOE SST is related to the dynamic state of Kuroshio Extension flow (Vivier et al. 2002; Qiu and Chen 2005, 2010; Qiu et al. 2014; Che et al. 2021). When the Kuroshio Extension axis remains unstable, the intensified (reduced) meridional (zonal) flow and increased eddy activity will transport more warm water to the KOE region (Qiu 2000; Sugimoto and Hanawa 2009; Sugimoto et al. 2014; Masunaga et al. 2016), causing warming of the KOE region and strengthening of the KOEF. The change of KOEF lags the change of Kuroshio dynamics by about 1 year (Che et al. 2021). Figures 6c and 6d show the composite difference of SST and turbulent heat flux (sensible + latent; positive values indicate the ocean is losing heat) between the positive and negative phases of the KOEF. As the results show, there are positive regression coefficients of SST and turbulent heat flux in the KOE region, which means the KOEF index successfully portrayed the forcing role of KOE SST anomalies (Tanimoto et al. 2003; Chelton and Xie 2010) in local sea–air interaction.
The contemporaneous correlation between the AKNA index and the OISST (HadISST) KOEF index is significant at 0.32 (0.29). Further lead–lag regression suggests the DJF mean AKNA pattern is significantly cross correlated with the value of the OISST KOEF when leading for 1–4 months or lagging for 1 month. Lead–lag regression also reveals the DJF mean AKNA pattern is significantly cross correlated with the value of the EP El Niño when leading for 1–3 months or lagging for 1–4 months. Considering the complexity of the lead–lag regression and the contamination by ENSO variability, it is difficult to determine the causality from observations alone. This study will focus on exploring whether KOE SSTa has a forcing effect on the formation of AKNA, as implied by Figs. 5a–c. The CAM6 numerical simulations are designed to explore the atmospheric response to the KOE SSTa. Thus, the KOEF-related SSTa is used as a forcing signal of CAM6 numeric simulation. The fixed forcing SSTa field added to the FORC run is shown in Fig. 6e, which is selected from the dashed box (140°–155°E, 32.5°–44°N) in Fig. 6c.
4. Response of atmospheric circulation and SAT to the KOE SSTa
a. Comparison between the NCEP reanalysis data and CTRL experiment
In this section, whether the AKNA pattern is forced by the KOE SSTa is investigated through CESM experiments. To test the ability of the CAM6 to represent the realistic wintertime atmospheric circulation, Fig. 7 compares the climatological wintertime zonal wind speed at 300 hPa (U300), stationary wave at Z500, and storm track at 300 hPa between NCEP reanalysis data and the CTRL run. The stationary wave is defined as the Z500 anomaly obtained by subtracting the zonal average. The model performed well in catching the distribution of the midlatitude westerly jet (Fig. 7b) and storm track (Fig. 7f), as their position and intensity are consistent with the result in NCEP reanalysis data (Figs. 7a,e). Similarly, Figs. 7c and 7d show a similar pattern of stationary wave (e.g., the East Asian trough and the Alaska ridge) in NCEP reanalysis data and the CTRL run. The comparisons confirm that the model has successfully depicted both the high- and low-frequency atmospheric processes, which increases the confidence in the model to test the atmospheric response to KOE SSTa.
Subsequently, the correlations between wintertime mean SST and turbulent heat flux are examined in North Pacific. According to Kalnay et al. (1996), the NCEP reanalysis model is forced by OISST, version 2, for the period 1982–present, and forced by GISST (replaced with the HadISST in this study) for earlier periods (1948–81). Thus, the correlations resolved by NCEP reanalysis data and observation were compared in two periods, 1949–80 (Figs. 8a,b) and 1982–2019 (Figs. 8c,d). The correlations between heat flux and SST depicted by NCEP data are basically consistent with the observation, except the NCEP reanalysis underestimates the correlations during 1982–2019. Over the KOE region, the result of NCEP reanalysis shows significant positive correlations during both periods, which indicates the NCEP reanalysis data can depict the forcing role of the KOE SSTa. The variability of the DJF mean turbulent heat flux resolved in the model (a combination of CTRL and FORC) is compared with that in observations. The variability of turbulent heat flux in model experiments is similar to the observed results but with much smaller magnitude. This is because the SST is prescribed as a monthly climatological mean in CAM6. The variation of turbulent heat flux reaches a peak in the KOE region in the CAM6 experiments, where the fixed SSTa is added.
b. Response of atmospheric circulation and SAT in NCEP reanalysis data and model experiments
To facilitate comparison of results in NCEP reanalysis data and model experiments, positive and negative AKNA winters are to be selected at time periods coinciding with positive and negative phases of the normalized NCEP AKNA index. From 1949 to 2019, 34 winters are identified as positive AKNA winters, and 37 winters are identified as negative AKNA winters. Figures 9a and 9b show the composite differences of Z500 and SAT between the two AKNA phrases (Fig. 9a) and between the two experiments (Fig. 9b). On both continents, the climatological high pressure systems (i.e., the Siberian high pressure and the atmospheric ridge of the Alaska Gulf) are weakened, consistent with warming southeast of East Asia and North America. Over the North Pacific, the significant, downstream, positive Z500 anomalies confirm the ridge response of the atmosphere to warm SSTa over the KOE region (Kushnir and Lau 1992; Peng and Whitaker 1999; Liu and Wu 2004; Liu et al. 2007; Frankignoul et al. 2011; Gan and Wu 2012; Taguchi et al. 2012; Okajima et al. 2014; Révelard et al. 2016, 2018). The response of extremely cold weather to the variation of KOE is consistent with the change of SAT in both model experiments and NCEP reanalysis data.
Naturally, there are differences between the model results and NCEP reanalysis data that cannot be ignored. Compared to the results in the NCEP data reanalysis, the response of atmospheric circulation and SAT locates more northerly in the model, with a weaker amplitude. In North America, the meridional dipole pattern changes into a northwest–southeast type. The negative Z500 anomaly along the west coast of North America extends eastward and the positive Z500 anomaly in eastern North America diminishes. Kohyama et al. (2021) highlighted the synergistic influences of the Kuroshio and Gulf Stream on the atmospheric jet stream, which may explain the discrepancy between NCEP reanalysis data and model. The low consistency of the AKNA-related patterns (e.g., SAT, extremely cold weather, and westerlies) in eastern North America may be caused by the absence of the synergistic effect of the Gulf Stream in the model. In general, the above comparisons between the NCEP reanalysis data and model results suggest the response of the AKNA pattern to the variation of KOE SSTa. The physical mechanisms are further investigated in the following section.
5. Diagnosis of physical mechanisms
a. Response of baroclinic instability and storm track
Previous studies have emphasized the important role of midlatitude SST in the variation of the storm track (Nakamura et al. 2004, 2008; Kwon and Deser 2007; Kwon et al. 2010; Taguchi et al. 2009; Sampe et al. 2010; Ogawa et al. 2012; Ma et al. 2017). Figures 12a and 12d depict the response of the DJF mean storm track at 850 and 300 hPa. The storm track is strongly intensified to the north of the storm track axis in both the upper and lower troposphere in the positive phase of AKNA and FORC run. The North Pacific storm track moves poleward in response to the warm SSTa over the KOE region (Taguchi et al. 2009; Ogawa et al. 2012; Yao et al. 2018; Wang et al. 2019). As suggested by Vallis (2006), baroclinic instability is the key source of atmospheric eddy. Figures 12e and 12f demonstrate the differences in EGR between the positive and negative phases of AKNA and the two experiments. At the lower troposphere (850 hPa), the change of EGR not only shows consistent results in the NCEP data reanalysis and model experiments but also corresponds to the change of the storm track. The increased (decreased) EGR to the north (south) of 40°N during the positive (negative) phase of AKNA promotes (inhibits) the development of eddy activity.
b. Response of eddy–mean flow interaction
Because the westerlies show a barotropic response to the KOE SSTa, the horizontal divergence of the two-dimensional E vector (Hoskins and White 1983) at 300 hPa is examined. During winter, the midlatitude North Pacific westerlies (around 40°N) are accelerated by synoptic eddy forcing over the North Pacific in both NCEP reanalysis data and model experiments (Figs. 13a,b). The differences between the two AKNA phases (Figs. 13c,d) indicate an enhanced acceleration of westerlies over the North Pacific and East Asia to the north of 40°N. Similar to the variation of zonal wind, the difference in divergence of the E vector between model experiments moves poleward compared with that in NCEP reanalysis data.
The differences in energy conversion at the upper (300 hPa) and lower (850 hPa) troposphere further demonstrate the response of eddy–mean flow interaction. The significant variation of BCEC at the lower troposphere (Figs. 14a,b) explains the distribution of the storm track. Available potential energy is gained from the mean state to promote eddy activity to the north of 40°N. The difference of BTEC is stronger at 300 hPa instead (Figs. 14g,h), showing similar but opposite-sign differences to the 300-hPa BCEC. The eddy intensified by available potential energy loses kinetic energy to the mean flow to intensify the westerly jet aloft the KOE region, while in the downstream region, the eddy gains kinetic energy to dampen the intensification of mean flow (Cai et al. 2007; Gan and Wu 2014).
c. Different responses of vertical motion in the western and eastern North Pacific
The response of vertical motion is investigated in this subsection. Since the warm KOE SSTa is restricted in KOE region, it is reasonable to separate the local atmosphere (hereafter western North Pacific) from the downstream part (hereafter eastern North Pacific). The longitude of the eastern boundary of the KOE region (155°E in this paper) is chosen to be the dividing line. Figure 15 presents the latitude–pressure plot for the vertical velocity (defined by omega, positive downward). There are significant differences in the vertical velocity anomalies between the eastern and western North Pacific. In the eastern North Pacific, there is enhanced sinking motion between 30° and 50°N during the positive AKNA winters, and that is reproduced in model experiments.
In the western North Pacific, the enhanced upward motion is concentrated in the range of 20°–40°N during the positive phase of AKNA. The results of model experiments show a stronger and more complicated structure of the omega anomaly in the western North Pacific. Negative vertical velocity anomalies cover the latitude range of 20°–50°N. Among them, there is significantly enhanced ascending motion over the KOE area (centered at 37.5°N), with two corresponding sinking motions on the north and south sides. On the one hand, this result is consistent with previous studies, which suggest that the air ascends above the warm SSTa and sinks at the cold sides (Lindzen and Nigam 1987; Feliks et al. 2004, 2007; Chelton and Xie 2010; Masunaga et al. 2020). The secondary circulation forced by KOE SSTa is believed to reach the middle troposphere (Brachet et al. 2012; Wang and Liu 2015; Z. Zhang et al. 2020). On the other hand, the model results are likely to exaggerate the effect of KOE SSTa on the vertical motion of the atmosphere. This is because the fixed SST anomaly in the model has been forcing the atmosphere as a constant heat source, thus causing the vertical motion anomalies to far exceed the observed values.
6. Discussion and conclusions
The AKNA pattern, a new wintertime pan–North Pacific SAT pattern, is identified using EOF analysis based on NCEP reanalysis data. The EOF analysis is performed in the pan–North Pacific region (85°E–85°W, 25°–65°N), and long-term signals reflective of global warming have been removed. As the fourth EOF mode, the AKNA pattern presents a meridional dipole anomaly over extratropical Asia and a zonal dipole anomaly over North America, accounting for 8.90% of the variance in DJF mean pan–North Pacific SAT. A new AKNA index is defined by the difference of regionally averaged SAT anomalies. Correlation analysis indicates the AKNA pattern is related to the variation of SSTa over the KOE region.
Two model experiments are carried out to explore whether the AKNA pattern arises as a response to the variation of KOE SSTa, using the CAM6 model forced by monthly climatological prescribed SST field. The differences in atmospheric circulation and SAT between the FORC and CTRL runs are compared to the differences between positive and negative AKNA winters produced by NCEP reanalysis data. After adding warm SSTa in the wintertime KOE region (positive AKNA winters in NCEP), the downstream tropospheric atmosphere presents a ridge response, accompanied by a reduced Siberia high pressure, a reduced Alaska ridge, and an anticyclonic circulation over eastern North America. Consequently, the cold air from the polar region is restricted to northern Asia and northwestern North America, which makes southern East Asia and southeastern North America experience warmer winters and less extremely cold weather days.
Further analysis shows similar variations in the westerlies and storm tracks from the NCEP reanalysis and model experiments. For the FORC run/positive AKNA winters, the zonal wind and eddy heat flux shift poleward under the forcing of warm KOE SSTa. The variation of baroclinic instability and eddy feedback is consistent with the variation of storm tracks and zonal wind, which indicates that the KOE SSTa can modulate atmospheric eddy activity and westerlies through changing baroclinic instability. At 850 hPa, the increased (decreased) baroclinic energy conversion promotes (inhibits) atmospheric eddy activity to the north (south) of the westerly jet, causing the storm track to shift poleward. At 300 hPa, diagnostic analysis reveals an opposite-sign response of energy conversion over the western and eastern North Pacific. The intensified eddy activity converts energy to the mean flow to maintain the intensified zonal wind aloft the KOE region and absorbs energy from the mean flow to dampen the intensified zonal wind downstream in the jet exit region. In addition, the atmospheric vertical motion over the western and eastern North Pacific was investigated separately. For the FORC run/positive AKNA winters, the atmospheric vertical motion shows intensified upward motion over the KOE region and downward motion over the eastern North Pacific. In particular, the model experiments show, but may exaggerate, the response of the local atmosphere to the KOE SSTa. The atmosphere rises over warmer SST and sinks on both sides, and this vertical motion can extend from the boundary layer to Z500 troposphere.
Overall, this study reveals a new SAT pattern influenced by the variation of KOE SSTa and contributes to the understanding of their influence on climate. Results of the model experiments suggest the contribution of KOE SSTa to the formation of the AKNA pattern. However, the stabler KOE SSTa is not the only factor affecting the AKNA pattern. EP El Niño partly contributes to the interannual variability of AKNA, and the impact of atmospheric internal physical processes on the AKNA index remains to be explored. In addition, as mentioned by Kohyama et al. (2021), the synergistic effect of the Gulf Stream cannot be ignored. Although the most significant atmospheric response is over the North Pacific, the physical mechanism of atmospheric circulation changes over the two continents still needs further investigation. In addition, the conclusions need to be confirmed in a fully coupled and high-resolution model in future work.
Acknowledgments.
The authors acknowledge the support from the Shenzhen Fundamental Research Program (Grant JCYJ20200109110220482) and National Natural Science Foundation of China (Grant U2006210).
Data availability statement.
Daily NCEP–NCAR reanalysis data and climate indices are obtained from the NOAA PSL website (https://www.esrl.noaa.gov/psd/data/gridded/). Monthly GISTEMP data are obtained from the National Aeronautics and Space Administration’s Goddard Institute for Space Studies (https://data.giss.nasa.gov/gistemp/). Monthly ERA5 reanalysis data are obtained from the Copernicus Climate Change Service (C3S) Climate Date Store (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=form). Monthly MERRA-2 reanalysis data are obtained from NASA website (https://disc.gsfc.nasa.gov/datasets/M2IMNXASM_5.12.4/summary). Monthly JRA-55 reanalysis data are obtained from the JMA website (https://search.diasjp.net/en/dataset/JRA55). HadISST data are obtained from Met Office Hadley Centre website (https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html). OISSTv2 data are obtained from the NOAA PSL website (https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html). OAFlux data are obtained from the WHOI Objectively Analyzed Air-Sea Fluxes (OAFlux) project through an FTP server (ftp://ftp.whoi.edu/pub/science/oaflux/data_v3/). The numerical model simulations upon which this study is based are too large to archive or transfer. Instead, we provide all the information needed to replicate the simulations. We used CESM 2.2.1. The model code and the default initial field have not been modified in any way. The compilation scripts and boundary condition files are available at https://zenodo.org/record/7513953#.Y7roeP5Bz2c.
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