It is argued that anthropogenic global warming may decrease the global occurrence of cold waves. However, a historical record-extreme cold wave, popularly called the “boss level” cold wave, attacked East Asia in January 2016, which gives rise to the discussion of why this boss-level cold wave occurred during the winter with the warmest recorded global mean surface air temperature (SAT). To explore the impacts of human-induced global warming and natural internal atmosphere variability, we investigated the cold-wave-related circulation regime (i.e., the large-scale atmospheric circulation pattern) and compared the observation with the large ensemble simulations of the MIROC5 model. Our results showed that this East Asian extreme cold-wave-related atmospheric circulation regime mainly exhibited an extremely strong anomaly of the Ural blocking high (UBH) and a record-breaking anomaly of the surface Siberian high (SH), and it largely originated from the natural internal atmosphere variability. However, because of the dynamic effect of Arctic amplification, anthropogenic global warming may increase the likelihood of extreme cold waves through shifting the responsible natural atmospheric circulation regime toward a stronger amplitude. The probability of occurrence of extreme anomalies of UBH, SH, and the East Asia area mean SAT have been increased by 58%, 57%, and 32%, respectively, as a consequence of anthropogenic global warming. Therefore, extreme cold waves in East Asia, such as the one in January 2016, may be an enhanced response to the larger internal atmospheric variability modulated by human-induced global warming.
A large amount of evidence has shown that global warming has increased (reduced) the probability of global heat (cold) waves (Stott et al. 2004; Peterson et al. 2012, 2013; Herring et al. 2014, 2015, 2016; Screen et al. 2015; Ma et al. 2017; Yeh et al. 2018). However, a series of unusual extreme cold events, such as the bitterly cold waves in Europe, the eastern and central United States, and southern Canada, occurred during the winter of 2010/11, 2013/14, and 2014/15, and an all-time record low temperature in New York occurred on 7 January 2014 (Peterson et al. 2012; Herring et al. 2016). These extreme cold waves happened along with recent global warming, reflecting a fascinating paradox, that is, that unusually cold extremes still occur frequently during boreal winter, despite global warming.
Some researchers have explored linkages between extreme cold events and recent global warming (i.e., Cattiaux et al. 2010; Kodra et al. 2011; Francis and Vavrus 2012; Liu et al. 2012; Cohen et al. 2014; Wallace et al. 2014; Screen et al. 2015; Shepherd 2016; Overland et al. 2016). The argument concerning the more frequent occurrence of cold events in the context of climate change has focused on the response of atmospheric dynamics (Shepherd 2016). Some studies have suggested that Arctic warming, along with decreased sea ice, has resulted in a wavier jet stream and slower Rossby waves because of a reduced meridional gradient of the air temperature. This favors blocking over higher latitudes, which leads to more frequent and intense cold spells in the midlatitudes over the Northern Hemisphere (Francis and Vavrus 2012; Liu et al. 2012; Mori et al. 2014). For instance, Cohen et al. (2012) proposed that Arctic warming may enhance the Siberian high by increasing the amount of snow cover, leading to stronger diabatic cooling, which dynamically induces widespread winter cooling. Other results have suggested that the enhanced Siberian high is induced by the remote response of Rossby waves and intensified anticyclones as a result of Arctic warming (Honda et al. 2009; Zhang et al. 2012; Cohen et al. 2014; Kug et al. 2015).
The upper-level Ural blocking high (UBH) and the surface Siberian high (SH) are the two dominant circulation components linked to East Asia cold waves, and they are strongly modulated by Arctic oscillations on the interannual time scale (Zhang et al. 1997; Park et al. 2011; Wang et al. 2010; Cheung et al. 2012). Emerging evidence has indicated that amplified Arctic warming favors more frequent UBH and strengthened SH, resulting in increased incidences of extreme cold spells and winters over Eurasia (Honda et al. 2009; Cohen et al. 2012; Francis and Vavrus 2012; Liu et al. 2012; Zhang et al. 2012; Mori et al. 2014; Kug et al. 2015; Yao et al. 2017). Observations have shown that East Asia has been repeatedly affected by cold waves in recent decades. For example, an unprecedented freezing disaster occurred in southern China in early January 2008 (Ding et al. 2008), and record-breaking blizzards and low temperatures frequently affected many areas of China during the winter of 2010/11 and January–February 2012 (Gong et al. 2014). These extreme cold events caused great damage to agriculture, transportation, power infrastructure, and people’s lives (Ding et al. 2008; Gong et al. 2014). Accumulated evidence implies that extreme cold waves have become more serious and frequent with recent global warming. Motivated by this counterintuitive hypothesis, we analyzed the changes of East Asian winter temperature extremes during the era of Arctic amplification (AA; 1988–2016) and found that more frequent cold extremes are observed because of the more frequent blocking over the Ural region and the stronger Siberian high, possibly induced by the AA phenomenon in recent decades (Ma et al. 2018). The AA phenomenon is one of the clearest manifestations of recent climate change (Hartmann et al. 2013). However, the quantitative contribution of anthropogenic climate change to the extremely cold waves such that occurred in East Asia in January 2016 has not been clearly evaluated.
While any chaotic system such as atmospheric circulation can give rise to extremes without a change in external forcing, the probability of their occurrence in a particular regime may be modulated when external forcing is varied (Palmer 1999). It is argued that anthropogenically forced changes in climate may in fact project principally onto modes of natural climate variability; therefore, recent climate change can be interpreted in terms of changes in the frequency of occurrence of natural atmospheric circulation regimes (Corti et al. 1999; Shepherd 2014). Changes in atmospheric circulation regimes could cause synchronization of extreme weather (Palmer 2013; Coumou et al. 2014; Shepherd 2014). Extreme cold events over East Asia are one of the most frequent climate disasters in winter and are usually associated with specific anomalous circulation regimes (Ding et al. 2008; Gong et al. 2014). It is still an open question whether AA influences the natural internal atmosphere variability and thus results in the strengthening or reducing of the atmospheric circulation regime related to East Asian cold waves, which would finally lead to more serious extreme cold events.
During the winter of 2015/16, the global surface air temperature (SAT) reached a historical high as a result of global warming and the strongest recorded El Niño event (WMO 2017), but East Asia unexpectedly experienced a severe cold event in late January 2016. This cold wave swept through eastern China and south to Thailand, leading to extremely low SATs in East Asia, producing the first-ever snowfall over northern Vietnam, and causing 14 human deaths in Thailand (www.ncc-cma.net/Website/index.php?ChannelID=100). This extreme cold wave was popularly referred to as the “boss level” cold wave in China because of the widespread temperature reduction and unprecedentedly cold temperature (CMA 2017). Thus, this study aims to understand why the unprecedentedly serious East Asian extreme cold wave occurred at the same time as new record high global temperatures and to further explore the links between East Asian extreme cold-wave events and anthropogenic global warming.
In this study, we started from the extreme cold wave that affected eastern Asia in 2016 and tried to identify the responsible atmospheric circulation regime. Then, we examined the relationship between the East Asian cold-wave atmospheric circulation regime and the natural internal atmosphere variability and recent rapid warming in the Arctic using simulations with large ensembles. Our results show that East Asian extreme cold waves are associated with an atmospheric circulation regime characterized by an extremely anomalous blocking high over the Urals and a Siberian high. The atmospheric circulation regime responsible for East Asian extreme cold waves is largely a behavior of natural internal atmospheric variability. However, we will show evidence that recent anthropogenic global warming, probably via the dynamic effect of the AA phenomenon, drove the shift of the East Asian cold-wave-related atmospheric circulation regime toward the direction of stronger intensity and thus strongly increased the probability of occurrence of extreme cold events over East Asia.
2. Data and methods
a. Description of data
A total of 2474 in situ daily minimum and mean SATs in China were provided by the National Meteorological Information Center (http://cdc.nmic.cn/home.do), covering 1961–2016. Before calculating the area-weighted mean SAT in eastern Asia (15°–50°N, 100°–130°E), we interpolated the station data onto a 0.5° × 0.5° grid resolution using iterative improvement objective analysis with the search radius of 3°–2°–1°–0.5° [using the function of “obj_anal_ic_Wrap” in the NCAR command language (www.ncl.ucar.edu/Document/Functions/Contributed/obj_anal_ic_Wrap.shtml)]. The 6-hourly mean surface and pressure fields from 1979 to 2016 were derived from the ERA-Interim dataset (Dee et al. 2011; http://apps.ecmwf.int/datasets). The global and monthly SATs during 1961–2016 were derived from the Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP) dataset (Hansen et al. 2010; https://data.giss.nasa.gov/gistemp).
Model simulation analysis was based on the atmospheric general circulation model of the Model for Interdisciplinary Research on Climate, version 5 (MIROC5; Shiogama et al. 2014), which is one of the phase 5 of the Coupled Model Intercomparison Project (CMIP5) models (Taylor et al. 2012). This model provides very large ensemble simulations with varying initial conditions under all historical (All-Hist) and only natural (Nat-Hist) scenario runs (see Table 1). The ensemble simulations in the All-Hist run were carried out using the observed sea surface temperatures (SSTs) and sea ice, with historical anthropogenic (greenhouse gas emissions, sulfate and carbon aerosols, tropospheric and stratospheric ozone, and land use changes) and natural (solar irradiance changes and large volcanic activity) external forcing factors. For the simulations since 2006 in the All-Hist run, the external forcing factors were included under the representative concentration pathways 4.5 scenario. In the Nat-Hist run, however, the model was forced by the anthropogenic radiative forcing and land cover/use fixed in the year 1850 with modified ocean boundary conditions, that is, the observed SST and sea ice were adjusted by removing the possible human-induced component (Shiogama et al. 2014). The All-Hist runs included 10 ensemble members for 1950–2016 (All-Hist long-length runs), whereas 100 and 50 ensemble members were simulated in both runs for 2006–16 and 2010–16, respectively. (The model experiments and detailed information can also be found at http://portal.nersc.gov/c20c.)
b. Criteria for the cold wave
We applied a methodology similar to that of Park et al. (2011) to identify cold-wave events over East Asia. We first calculated the area-weighted daily mean SAT index averaged over East Asia (15°–50°N, 100°–130°E) during the winter monsoon season (from November to the following March) over the period of 1979/80–2015/16. We then obtained the daily mean SAT anomalies relative to the daily climatology, as well as the day-to-day decrease in the daily mean SAT A cold-wave event occurrence is defined as at least three consecutive days during which the decreases in the daily mean SAT on at least one day were less than −1σ and the daily mean SAT anomalies on the other days were less than −1σ. The value of σ is defined here as the standard deviation of the daily mean SAT anomalies over the period 1979/80–2015/16.
To examine the impact of anthropogenic global warming on extreme cold waves such as the boss-level cold wave, we calculated the probability ratio of the occurrence of an extreme cold wave and its corresponding circulation regime in the current world, with and without anthropogenic forcing, before obtaining the corresponding fraction of attributable risk. The probability ratio (PR) and fraction of attributable risk (FAR) are defined as follows (Stone and Allen 2005; NAS 2016):
where is the probability of the occurrence of extreme cold waves as severe as the observed boss-level event in late January 2016 in the All-Hist ensemble simulations. The variable is the counterpart of in the Nat-Hist ensemble simulations. The probability ratio PR is defined as the ratio of to , representing the change in the probability of extreme cold waves under anthropogenic forcing. The fraction of attributable risk FAR is the fractional contribution of human-induced cold waves. The fraction of attributable risk measures how much the likelihood of extreme cold waves will increase under human-induced global warming in the current climate system (Stone and Allen 2005; NAS 2016). Bootstrap resampling with replacement (resampled 1000 times) was used to estimate the sampling uncertainty of the probability ratio and the fraction of attributable risk. The corresponding best estimate was approximated by the median value.
a. The cold-wave-related atmospheric circulation regime
We used the daily and monthly SATs to identify the extreme cold wave that occurred over East Asia in late January 2016 and its corresponding historical context. Figure 1a shows the time series of the daily mean SAT over East Asia and the annual global and Arctic mean SATs during the winters of 1961–2016. The Arctic is the source region of mid- to high-latitude cold waves, and greater warming of the Arctic SATs compared with those of lower latitudes under anthropogenic forcing is suggested to result in fewer cold-wave events (Screen et al. 2015; Shepherd 2016). In the winter of 2015/16, East Asia was repeatedly hit by freezing events, although both the global and Arctic mean SAT anomaly (approximately 0.67° and 2.09°C above normal, respectively) set new historical records under a long-term warming trend over 1961–2016 (0.16° and 0.46°C decade−1, respectively, with both trends significant at the 0.01 confidence level). A deadly cold wave occurred on 20–25 January 2016 and the average daily mean SAT over East Asia (−5.50°C on 24 January 2016) broke the historical record low, with the largest SAT anomaly of −7.72°C. This cold wave swept through East Asia and caused negative SAT anomalies in most areas in China (Fig. 1b). Some station-observed daily mean SAT anomalies exceeded −8°C, and the daily minimum SAT was less than the historical record low. Because of the widespread decrease in the SAT and the record-breaking low SAT, this event was nicknamed the boss-level cold wave in public discourse.
The UBH and SH both exerting a dominant cold northerly flow are the key circulation systems responsible for cold waves occurring over East Asia (Zhang et al. 1997; Park et al. 2011). During cold-wave events, 500-hPa geopotential height (Z500) anomalies generally exhibit a wave train across the Eurasian continent, with ridge-trough-ridge circulation anomalies prevailing alternately over the Ural Mountains, the eastern seaboard of East Asia, and the North Pacific, and positive sea level pressure anomalies are superimposed on the region of the Siberian high (Fig. 2). The deepened UBH and amplified SH jointly enhance cold advection, causing the cold air activity to move southward and resulting in widespread cold anomalies in East Asia. Similarly, during the extreme cold wave in late January 2016, the widespread cold anomalies over East Asia occurred in tandem with a strengthened UBH, a deepened East Asian trough (EAT), and an amplified SH, accompanied by a southeastward-propagating Rossby wave train [according to the wave activity flux (WAF) defined in Takaya and Nakamura (2001)] over the Eurasian continent (Figs. 3a,c,e). Compared with the mean state of cold waves during the reference period, this boss-level extreme cold wave had stronger anomalies of the blocking high over the Urals and the SH intensity and, correspondingly, a stronger northerly anomaly over East Asia, resulting in deadly cold weather over East Asia. In brief, the record low SAT over East Asia during the extreme cold wave in late January 2016 occurred concurrently with an extreme amplification of the UBH and the SH during the warmest Arctic winter of 2016 (Fig. 4).
b. Linkage to the atmospheric internal variability
The Arctic is the source region of cold air affecting climates at northern mid–high latitudes. The SAT anomaly in East Asia was negatively and significantly correlated with the SAT anomaly over the regions of the Barents and Kara Seas (BKS; Fig. 5), indicating that the colder East Asia SATs correspond to warmer Arctic SATs over BKS, which is consistent with the study of Kug et al. (2015). A number of studies suggest that the circulation anomaly related to enhanced UBH and SH can be trigged by both the internal variability of the climate system and anthropogenic forcing (Zhang et al. 1997; Cattiaux et al. 2010; Park et al. 2011; Ma et al. 2012; Liu et al. 2012; Mori et al. 2014; Kug et al. 2015; Yao et al. 2017). Both the frequency of UBH and the intensity of SH exhibited an increasing trend during the AA era and favored more cold extremes over East Asia [Fig. 8d of Ma et al. (2018)].
We examined the model-simulated atmospheric circulation regime with respect to the cold wave to distinguish the impacts of external forcing and internal variability on the extreme cold-wave events over East Asia. We first explored the long-term global and Arctic mean SATs and the general features of atmospheric circulation during East Asia cold waves in the model simulations. The ensemble mean simulations in the All-Hist runs were able to reproduce the observed interannual variability and long-term warming trends of the global and Arctic SAT anomalies (Fig. 1, inset plot). The correlation coefficients r for the global and Arctic SAT during 1961–2016 between the model simulation ensemble mean and the observations are 0.95 and 0.74, respectively, with significant warming trends of 0.13° and 0.42°C decade−1 in the ensemble mean simulation and 0.16° and 0.46°C decade−1 in the observations. The observed atmospheric circulation regime with respect to the cold wave can be realistically captured by the simulation (Fig. 2). The anomaly correlation coefficient (ACC) between the observation and the model composite simulation is +0.90 for the Z500, +0.88 for the SLP, and +0.92 for the SAT. Meanwhile, the model simulation captures well the dipole correlation pattern over the Eurasian continent, with negative correlations prevailing over the Arctic region northwest of Lake Baikal and strong positive correlations over the East Asia region (Fig. 5b). The pattern correlation between the observations and the model simulation is 0.94, indicating high similarity between model responses and the observations. These statistical verifications solidify the attribution analysis of the extreme cold wave based on the model simulations.
Taking the extreme cold wave that occurred in late January 2016 as representative of East Asian extreme cold waves, for the simulated atmospheric circulation conditions on 20–25 January 2016, the ensemble mean of 160 members under the All-Hist scenario does not capture the strong positive anomalies of Z500 over the Urals and exhibits a weakening instead of deepening of the East Asia trough (Fig. 3b). The strong positive SLP anomalies over the Asian continent and cold SAT anomalies over East Asia in the observations also cannot been seen in the 160-member ensemble mean circulations (Figs. 3d,f). The small amount of similarity between the anomalous atmospheric circulation patterns during this boss-level extreme cold wave according to observations and the 160-member ensemble mean of All-Hist runs implies that the internal variability of the atmosphere, which is independent on the ocean boundary conditions and background forcings, that is, the observed ocean or sea ice states and external forcing factors, possibly played an important role in this extreme cold wave.
To verify our hypothesis, we examined each All-Hist ensemble simulation and identified the five members that exhibited the largest negative SAT anomalies on 20–25 January 2016 over East Asia. Their ensemble mean shows a clear southeastward-propagating Rossby wave train over the Eurasian continent and an amplification of both the blocking high over the Urals and the major trough over East Asia (Fig. 6a). Meanwhile, positive SLP anomalies override the Siberian high domain, with strong northerly winds and cold SAT anomalies over East Asia (Figs. 6c,e). The ACC between the observation and the average of the five members with the largest East Asia SAT cold anomalies is 0.84 for the Z500, 0.85 for the SLP, and 0.72 for the SAT. These sufficient similarities of atmospheric circulations indicate that the model-simulated stochastic atmospheric internal variability can be regarded as the dominant contributor to East Asian extreme cold-wave events.
To further examine the cold-wave circulation regime of the model simulation, we applied multivariate empirical orthogonal function analysis (MV-EOF) to the Z500, SLP, and SAT anomalies of all members in the All-Hist runs during 20–25 January, 2016. The first leading MV-EOF accounts for 38% of the total variance and resembles a cold-wave circulation regime (Figs. 6b,d,f). Similar to the observations (Fig. 3a), the spatial patterns of the first MV-EOF mode show a “ridge trough ridge” pattern over the Eurasian continent, with a strong positive center of Z500 anomalies over the Urals and a negative center over the eastern coast of East Asia (Figs. 3a, 6b). At the surface, a prominent feature is the anomalous SH with enhanced northerly winds prevailing on its east flank, alongside a strong cold anomaly centered over East Asia (Figs. 6d,f). That is, the largest intraensemble spreads of Z500, SLP, and SAT anomalies during 20–25 January 2016 in All-Hist simulations are characterized by the amplification of both the UBH and SH and strong cold anomalies over East Asia.
For the Nat-Hist runs, the 150-member ensemble mean was also unable to reproduce the observed cold-wave circulation regime. However, both the average of those members with the coldest East Asia SAT anomalies and the first MV-EOF of 150-members exhibit a similar cold-wave circulation regime in the Z500, SLP, and SAT anomalies, which was also revealed by observations (Fig. 7). Together, these findings suggest that the boss-level cold wave itself is unlikely to be attributable to boundary forcing but was dominantly induced by the stochastic behavior of atmospheric circulation.
c. The modification of atmospheric internal variability by Arctic warming
A particular climate extreme event is hardly attributed to the direct impact of climate change, but its occurrence probability, to some extent, can be identifiable under a warming climate (Huber and Gulledge 2011; Peterson et al. 2012, 2013; Herring et al. 2014, 2015, 2016; NAS 2016). The probability-based attribution of individual extreme events is a particularly powerful approach, which has been used to link a single climate extreme with global warming (Huber and Gulledge 2011; NAS 2016). Therefore, we can evaluate the probability of extreme cold waves, such as the case in late January 2016, and explore the possible impact of human-induced climate change—for example, the AA phenomenon (Fig. 8), one of the clearest manifestations of recent climate change.
During the boss-level extreme cold-wave event, the intensity anomalies of the UBH and SH are the fifth-highest record and the highest record, respectively, of all cold waves occurring since 1979, with the lowest average SAT cold anomaly in East Asia (Fig. 4). Previous studies have suggested that the substantially greater warming over the Arctic than over lower latitudes has led to more frequent Eurasian blockings with an intensified SH, which provides a favorable circulation anomaly for severe extreme cold events in Eurasia (Honda et al. 2009; Liu et al. 2012; Mori et al. 2014; Kug et al. 2015; Ma et al. 2018).
A number of studies have suggested that the response of atmospheric circulation to external forcing appears to project strongly onto the existing patterns of atmospheric variability (Corti et al. 1999; Palmer 2013; Shepherd 2014; Overland et al. 2016). Therefore, we speculate that recent anthropogenic global warming through the dynamic effect of the AA phenomenon (Fig. 8) may project onto the East Asian cold-wave-related circulation regime, which is conducive to the occurrence of extreme cold events. To verify our speculation, we calculated the occurrence rate of the cold-wave-related circulation regime, which was derived from the All-Hist and Nat-Hist runs, respectively. Figure 9 shows the probability density functions of the anomalous intensity of the UBH, the SH, and the East Asia SAT related to cold waves during the winters of 2006/07–2015/16 and 1950/51–2005/06. The AA phenomenon is obvious in the recent climate change (Fig. 8a). Compared with the less warm Arctic period from 1950/51 to 2005/06, the East Asian cold-wave circulation regime during the recent warmest Arctic 10-yr period of 2006/07–2015/16 become stronger, with stronger positive anomalies of UBH and SH and negative anomalies of the East Asia SAT (Fig. 9, red curves and blue curves). During 2006/07–2015/16, the AA phenomenon become more obvious in the All-Hist ensemble simulations relative to the Nat-Hist ensemble simulations (Fig. 8b). The PDFs of both the UBH and SH intensity anomalies in the All-Hist ensemble simulations are shifted to stronger UBH and SH anomalies relative to the Nat-Hist ensemble simulations without AA (Fig. 8b), and the PDFs of the East Asia SAT anomalies are shifted to colder anomalies (Fig. 9, red curves and green curves). Corresponding to the stronger AA phenomenon, the shifts of the PDFs during 2006/07–2015/16 in the All-Hist ensemble simulation relative to the Nat-Hist ensemble simulations are stronger than those relative to the period of 1950/51–2005/06 in the All-Hist ensemble simulations. This implies that human-induced Arctic warming may enhance the probability of extreme cold-wave events by intensifying the UBH and the SH.
We chose the observed anomalies (209.72 gpm for UBH, 13.39 hPa for SH, and −4.80°C for East Asia SAT) during the boss-level cold wave as the threshold of extreme cold-wave events and estimated the contribution of anthropogenic amplified Arctic warming to the likelihood of an East Asia extreme cold wave with the boss-level cold wave’s severity. Specifically, for the simulated cold waves over East Asia during the AA period of 2006–16 from the All-Hist (Nat-Hist) experiments, the best estimates of the probabilities of exceeding the extreme threshold in the All-Hist (Nat-Hist) experiments are 0.79% (0.34%) for the UBH, 4.64% (3.13%) for the SH, and 5.79% (2.41%) for the East Asia SAT anomaly. This suggests that anthropogenic forcing has doubled the probability of the cold-wave circulation regime and increased the probability of extreme cold waves in East Asia by about a factor of 1.47. The corresponding best estimate of the fraction of attributable risk exceeding the extreme threshold for the UBH, the SH, and the East Asia SAT is 0.58 (5%–95% uncertainty range is 0.49–0.64), 0.57 (5%–95% uncertainty range is 0.35–0.72), and 0.32 (5%–95% uncertainty range is 0.19–0.44), respectively (Fig. 9d).
4. Summary and discussion
Our previous study suggests that the East Asian cold extremes show a more extreme tendency during the Arctic rapid warming era because of the dynamic effect of the Arctic amplification phenomenon (Ma et al. 2018). An exceptionally extreme cold wave occurred in East Asia on 20–25 January during the warmest Arctic winter of 2016. As the anthropogenic atmospheric circulation responses usually project onto the natural internal variability, it is necessary to explore how the possible impacts of anthropogenic Arctic warming impose on the internal atmospheric variability and then act on East Asian extreme cold waves. Thus, in the present study, we started from this extreme cold wave in 2016 and tried to investigate the atmospheric circulation regime responsible for East Asian extreme cold waves based on observations and the huge pool of MIROC5 ensemble simulations. Our results suggest that the cold-wave-related circulation regime is mainly characterized by an intensified UBH, an amplified SH, and a strengthened cold northerly wind anomaly over East Asia. These atmospheric circulation anomalies are closely related to the stochastic internal variability of the atmosphere. However, human-induced Arctic warming strongly increased the probability of such extreme cold-wave events. According to model simulation, the odds of cold-wave circulation regime occurrence have increased by 58%, 57%, and 32%, respectively, for the UBH, SH, and East Asia SAT as a consequence of anthropogenic Arctic warming. Therefore, the extreme cold wave over East Asia in 2016 may have been a response to the larger internal variability of the atmosphere as a result of the projection of human-induced global warming through the AA dynamic effect.
Our previous study suggested that East Asian cold extremes have become more frequent and intense during the AA era, possibly because of the dynamic effect of AA (Ma et al. 2018). In this study, we show evidence that anthropogenic global warming increases the occurrence probability of East Asian extremes cold waves, such as the boss-level cold wave in January 2016, through inducing larger internal atmospheric variability, probably as a consequence of projection of the dynamic effects of the largely human-induced amplified Arctic warming. Although the AA phenomenon during recent decades is tremendous, it is only one of the clearest manifestations of recent climate change. The hiatus in surface warming, which is mainly evident in the central and eastern Pacific, is also one of the clearest manifestations of recent surface warming (Kosaka and Xie 2013; Trenberth 2015). It was also suggested that tropical Pacific forcing of the atmosphere, such as that associated with a negative phase of the PDO during the recent hiatus, is mainly responsible for the global quasi-stationary waves in the upper troposphere, which in turn create persistent regional climate anomalies and increase the odds of cold winters in Eurasia (Trenberth et al. 2014). The winter cooling over the early 2000s in northwest North America was primarily a remote response to climate fluctuations in the tropical Pacific (Sigmond and Fyfe 2016). The compounding effects of the anomalous Pacific SST and reduced Arctic sea ice contributed to the severe North American winter in 2013/14 (Lee et al. 2015). Meanwhile, Arctic warming is modulated by tropical forcing (Ding et al. 2014; Tokinaga et al. 2017). The Arctic and the tropics are the source regions of cold and warm air activity affecting the East Asian climate, respectively. Therefore, remote responses to the climate change both over Arctic and tropics may affect the East Asian climate. Although separating out Arctic warming’s influence on East Asian cold waves from tropical forcing is not easy because of the high interconnection between Arctic and tropics, how tropical forcing influences the extreme cold waves in East Asia is a very interesting scientific question and merits further investigations.
Note that our estimation of the fraction of attributable risk FAR presented here is dependent of MIROC5 and the boundary conditions used. Thus, the estimation of FAR may change as the models are refined or if a different subset of models and experiments are used.
This study was jointly supported by the National Natural Science Foundation of China (41705052, 41830969, and 41775052), the Special Funds for Climate Change (CCSF201830), the National Key R&D Program (2018YFC1505904), and the Basic Scientific Research and Operation Foundation of CAMS (2018Z006). This study was also supported by the Jiangsu Collaborative Innovation Center for Climate Change. The datasets are all freely available on the official websites.