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Review

A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary

1
Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
2
Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate, Ministry of Education, Fudan University, Shanghai 200438, China
3
CMA-FDU Joint Laboratory of Marine Meteorology, Shanghai 200438, China
4
School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610103, China
5
Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(9), 1115; https://doi.org/10.3390/atmos15091115
Submission received: 9 July 2024 / Revised: 5 September 2024 / Accepted: 9 September 2024 / Published: 13 September 2024
(This article belongs to the Special Issue Arctic Atmosphere–Sea Ice Interaction and Impacts)

Abstract

:
In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research.

1. Introduction

The latest Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) revealed that the global surface temperature during 2011–2020 was 1.09 °C higher than that of 1850–1900, with land warming (1.59 °C) being more significant than the ocean (0.88 °C) [1]. In recent years, the Arctic has experienced unprecedented changes, including rapid surface warming and shrinking sea ice extent, which have profound impacts on the Arctic environment [2,3]. As one of the most sensitive climate system responses to global warming, previous studies have indicated that the rate of surface warming in the Arctic is approximately two to four times faster than the global average, a phenomenon known as the Arctic Amplification (AA) effect [4,5,6,7]. Multiple Arctic local feedback processes, such as sea ice–albedo feedback [8], cloud feedback [9], water vapor feedback [10], and Planck feedback [11], in addition to poleward energy transport from mid-low latitudes driven by atmospheric (e.g., Arctic/North Atlantic Oscillation, AO/NAO; Ural blocking, UB) [4,12,13] and oceanic (e.g., Pacific Decadal Oscillation, PDO; Atlantic Multidecadal Oscillation, AMO) [14,15,16] circulation patterns, collectively contribute to AA [17,18]. As the lower boundary of the Arctic climate system, sea ice changes directly regulate the exchange of heat, momentum, and moisture between the cold atmosphere and warm ocean by altering open water extent and surface reflectivity, thus affecting local atmospheric variability [19]. The associated sea ice–albedo feedback [8,20], which refers to the sea ice loss caused by rising temperatures intensifying surface warming in turn via decreasing albedo and increasing incoming solar radiation, plays a crucial role in driving AA [21,22]. For instance, the bidirectional interactions between sea ice and the atmosphere enhance the multidecadal variability in Arctic Sea ice and sea surface temperatures (SST) across the North Atlantic and Barents–Kara (BK) Seas, as well as the Atlantic Meridional Overturning Circulation (AMOC), while these fluctuations are significantly diminished in the absence of changes in Arctic Sea ice [23,24].
In addition to the local climate effects, an increasing number of studies focus on whether the impact of Arctic Sea ice can extend to mid- and low latitudes, especially on interannual timescales. This is attributed to the accelerated decline in Arctic Sea ice observed by satellite since 1979 [8] and the associated heightened air–sea ice interactions, which are concurrent with the rapid Arctic warming signal [4]. Meanwhile, there is an increasing frequency of extreme weather and climate events in the midlatitudes, such as heavy precipitation, extreme cold surges, and persistent heatwaves [25,26]. In such a climate context, the intensification and more frequent occurrence of extreme weather and climate disasters may be related to the reduced Arctic Sea ice. The rate of decline in yearly Arctic Sea ice extent has been approximately 3.8% per decade during the 1979–2012 period, accompanied by reduced thickness, prolonged melting periods, and increased seasonal ice replacing multiyear ice [27]. Particularly since the 21st century, Arctic Sea ice extent has consistently reached historically unprecedented low levels (e.g., 2007, 2011, 2012, 2016, 2018, 2019, 2020, 2021), with the minimum extent recorded in September 2012 at approximately 3.6 × 106 km2 (Figure 1) [28].
These low sea ice years are generally followed by extreme temperatures and precipitation events. In early 2008, southern China experienced a rare snowstorm and freezing disaster [29]; During the winter of 2010, Europe experienced three successive cold outbreaks with unusual persistence of snow cover and several states of the Eastern United States recorded their snowiest winter ever, coinciding with an unprecedented duration of the negative NAO phase [30]; From 17 January to 1 February in 2012, a cold wave swept across the entire Eurasian continent, resulting in over 700 freezing deaths [31]; In mid-to-late December 2012, Russia experienced the strongest cold surge since 1938, with temperatures dropping to as low as −50 °C in Siberia, leading to at least 88 freezing deaths and over 1200 cases of frostbite; In February 2018, the outbreak of a robust anticyclone brought heavy snowfall and freezing weather to Europe [32]; Eurasia experienced a lasting 26-day extreme heatwave from 2 August to 27 August in 2016, which had severe impacts on the economy and ecosystems [33,34]; The extreme Mei-yu rainfall during the summer of 2020 along the Yangtze River reached its heaviest level since 1961 [35,36], and record-breaking July-mean rainfall hit North China in 2021 [37], resulting in historic flooding with devastating economic losses and human casualties. The crucial contribution of reduced Arctic Sea ice to some of the aforementioned extreme weather and climate events has been identified through case studies and numerical experiments [31,33,36,37]. Furthermore, numerous studies have proposed a significant statistical connection between the Arctic and midlatitudes on interannual timescales, manifested as the “warm Arctic–cold Eurasia (WACE)” pattern in winter and “cold Arctic-warm Eurasia” in summer due to the reduction of Arctic Sea ice, which is partially supported by sea ice sensitivity experiments [34,38,39,40,41,42,43,44,45,46]. Nevertheless, the attribution of midlatitude atmospheric variations to Arctic anomalies remains a topic of debate [6,47,48,49,50,51,52], as the Arctic–midlatitudes connection is unstable and only manifests during specific epochs, varying with the magnitude and location of sea ice anomalies, seasonality, the study period, and interference from other external forcing [53,54,55,56]. Some studies have even suggested that the fluctuation of the Arctic–midlatitudes connection over time is dominated by atmospheric internal variability [57].
The variability of midlatitudes atmospheric circulation exerts an influence on Arctic changes in turn, forming a two-way interaction between the Arctic and midlatitudes. Several predominant low-frequency atmospheric pattern in the midlatitudes, including NAO, UB, and Pacific–North American (PNA) patterns, contribute to the Arctic thermal and sea ice anomalies through anomalous meridional transport of heat and moisture [58,59,60]. Tropical anomalous signals (e.g., El Niño–Southern Oscillation, ENSO) can also induce Arctic anomalies by generating Rossby wave trains and then disturbing atmospheric prevailing modes in the midlatitudes [61], such as the PNA.
Numerous prior studies have reviewed the linkage between Arctic changes and midlatitude weather, addressing potential remote effects and physical pathways associated with Arctic amplification or sea ice reduction, as well as the uncertainties and intermittencies inherent in this connection, along with discrepancies between model simulations and observations or among intramodel [3,6,46,52]. However, with ongoing global warming, expanded sea-ice datasets, and improved models, some scholars have gained new insights and perspectives into the Arctic–midlatitude connection. This review has focused on the bidirectional impact between the Arctic and midlatitudes, including further comparison of the different climate effects of autumn–winter sea ice changes in distinct Arctic regions, the possible role of spring–summer Arctic changes in midlatitude weather, and how midlatitude variability or even tropical signals affect Arctic variations. In addition, the Arctic–midlatitude connection fluctuates over time, and the causes of this nonstationary relationship have been widely discussed, representing another focal point of this review. Hence, we provide a comprehensive summary of recent progress and an outlook on the relationship between midlatitude variability and Arctic Sea ice or thermal conditions, with a specific focus on the interannual timescale.
The potential impact and underlying physical mechanisms of autumn–winter, as well as spring–summer Arctic variations on midlatitude temperature and precipitation are summarized in Section 2, particularly emphasizing the distinct role of regional sea ice. In Section 3, the contribution of atmospheric patterns in the midlatitudes and even tropical forcing to Arctic variations is reviewed. Section 4 overviews the characteristics of the nonstationary Arctic–midlatitude connection and possible explanations. Section 5 provides a conceptual summary and emphasizes the pressing issues that require attention, as well as the direction for future advancements.

2. Possible Role of Arctic Variations in Mid-Low Latitude Climate/Weather

2.1. Autumn–Winter Arctic Variations

In the context of global warming, the autumn–winter Arctic Sea ice extent (SIE) or concentration (SIC) has shown an increasing decline rate since 1979, with frequent historically unprecedented low levels in recent years. Specifically, the autumn Arctic Sea ice cover exhibits the strongest declining trend and reached a record low in September 2012, declining by 40% (IPCC; https://www.ipcc.ch/srocc/, accessed on 15 June 2024). Some studies have projected that by 2050, the summer Arctic Ocean may experience an “ice-free” phenomenon [62,63,64,65]. The rapid changes in the Arctic, such as continuous sea ice melting, accelerated tropospheric warming, and increased precipitation [4,66], may potentially contribute to diverse climate and weather variability in the midlatitudes and have attracted intensive scientific attention [67,68,69,70,71,72,73,74,75,76,77].
Early studies have revealed that the autumn–winter decline of pan-Arctic Sea ice is associated with the negative phase of AO/NAO responses [78,79,80,81]. Deser et al. [80] utilized CCM3 models with sea ice and sea surface temperature anomalies to conduct simulations, which indicated that the atmospheric circulation exhibits a quasi-stationary NAO-like structure after 2–2.5 months. Liu et al. [81] identified that winter atmospheric responses to the reduction of pan-Arctic Sea ice display broader meridional meanders in midlatitudes, with more frequent episodes of blocking patterns, resulting in increased cold surges and heavy snowfall over northern continents. It seems that Arctic warm anomalies linked to sea ice decrease may contribute to the negative AO/NAO phase and modulate the North Atlantic (NA) storm tracks, thereby indirectly influencing midlatitude variability. However, several modeling studies have cast doubt on this relationship due to the weak and insignificant atmospheric responses to pan-Arctic Sea ice anomalies, which quite differ from the AO/NAO-like distribution [82,83,84,85].
To address the aforementioned inconsistencies, certain studies have initiated investigations into the linkage of midlatitude variability to regional Arctic Sea ice changes, indicating that atmospheric responses are highly sensitive to the geographical location of sea ice anomalies [6,86,87,88,89]. Chen et al. [88] objectively categorized the Arctic region into smaller subregions based on interannual sea ice variability and established robust statistical associations between winter Eurasian cooling and autumn–winter sea ice loss in the BK Seas, as well as in the Beaufort Sea. Screen et al. [89] conducted an analysis of ensemble simulations forced with sea ice loss separately in nine distinct regions of the Arctic and proposed that atmospheric responses to pan-Arctic Sea loss are not simply a linear addition of the responses to regional sea ice loss. Different physical processes dominate the atmospheric anomalies induced by sea ice loss in distinct regions, with dynamical responses extending further to midlatitudes and thermodynamical responses only constraining the Arctic region. The BK Seas have consistently been emphasized in numerous previous studies as a crucial region where the reduction of autumn–winter sea ice promotes a more meandering atmospheric circulation, an intensified Siberian High, increased blocking patterns, and enhanced southward transport of abundant water vapor, which contributes to significant cooling, heavy snow, and freezing weather over northern Eurasia during winter [32,38,40,42,67,68,69,74,75,90] (Figure 2b). Several studies suggested that September SIC in the eastern Arctic Ocean is a potentially important precursor for the winter Siberian High [69], northern mode of the East Asian winter monsoon (EAWM) [72], and the occurrence of extreme cold events [31,67,71,91]. While the autumn BK sea ice loss (the second EOF of early autumn SIC interannual variability) is more effective in influencing and predicting the winter large-scale atmospheric circulation [66,88,89], the potential climate implications of autumn sea ice loss in the East Siberian–Chukchi–Beaufort (EsCB) Seas (the first EOF of early autumn SIC interannual variability) are increasingly significant due to the increased interannual variation of SIC and enhanced local air–sea ice interaction along with global warming [76,92]. The most pronounced responses to EsCB sea ice loss can be observed in early winter, manifested as anticyclonic anomalies over northern Europe, cooling in central-western Eurasia, and more frequent extreme low temperature in central-western China [76,93] (Figure 2a). In contrast, atmospheric responses to BK sea ice loss peak in midwinter, with larger-scale anticyclonic anomalies, widespread cooling across northern Eurasia, and more frequent extreme low temperatures in northwestern-northeastern China [75,93] (Figure 2). It is noteworthy that the role of autumn EsCB sea ice loss in northern Eurasian cooling can persist into early spring via modulating or maintaining AO-like atmospheric anomalies [92,94]. Additionally, it has been observed that during winter, sea ice melting around the Greenland Sea influences temperature variability over eastern North America and Northern Europe [95,96], sea ice melting in the Sea of Okhotsk is closely linked to North American climate anomalies and North Atlantic cyclone formation [89,97], and sea ice melting in the Chukchi-East Siberian Seas is conducive to central North American cooling [98]. Kug et al. [41] also reported different impacts of Arctic warming in distinct regions on continental cold anomalies. The BK warming is associated with Eurasian cold anomalies, while the Chukchi warming is linked to American cold anomalies.
The complex physical mechanisms involving various interactions and feedback processes that seek to elucidate the causality of the Arctic–midlatitude connection have received wide attention and deep discussion [6,99]. Cohen et al. [99] provided a comprehensive overview of three potential dynamic pathways through which the Arctic influences midlatitude climate variability, including storm tracks [100,101], jet streams [102], and large-scale planetary waves [103]. Recent studies have further categorized the physical processes into stratospheric and tropospheric pathways. The stratospheric pathway generally involves the vertical propagation of quasi-stationary planetary waves [75,104]. By conducting several stratospheric nudging experiments, Zhang et al. [75] underscored the crucial role of stratospheric processes in inducing robust cold Siberia in winter due to BK sea ice loss, while tropospheric processes merely contribute to Arctic warm anomalies. The enhancement of upward propagated quasi-stationary planetary waves, resulting from the resonance of planetary wave 1 with increased amplitude, leads to a deceleration of the zonal westerly winds through wave-mean flow interaction and a weakening of the stratospheric polar vortex. Subsequently, the downward propagation of self-persistent stratospheric signals enhances tropospheric anticyclonic anomalies in the following 1–2 months, facilitating Eurasian cooling and cold surge events. Similar stratospheric processes can be observed in the impact of EsCB sea ice loss, with a predominant contribution from the resonance of planetary wave 2 [92,93,94] (Figure 2).
The tropospheric pathways primarily encompass variations in the westerly jet, alteration in storm track activity [71,105,106], horizontal propagation of quasi-stationary planetary waves [38,72,76], and the formation of blocking. Significant warm anomalies in the Arctic region lead to a decrease in the meridional temperature gradient and induce significant easterly wind anomalies, which decelerate westerly winds according to the thermal wind balance relation [81,107]. The weakened polar jet stream not only increases the meridional amplitude of Rossby waves, facilitating the formation of Arctic anticyclonic anomalies [102], but also provides more favorable conditions for the upward propagation of planetary waves from the troposphere into the lower stratosphere [108,109,110]. Through a southeastward stationary Rossby wave train, the atmospheric perturbation energy caused by sea ice loss propagates downstream and subsequently deepens the continental troughs [38,72,76], thereby guiding more Arctic cold air to penetrate southward and resulting in an increased frequency and intensity of extreme cold weather events in the midlatitudes [68,102,107]. Accompanied by the deceleration of zonal westerly winds, significant changes occur in the storm track, and the associated synoptic eddy–mean flow interaction further maintains or strengthens the quasi-stationary Rossby wave train structure, particularly in the North Atlantic–Eurasia sector [93,111]. Moreover, the wavier winter atmospheric circulation is seemingly conducive to the development of blocking patterns [102,107]. The decrease in the meridional temperature gradient and potential vorticity gradient due to Arctic Sea ice loss and icesheet melting leads to a weakening of UB energy dispersion and a significant prolongation in the periods of UB events, thus contributing to wintertime cold extremes over Eurasia and North American midlatitudes [15,59,112,113,114,115,116]. It is worth noting that severe Arctic Sea ice loss and deep Arctic warming during autumn–early winter can prolong and intensify UB due to the weakening of the polar vortex and potential vorticity gradient, thereby amplifying Eurasian cold extremes in late winter [117].
While statistical analyses and a few sea ice sensitivity experiments provide some evidence for the crucial role of Arctic Sea ice in winter midlatitude cooling, doubts and controversy regarding this causal connection still persist. Whether Arctic Sea ice loss or warming favors a wavier circulation and more frequent midlatitude extreme weather has encountered some skepticism, as observed waviness has reversed in recent years, while simulated waviness remains largely unchanged, despite ongoing Arctic amplification [47,48]. Significant midlatitude atmospheric anomalies associated with Arctic Sea ice anomalies are present only under specific conditions and generally lack robustness [118]. The very weak or even negligible atmospheric responses to Arctic Sea ice loss in large-ensemble simulations primarily attribute the occurrence of continental cooling or extreme cold to internal atmospheric variability [119,120,121,122]. The WACE pattern is also not reproducible in certain climate models driven by observed Arctic Sea ice loss [85,119]. After separating atmospheric forcing and sea ice forcing through the direction of turbulent heat fluxes, Blackport et al. [48] suggested that numerical models can only capture the observed WACE pattern when sea ice decreases are accompanied by anomalous heat transfer from the atmosphere to the ocean (atmosphere-driven sea ice), while the role of sea ice forcing is minimal and confined to the Arctic Ocean. Divergent views and inconsistent findings may be partially attributed to the nonstationary Arctic–midlatitude connection, which is interfered by various factors such as the continuous shrinking and northward shift of climatological sea ice [53], Siberian snow cover [43,123], PDO [124,125], AMO [15,126], ENSO [127,128], quasi-biennial oscillation (QBO) [56], and atmospheric internal variability [57]. This aspect will be thoroughly reviewed in Section 4.

2.2. Spring–Summer Arctic Variations

The Northern Hemisphere has witnessed a high frequency of extreme climate events (e.g., extreme precipitation, heatwaves) during spring–summer in recent decades [35,129,130], and some scholars have investigated the potential connection between these events and Arctic rapid change [36,131,132]. Arctic Sea ice loss is not only conducive to the southward shift of the European jet stream, resulting in more abundant precipitation in northern Europe [133], but also triggers anomalous wave trains over Eurasia. This promotes anticyclonic anomalies in southwest China with decreased cloud cover and increased surface short-wave radiation, thus leading to more frequent occurrences of heatwaves [134]. Concomitant with the decline of Arctic Sea ice, the meridional temperature gradient decreases in the mid-high latitudes and generates an enhanced meridional atmospheric circulation, seemingly more favorable for extreme climate events [107,135]. For instance, the reduced Arctic Sea ice concentration tends to decelerate midlatitude westerlies and increase high-latitude westerly winds, which create favorable background conditions for sustained positive geopotential height anomalies, then dynamically facilitate more frequent and intensified heatwaves in the Northern Hemisphere [34,131,135]. Recent studies have indicated that Arctic Sea ice loss can also impact climate variability in the mid-low latitudes by modulating the AO [136], the storm tracks, and the jet streams [7,137,138]. Most previous studies have primarily concentrated on the potential role of monthly or seasonal mean Arctic Sea ice anomalies in atmospheric variability in the mid-high latitudes, with only a few recent studies undertaking on how the rate of Arctic Sea ice melt affects Eurasian climate variation. The Arctic Sea ice melt rate is generally described as the difference between large sea ice cover in winter and small sea ice cover in summer [139,140]. A higher spring melt rate of Arctic Sea ice can cause an east–west-oriented dipole pattern over Eurasia, with European warming and East Asian cooling [140]. Since 2007, there has been a frequent occurrence of enhanced summer sea ice melting, which dynamically corresponds to the positive phase of the summer Arctic dipole anomaly. The high summer sea ice melt rate significantly increases surface air temperatures and heatwave frequencies in the mid-high latitudes of both Asia and North America [139,141], except causing cooling effects in Europe and parts of the Asian continent [141]. However, the influence of summer Arctic Sea ice on East Asian precipitation during summer remains uncertain and controversial. Based on extensive numerical experiments, Wu et al. [142] considered that although summer Arctic Sea ice can affect atmospheric circulation in high latitudes, its impacts on summer precipitation in the Yangtze River valley and South China, which mainly relies on tropical water sources, are limited.
The winter–spring Arctic Sea ice anomalies also have the potential to predict changes in Northern Hemisphere atmospheric circulation in subsequent seasons. The Arctic Sea ice anomalies persist from winter through the ensuing spring and trigger downstream teleconnections characterized by a distinct Rossby wave train prevailing over the Eurasian continent [143]. Reduced Arctic Sea ice is typically associated with a wave train pattern featuring a low-pressure anomaly center over the Mongolian Plateau, which accelerates the East Asian subtropical westerly jet. Concurrent with lower-level convergence and upper-level divergence, the intensified subtropical westerly jet enhances local convection and consequently favors increased summer precipitation over East Asia [144,145]. Spring atmospheric circulation anomalies over the North Atlantic, linked to persistent winter–spring sea ice anomalies and a horseshoe-like pattern of sea surface temperature (SST) anomalies in the North Atlantic, play a crucial role as a bridge connecting winter–spring sea ice anomalies with ensuing summer atmospheric circulation anomalies over northern Eurasia. This leads to deficient precipitation in Northeast China [146,147,148] and excessive precipitation in Japan and South Korea [147]. The unprecedented 2020 Meiyu-Baiu rainfall in the mid-lower regions of the Yangtze River is also partly attributed to the diminished Arctic Sea ice cover along the Siberian coast during late spring–early summer, as the associated excessive atmospheric blockings over East Siberia strengthen cold air southward outbreaks and halt the seasonal northward march of the Meiyu-Baiu front [36]; however, it remains unclear whether there exists a causal relationship between Arctic Sea ice anomalies and extreme precipitation events [45,149,150]. Additionally, numerous studies have examined the possible contribution of sea ice anomalies in the North Pacific (NP) flank to climate variability over East Asia and even tropical regions. When the Bering Sea ice diminishes, a high-pressure anomaly over Baikal−Northeast China hinders the northward shift of the East Asian summer monsoon, leading to decreased rainfall in Northeast China [151]. The spring Bering Sea ice loss can also result in the westward shift and intensification of the Aleutian Low, thereby enhancing the southward intrusion of cold air and causing SST cooling in the Japan Sea and its adjacent regions. Subsequently, the persistent cold SST anomalies until June–September induce a strengthened and southward shift of the East Asian subtropical westerly jet, increasing convective available potential energy. These conditions promote the formation of more tropical cyclones over the western North Pacific during summer [152].
In addition to the influence of underlying surface external forcing, some scholars have examined the potential simultaneous relationship between Arctic atmospheric anomalies and weather and climate change in mid-low latitudes (Figure 3). Rinke et al. [153] highlighted that the evolution of summer cloud radiation feedback associated with sea ice melt exerts the opposite role of low-level clouds in the reanalysis [154] and simulation results. Liu et al. [155] proposed that positive shortwave cloud radiative effect (SWCRE) anomalies over northern Russia promote the intensification of Ural anticyclonic anomalies, dynamically triggering downstream development of a positive Eurasian pattern, leading to positive (negative) precipitation anomalies in northern (southeastern) China and persistent East Asian heatwaves between 20° N and 40° N. Particularly during the North Atlantic Oscillation (SNAO) positive phase, positive northern Russian SWCRE events are more conducive to strengthening the upstream wave train, as the southward extended Ural anticyclone anomaly is easily trapped by the northward shifted South Asian jets, subsequently propagating to low latitudes and causing extreme heat events in East Asia [156]. Wu et al. [45] found that the second EOF mode of the atmospheric thickness pattern exhibits strong interannual variability without any discernible trend. The positive phase is associated with notable Arctic cold anomalies in the mid-low troposphere and is accompanied by intensified tropospheric westerly winds over most of the Arctic and weakened westerlies over the mid-low latitudes of Asia. The enhanced Arctic zonal westerly winds significantly increase local baroclinicity, which dynamically contributes to the increased frequency of anomalous low surface pressure during summer along with decreased frequency over the high latitudes of Eurasia and North America. The weakened Asian westerly winds are exhibited by sustained high-pressure anomalies in the mid-low troposphere that dynamically facilitate the occurrence of East Asian heatwaves [45]. Additionally, these findings suggest that the intensified Arctic westerlies during summer may serve as a precursor for improved predictions of the East Asian winter monsoon [149].

3. Contribution of Midlatitudes and Tropical Systems to Arctic Variation/Anomalies

3.1. Influences of Midlatitude Systems on the Arctic

Previous research has generally explored the Arctic–midlatitudes connection from the perspective of how Arctic warm anomalies or sea ice loss may favor midlatitude cooling. Nevertheless, an increasing body of literature discusses the impact of midlatitude climate systems on Arctic variations. Some scholars have also begun to discuss the contribution of midlatitude climate systems to Arctic variability because the Arctic Sea ice loss, as the primary contributor to the surface warming trend [4], only explains about 20% of the magnitude of lower-middle tropospheric warming in autumn and early winter [157]. Furthermore, multiple atmospheric systems in the midlatitudes are also conducive to the remaining Arctic warming through atmospheric poleward energy transport.
The NAO pattern, as the predominant low-frequency atmospheric pattern over the North Atlantic-Arctic sector, encompasses concurrent variations in the Azores High, the Icelandic Low, and the North Atlantic (NA) storm track [158]. NAO-related changes in the NA storm track may induce Arctic Rapid Tropospheric Daily Warming (RTDW) by transporting warm and humid air masses across the North Pole [159,160,161,162]. During the positive phase of NAO, southerly wind anomalies at 500 hPa occupy the NA region and steer cyclone tracks or storms into the Arctic [163,164,165], thereby increasing RTDW events in the NA sector [58]. Additionally, the secondary atmospheric mode is manifested as the Barents Oscillation (BO), also regulating the interdecadal change of the wintertime Barents Sea air temperature by altering meridional atmospheric heat and moisture transport [165]. In the NP sector, the PNA pattern plays a crucial role in driving the variability of western Arctic Sea ice, with its positive phase leading to an acceleration of sea ice decline through increased heat and moisture flux as a result of local processes and advection from NP air masses [60].
UB is another major internal mode of tropospheric atmospheric flow [166,167] and a key climate system connecting Arctic warming with Eurasian cooling [40,168,169]. Chen et al. [170] emphasized the significant influence of the zonal movement of the UB on the variability of sea ice in the BK Seas. Specifically, they found that only the quasi-stationary UB reduces BK sea ice concentration for nearly a month, with an average decrease of about 5%. Luo and Yao [171] further confirmed that the increased duration and frequency of UB events can warm the BK Seas through enhanced downward infrared radiation. Luo et al. [15,59] were the first to propose that the combined impact of UB and the positive phase of NAO is the most optimal regime for winter BK sea ice melting, offering valuable insights into the Arctic–midlatitudes connection. When the midlatitude westerly jet sufficiently attenuates in November, the UB is further intensified in the subsequent months due to enhanced upward propagation of planetary waves and prolonged weakening of the stratospheric polar vortex, leading to a warmer Arctic and colder Eurasia [172].
In addition to the aforementioned atmospheric pattern, cold-Eurasia also induces Arctic warm anomalies in turn, particularly in the BK Seas [13]. Observational analyses and simulation experiments conducted with enhanced regional ground albedo over Eurasia have demonstrated that regional Eurasian cooling reinforces the Siberian High and generates Ural anticyclonic anomalies through anomalous atmospheric subsidence and energy propagation, contributing to BK warm anomalies. The increased winter snow cover over Eurasia partially accounts for the enhancement of the ground albedo, which promotes the occurrence of a warm Arctic–cold Eurasia pattern.

3.2. Influences of the Tropic to the Arctic

The impact of ENSO, which is the primary mode of interannual tropical climate variability, on the high latitudes of the Northern Hemisphere has generated considerable interest among researchers. Numerous studies have indicated that ENSO events significantly influence the variability of temperature and sea ice in the Arctic region [173,174,175,176,177,178,179]. The pathways of ENSO affecting the high latitudes include northward-propagating Rossby waves caused by tropical convection anomalies, westerly and jet stream changes due to anomalies in the tropical sea-air coupled system, anomalous meridional heat transport, and the stratospheric polar vortex. It is noteworthy that some recent studies suggest that excessive convection/precipitation in the Asian Monsoon region, particularly over South Asia, can also affect Arctic temperature and sea ice via teleconnections carrying a large amount of heat and moisture [180,181,182]. This is a potential new approach for ENSO affecting the Arctic. Furthermore, the increased amplitudes of ENSO due to global warming partly explain the interannual variations of BK sea ice [183]. However, emerging work has found that the robustness of the impact of the ENSO teleconnection on the Arctic relies strongly on the amplitude and pattern of the ENSO event [184,185,186,187], suggesting the significance of ENSO diversity in the tropics affecting the Arctic.
Tropical variability on the intraseasonal time scale also influences Arctic Sea ice variability, air temperature, and atmospheric circulation [188,189]. Generally, on the intraseasonal time scale, it has been suggested that MJO convection in the tropical Indian Ocean (IO) favors the positive AO. In contrast, on the time scale of decades, the increase in MJO convection around the Maritime Continent and the decrease in MJO heating in the IO have contributed significantly to Arctic warming and sea-ice loss since 1979 [190]. These different conclusions may arise because the MJO’s influence on the Arctic varies considerably with season [191]. MJO convection is associated with poleward heat transport to the Arctic through the Rossby wave connection [192,193]. Nevertheless, MJO-induced Arctic warming accounts for only a small fraction (10–20%) of the observed Arctic warming from 1979 to 2008 [190]. In addition to the connective mechanisms through the troposphere, teleconnections can also take stratospheric pathways. Some recent studies found that MJO activity can affect stratospheric wave activity, the Arctic stratospheric polar vortex, and thus the tropospheric AO a few months later [193].
At multidecadal time scales, based on observational data and forced model experiments, Ding et al. [194] showed that the rapid warming in northeast Canada and Greenland is related to the cooling trend in the eastern tropical Pacific since 1979 via the Rossby wave train, which has been referred to as the Pacific–Arctic teleconnection. This teleconnection is mostly internally generated by the climate system rather than anthropogenic or natural forcing [194,195]. Additionally, the associated atmospheric circulation anomalies explain more than half of the summertime Arctic Sea ice decline trend since 1979 [194,196]. The tropical forcing that influences the high latitudes and the Arctic has also been found in the tropical IO [197].

4. Nonstationary Arctic–Midlatitude Connection

Along with the rapid and dramatic changes in the Arctic, numerous studies have sought to construct schematic diagrams illustrating the plausible physical connection of Arctic anomalies to midlatitude weather and climate in the Northern Hemisphere, utilizing reanalysis data and numerical simulations. The WACE pattern with a strengthened Siberian High (SH) has been consistently observed, particularly pronounced during the winter of 1990–2013, while weakening or diminishing in other periods [6,40,42,99,107]. Controversy surrounding the Arctic–midlatitude connection has ensued due to varying conclusions across different studies. Some studies have indicated a strong correlation between colder winters in midlatitudes and autumn–winter sea ice loss or warm anomalies in the Arctic region, while others have argued that Arctic variations exert weak or negligible impacts on midlatitude climate [48,119,198,199,200]. To address the ongoing debate, recent research has increasingly focused on investigating the nonstationary relationship between Arctic variations and midlatitude climate anomalies [54,201].
Some scholars have noted that the link between midlatitude atmospheric systems and Arctic Sea ice is unstable and undergoes significant interdecadal changes, leading to varying results depending on the analysis period. Observational analysis has revealed a pronounced interdecadal weakening in the influence of autumn Arctic SIC over the Kara-Laptev Seas on subsequent winter SH or UB around the late 1990s, possibly due to the northward retreat of climatological mean Arctic Sea ice [53,93]. After the late 1990s, with more open water replacing sea ice in the Kara-Laptev Seas, autumn SIC interannual variability has become much weaker than before, thus attenuating associated surface heat flux anomalies, Arctic warming, and Eurasian atmospheric responses. Conversely, as autumn sea ice gradually thins over the EsCB Seas, larger SIC anomalies indicate increasingly intensified local air–sea ice interaction, inducing stronger surface heat flux anomalies and larger-scale Arctic atmospheric responses. Consequently, only after the late 1990s can a significant connection be detected between autumn EsCB sea ice and subsequent winter–spring Eurasian anomalies, that is sea ice loss promotes negative AO-like atmospheric anomalies, evident cooling over northern Eurasia, and more frequent extreme low-temperature events in western-central China [76,93]. Following the continuous melting and northward retreat of Arctic Sea ice, the Arctic–midlatitudes connection involving distinct sea ice regions exhibits opposite changes, further complicating the understanding of whether midlatitude climate anomalies are influenced by Arctic variations [93]. Furthermore, some works utilize centennial reanalyses to examine the multi-decadal variations in the Arctic-Eurasian connection over a longer historical period in the 20th century [57,201,202]. The association between autumn BK sea ice and winter Eurasian temperature undergoes a transition from a weak negative correlation prior to the mid-1940s (1901–1945) to a significantly positive correlation after the mid-1980s (1986–2013). The greater (lesser) magnitude and prolonged (shortened) persistence of sea ice decrease suggest that sea ice (atmosphere) may dominate the Arctic–midlatitudes connection in the latter (former) period, partially explaining the aforementioned inverse correlations. Only a limited number of CMIP6 models that accurately capture the realistic stratospheric pathway are able to reproduce the historically fluctuating Arctic-Eurasian connection [201]. Since the early 2010s, there has been a gradual weakening of the positive correlation albeit continuous Arctic Sea ice melting, leading to ongoing debates regarding causality in the Arctic–midlatitudes connection. Extensive climate model ensembles have indicated that internal climate variability may play a dominant role in the infrequent occurrence of strong positive correlation observed recently between autumn BK sea ice and winter NAO due to weaker NAO-like responses to sea ice loss compared with internal variability [57].
In addition to external forcing, the WACE pattern, which represents the predominant atmospheric mode of the Arctic–midlatitude connection, also exhibits interdecadal variability with a cycle of 20–30 years and is modulated by the BO [202,203]. The positive BO phase enhances the formation of the WACE pattern through northward (southward) transport of warm (cold) and moist (dry) air to the BK Seas (Central Eurasia) from the midlatitudes (polar). Wu et al. [54] have also identified a recent weakening of the Arctic-Asia temperature association around 2012/13, characterized by a phase transition from a warm Arctic-cold Eurasia to a warm Arctic-warm Eurasia with southward amplified and expanded Arctic warm anomalies. Observed sea ice-forced experiments further confirm that Arctic Sea ice loss seemingly contribute to the low-frequency fluctuation of the winter Arctic-Asia temperature association, increasing the challenge of accurately predicting Asian winter temperatures.
Nonlinear winter atmospheric responses to sea ice anomalies of varying magnitudes and seasonality also contribute to the nonstationary Arctic–midlatitudes connection [55,68,118,204]. Petoukhov and Semenov [68] simulated the nonlinear response of large-scale circulation and midlatitude temperature to a gradual decrease in BK sea ice cover in GCM for the first time, providing a physical mechanism for the nonlinear response based on an analytical model. Similar nonlinear responses were reproduced in simulations with realistic sea ice anomalies in the past four decades [204]. Zhang et al. [55] identified an intermittent causal relation between BK sea ice and Eurasian winter temperature through designing multimodel large-ensemble simulation experiments with distinct seasonality, sign, and magnitude of sea ice anomalies. Autumn BK sea ice exerts nonlinear impacts on Eurasian surface temperature, manifested by moderate sea ice loss leading to the strongest Eurasian cooling due to nonlinear wave-mean flow interactions through a stratospheric pathway. Unlike the impacts of autumn sea ice loss, dominated by dynamical effects, winter sea ice loss approximately linearly induces Eurasian warming via thermodynamical effects. The complex nature of the Arctic–midlatitudes connection is further increased by the large difference in the roles of autumn and winter sea ice—for example, the complete loss of Arctic Sea ice favors Eurasian warming because thermodynamical processes dominate. Using persistent autumn–winter sea ice as a predictor of Eurasian temperature presents significant challenges, as accurate evaluation is required for the respective contributions of dynamical and thermodynamical processes. Furthermore, various other factors also modulate or interfere with the Arctic–midlatitudes connection [205]. Different phases of the AMO or PDO can lead to differing atmospheric and Eurasian temperature anomalies in response to Arctic Sea ice loss [15,125,203,206]. Recent scientific findings have emphasized that a combination of reduced Arctic Sea ice and a warm PDO phase or La Niña potentially drives colder winters with more frequent extreme low-temperature events over Eurasia [101,207,208,209], while the easterly phase of QBO could disrupt this connection and produce opposite effects [56].

5. Summary and Perspectives

This review provides a comprehensive overview of recent understanding and progress in the Arctic–midlatitudes connection, with a special focus on the role of regional sea ice anomalies on interannual timescales. It includes reciprocal influences, possible physical mechanisms, and nonstationary. The main findings can be summarized as follows (Figure 4):
  • For autumn–winter Arctic variations, the Barents–Kara (BK) and East Siberia–Chukchi–Beaufort (EsCB) Seas are the primary regions where sea ice or thermal anomalies significantly impact midlatitude atmospheric variability, particularly the latter playing a more crucial role due to its increasingly enhanced interannual amplitude in the context of global warming.
  • The reduction of autumn–winter sea ice in both BK and EsCB Seas generally favors the development of strong anticyclonic anomalies over northern Eurasia, accompanied by a deepened East Asian trough and intensified Siberian High. This results in significant temperature drops, more frequent extreme low temperatures, and heavy snowfall in the midlatitudes of Eurasia.
  • The various mechanisms currently proposed to explain the impact of Arctic variations on midlatitude variability can be categorized into tropospheric and stratospheric pathways. The tropospheric pathways involve surface heat flux–circulation interactions, changes in the intensity and position of the jet stream due to altered meridional temperature gradients, horizontal propagation of Rossby waves, and synoptic eddy–mean flow interactions associated with storm track anomalies. The stratospheric pathways encompass anomalous vertical propagation of quasi-stationary planetary waves, as well as the disturbance and downward control of the polar vortex. The tropospheric and stratospheric processes are coupled, as upward quasi-stationary planetary waves are influenced by the tropospheric wave pattern and westerlies’ intensity, while anomalous stratospheric signals, in turn, modulate underlying tropospheric atmospheric anomalies.
  • The impact of sea ice anomalies in distinct Arctic regions on midlatitudes exhibits certain differences in influencing months, range, intensity, and mechanisms. For instance, autumn EsCB sea ice loss generates moderate Arctic anticyclonic anomalies over northern Europe, peaking in early winter, resulting in a noticeable temperature decrease, while autumn BK sea ice loss favors stronger and larger-scale Arctic anticyclonic anomalies, peaking in midwinter, leading to broader cooling across northern Eurasia. The stratospheric pathways associated with EsCB and BK sea ice loss are respectively dominated by planetary waves 2 and 1.
  • Summer sea ice decreases, cold anomalies and positive SWCRE in the Arctic region create favorable conditions for sustained anticyclonic anomalies and strengthen the zonal eastward wave train, propagating further downstream in the mid-high latitudes. This facilitates more frequent and stronger heatwaves in the midlatitude continent but has limited impacts on summer precipitation in central and southern China. The spring Arctic Sea ice in both the North Atlantic and NP sectors has the potential to serve as a predictor for summer Eurasian precipitation—for instance, sea ice decreases correspond to deficient precipitation in Northeast China and excessive precipitation around the Yangtze River.
  • The prevailing atmospheric patterns in midlatitudes, such as AO/NAO, UB, and PNA patterns, can lead to Arctic thermal and sea ice anomalies through the meridional transport of heat and moisture. These atmospheric intrinsic modes may be associated with changes in the ground albedo of midlatitude continents and teleconnection wave trains triggered by tropical forcing.
  • The Arctic–midlatitudes connection is nonstationary and fluctuates over time, following the continuous melting and northward shrinking of climatological sea ice. The role of autumn sea ice in distinct Arctic regions at midlatitudes has become opposite, with a strengthened linkage for EsCB sea ice and a weakened linkage for BK sea ice since the late 1990s. Furthermore, the nonstationary Arctic–midlatitudes connection is influenced by atmospheric intrinsic modes, other external forcings, and nonlinear responses to sea ice anomalies of varying magnitudes and seasonality.
Previous studies generally use a combination of statistical analyses and numerical experiments to investigate the Arctic–midlatitude connection. Linear statistical methods remain the primary tools (correlation, regression, SVD, etc.), which may overlook the nonlinear interactions between sea ice and the atmosphere. The nonlinear effects of sea ice are often qualitatively analyzed by means of multiple numerical experiments with different signs and magnitudes [52,55]. Identifying and measuring the nonlinear air–sea ice coupling process remains a challenging problem to address, for example, through the use of the Conditional Non-linear Optimal Perturbation (CNOP) method [210]. Present models also have some defects in describing the turbulent heat flux responses over the Arctic Sea ice surface, thus underestimating the Arctic–midlatitude connection [211]. A precise representation of the air–sea ice interaction and stratosphere in models, along with utilizing machine learning may reduce discrepancies between observational and model results.
This review primarily summarizes the interaction between Arctic changes and midlatitude variability; however, the causality of the Arctic–midlatitude connection remains unclear. During certain periods, autumn–winter Arctic Sea ice loss can indeed cause Ural anticyclonic anomalies and midlatitude cooling, which in turn may lead to warm anomalies over the Arctic marginal seas. In addition, the autumn sea ice changes are also influenced by preceding summer Arctic atmospheric circulation anomalies. In the warm seasons or during some historical periods, atmospheric intrinsic modes dominate changes in sea ice and midlatitude climate, but the feedback effect of sea ice should not be overlooked. It is challenging to determine the causality of the Arctic–midlatitude connection using existing methodologies due to the complex interactions involved.
In the future, how the Arctic–midlatitudes connection varies over time under different CO2 scenarios warrants further investigation, particularly focusing on the impacts of Arctic Sea ice on midlatitudes, including the key seas, influencing ranges and intensities, and the physical processes involved. The combined climate effects of distinct regional Arctic Sea ice in different seasons should receive more attention, as strong air–sea ice interaction primarily occurs in autumn EsCB Seas and winter BK Seas since the late 1990s, replacing the persistent decrease of BK sea ice from autumn to winter prior to the 1990s. In addition, it is necessary to comprehend the synergistic impact of the Arctic and other factors (e.g., ENSO, PDO, AMO, NAO), including quantitatively assessing the contribution of each variable and investigating the specific mechanisms. Improvements in models facilitate a more precise identification of the respective contributions of tropospheric and stratospheric pathways to the Arctic–midlatitudes connection. Recently, there has been a growing focus on investigating the possible linkage of Arctic variations to ENSO [212,213], thus exploring the Arctic–tropics interaction is another potential hot theme for future research.

Author Contributions

Writing Section 1, Section 2.1, Section 3.1, Section 4 and Section 5 and editing—original draft preparation, S.D.; Writing Section 2.1, Section 3.1 and Section 3.2, X.C.; Writing Section 2.2, X.Z. (Xuanwen Zhang); Writing Section 2.1, X.Z. (Xiang Zhang); Editing, P.X. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (Grant 42375026, 41905058) and the Natural Science Foundation of Sichuan, China (2024NSFSC0778).

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Jarraud, M.; Steiner, A. Summary for Policymakers. In Climate Change 2021—The Physical Science Basis; Cambridge University Press: Navi Mumbai, India, 2023; Volume 9781107025, pp. 3–32. ISBN 9781139177245. [Google Scholar]
  2. Shepherd, T.G. Effects of a Warming Arctic. Science 2016, 353, 989–990. [Google Scholar] [CrossRef] [PubMed]
  3. Walsh, J.E. Intensified Warming of the Arctic: Causes and Impacts on Middle Latitudes. Glob. Planet. Change 2014, 117, 52–63. [Google Scholar] [CrossRef]
  4. Screen, J.A.; Simmonds, I. The Central Role of Diminishing Sea Ice in Recent Arctic Temperature Amplification. Nature 2010, 464, 1334–1337. [Google Scholar] [CrossRef] [PubMed]
  5. Boeke, R.C.; Taylor, P.C. Seasonal Energy Exchange in Sea Ice Retreat Regions Contributes to Differences in Projected Arctic Warming. Nat. Commun. 2018, 9, 5017. [Google Scholar] [CrossRef]
  6. Cohen, J.; Zhang, X.; Francis, J.; Jung, T.; Kwok, R.; Overland, J.; Ballinger, T.J.; Bhatt, U.S.; Chen, H.W.; Coumou, D.; et al. Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Clim. Change 2020, 10, 20–29. [Google Scholar] [CrossRef]
  7. Coumou, D.; Di Capua, G.; Vavrus, S.; Wang, L.; Wang, S. The Influence of Arctic Amplification on Mid-Latitude Summer Circulation. Nat. Commun. 2018, 9, 2959. [Google Scholar] [CrossRef]
  8. Comiso, J.C.; Parkinson, C.L.; Gersten, R.; Stock, L. Accelerated decline in the Arctic sea ice cover. Geophys. Res. Lett. 2008, 35, L01703. [Google Scholar] [CrossRef]
  9. Vavrus, S. The impact of cloud feedbacks on Arctic climate under greenhouse forcing. J. Clim. 2004, 17, 603–615. [Google Scholar] [CrossRef]
  10. Ruckstuhl, C.; Philipona, R.; Morland, J.; Ohmura, A. Observed relationship between surface specific humidity, integrated water vapor, and longwave downward radiation at different altitudes. J. Geophys. Res. Atmos. 2007, 112, D03302. [Google Scholar] [CrossRef]
  11. Pithan, F.; Mauritsen, T. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci. 2014, 7, 181–184. [Google Scholar] [CrossRef]
  12. Lee, S.; Gong, T.; Feldstein, S.B.; Screen, J.A.; Simmonds, I. Revisiting the cause of the 1989–2009 Arctic surface warming using the surface energy budget: Downward infrared radiation dominates the surface fluxes. Geophys. Res. Lett. 2017, 44, 10654–10661. [Google Scholar] [CrossRef]
  13. Wu, B.; Ding, S. Cold-Eurasia contributes to arctic warm anomalies. Clim. Dyn. 2023, 60, 4157–4172. [Google Scholar] [CrossRef]
  14. Serreze, M.C.; Barry, R.G. Processes and impacts of Arctic amplification: A research synthesis. Glob. Planet. Change 2011, 77, 85–96. [Google Scholar] [CrossRef]
  15. Luo, D.; Chen, Y.; Dai, A.; Mu, M.; Zhang, R.; Ian, S. Winter Eurasian cooling linked with the Atlantic Multidecadal Oscillation. Environ. Res. Lett. 2017, 12, 125002. [Google Scholar] [CrossRef]
  16. Wang, Q.; Wang, X.; Wekerle, C.; Danilov, S.; Jung, T.; Koldunov, N.; Lind, S.; Sein, D.; Shu, Q.; Sidorenko, D. Ocean heat transport into the Barents Sea: Distinct controls on the upward trend and interannual variability. Geophys. Res. Lett. 2019, 46, 13180–13190. [Google Scholar] [CrossRef]
  17. Cao, Y.; Liang, S. Recent advances in driving mechanisms of the Arctic amplification: A review. Chin. Sci. Bull. 2018, 63, 2757–2771. [Google Scholar] [CrossRef]
  18. You, Q.; Cai, Z.; Pepin, N.; Chen, D.; Ahrens, B.; Jiang, Z.; Wu, F.; Kang, S.; Zhang, R.; Wu, T.; et al. Warming amplification over the Arctic Pole and Third Pole: Trends, mechanisms and consequences. Earth-Sci. Rev. 2021, 217, 103625. [Google Scholar] [CrossRef]
  19. Serreze, M.C.; Holland, M.M.; Stroeve, J. Perspectives on the Arctic’s shrinking sea-ice cover. Science 2007, 315, 1533–1536. [Google Scholar] [CrossRef]
  20. Curry, J.A.; Schramm, J.L.; Ebert, E.E. Sea ice-albedo climate feedback mechanism. J. Clim. 1995, 8, 240–247. [Google Scholar] [CrossRef]
  21. Dai, A.; Luo, D.; Song, M.; Liu, J. Arctic amplification is caused by sea-ice loss under increasing CO2. Nat. Commun. 2019, 10, 121. [Google Scholar] [CrossRef]
  22. Gao, K.; Duan, A.; Chen, D.; Wu, G. Surface energy budget diagnosis reveals possible mechanism for the different warming rate among Earth’s three poles in recent decades. Sci. Bull. 2019, 64, 1140–1143. [Google Scholar] [CrossRef] [PubMed]
  23. Deng, J.; Dai, A. Sea ice–air interactions amplify multidecadal variability in the North Atlantic and Arctic region. Nat. Commun. 2022, 13, 2100. [Google Scholar] [CrossRef] [PubMed]
  24. Bengtsson, L.; Semenov, V.A.; Johannessen, O.M. The early twentieth-century warming in the Arctic—A possible mechanism. J. Clim. 2004, 17, 4045–4057. [Google Scholar] [CrossRef]
  25. Kim, B.M.; Son, S.W.; Min, S.K.; Jeong, J.H.; Kim, S.J.; Zhang, X.; Shim, T.; Yoon, J.H. Weakening of the stratospheric polar vortex by Arctic sea-ice loss. Nat. Commun. 2014, 5, 4646. [Google Scholar] [CrossRef]
  26. Smith, E.T.; Sheridan, S.C. The influence of extreme cold events on mortality in the United States. Sci. Total Environ. 2019, 647, 342–351. [Google Scholar] [CrossRef]
  27. Comiso, J.C.; Hall, D.K. Climate trends in the Arctic as observed from space. WIREs Clim. Change 2014, 5, 389–409. [Google Scholar] [CrossRef]
  28. Parkinson, C.L.; DiGirolamo, N.E. Sea ice extents continue to set new records: Arctic, Antarctic, and global results. Remote Sens. Environ. 2021, 267, 112753. [Google Scholar] [CrossRef]
  29. Ding, Y.H.; Wang, Z.Y.; Song, Y.F. Cause of the unprecedented freezing disaster in January 2008 and its possible association with the global warming. Acta. Meteor. Sin. 2008, 66, 808–825. [Google Scholar]
  30. Cattiaux, J.; Vautard, R.; Cassou, C.; Yiou, P.; Masson-Delmotte, V.; Codron, F. Winter 2010 in Europe: A cold extreme in a warming climate. Geophys. Res. Let. 2010, 37, L20704. [Google Scholar] [CrossRef]
  31. Wu, B.; Yang, K.; Francis, J.A. A cold event in Asia during January–February 2012 and its possible association with Arctic sea ice loss. J. Clim. 2017, 30, 7971–7990. [Google Scholar] [CrossRef]
  32. Bailey, H.; Hubbard, A.; Klein, E.S.; Mustonen, K.R.; Akers, P.D.; Marttila, H.; Welker, J.M. Arctic sea-ice loss fuels extreme European snowfall. Nat. Geosci. 2021, 14, 283–288. [Google Scholar] [CrossRef]
  33. Seo, E.; Lee, M.I.; Schubert, S.D.; Koster, R.D.; Kang, H.S. Investigation of the 2016 Eurasia heat wave as an event of the recent warming. Environ. Res. Let. 2020, 15, 114018. [Google Scholar] [CrossRef]
  34. Zhang, X.; Wu, B.; Ding, S. Summer Russian Heat Waves Linked to Arctic Sea Ice Anomalies in 2010 and 2016. J. Clim. 2024, 37, 1597–1611. [Google Scholar] [CrossRef]
  35. Zhou, Z.Q.; Xie, S.P.; Zhang, R. Historic Yangtze flooding of 2020 tied to extreme Indian Ocean conditions. Proc. Natl. Acad. Sci. USA 2021, 118, e2022255118. [Google Scholar] [CrossRef] [PubMed]
  36. Chen, X.; Dai, A.; Wen, Z.; Song, Y. Contributions of Arctic sea-ice loss and East Siberian atmospheric blocking to 2020 record-breaking Meiyu-Baiu rainfall. Geophys. Res. Lett. 2021, 48, e2021GL092748. [Google Scholar] [CrossRef]
  37. Liu, X.; Zhu, Z.; Lu, R.; Miao, Z.; Li, W.; Hsu, P. Unprecedented July rainfall in North China in 2021: Combined effect of Atlantic warming and Arctic sea-ice loss. J. Geophys. Res. Atmos. 2023, 128, e2022JD038068. [Google Scholar] [CrossRef]
  38. Honda, M.; Inoue, J.; Yamane, S. Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters. Geophys. Res. Lett. 2009, 36. [Google Scholar] [CrossRef]
  39. Overland, J.E.; Wood, K.R.; Wang, M. Warm Arctic—Cold continents: Climate impacts of the newly open Arctic Sea. Polar Res. 2011, 30, 15787. [Google Scholar] [CrossRef]
  40. Mori, M.; Watanabe, M.; Shiogama, H.; Inoue, J.; Kimoto, M. Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades. Nat. Geosci. 2014, 7, 869–873. [Google Scholar] [CrossRef]
  41. Kug, J.S.; Jeong, J.H.; Jang, Y.S.; Kim, B.M.; Folland, C.K.; Min, S.K.; Son, S.W. Two Distinct Influences of Arctic Warming on Cold Winters over North America and East Asia. Nat. Geosci. 2015, 8, 759–762. [Google Scholar] [CrossRef]
  42. Mori, M.; Kosaka, Y.; Watanabe, M.; Nakamura, H.; Kimoto, M. A reconciled estimate of the influence of Arctic sea-ice loss on recent Eurasian cooling. Nat. Clim. Change 2019, 9, 123–129. [Google Scholar] [CrossRef]
  43. Xu, X.; He, S.; Gao, Y.; Furevik, T.; Wang, H.; Li, F.; Ogawa, F. Strengthened Linkage between Midlatitudes and Arctic in Boreal Winter. Clim. Dyn. 2019, 53, 3971–3983. [Google Scholar] [CrossRef]
  44. Pang, X.; Wu, B.; Ding, S. Strengthened Connection between Meridional Location of Winter Polar Front Jet and Surface Air Temperature since the Mid-1990s. Clim. Dyn. 2023, 60, 3211–3224. [Google Scholar] [CrossRef]
  45. Wu, B.; Francis, J.A. Summer Arctic cold anomaly dynamically linked to East Asian heat waves. J. Clim. 2019, 32, 1137–1150. [Google Scholar] [CrossRef]
  46. Vihma, T. Effects of Arctic sea ice decline on weather and climate: A review. Surv. Geophys. 2014, 35, 1175–1214. [Google Scholar] [CrossRef]
  47. Barnes, E.A. Revisiting the evidence linking Arctic amplification to extreme weather in midlatitudes. Geophys. Res. Lett. 2013, 40, 4734–4739. [Google Scholar] [CrossRef]
  48. Blackport, R.; Screen, J.A.; van der Wiel, K.; Bintanja, R. Minimal influence of reduced Arctic sea ice on coincident cold winters in mid-latitudes. Nat. Clim. Change 2019, 9, 697–704. [Google Scholar] [CrossRef]
  49. Blackport, R.; Screen, J.A. Insignificant effect of Arctic amplification on the amplitude of midlatitude atmospheric waves. Sci. Adv. 2020, 6, eaay2880. [Google Scholar] [CrossRef]
  50. Blackport, R.; Screen, J.A. Observed statistical connections overestimate the causal effects of Arctic sea ice changes on midlatitude winter climate. J. Clim. 2021, 34, 3021–3038. [Google Scholar] [CrossRef]
  51. Meleshko, V.P.; Johannessen, O.M.; Baidin, A.V.; Pavlova, T.V.; Govorkova, V.A. Arctic amplification: Does it impact the polar jet stream? Tellus A Dyn. Meteorol. Oceanogr. 2016, 68, 32330. [Google Scholar] [CrossRef]
  52. Overland, J.E.; Dethloff, K.; Francis, J.A.; Hall, R.J.; Hanna, E.; Kim, S.J.; Screen, J.A.; Shepherd, T.G.; Vihma, T. Nonlinear response of mid-latitude weather to the changing Arctic. Nat. Clim. Change 2016, 6, 992–999. [Google Scholar] [CrossRef]
  53. Chen, S.; Wu, R.; Chen, W.; Song, L.; Cheng, W.; Shi, W. Weakened impact of autumn Arctic sea ice concentration change on the subsequent winter Siberian High variation around the late-1990s. Int. J. Climtol. 2021, 41, E2700–E2717. [Google Scholar] [CrossRef]
  54. Wu, B.; Li, Z.; Francis, J.A.; Ding, S. A recent weakening of winter temperature association between Arctic and Asia. Environ. Res. Lett. 2022, 17, 034030. [Google Scholar] [CrossRef]
  55. Zhang, R.; Screen, J.A. Diverse Eurasian winter temperature responses to Barents-Kara sea ice anomalies of different magnitudes and seasonality. Geophys. Res. Lett. 2021, 48, e2021GL092726. [Google Scholar] [CrossRef]
  56. Ma, X.; Wang, L.; Smith, D.; Hermanson, L.; Eade, R.; Dunstone, N.; Hardiman, S.; Zhang, J. ENSO and QBO modulation of the relationship between Arctic sea ice loss and Eurasian winter climate. Environ. Res. Lett. 2022, 17, 124016. [Google Scholar] [CrossRef]
  57. Kolstad, E.W.; Screen, J.A. Nonstationary relationship between autumn Arctic sea ice and the winter North Atlantic Oscillation. Geophys. Res. Lett. 2019, 46, 7583–7591. [Google Scholar] [CrossRef]
  58. Wang, C.; Ren, B.; Li, G.; Zheng, J.; Jiang, L.; Xu, D. An interdecadal change in the influence of the NAO on Atlantic-induced Arctic daily warming around the mid-1980s. Adv. Atmos. Sci. 2023, 40, 1285–1297. [Google Scholar] [CrossRef]
  59. Luo, D.; Chen, X.; Dai, A.; Simmonds, I. Changes in atmospheric blocking circulations linked with winter Arctic warming: A new perspective. J. Clim. 2018, 31, 7661–7678. [Google Scholar] [CrossRef]
  60. Liu, Z.; Risi, C.; Codron, F.; He, X.; Poulsen, C.J.; Wei, Z.; Chen, D.; Li, S.; Bowen, G.J. Acceleration of western Arctic sea ice loss linked to the Pacific North American pattern. Nat. Commun. 2021, 12, 1519. [Google Scholar] [CrossRef]
  61. Ding, Q.; Wallace, J.M.; Battisti, D.S.; Steig, E.J.; Gallant, A.J.; Kim, H.J.; Geng, L. Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland. Nature 2014, 509, 209–212. [Google Scholar] [CrossRef]
  62. Overland, J.E.; Wang, M. When Will the Summer Arctic Be Nearly Sea Ice Free? Geophys. Res. Lett. 2013, 40, 2097–2101. [Google Scholar] [CrossRef]
  63. Stroeve, J.; Notz, D. Changing State of Arctic Sea Ice across All Seasons. Environ. Res. Lett. 2018, 13, 103001. [Google Scholar] [CrossRef]
  64. Notz, D.; SIMIP Community. Arctic sea ice in CMIP6. Geophys. Res. Let. 2020, 47, e2019GL086749. [Google Scholar] [CrossRef]
  65. Jahn, A.; Holland, M.M.; Kay, J.E. Projections of an ice-free Arctic Ocean. Nat. Rev. Earth Environ. 2024, 5, 164–176. [Google Scholar] [CrossRef]
  66. Deser, C.; Teng, H. Recent trends inArctic sea ice and the evolving role of atmospheric circulation forcing, 1979–2007. In Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications; Geophysical Monograph Series; American Geophysical Union: Washington, DC, USA, 2008; Volume 180, pp. 7–26. [Google Scholar] [CrossRef]
  67. Francis, J.A.; Chan, W.; Leathers, D.J.; Miller, J.R.; Veron, D.E. Winter Northern Hemisphere weather patterns remember summer Arctic sea-ice extent. Geophys. Res. Lett. 2009, 36, L07503. [Google Scholar] [CrossRef]
  68. Petoukhov, V.; Semenov, V.A. A link between reduced Barents-Kara sea ice and cold winter extremes over northern continents. J. Geophys. Res. Atmos. 2010, 115, D21111. [Google Scholar] [CrossRef]
  69. Wu, B.; Su, J.; Zhang, R. Effects of autumn-winter Arctic sea ice on winter Siberian High. Chin. Sci. Bull. 2011, 56, 3220–3228. [Google Scholar] [CrossRef]
  70. Inoue, J.; Hori, M.E.; Takaya, K. The role of Barents Sea ice in the wintertime cyclone track and emergence of a warm-Arctic cold-Siberian anomaly. J. Clim. 2012, 25, 2561–2568. [Google Scholar] [CrossRef]
  71. Jaiser, R.; Dethloff, K.; Handorf, D.; Rinke, A.; Cohen, J. Impact of sea ice cover changes on the Northern Hemisphere atmospheric winter circulation. Tellus A Dyn. Meteorol. Oceanogr. 2012, 64, 11595. [Google Scholar] [CrossRef]
  72. Chen, Z.; Wu, R.; Chen, W. Impacts of autumn Arctic sea ice concentration changes on the East Asian winter monsoon variability. J. Clim. 2014, 27, 5433–5450. [Google Scholar] [CrossRef]
  73. Nakamura, T.; Yamazaki, K.; Iwamoto, K.; Honda, M.; Miyoshi, Y.; Ogawa, Y.; Ukita, J. A negative phase shift of the winter AO/NAO due to the recent Arctic sea-ice reduction in late autumn. J. Geophys. Res. Atmos. 2015, 120, 3209–3227. [Google Scholar] [CrossRef]
  74. Zhang, P.; Wu, Y.; Smith, K.L. Prolonged effect of the stratospheric pathway in linking Barents–Kara Sea sea ice variability to the midlatitude circulation in a simplified model. Clim. Dyn. 2018, 50, 527–539. [Google Scholar] [CrossRef]
  75. Zhang, P.; Wu, Y.; Simpson, I.R.; Smith, K.L.; Zhang, X.; De, B.; Callaghan, P. A stratospheric pathway linking a colder Siberia to Barents-Kara Sea sea ice loss. Sci. Adv. 2018, 4, eaat6025. [Google Scholar] [CrossRef] [PubMed]
  76. Ding, S.; Wu, B.; Chen, W. Dominant characteristics of early autumn Arctic sea ice variability and its impact on winter Eurasian climate. J. Clim. 2021, 34, 1825–1846. [Google Scholar] [CrossRef]
  77. Overland, J.E.; Ballinger, T.J.; Cohen, J.; Francis, J.A.; Hanna, E.; Jaiser, R.; Kim, B.M.; Kim, S.J.; Ukita, J.; Vihma, T.; et al. How do intermittency and simultaneous processes obfuscate the Arctic influence on midlatitude winter extreme weather events? Environ. Res. Lett. 2021, 16, 043002. [Google Scholar] [CrossRef]
  78. Alexander, M.A.; Bhatt, U.S.; Walsh, J.E.; Timlin, M.S.; Miller, J.S.; Scott, J.D. The atmospheric response to realistic Arctic sea ice anomalies in an AGCM during winter. J. Clim. 2004, 17, 890–905. [Google Scholar] [CrossRef]
  79. Magnusdottir, G.; Deser, C.; Saravanan, R. The effects of North Atlantic SST and sea ice anomalies on the winter circulation in CCM3. Part I: Main features and storm track characteristics of the response. J. Clim. 2004, 17, 857–876. [Google Scholar] [CrossRef]
  80. Deser, C.; Magnusdottir, G.; Saravanan, R.; Phillips, A. The effects of North Atlantic SST and sea ice anomalies on the winter circulation in CCM3. Part II: Direct and indirect components of the response. J. Clim. 2004, 17, 877–889. [Google Scholar] [CrossRef]
  81. Liu, J.; Curry, J.A.; Wang, H.; Song, M.; Horton, R.M. Impact of declining Arctic sea ice on winter snowfall. Proc. Nat. Acad. Sci. USA 2012, 109, 4074–4079. [Google Scholar] [CrossRef]
  82. Singarayer, J.S.; Valdes, P.J.; Bamber, J.L. The atmospheric impact of uncertainties in recent Arctic sea ice reconstructions. J. Clim. 2005, 18, 3996–4012. [Google Scholar] [CrossRef]
  83. Sorokina, S.A.; Li, C.; Wettstein, J.J.; Kvamstø, N.G. Observed atmospheric coupling between Barents Sea ice and the warm-Arctic cold-Siberian anomaly pattern. J. Clim. 2016, 29, 495–511. [Google Scholar] [CrossRef]
  84. Blackport, R.; Kushner, P.J. Isolating the atmospheric circulation response to Arctic sea ice loss in the coupled climate system. J. Clim. 2017, 30, 2163–2185. [Google Scholar] [CrossRef]
  85. Ogawa, F.; Keenlyside, N.; Gao, Y.; Koenigk, T.; Yang, S.; Suo, L.; Wang, T.; Gastineau, G.; Nakamura, T.; Cheung, H.N.; et al. Evaluating impacts of recent Arctic sea ice loss on the northern hemisphere winter climate change. Geophys. Res. Lett. 2018, 45, 3255–3263. [Google Scholar] [CrossRef]
  86. Rinke, A.; Dethloff, K.; Dorn, W.; Handorf, D.; Moore, J.C. Simulated Arctic atmospheric feedbacks associated with late summer sea ice anomalies. J. Geophys. Res. Atmos. 2013, 118, 7698–7714. [Google Scholar] [CrossRef]
  87. Pedersen, R.A.; Cvijanovic, I.; Langen, P.L.; Vinther, B.M. The impact of regional Arctic sea ice loss on atmospheric circulation and the NAO. J. Clim. 2016, 29, 889–902. [Google Scholar] [CrossRef]
  88. Chen, H.W.; Alley, R.B.; Zhang, F. Interannual Arctic sea ice variability and associated winter weather patterns: A regional perspective for 1979–2014. J. Geophys. Res. Atmos. 2016, 121, 14–433. [Google Scholar] [CrossRef]
  89. Screen, J.A. Simulated atmospheric response to regional and pan-Arctic sea ice loss. J. Clim. 2017, 30, 3945–3962. [Google Scholar] [CrossRef]
  90. Yang, X.Y.; Yuan, X.; Ting, M. Dynamical link between the Barents–Kara sea ice and the Arctic Oscillation. J. Clim. 2016, 29, 5103–5122. [Google Scholar] [CrossRef]
  91. Hopsch, S.; Cohen, J.; Dethloff, K. Analysis of a link between fall Arctic sea ice concentration and atmospheric patterns in the following winter. Tellus A Dyn. Meteorol. Oceanogr. 2012, 64, 18624. [Google Scholar] [CrossRef]
  92. Chen, S.; Wu, R. Impacts of early autumn Arctic sea ice concentration on subsequent spring Eurasian surface air temperature variations. Clim. Dyn. 2018, 51, 2523–2542. [Google Scholar] [CrossRef]
  93. Ding, S.; Wu, B.; Chen, W.; Graf, H.F.; Zhang, X. Possible Linkage Between Winter Extreme Low Temperature Over Western-Central China and Autumn Sea Ice Loss. J. Geophys. Res. Atmos. 2023, 128, e2023JD038547. [Google Scholar] [CrossRef]
  94. Ding, S.; Wu, B. Linkage between autumn sea ice loss and ensuing spring Eurasian temperature. Clim. Dyn. 2021, 57, 2793–2810. [Google Scholar] [CrossRef]
  95. Cohen, J.; Pfeiffer, K.; Francis, J.A. Warm Arctic episodes linked with increased frequency of extreme winter weather in the United States. Nat. Commun. 2018, 9, 869. [Google Scholar] [CrossRef] [PubMed]
  96. Vihma, T.; Graversen, R.; Chen, L.; Handorf, D.; Skific, N.; Francis, J.A.; Tyrrell, N.; Hall, R.; Hanna, E.; Uotila, P.; et al. Effects of the tropospheric large-scale circulation on European winter temperatures during the period of amplified Arctic warming. Int. J. Climtol. 2020, 40, 509. [Google Scholar] [CrossRef]
  97. Sun, L.; Deser, C.; Tomas, R.A. Mechanisms of stratospheric and tropospheric circulation response to projected Arctic sea ice loss. J. Clim. 2015, 28, 7824–7845. [Google Scholar] [CrossRef]
  98. Overland, J.E.; Wang, M. Resolving future Arctic/midlatitude weather connections. Earth’s Future 2018, 6, 1146–1152. [Google Scholar] [CrossRef]
  99. Cohen, J.; Screen, J.A.; Furtado, J.C.; Barlow, M.; Whittleston, D.; Coumou, D.; Francis, J.; Dethloff, K.; Entekhabi, D.; Overland, J.; et al. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 2014, 7, 627–637. [Google Scholar] [CrossRef]
  100. Wang, J.; Kim, H.M.; Chang, E.K. Changes in Northern Hemisphere winter storm tracks under the background of Arctic amplification. J. Clim. 2017, 30, 3705–3724. [Google Scholar] [CrossRef]
  101. Zhang, X.; Wu, B.; Ding, S. Combined effects of La Niña events and Arctic tropospheric warming on the winter North Pacific storm track. Clim. Dyn. 2023, 60, 1351–1368. [Google Scholar] [CrossRef]
  102. Francis, J.A.; Vavrus, S.J. Evidence for a wavier jet stream in response to rapid Arctic warming. Environ. Res. Lett. 2015, 10, 014005. [Google Scholar] [CrossRef]
  103. Wang, S.; Nath, D.; Chen, W.; Wang, L. Changes in winter stationary wave activity during weak mid-latitude and Arctic thermal contrast period. Int. J. Climtol. 2020, 40, 1755–1768. [Google Scholar] [CrossRef]
  104. Cohen, J.; Agel, L.; Barlow, M.; Garfinkel, C.I.; White, I. Linking Arctic variability and change with extreme winter weather in the United States. Science 2021, 373, 1116–1121. [Google Scholar] [CrossRef] [PubMed]
  105. Deser, C.; Walsh, J.E.; Timlin, M.S. Arctic sea ice variability in the context of recent atmospheric circulation trends. J. Clim. 2000, 13, 617–633. [Google Scholar] [CrossRef]
  106. Newson, R.L. Response of a general circulation model of the atmosphere to removal of the Arctic ice-cap. Nature 1973, 241, 39–40. [Google Scholar] [CrossRef]
  107. Francis, J.A.; Vavrus, S.J. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys. Res. Lett. 2012, 39, L06801. [Google Scholar] [CrossRef]
  108. Mohanakumar, K. Stratosphere Troposphere Interactions: An Introduction; Springer: Berlin/Heidelberg, Germany, 2008; p. 416. [Google Scholar]
  109. Castanheira, J.M.; Liberato, M.L.R.; De La Torre, L.; Graf, H.F.; DaCamara, C.C. Baroclinic Rossby wave forcing and barotropic Rossby wave response to stratospheric vortex variability. J. Atmos. Sci. 2009, 66, 902–914. [Google Scholar] [CrossRef]
  110. Graf, H.F.; Zanchettin, D.; Timmreck, C.; Bittner, M. Observational constraints on the tropospheric and near-surface winter signature of the Northern Hemisphere stratospheric polar vortex. Clim. Dyn. 2014, 43, 3245–3266. [Google Scholar] [CrossRef]
  111. Ding, S.; Chen, W.; Graf, H.F.; Chen, Z.; Ma, T. Quasi-stationary extratropical wave trains associated with distinct tropical Pacific seasonal mean convection patterns: Observational and AMIP model results. Clim. Dyn. 2019, 53, 2451–2476. [Google Scholar] [CrossRef]
  112. Chen, X.; Luo, D. Arctic sea ice decline and continental cold anomalies: Upstream and downstream effects of Greenland blocking. Geophys. Res. Lett. 2017, 44, 3411–3419. [Google Scholar] [CrossRef]
  113. Luo, D.; Chen, X.; Overland, J.; Simmonds, I.; Wu, Y.; Zhang, P. Weakened potential vorticity barrier linked to recent winter Arctic sea ice loss and midlatitude cold extremes. J. Clim. 2019, 32, 4235–4261. [Google Scholar] [CrossRef]
  114. Zhang, W.; Luo, D. A nonlinear theory of atmospheric blocking: An application to Greenland blocking changes linked to winter Arctic sea ice loss. J. Atmos. Sci. 2019, 77, 723–751. [Google Scholar] [CrossRef]
  115. Wang, H.; Luo, D. North Atlantic footprint of summer Greenland ice sheet melting on interannual to interdecadal time scales: A Greenland blocking perspective. J. Clim. 2022, 35, 1939–1961. [Google Scholar] [CrossRef]
  116. Yao, Y.; Zhuo, W.; Gong, Z.; Luo, B.; Luo, D.; Zheng, F.; Zhong, L.; Huang, F.; Ma, S.; Zhu, C.; et al. Extreme cold events in North America and Eurasia in November-December 2022: A potential vorticity gradient perspective. Adv. Atmos. Sci. 2023, 40, 953–962. [Google Scholar] [CrossRef]
  117. Song, Y.; Yao, Y.; Luo, D.; Li, Y. Loss of autumn Kara-East Siberian Sea ice intensifies winter Ural blocking and cold anomalies in high latitudes of Eurasia. Atmos. Res. 2023, 295, 107038. [Google Scholar] [CrossRef]
  118. Chen, H.W.; Zhang, F.; Alley, R.B. The robustness of midlatitude weather pattern changes due to Arctic sea ice loss. J. Clim. 2016, 29, 7831–7849. [Google Scholar] [CrossRef]
  119. McCusker, K.E.; Fyfe, J.C.; Sigmond, M. Twenty-five winters of unexpected Eurasian cooling unlikely due to Arctic sea-ice loss. Nat. Geosci. 2016, 9, 838–842. [Google Scholar] [CrossRef]
  120. Smith, D.M.; Dunstone, N.J.; Scaife, A.A.; Fiedler, E.K.; Copsey, D.; Hardiman, S.C. Atmospheric response to Arctic and Antarctic sea ice: The importance of ocean–atmosphere coupling and the background state. J. Clim. 2017, 30, 4547–4565. [Google Scholar] [CrossRef]
  121. Collow, T.W.; Wang, W.; Kumar, A. Simulations of Eurasian winter temperature trends in coupled and uncoupled CFSv2. Adv. Atmos. Sci. 2018, 35, 14–26. [Google Scholar] [CrossRef]
  122. Koenigk, T.; Gao, Y.; Gastineau, G.; Keenlyside, N.; Nakamura, T.; Ogawa, F.; Orsolini, Y.; Semenov, V.; Suo, L.; Tian, T.; et al. Impact of Arctic sea ice variations on winter temperature anomalies in northern hemispheric land areas. Clim. Dyn. 2019, 52, 3111–3137. [Google Scholar] [CrossRef]
  123. Orsolini, Y.J.; Senan, R.; Vitart, F.; Balsamo, G.; Weisheimer, A.; Doblas-Reyes, F.J. Influence of the Eurasian snow on the negative North Atlantic Oscillation in subseasonal forecasts of the cold winter 2009/2010. Clim. Dyn. 2016, 47, 1325–1334. [Google Scholar] [CrossRef]
  124. Dai, A.; Fyfe, J.C.; Xie, S.P.; Dai, X. Decadal modulation of global surface temperature by internal climate variability. Nat. Clim. Change 2015, 5, 555–559. [Google Scholar] [CrossRef]
  125. Luo, B.; Luo, D.; Dai, A.; Simmonds, I.; Wu, L. Decadal variability of winter warm Arctic-cold Eurasia dipole patterns modulated by Pacific decadal oscillation and Atlantic multidecadal oscillation. Earth’s Future 2022, 10, e2021EF002351. [Google Scholar] [CrossRef]
  126. Wang, H.; Zuo, Z.; Qiao, L.; Zhang, K.; Sun, C.; Xiao, D.; Lin, Z.; Bu, L.; Zhang, R. Frequency of the winter temperature extremes over Siberia dominated by the Atlantic Meridional Overturning Circulation. NPJ Clim. Atmos. Sci. 2022, 5, 84. [Google Scholar] [CrossRef]
  127. Matsumura, S.; Kosaka, Y. Arctic–Eurasian climate linkage induced by tropical ocean variability. Nat. Commun. 2019, 10, 3441. [Google Scholar] [CrossRef]
  128. Sato, K.; Inoue, J.; Watanabe, M. Influence of the Gulf Stream on the Barents Sea ice retreat and Eurasian coldness during early winter. Environ. Res. Lett. 2014, 9, 084009. [Google Scholar] [CrossRef]
  129. Rousi, E.; Kornhuber, K.; Beobide-Arsuaga, G.; Luo, F.; Coumou, D. Accelerated western European heatwave trends linked to more-persistent double jets over Eurasia. Nat. Commun. 2022, 13, 3851. [Google Scholar] [CrossRef]
  130. Di Capua, G.; Sparrow, S.; Kornhuber, K.; Rousi, E.; Osprey, S.; Wallom, D.; van den Hurk, B.; Coumou, D. Drivers behind the summer 2010 wave train leading to Russian heatwave and Pakistan flooding. NPJ Clim. Atmos. Sci. 2021, 4, 55. [Google Scholar] [CrossRef]
  131. Zhang, R.; Sun, C.; Zhu, J.; Zhang, R.; Li, W. Increased European heat waves in recent decades in response to shrinking Arctic sea ice and Eurasian snow cover. NPJ Clim. Atmos. Sci. 2020, 3, 7. [Google Scholar] [CrossRef]
  132. Wang, H.; Luo, D. Summer Russian heat waves and their links to Greenland’s ice melt and sea surface temperature anomalies over the North Atlantic and the Barents–Kara Seas. Environ. Res. Lett. 2020, 15, 114048. [Google Scholar] [CrossRef]
  133. Screen, J.A. Influence of Arctic sea ice on European summer precipitation. Environ. Res. Lett. 2013, 8, 044015. [Google Scholar] [CrossRef]
  134. Deng, K.; Jiang, X.; Hu, C.; Chen, D. More frequent summer heat waves in southwestern China linked to the recent declining of Arctic sea ice. Environ. Res. Lett. 2020, 15, 074011. [Google Scholar] [CrossRef]
  135. Zhang, R.; Sun, C.; Zhang, R.; Jia, L.; Li, W. The impact of Arctic sea ice on the inter-annual variations of summer Ural blocking. Int. J. Climtol. 2018, 38, 4632–4650. [Google Scholar] [CrossRef]
  136. Wu, Q.; Cheng, L.; Chan, D.; Yao, Y.; Hu, H.; Yao, Y. Suppressed midlatitude summer atmospheric warming by Arctic sea ice loss during 1979–2012. Geophys. Res. Lett. 2016, 43, 2792–2800. [Google Scholar] [CrossRef]
  137. Tang, Q.; Zhang, X.; Francis, J.A. Extreme summer weather in northern mid-latitudes linked to a vanishing cryosphere. Nat. Clim. Change 2014, 4, 45–50. [Google Scholar] [CrossRef]
  138. Petrie, R.E.; Shaffrey, L.C.; Sutton, R. Atmospheric response in summer linked to recent Arctic sea ice loss. Q. J. R. Meteorol. Soc. 2015, 141, 2070–2076. [Google Scholar] [CrossRef]
  139. Knudsen, E.M.; Orsolini, Y.J.; Furevik, T.; Hodges, K.I. Observed anomalous atmospheric patterns in summers of unusual Arctic sea ice melt. J. Geophys. Res. Atmos. 2015, 120, 2595–2611. [Google Scholar] [CrossRef]
  140. Zhang, X.; Wu, B.; Ding, S. Influence of spring Arctic sea ice melt on Eurasian surface air temperature. Clim. Dyn. 2022, 59, 3305–3316. [Google Scholar] [CrossRef]
  141. Wu, B.; Li, Z. Possible impacts of anomalous Arctic sea ice melting on summer atmosphere. Int. J. Climtol. 2022, 42, 1818–1827. [Google Scholar] [CrossRef]
  142. Wu, B.; Li, Z.; Zhang, X.; Sha, Y.; Duan, X.; Pang, X.; Ding, S. Has Arctic sea ice loss affected summer precipitation in North China? Int. J. Climtol. 2023, 43, 4835–4848. [Google Scholar] [CrossRef]
  143. Wu, Z.; Li, X.; Li, Y.; Li, Y. Potential influence of Arctic sea ice to the interannual variations of East Asian spring precipitation. J. Clim. 2016, 29, 2797–2813. [Google Scholar] [CrossRef]
  144. Sun, L.; Shen, B.; Sui, B.; Huang, B. The influences of East Asian Monsoon on summer precipitation in Northeast China. Clim. Dyn. 2017, 48, 1647–1659. [Google Scholar] [CrossRef]
  145. Zhou, J.; Zuo, Z.; Rong, X.; Wen, J. Role of May surface temperature over eastern China in East Asian summer monsoon circulation and precipitation. Int. J. Climtol. 2020, 40, 6396–6409. [Google Scholar] [CrossRef]
  146. Wu, B.; Zhang, R.; D’Arrigo, R.; Su, J. On the relationship between winter sea ice and summer atmospheric circulation over Eurasia. J. Clim. 2013, 26, 5523–5536. [Google Scholar] [CrossRef]
  147. Zhang, P.; Wu, Z.; Jin, R. How can the winter North Atlantic Oscillation influence the early summer precipitation in Northeast Asia: Effect of the Arctic sea ice. Clim. Dyn. 2021, 56, 1989–2005. [Google Scholar] [CrossRef]
  148. Du, Y.; Zhang, J.; Zhao, S.; Chen, Z. A mechanism of spring Barents Sea ice effect on the extreme summer droughts in northeastern China. Clim. Dyn. 2022, 58, 1033–1048. [Google Scholar] [CrossRef]
  149. Yu, Q.; Wu, B. Summer Arctic atmospheric circulation and its association with the ensuing East Asian Winter Monsoon variability. J. Geophys. Res. Atmos. 2023, 128, e2022JD037104. [Google Scholar] [CrossRef]
  150. Chen, X.; Wen, Z.; Song, Y.; Guo, Y. Causes of extreme 2020 Meiyu-Baiu rainfall: A study of combined effect of Indian Ocean and Arctic. Clim. Dyn. 2022, 59, 3485–3501. [Google Scholar] [CrossRef]
  151. Tian, Y.; Gao, Y.; Guo, D. The relationship between melt season sea ice over the Bering Sea and summer precipitation over mid-latitude East Asia. Adv. Atmos. Sci. 2021, 38, 918–930. [Google Scholar] [CrossRef]
  152. Fu, H.; Zhan, R.; Wu, Z.; Wang, Y.; Zhao, J. How does the Arctic sea ice affect the interannual variability of tropical cyclone activity over the western North Pacific? Front. Earth Sci. 2021, 9, 675150. [Google Scholar] [CrossRef]
  153. Rinke, A.; Knudsen, E.M.; Mewes, D.; Dorn, W.; Handorf, D.; Dethloff, K.; Moore, J.C. Arctic summer sea ice melt and related atmospheric conditions in coupled regional climate model simulations and observations. J. Geophys. Res. Atmos. 2019, 124, 6027–6039. [Google Scholar] [CrossRef]
  154. Gultepe, I.; Isaac, G.A.; Williams, A.; Marcotte, D.; Strawbridge, K.B. Turbulent heat fluxes over leads and polynyas, and their effects on arctic clouds during FIRE. ACE: Aircraft observations for April 1998. Atmos. Ocean. 2003, 41, 15–34. [Google Scholar] [CrossRef]
  155. Liu, L.; Wu, B.; Ding, S. On the association of the summertime shortwave cloud radiative effect in northern Russia with atmospheric circulation and climate over East Asia. Geophys. Res. Lett. 2022, 49, e2021GL096606. [Google Scholar] [CrossRef]
  156. Liu, L.; Wu, B.; Ding, S. Combined impact of summer NAO and northern Russian shortwave cloud radiative effect on Eurasian atmospheric circulation. Environ. Res. Lett. 2023, 18, 014015. [Google Scholar] [CrossRef]
  157. Perlwitz, J.; Hoerling, M.; Dole, R. Arctic tropospheric warming: Causes and linkages to lower latitudes. J. Clim. 2015, 28, 2154–2167. [Google Scholar] [CrossRef]
  158. Hurrell, J.W.; Deser, C. North Atlantic climate variability: The role of the North Atlantic Oscillation. J. Marine. Syst. 2010, 79, 231–244. [Google Scholar] [CrossRef]
  159. Overland, J.E.; Wang, M.Y. Recent extreme Arctic temperatures are due to a split polar vortex. J. Clim. 2016, 29, 5609–5616. [Google Scholar] [CrossRef]
  160. Graham, R.M.; Cohen, L.; Petty, A.A.; Boisvert, L.N.; Rinke, A.; Hudson, S.R.; Nicolaus, M.; Granskog, M.A. Increasing frequency and duration of Arctic winter warming events. Geophys. Res. Lett. 2017, 44, 6974–6983. [Google Scholar] [CrossRef]
  161. Kim, B.M.; Hong, J.Y.; Jun, S.Y.; Zhang, X.; Kwon, H.; Kim, S.J.; Kim, J.H.; Kim, S.W.; Kim, H.K. Major cause of unprecedented Arctic warming in January 2016: Critical role of an Atlantic windstorm. Sci. Rep. 2017, 7, 40051. [Google Scholar] [CrossRef]
  162. Wang, C.; Ren, B.; Zheng, J. Two impacts of arctic rapid tropospheric daily warming from different warm temperature advection on cold winters over northern hemisphere. Earth Space Sci. 2019, 6, 1667–1674. [Google Scholar] [CrossRef]
  163. Hoskins, B.J.; Hodges, K.I. New perspectives on the northern hemisphere winter storm tracks. J. Atmos. Sci. 2002, 59, 1041–1061. [Google Scholar] [CrossRef]
  164. Pinto, J.G.; Spangehl, T.; Ulbrich, U.; Speth, P. Sensitivities of a cyclone detection and tracking algorithm: Individual tracks and climatology. Meteorol. Z. 2005, 14, 823–838. [Google Scholar] [CrossRef] [PubMed]
  165. Chen, H.W.; Zhang, Q.; Körnich, H.; Chen, D. A robust mode of climate variability in the Arctic: The Barents Oscillation. Geophys. Res. Lett. 2013, 40, 2856–2861. [Google Scholar] [CrossRef]
  166. Martius, O.; Polvani, L.M.; Davies, H.C. Blocking precursors to stratospheric sudden warming events. Geophys. Res. Lett. 2009, 36, L14806. [Google Scholar] [CrossRef]
  167. Peings, Y. Ural blocking as a driver of early-winter stratospheric warmings. Geophys. Res. Lett. 2019, 46, 5460–5468. [Google Scholar] [CrossRef]
  168. Yao, Y.; Luo, D.; Dai, A.; Simmonds, I. Increased quasi stationarity and persistence of winter Ural blocking and Eurasian extreme cold events in response to Arctic warming. Part I Insights Obs. Analyses. J. Clim. 2017, 30, 3549–3568. [Google Scholar]
  169. Tyrlis, E.; Bader, J.; Manzini, E.; Ukita, J.; Nakamura, H.; Matei, D. On the role of Ural Blocking in driving the Warm Arctic–Cold Siberia pattern. Q. J. R. Meteorol. Soc. 2020, 146, 2138–2153. [Google Scholar] [CrossRef]
  170. Chen, X.; Luo, D.; Feldstein, S.B.; Lee, S. Impact of winter Ural blocking on Arctic sea ice: Short-time variability. J. Clim. 2018, 31, 2267–2282. [Google Scholar] [CrossRef]
  171. Luo, B.; Yao, Y. Recent rapid decline of the Arctic winter sea ice in the Barents–Kara Seas owing to combined effects of the Ural blocking and SST. J Meteorol. Res. 2018, 32, 191–202. [Google Scholar] [CrossRef]
  172. Xu, X.; He, S.; Zhou, B.; Wang, H.; Outten, S. The role of mid-latitude westerly jet in the impacts of november ural blocking on early-winter warmer arctic-colder eurasia pattern. Geophys. Res. Lett. 2022, 49, e2022GL099096. [Google Scholar] [CrossRef]
  173. Jevrejeva, S.; Moore, J.C.; Grinsted, A. Influence of the Arctic Oscillation and El Niño-Southern Oscillation (ENSO) on ice conditions in the Baltic Sea: The wavelet approach. J. Geophys. Res. Atmos. 2003, 108, D214677. [Google Scholar] [CrossRef]
  174. Pozo-Vázquez, D.; Gámiz-Fortis, S.R.; Tovar-Pescador, J.; Esteban-Parra, M.J.; Castro-Díez, Y. North Atlantic winter SLP anomalies based on the autumn ENSO state. J. Clim. 2005, 18, 97–103. [Google Scholar] [CrossRef]
  175. Bell, C.J.; Gray, L.J.; Charlton-Perez, A.J.; Joshi, M.M.; Scaife, A.A. Stratospheric communication of El Niño teleconnections to European winter. J. Clim. 2009, 22, 4083–4096. [Google Scholar] [CrossRef]
  176. Ineson, S.; Scaife, A.A. The role of the stratosphere in the European climate response to El Niño. Nat. Geosci. 2009, 2, 32–36. [Google Scholar] [CrossRef]
  177. OrtizBevia, M.J.; Pérez-González, I.; Alvarez-García, F.J.; Gershunov, A. Nonlinear estimation of El Niño impact on the North Atlantic winter. J. Geophys. Res. Atmos. 2010, 115, D21123. [Google Scholar] [CrossRef]
  178. Lee, S. Testing of the tropically excited Arctic warming mechanism (TEAM) with traditional El Niño and La Niña. J. Clim. 2012, 25, 4015–4022. [Google Scholar] [CrossRef]
  179. Clancy, R.; Bitz, C.; Blanchard-Wrigglesworth, E. The influence of ENSO on Arctic sea ice in large ensembles and observations. J. Clim. 2021, 34, 9585–9604. [Google Scholar] [CrossRef]
  180. Krishnamurti, T.N.; Krishnamurti, R.; Das, S.; Kumar, V.; Jayakumar, A.; Simon, A. A pathway connecting the monsoonal heating to the rapid Arctic ice melt. J. Atmos. Sci. 2015, 72, 5–34. [Google Scholar] [CrossRef]
  181. Krishnamurti, T.N.; Kumar, V. Prediction of a thermodynamic wave train from the monsoon to the Arctic following extreme rainfall events. Clim. Dyn. 2017, 48, 2315–2337. [Google Scholar] [CrossRef]
  182. Chatterjee, S.; Ravichandran, M.; Murukesh, N.; Raj, R.P.; Johannessen, O.M. A possible relation between Arctic sea ice and late season Indian summer monsoon rainfall extremes. NPJ Clim. Atmos. Sci. 2021, 4, 36. [Google Scholar] [CrossRef]
  183. Luo, B.; Luo, D.; Ge, Y.; Dai, A.; Wang, L.; Simmonds, I.; Xiao, C.; Wu, L.; Yao, Y. Origins of Barents-Kara sea-ice interannual variability modulated by the Atlantic pathway of El Niño–Southern Oscillation. Nat. Commun. 2023, 14, 585. [Google Scholar] [CrossRef]
  184. Hu, C.; Yang, S.; Wu, Q.; Li, Z.; Chen, J.; Deng, K.; Zhang, T.; Zhang, C. Shifting El Niño inhibits summer Arctic warming and Arctic sea-ice melting over the Canada Basin. Nat. Commun. 2016, 7, 11721. [Google Scholar] [CrossRef] [PubMed]
  185. Li, Z.; Zhang, W.; Stuecker, M.F.; Xu, H.; Jin, F.F.; Liu, C. Different effects of two ENSO types on Arctic surface temperature in boreal winter. J. Clim. 2019, 32, 4943–4961. [Google Scholar] [CrossRef]
  186. Jeong, H.; Park, H.S.; Stuecker, M.F.; Yeh, S.W. Distinct impacts of major El Niño events on Arctic temperatures due to differences in eastern tropical Pacific sea surface temperatures. Sci. Adv. 2022, 8, eabl8278. [Google Scholar] [CrossRef] [PubMed]
  187. Wang, C.; Ren, B.; Li, G.; Zheng, J.; Jiang, L.; Zhang, Z. Why could ENSO directly affect the occurrence frequency of Arctic daily warming events after the late 1970s? Environ. Res. Lett. 2023, 18, 024009. [Google Scholar] [CrossRef]
  188. L’Heureux, M.L.; Higgins, R. Boreal Winter Links between the Madden-Julian Oscillation and the Arctic Oscillation. J. Clim. 2008, 21, 3040–3050. [Google Scholar] [CrossRef]
  189. Flatau, M.; Kim, Y.J. Interaction between the MJO and Polar Circulations. J. Clim. 2013, 26, 3562–3574. [Google Scholar] [CrossRef]
  190. Yoo, C.; Feldstein, S.B.; Lee, S. The impact of the Madden-Julian oscillation trend on the Arctic amplification of surface air temperature during the 1979–2008 boreal winter. Geophys. Res. Lett. 2011, 38, L24804. [Google Scholar] [CrossRef]
  191. Henderson, G.R.; Barrett, B.S.; Lafleur, D.M. Arctic sea ice and the Madden–Julian oscillation (MJO). Clim. Dyn. 2014, 43, 2185–2196. [Google Scholar] [CrossRef]
  192. Graversen, R.G. Do changes in the midlatitude circulation have any impact on the Arctic surface air temperature trend? J. Clim. 2006, 19, 5422–5438. [Google Scholar] [CrossRef]
  193. Garfinkel, C.I.; Feldstein, S.B.; Waugh, D.W.; Yoo, C.; Lee, S. Observed connection between stratospheric sudden warmings and the Madden-Julian Oscillation. Geophys. Res. Lett. 2012, 39, L18807. [Google Scholar] [CrossRef]
  194. Ding, Q.; Schweiger, A.; L’Heureux, M.; Battisti, D.S.; Po-Chedley, S.; Johnson, N.C.; Blanchard-Wrigglesworth, E.; Harnos, K.; Zhang, Q.; Eastman, R.; et al. Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Clim. Change 2017, 7, 289–295. [Google Scholar] [CrossRef]
  195. Ding, Q.; Schweiger, A.; L’Heureux, M.; Steig, E.J.; Battisti, D.S.; Johnson, N.C.; Blanchard-Wrigglesworth, E.; Po-Chedley, S.; Zhang, Q.; Harnos, K.; et al. Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nat. Geosci. 2019, 12, 28–33. [Google Scholar] [CrossRef]
  196. Ding, Q.; Schweiger, A.; Baxter, I. Nudging observed winds in the Arctic to quantify associated sea ice loss from 1979 to 2020. J. Clim. 2022, 35, 6797–6813. [Google Scholar] [CrossRef]
  197. Jeong, Y.C.; Yeh, S.W.; Lim, Y.K.; Santoso, A.; Wang, G. Indian Ocean warming as key driver of long-term positive trend of Arctic Oscillation. NPJ Clim. Atmos. Sci. 2022, 5, 56. [Google Scholar] [CrossRef]
  198. Barnes, E.A.; Screen, J.A. The impact of Arctic warming on the midlatitude jet-stream: Can it? Has it? Will it? WIREs Clim. Change 2015, 6, 277–286. [Google Scholar] [CrossRef]
  199. Dai, A.; Song, M. Little influence of Arctic amplification on mid-latitude climate. Nat. Clim. Change 2020, 10, 231–237. [Google Scholar] [CrossRef]
  200. Screen, J.A.; Deser, C.; Smith, D.M.; Zhang, X.; Blackport, R.; Kushner, P.J.; Oudar, T.; McCusker, K.E.; Sun, L. Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. Nat. Geosci. 2018, 11, 155–163. [Google Scholar] [CrossRef]
  201. He, X.; Zhang, R.; Ding, S.; You, Q.; Cai, Z. Decadal changes in the linkage between autumn sea ice and the winter Eurasian temperature in the 20th century. Geophys. Res. Lett. 2023, 50, e2023GL103851. [Google Scholar] [CrossRef]
  202. Cai, Z.; You, Q.; Chen, H.W.; Zhang, R.; Zuo, Z.; Dai, G.; Chen, D.; Cohen, J.; Zolina, O.; Gulev, S.K. Interdecadal variability of the warm Arctic-cold Eurasia pattern linked to the Barents oscillation. Atmos. Res. 2023, 287, 106712. [Google Scholar] [CrossRef]
  203. Sung, M.K.; Kim, S.H.; Kim, B.M.; Choi, Y.S. Interdecadal variability of the warm Arctic and cold Eurasia pattern and its North Atlantic origin. J. Clim. 2018, 31, 5793–5810. [Google Scholar] [CrossRef]
  204. Semenov, V.A.; Latif, M. Nonlinear winter atmospheric circulation response to Arctic sea ice concentration anomalies for different periods during 1966–2012. Environ. Res. Lett. 2015, 10, 054020. [Google Scholar] [CrossRef]
  205. Komatsu, K.K.; Takaya, Y.; Toyoda, T.; Hasumi, H. Response of Eurasian Temperature to Barents–Kara Sea Ice: Evaluation by Multi-Model Seasonal Predictions. Geophys. Res. Lett. 2022, 49, e2021GL097203. [Google Scholar] [CrossRef]
  206. Screen, J.A.; Francis, J.A. Contribution of sea-ice loss to Arctic amplification is regulated by Pacific Ocean decadal variability. Nat. Clim. Change 2016, 6, 856–860. [Google Scholar] [CrossRef]
  207. Zhang, R.; Screen, J.A.; Zhang, R. Arctic and Pacific Ocean conditions were favorable for cold extremes over Eurasia and North America during winter 2020/21. Bull. Am. Meteorol. Soc. 2022, 103, E2285–E2301. [Google Scholar] [CrossRef]
  208. Zhang, R.; Zhang, R.; Dai, G. Intraseasonal contributions of Arctic sea-ice loss and Pacific decadal oscillation to a century cold event during early 2020/21 winter. Clim. Dyn. 2022, 58, 741–758. [Google Scholar] [CrossRef]
  209. Zheng, F.; Yuan, Y.; Ding, Y.; Li, K.; Fang, X.; Zhao, Y.; Sun, Y.; Zhu, J.; Ke, Z.; Wang, J.; et al. The 2020/21 extremely cold winter in China influenced by the synergistic effect of La Niña and warm Arctic. Adv. Atmos. Sci. 2022, 39, 546–552. [Google Scholar] [CrossRef]
  210. Wang, Q.; Mu, M.; Sun, G. A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: Conditional non-linear optimal perturbation. Nat. Sci. Rev. 2020, 7, 214–223. [Google Scholar] [CrossRef]
  211. Yu, Q.; Wu, B.; Zhang, W. The atmospheric connection between the Arctic and Eurasia is underestimated in simulations with prescribed sea ice. Commun. Earth Environ. 2024, 5, 435. [Google Scholar] [CrossRef]
  212. Liu, J.; Song, M.; Zhu, Z.; Horton, R.M.; Hu, Y.; Xie, S.P. Arctic sea-ice loss is projected to lead to more frequent strong El Niño events. Nat. Commun. 2022, 13, 4952. [Google Scholar] [CrossRef]
  213. Deng, J.; Dai, A. Arctic sea ice–air interactions weaken El Niño–Southern Oscillation. Sci. Adv. 2024, 10, eadk3990. [Google Scholar] [CrossRef]
Figure 1. Per ten-year variation in the annual cycle of Arctic Sea ice extent with eight lowest sea ice years (e.g., 2007, 2011, 2012, 2016, 2018, 2019, 2020, 2021). Figure and Table is from the National Snow and Ice Data Center (NSIDC; https://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/, accessed on 1 June 2024) and ref. [28].
Figure 1. Per ten-year variation in the annual cycle of Arctic Sea ice extent with eight lowest sea ice years (e.g., 2007, 2011, 2012, 2016, 2018, 2019, 2020, 2021). Figure and Table is from the National Snow and Ice Data Center (NSIDC; https://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/, accessed on 1 June 2024) and ref. [28].
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Figure 2. Schematic diagram of how the autumn (a) EsCB and (b) BK sea ice loss affects winter Eurasian temperature anomalies. Shadings in cylindrical equidistant and polar projection maps denote air temperature anomalies at 1000 hPa and sea ice anomalies, respectively. Red (blue) represents positive (negative) anomalies. Contours in cylindrical equidistant and polar projection maps denote geopotential anomalies at 1000 and 500 hPa, respectively. Dots denote more frequent extreme low temperatures. Black arrows denote the horizontal propagation of planetary waves. Orange (green) empty arrows denote stratospheric (tropospheric) pathways.
Figure 2. Schematic diagram of how the autumn (a) EsCB and (b) BK sea ice loss affects winter Eurasian temperature anomalies. Shadings in cylindrical equidistant and polar projection maps denote air temperature anomalies at 1000 hPa and sea ice anomalies, respectively. Red (blue) represents positive (negative) anomalies. Contours in cylindrical equidistant and polar projection maps denote geopotential anomalies at 1000 and 500 hPa, respectively. Dots denote more frequent extreme low temperatures. Black arrows denote the horizontal propagation of planetary waves. Orange (green) empty arrows denote stratospheric (tropospheric) pathways.
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Figure 3. Schematic diagram of the connections between summer Arctic changes and midlatitude temperature and precipitation anomalies. Shadings denote air temperature anomalies at 1000 hPa. Red (blue) represents positive (negative) anomalies. Contours denote geopotential anomalies at 500 hPa. Green (brown) dots denote excessive (deficient) rainfall. Black arrows denote the horizontal propagation of planetary waves.
Figure 3. Schematic diagram of the connections between summer Arctic changes and midlatitude temperature and precipitation anomalies. Shadings denote air temperature anomalies at 1000 hPa. Red (blue) represents positive (negative) anomalies. Contours denote geopotential anomalies at 500 hPa. Green (brown) dots denote excessive (deficient) rainfall. Black arrows denote the horizontal propagation of planetary waves.
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Figure 4. A brief synopsis of the Arctic–midlatitudes connection: interactive impacts, possible physical mechanisms (tropospheric and stratospheric pathways), and nonstationary. BK Seas denotes Barents–Kara Seas, EsCB Seas denotes East Siberian–Chukchi–Beaufort Seas, NA denotes North Atlantic, NP denotes North Pacific, AO/NAO denotes Arctic/North Atlantic Oscillation, UB denotes Ural blocking, PNA denotes Pacific–North American, ENSO denotes El Niño–Southern Oscillation, and MJO denotes Madden–Julian Oscillation.
Figure 4. A brief synopsis of the Arctic–midlatitudes connection: interactive impacts, possible physical mechanisms (tropospheric and stratospheric pathways), and nonstationary. BK Seas denotes Barents–Kara Seas, EsCB Seas denotes East Siberian–Chukchi–Beaufort Seas, NA denotes North Atlantic, NP denotes North Pacific, AO/NAO denotes Arctic/North Atlantic Oscillation, UB denotes Ural blocking, PNA denotes Pacific–North American, ENSO denotes El Niño–Southern Oscillation, and MJO denotes Madden–Julian Oscillation.
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MDPI and ACS Style

Ding, S.; Chen, X.; Zhang, X.; Zhang, X.; Xu, P. A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary. Atmosphere 2024, 15, 1115. https://doi.org/10.3390/atmos15091115

AMA Style

Ding S, Chen X, Zhang X, Zhang X, Xu P. A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary. Atmosphere. 2024; 15(9):1115. https://doi.org/10.3390/atmos15091115

Chicago/Turabian Style

Ding, Shuoyi, Xiaodan Chen, Xuanwen Zhang, Xiang Zhang, and Peiqiang Xu. 2024. "A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary" Atmosphere 15, no. 9: 1115. https://doi.org/10.3390/atmos15091115

APA Style

Ding, S., Chen, X., Zhang, X., Zhang, X., & Xu, P. (2024). A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary. Atmosphere, 15(9), 1115. https://doi.org/10.3390/atmos15091115

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