Extreme Surface Energy Budget Anomalies in the High Arctic in Winter

Sonja Murto aDepartment of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

Search for other papers by Sonja Murto in
Current site
Google Scholar
PubMed
Close
,
Lukas Papritz bInstitute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland

Search for other papers by Lukas Papritz in
Current site
Google Scholar
PubMed
Close
,
Gabriele Messori cDepartment of Earth Sciences and Centre of Natural Hazards and Disaster Science, Uppsala University, Uppsala, Sweden
aDepartment of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

Search for other papers by Gabriele Messori in
Current site
Google Scholar
PubMed
Close
,
Rodrigo Caballero aDepartment of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

Search for other papers by Rodrigo Caballero in
Current site
Google Scholar
PubMed
Close
,
Gunilla Svensson aDepartment of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

Search for other papers by Gunilla Svensson in
Current site
Google Scholar
PubMed
Close
, and
Heini Wernli bInstitute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland

Search for other papers by Heini Wernli in
Current site
Google Scholar
PubMed
Close
Open access

Abstract

In recent decades, the Arctic has warmed faster than the global mean, especially during winter. This has been attributed to various causes, with recent studies highlighting the importance of enhanced downward infrared radiation associated with anomalous inflow of warm, moist air from lower latitudes. Here, we study wintertime surface energy budget (SEB) anomalies over Arctic sea ice on synoptic time scales, using ERA5 (1979–2020). We introduce a new algorithm to identify areas with extreme, positive daily mean SEB anomalies and connect them to form spatiotemporal life cycle events. Most of these events are associated with large-scale inflow from the Atlantic and Pacific Oceans, driven by poleward deflection of the storm track and blocks over northern Eurasia and Alaska. Events originate near the ice edge, where they have roughly equal contributions of net longwave radiation and turbulent fluxes to the positive SEB anomaly. As the events move farther into the Arctic, SEB anomalies decrease due to weakening sensible and latent heat-flux anomalies, while the surface temperature anomaly increases toward the peak of the events along with the downward longwave radiation anomaly. Due to these temporal and spatial differences, the largest SEB anomalies are not always related to strongest surface warming. Thus, studying temperature anomalies alone might not be sufficient to determine sea ice changes. This study highlights the importance of turbulent fluxes in driving SEB anomalies and downward longwave radiation in determining local surface warming. Therefore, both processes need to be accurately represented in climate models.

Significance Statement

Mechanisms behind wintertime rapid Arctic warming and sea ice growth changes are not well understood. While much is known about the impact of radiative fluxes on both sea ice variability and surface warming, the relative importance of radiative and turbulent fluxes remains unclear. The purpose of this study is to clarify what controls surface energy budget (SEB) anomalies over sea ice. Along the life cycle of synoptic-scale events, positive SEB anomalies are shown to decrease and surface temperature anomalies increase after their onset. Additionally, variations in SEB anomalies are primarily controlled by turbulent fluxes, while downward longwave radiative fluxes are mainly responsible for surface temperature variations. These results highlight the need for accurate representations of these fluxes for predicting future Arctic climate.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

Corresponding author: Sonja Murto, sonja.murto@misu.su.se

Abstract

In recent decades, the Arctic has warmed faster than the global mean, especially during winter. This has been attributed to various causes, with recent studies highlighting the importance of enhanced downward infrared radiation associated with anomalous inflow of warm, moist air from lower latitudes. Here, we study wintertime surface energy budget (SEB) anomalies over Arctic sea ice on synoptic time scales, using ERA5 (1979–2020). We introduce a new algorithm to identify areas with extreme, positive daily mean SEB anomalies and connect them to form spatiotemporal life cycle events. Most of these events are associated with large-scale inflow from the Atlantic and Pacific Oceans, driven by poleward deflection of the storm track and blocks over northern Eurasia and Alaska. Events originate near the ice edge, where they have roughly equal contributions of net longwave radiation and turbulent fluxes to the positive SEB anomaly. As the events move farther into the Arctic, SEB anomalies decrease due to weakening sensible and latent heat-flux anomalies, while the surface temperature anomaly increases toward the peak of the events along with the downward longwave radiation anomaly. Due to these temporal and spatial differences, the largest SEB anomalies are not always related to strongest surface warming. Thus, studying temperature anomalies alone might not be sufficient to determine sea ice changes. This study highlights the importance of turbulent fluxes in driving SEB anomalies and downward longwave radiation in determining local surface warming. Therefore, both processes need to be accurately represented in climate models.

Significance Statement

Mechanisms behind wintertime rapid Arctic warming and sea ice growth changes are not well understood. While much is known about the impact of radiative fluxes on both sea ice variability and surface warming, the relative importance of radiative and turbulent fluxes remains unclear. The purpose of this study is to clarify what controls surface energy budget (SEB) anomalies over sea ice. Along the life cycle of synoptic-scale events, positive SEB anomalies are shown to decrease and surface temperature anomalies increase after their onset. Additionally, variations in SEB anomalies are primarily controlled by turbulent fluxes, while downward longwave radiative fluxes are mainly responsible for surface temperature variations. These results highlight the need for accurate representations of these fluxes for predicting future Arctic climate.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).

Corresponding author: Sonja Murto, sonja.murto@misu.su.se

1. Introduction

In recent decades, Northern Hemisphere high-latitude regions have experienced a faster surface warming than global average, with the most pronounced warming trend observed in late fall and early winter. This phenomenon, known as Arctic amplification (e.g., Serreze and Barry 2011; Cohen et al. 2014; Previdi et al. 2021), is associated with a dramatic reduction in the extent and thickness of Arctic sea ice cover (Serreze and Barry 2011; Screen and Simmonds 2010a,b), especially in summer and early fall (Serreze et al. 2007b; Simmonds 2015). Though Arctic amplification is robustly captured by climate models (Hahn et al. 2021), there remains substantial uncertainty regarding the mechanisms responsible for it (Previdi et al. 2021). A leading theory points to the role of ice–albedo feedback, where the thinning and retreating ice cover in summer enables more absorption of solar radiation by the ocean. This oceanic heat is then released by enhanced upward, turbulent heat fluxes in fall or winter, leading to bottom-amplified warming, which, in turn, limits sea ice growth (Serreze and Francis 2006; Screen and Simmonds 2010a,b).

However, model studies reveal that polar amplification occurs even without ice–albedo feedback (e.g., Alexeev et al. 2005; Graversen and Wang 2009), highlighting the important role of other processes. Local physical processes are, for instance, related to temperature feedbacks and enhanced local cloud cover and water vapor content (e.g., Graversen and Wang 2009; Serreze et al. 2012; Pithan and Mauritsen 2014), whereas remote processes are related to enhanced poleward energy transport in the ocean (Årthun and Eldevik 2016) and the atmosphere (e.g., Woods et al. 2013; Woods and Caballero 2016; Boisvert et al. 2016; Graversen and Burtu 2016; Binder et al. 2017; Naakka et al. 2019). These processes are not independent; for instance, northward transport of moist and warm air, that is, moist intrusions, result in enhanced humidity and cloudiness and promote local warming in the Arctic (e.g., Doyle et al. 2011; Woods et al. 2013; Woods and Caballero 2016; Messori et al. 2018, 2020). Northward moisture transport events are highly episodic (Woods et al. 2013; Liu and Barnes 2015; Naakka et al. 2019; Papritz et al. 2022) and typically enter the Arctic via the North Atlantic and the North Pacific, the former clearly having the largest frequency and intensity (e.g., Woods et al. 2013; Papritz et al. 2022).

The poleward transport of warm and moist air is favored by specific large-scale circulation patterns that are also the primary drivers of temporal variations of the wintertime vertical thermodynamic structure affecting local processes (Nygård et al. 2021). Moisture transport into the Arctic from the North Atlantic is characterized by a dipole of geopotential height anomalies, with the positive lobe over Eurasia and the negative lobe southeast of Greenland or in the high Arctic (e.g., Luo et al. 2017; Messori et al. 2018; Fearon et al. 2021; Papritz 2020; Papritz et al. 2022; Murto et al. 2022). The negative lobe is associated with a northward deflection of North Atlantic storm tracks and higher cyclone frequencies in the Nordic seas and North Atlantic (Sorteberg and Walsh 2008; Simmonds et al. 2008; Pinto et al. 2009; Papritz and Dunn-Sigouin 2020; Fearon et al. 2021), as expressed by the positive phase of the North Atlantic Oscillation (e.g., Liu and Barnes 2015; Luo et al. 2017), whereas the positive lobe is related to blocking over Scandinavia, the Barents Sea, or the Ural Mountains (Woods et al. 2013; Liu and Barnes 2015; Gong and Luo 2017). Enhanced moisture and heat transport is shown to result from an interplay between blocking and cyclones (Madonna et al. 2020; Papritz and Dunn-Sigouin 2020). Such transport has been shown to cause warming and sea ice decline over the Barents and Kara Seas (BKS; Luo et al. 2016, 2019). Similar synoptic patterns leading to the transport of midlatitude air into the Arctic are also found in the Pacific sector (Baggett et al. 2016; Nygård et al. 2019; Papritz and Dunn-Sigouin 2020).

Previous work has examined Arctic extreme events defined in terms of surface temperature anomalies (Messori et al. 2018; Papritz 2020), northward moisture transport (Woods et al. 2013; Fearon et al. 2021; Papritz et al. 2022), downward longwave radiation (LW; H.-S. Park et al. 2015), sea ice concentration (Zheng et al. 2022), and large-scale circulation patterns (e.g., Gong and Luo 2017). A common factor for all of these event definitions is their association with moist intrusions and their related atmospheric circulation patterns, as discussed above. The arrival of a moist intrusion at a given location in the winter Arctic typically leads to a transition from a clear-sky cold state with strong surface radiative cooling to a cloudy and warm state with near-zero surface radiative cooling (e.g., Doyle et al. 2011; Stramler et al. 2011; Woods and Caballero 2016; Pithan et al. 2018).

Here, we focus on extreme positive anomalies in the net surface energy budget (SEB) during Arctic winter. SEB anomalies are defined as
ΔSEB=ΔLWnet+ΔSWnet+ΔSHF+ΔLHF,
where LWnet = LW + LW is the net surface longwave radiative flux, with LW and LW referring to the downward and upward fluxes respectively; SWnet is the net surface shortwave radiative flux; SHF and LHF are turbulent sensible and latent heat fluxes, respectively; and the Δ symbol indicates an anomaly from climatology. All fluxes are defined positive downward. Physically, positive SEB implies a net energy transfer from the atmosphere into the surface. Climatologically, SEB is negative in the Arctic winter, promoting surface cooling and sea ice formation. Positive SEB anomalies (ΔSEB) over pack ice retard thickening of the ice layer (Persson et al. 2017), while in marginal ice zone regions such as the BKS they can lead to melting and reduction in sea ice cover (Boisvert et al. 2016; Zheng et al. 2022). Fluctuations in the SEB, therefore, play an important role in controlling sea ice volume and extent, motivating interest in the study of the statistics of these fluctuations and their drivers.

As detailed in section 2, we proceed by identifying patches of strong positive ΔSEB within the ice-covered Arctic Ocean on daily time scales, and we track these patches spatially and temporally to form “life cycle events.” Section 3 presents an illustrative case study of such a life cycle event, while section 4 provides an overview of the statistics of such events. Our overarching aim is to characterize the evolution of these events. We study how patches of positive ΔSEB travel across the Arctic Basin over the course of the events, the circulation patterns associated with them, and the time evolution of radiative and turbulent SEB components within the patches. We focus on two specific questions:

  1. What is the relative role of turbulent and radiative fluxes in giving rise to extreme ΔSEB? Recent work focused on the BKS has emphasized the importance of sensible heat flux in driving large, positive ΔSEB and short-time-scale sea ice variability there (Boisvert et al. 2016; Zheng et al. 2022). This salient role for turbulent fluxes is in agreement with the findings of Cardinale and Rose (2022), who study surface fluxes on daily time scales but spatially averaged over the entire Arctic. Our goal is to generalize these previous findings to encompass the whole Arctic Basin in a spatially and temporally resolved way.

  2. How are SEB and surface temperature anomalies (ΔTs) related? We generally expect positive SEB anomalies to be associated with warm surface temperature, but do the greatest positive ΔSEBs coincide with the warmest ΔTs? A number of previous studies (e.g., Francis and Hunter 2006; Doyle et al. 2011; Woods et al. 2013; H.-S. Park et al. 2015; Woods and Caballero 2016; Graversen and Burtu 2016; Cullather et al. 2016; Kim et al. 2017; Gong et al. 2017; Lee et al. 2017; Persson et al. 2017; Nygård et al. 2019) have linked warm surface temperature anomalies—on time scales from daily to decadal—primarily to fluctuations in downward longwave fluxes, with little role for turbulent fluxes. The relationship between SEB and Ts anomalies has not, to our knowledge, been explicitly addressed before.

Our main results concerning the evolution of surface fluxes over the life cycle events and addressing the questions above are presented in section 5, while section 6 examines the large-scale circulation patterns associated with the events. A summary and discussion are presented in section 7.

2. Data and methodology

a. Data

We employ the ERA5 product (Hersbach et al. 2020) interpolated to 0.5° × 0.5°. For the calculation of air-parcel trajectories, vertical profiles, and the detection of atmospheric blocks and cyclones, we use hourly data on model levels. We also use daily mean 500 hPa geopotential height, surface (skin) temperature, sea ice concentration, and the surface radiative and turbulent fluxes required to compute the ΔSEB [Eq. (1)]. The study period includes 41 extended winters (November–March) from 1979/80 to 2019/20. In midwinter, the contribution of solar radiation is negligible (e.g., Serreze et al. 2007a), but since we include the months of November and March, we include shortwave radiation components in all computations.

Where not stated otherwise, anomalies are defined relative to a daily climatology following Messori et al. (2018) and Papritz (2020). Specifically, the climatologies are computed by smoothing with a 31-day running-mean filter. For ΔSEB [Eq. (1)] and its components, the climatology is transient, computed on a 9-yr running window, which removes the nonlinear, long-term trend from ΔSEB (see section 2b). This step is important, as otherwise the number of SEB events would be nonuniformly distributed between the earlier and later decades of the study period. For other quantities, the climatology is kept constant across years.

b. Identification of SEB anomaly patches

To define extreme Arctic SEB events, we first identify coherent areas of anomalously high SEB over Arctic sea ice (herein referred to as SEB anomaly patches or simply “patches”) as follows:

  1. For each day, we identify grid points poleward of 60°N with sea ice concentration ≥ 0.7. This excludes grid points from the analysis with a notable open ocean fraction, where the SEB is strongly influenced by intense turbulent fluxes from the ocean to the atmosphere (e.g., Parmiggiani 2006). Furthermore, we exclude grid points where the sea ice concentration is below 0.7 for more than 30% of the days within a 31-day window around the day considered and a centered 9-yr interval. This latter criterion ensures that sufficient days are available for computing the transient climatology and interquartile range in the subsequent step. The remaining grid points are hereafter referred to as valid grid points.

  2. We then remove the seasonality and long-term trend from the SEB by computing the median (SEB50) and interquartile range (SEBiqr) of the SEB at each valid grid point. This is done consistently with the computation of climatologies as detailed in section 2a; that is, using 31-day and 9-yr running windows. This yields the following standardized and nondimensional SEB anomaly:

ΔSEB˜=SEBSEB50SEBiqr
  • 3) To select the most intense SEB anomalies, we impose a seasonally varying but spatially uniform threshold defined as the area-weighted 95th percentile of ΔSEB˜ at all valid grid points within 31 days around the given calendar day. Note that in contrast to the median and interquartile range, this threshold is kept constant across all years. Spatially contiguous grid points with ΔSEB˜ exceeding this threshold define the SEB anomaly patches.

  • 4) Finally, we discard small patches with an area < 105 km2 and rank the remaining patches according to an intensity metric defined as

intensity=ΔSEB˜¯×area,

where the overbar denotes a spatial average over the patch. This metric, with units of kilometers squared, measures the impact of an SEB anomaly patch, reflecting both the amplitude (magnitude of ΔSEB˜) and the affected area. Furthermore, we define the equivalent radius of a patch as the radius of a circle with the same area as the patch.

Applying this approach to the 41 extended winters results in 6454 patches (Fig. 1), with a positive correlation between the area of the patches and ΔSEB˜ (r = 0.46), yet with a large case-to-case variability.

Fig. 1.
Fig. 1.

Joint probability distribution of area (histogram at the bottom:106 km2; minimum patch area, 105 km2; dashed black line) and rescaled nondimensional ΔSEB˜ (histogram to the right) for all wintertime SEB patches (blue dots). Patches associated with LCEs (thresholds indicated with red lines) and peaks of LCEs are shown by small cyan circles and black circles, respectively. Note that ΔSEB is defined as the sum of the radiative and turbulent flux anomalies [rhs of Eq. (1)]. ΔSEB˜ is then computed following Eq. (2).

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

c. Trajectory calculations

To identify the origin of the air masses associated with the patches, used in the attribution of patches to life cycle events (LCEs) (section 2d), we compute kinematic backward trajectories using the Lagrangian analysis tool (LAGRANTO; Sprenger and Wernli 2015). For a patch on a given day, trajectories are initialized at 0300, 0900, 1500, and 2100 UTC from 200 hPa AGL on an equidistant 80 km × 80 km grid within the patch (note that the trajectory starting points are not on the same grid as the ERA5 data used). From the set of trajectories of a given patch, the centroid trajectory is computed by minimizing the sum of squared distances to the trajectory positions.

d. Definition of life cycle events

We connect individual patches occurring on consecutive days into multiday LCEs as follows. We first select patches that exceed the 90th percentiles of both the area and ΔSEB˜ (thresholds marked with red lines in Fig. 1). This yields a subset of 218 intense patches. Individual patches from the entire set of 6454 patches (section 2b) are then attributed to one of the intense patches to form LCEs. Specifically, our algorithm first takes the time step of the most intense patch, t, and defines an LCE around that patch. In the first step, candidates are selected among all patches at time step t + 1. For a candidate to be attributed to this LCE, the following two criteria must be satisfied:

  1. The centroid of the area of the candidate patch at t + 1 must be within two equivalent radii of that of the patch at t and

  2. the centroids of backward air-parcel trajectories initialized from the patches at 200 hPa AGL (see also section 2c) are within 1500 km of each other two days prior to arrival at the patches.

If these two criteria are met, the patch at t + 1 is attributed to the LCE. For each of the attributed patches at t + 1, the algorithm then searches for candidate patches at t + 2 and so forth until no patches satisfying the two criteria can be found. Subsequently, the same is done backward in time (i.e., considering candidate events at t − 1, t − 2, …). The algorithm then proceeds with the highest ranked of the 218 intense patches that has not yet been attributed, starting again a new LCE. This approach yields 142 LCEs, consisting of a total of 869 patches (cyan dots in Fig. 1). Note that the number of LCEs is smaller than the 218 most intense patches because some of these patches are attributed to the same LCE. Patches that are not attributed to an LCE are not considered further.

The first and the last time steps of an LCE represent its onset and decay, respectively, and the peak of an LCE is defined as the time step when the highest-ranked patch occurs [Eq. (3)]. Additionally, the timing of the peak with respect to the duration of the LCE is quantified by the peak index, which is defined as
peak-index=daysuntilpeaktotaldurationofLCE[0,1]
Each LCE can then be assigned to a group based on the origin of the LCE, i.e., the location of the centroid trajectory 2 days prior to arrival in the peak LCE. For this, we define four sectors (shown in Fig. 2a): two main sectors for Atlantic (A, 45°W–60°E) and Pacific (P, 150°E–150°W) origins and two sectors closing the latitude band with continental origins over Canada (C) and Siberia (S).
Fig. 2.
Fig. 2.

(a)–(e) Five-day evolution, relative to the peak of the LCE on 12 Dec 1986 [LCE 44 in (c)], of the daily mean SEB anomaly (red shading; W m−2; spatial average given in text boxes) and centroids of backward trajectories (black solid line). Trajectory centroids are highlighted every 24 h by black circles, with the larger circle indicating the position 2 days prior to arrival at the patches. Black dashed circles surrounding the patches correspond to two equivalent radii (see text for details). Green lines show mean sea ice concentration at 0.15 (solid) and 0.7 (dashed), respectively. Four sectors (A, S, P, and C) are labeled in (a) used to distinguish the origin of the trajectories (magenta and blue lines) as well as the Arctic circle (black dashed circle).

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

e. Illustrative case

To illustrate the above algorithm, Fig. 2 shows an example LCE consisting of a sequence of patches extending from the Greenland Sea across the central Arctic toward eastern Siberia, spanning 5 days but composed of six individual patches, two of which occur on the same day (lag 1). The centroid trajectories reveal that the LCE is related to a moist-air intrusion from the Atlantic sector.

To illustrate the attribution of patches to LCEs, consider the patch on 12 December 1986 (Fig. 2c). The equivalent radius of this patch includes the centroids of two events that occur on the next day (13 December; Fig. 2d). In addition, 2 days prior to 13 December, the trajectory centroids associated with these two patches are within 1500 km of the corresponding trajectory centroid associated with the patch on 12 December. Hence, the two patches on 13 December satisfy both criteria defined in section 2d and are attributed to this LCE.

f. Large-scale flow composites

The role of large-scale flow features for the LCEs is explored by a composite analysis using daily mean anomalies of 500 hPa geopotential height (Z500) and mean sea level pressure (SLP). Anomalies are computed as described in section 2a. We also consider frequency anomalies of blocks and cyclones. Blocks are identified as 5-day, persistent, negative, upper-level (vertically averaged between 150 and 500 hPa), potential vorticity anomalies below −1.3 PVU (1 PVU = 10−6 K kg−1 m2 s−1), following the algorithm by Schwierz et al. (2004) and Croci-Maspoli et al. (2007). Cyclones are identified using the SLP-based method by Wernli and Schwierz (2006). The blocking and the cyclone identification algorithms output binary fields that indicate the presence of the respective feature at a given grid point and time step. Averaging these binary fields over a set of time steps results in composite frequencies of these features.

Large-scale composites are computed at the peak, onset, and decay of the LCEs and at positive and negative lags relative to these dates. We further implement a procedure to avoid double counting by excluding time steps that are associated with both positive lags from one patch and negative lags from the subsequent patch. For example, if the peaks of two LCEs are 6 days apart, then lag +3 days relative to the peak of the first LCE would be the same as lag −3 days from the peak of the second LCE. In this case, when we produce the lag +3 days composite for all patches, it will not include the lag +3 time step from the first LCE in our example. Similarly, when we produce the lag −3 days composite for all patches, it will not include the lag −3 time step from the second LCE in our example.

3. Case study: Atlantic event in December 1986

The LCE discussed in section 2e coincides with one of the most extreme wintertime warm events discussed in Messori et al. (2018). Here, its intensity in terms of the SEB [Eq. (3)] ranks in the upper tercile. Its spatial evolution is show in Fig. 2. The onset of the event, on 10 December, is detected in the Greenland Sea (Fig. 2a). It covers a rather small area (roughly 0.1 × 106 km2), following the shape of the ice edge and the coastline of Greenland, while growing in size and intensity until the peak on 12 December (Fig. 2c). At peak intensity, ΔSEB˜¯ is 2.5 and the patch area is 1.5 × 106 km2, covering a large part of the Arctic Ocean (Fig. 2c). The ΔSEB field has two distinct maxima: one over the central Arctic Ocean and one at the marginal ice zone close to the location of onset (Fig. 2c).

At the peak of the LCE, an upper-level block is identified north of Eurasia, northwest of the surface high pressure located over the Kara and Laptev Seas (Fig. 3d). This ridge, together with a low pressure center over Greenland, creates a dipole over the North Atlantic favoring northward flow and bringing warm and moist air toward the pole. The abundance of moisture favors cloud formation of both liquid (Fig. 3e) and ice (Fig. 3f) phases. Interestingly, cloud ice water is concentrated near the marginal ice zone and cloud liquid water mainly in the central Arctic, though with relatively large values also close to the sea ice edge. We notice two maxima in the surface longwave cloud radiative effect (computed as the difference between the net longwave radiation for all skies and clear skies; Fig. 3c) collocated with the two maxima in ΔSEB (Fig. 2c).

Fig. 3.
Fig. 3.

(a),(b) Daily mean fields at the peak of LCE 44 (12 Dec 1986) of anomalies of (a) surface net longwave radiation and (b) surface sensible heat flux (in W m−2) within the SEB patch (black solid line) with average values at the top right and mean sea ice concentration at 0.15 (green solid) and 0.7 (green dashed), respectively. (c) Daily mean cloud radiative effect (W m−2), (d) total column water vapor (kg m−2), (e) total column cloud liquid and (f) ice water in (g m−2; note the nonlinear scale). All panels show the outline of the SEB patch (black solid line); (d) also shows atmospheric blocks (green contour), mean sea level pressure (hPa; black contours every 10 hPa, solid for >1000 hPa), and the 0°C isotherm of 2 m temperature (purple).

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

Figures 3a and 3b show the net longwave ΔLWnet and sensible heat flux ΔSHF contributions to ΔSEB. We focus here and subsequently on sensible heat fluxes since latent heat flux anomalies are typically much smaller. ΔSHF is dominant close to the ice edge and in the patch center but has decreasing importance toward the downwind edge of the patch. ΔLWnet clearly shows the same two anomalous regions as ΔSEB (Fig. 3a) but with the maximum in the Arctic Ocean shifted downwind relative to ΔSHF. Interestingly, ΔLWnet is negative in the western part of the region, north of Greenland, overlapping with a strong positive ΔSHF. Trajectory analysis shows that the air flowing into this small region originates over Greenland rather than following the centroid trajectory depicted in Fig. 2c (not shown). This suggests that a Föhn effect brings dry air with high potential temperature descending from Greenland over the sea ice, creating a clear but warm state and thus a strong local positive ΔSEB due to the enhanced downward SHF. As a result, the dominant spatial-mean ΔSEB term at the peak is SHF, exceeding the radiative contribution despite the moist and cloudy state over most of the patch’s area.

The LCE separates into two patches the day after the peak (13 December; Fig. 2d), and both area and ΔSEB begin to decrease. The leading edge of the event finally reaches the eastern Siberian Sea north of the Bering Strait (Fig. 2e).

Further insight into the processes controlling the temporal evolution of ΔSEB and surface temperature along the LCE can be gained by looking at daily area-mean values of the ΔSEB terms and ΔTs, shown in Fig. 4. At the LCE onset, radiative and turbulent fluxes contribute about equally to ΔSEB, but on the second and third days, the turbulent fluxes grow larger, driving the largest ΔSEB in the life cycle. Though ΔSEB peaks on the second day, the event as a whole peaks on the third day (12 December) because that is when patch area is largest [recall that our intensity metric combines SEB anomaly with extent of the area affected; Eq. (3)]. After the peak, the turbulent flux contribution weakens and finally becomes almost negligible as the LCE moves into the high Arctic. On the contrary, ΔLWnet varies little throughout the LCE. As a result, the time evolution of the ΔSEB largely reflects the variation in turbulent fluxes. By contrast, ΔTs (red dots in Fig. 4) more closely follows variations in the downward longwave flux; both remain roughly constant from the second to the fourth day. Overall, it appears that variations in ΔSEB over the life cycle are mostly associated with variations in ΔSHF, while variations in ΔLW follow variations in ΔTs.

Fig. 4.
Fig. 4.

Daily evolution of ΔSEB terms [Eq. (1)] spatially averaged over the patches identified for each day associated with LCE 44. The net longwave radiation (hatched) is divided into its upward and downward components (blue). Red dots (right y axis) show the ΔTs averaged over the same patches. Shortwave fluxes are weak and not shown for clarity.

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

4. General statistics of LCEs

Next, we examine the statistics of the 142 identified LCEs with respect to their origin and seasonality. Grouping the LCEs by origin (see section 2d), we find a majority originating over the oceans (72 Atlantic and 60 Pacific LCEs), whereas only eight and two LCEs have Canadian or Siberian origin, respectively. In the rest of the paper, we focus on the two main groups: Atlantic and Pacific. The majority of the LCEs (109) at their peak intensity are dominated by ΔLWnet, whereas ΔSHF dominates for only 33 LCEs, two-thirds of which have an Atlantic and one-third a Pacific origin.

Figure 5a shows the distribution of LCEs over the entire period. Seasonal frequency ranges from zero (1991/92) to eight (1993/94) LCEs, with a mean of 3.5 LCEs per season. There is year-to-year variability in the number of Atlantic and Pacific LCEs, with an average occurrence of 1.8 and 1.5 LCEs per season, respectively. The average LCE duration is 5 days, ranging from 2 (four LCEs) to 12 (three LCEs) days (Fig. 5b). The mean duration for each of the geographical groups differs little from the overall average duration (except for the Siberian group, consisting of only two LCEs that last for 4 and 12 days). When the LCEs are categorized by month or dominant ΔSEB term at the peak, LCEs identified in November or dominated by ΔLWnet tend to have the longest duration (Figs. S1b,f in the online supplemental material). Multiple SEB anomaly patches are often identified on a given day (e.g., in the case study; Fig. 2d), most frequently for Atlantic LCEs; on average, we find six patches per LCE (not shown).

Fig. 5.
Fig. 5.

Characteristics of LCEs, partitioned by sector of origin (see Fig. 2a): Atlantic (magenta), Pacific (light blue), Canada (yellow), and Siberia (brown). (a) Number of LCEs per November–March (NDJFM) season (gray bars, right y axis) and mean duration per season (black circles), as well as duration of individual LCEs (colored circles, left y axis). The case study (LCE 44) is highlighted by a star. Also shown are histograms of (b) LCE duration, (c) number of days per each lag day relative to the peak for all LCEs (insets for the tails of the distribution), (d) LCEs per month, and (e) area of patches associated with peaks of the LCEs (threshold area of 105 km2 in dotted line) along with relative percentages within each group.

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

Figure 5c shows the frequency distribution over LCEs relative to their peak (lag 0). The peak usually occurs in the middle of an LCE [mean peak index of 0.5; see Eq. (4)]. The distribution of the peak index over LCEs is slightly negatively skewed (not shown), which is influenced by the long-lasting Siberian LCE peaking 1 day before its decay (see left insert in Fig. 5c). Interestingly, for LCEs where the ΔSEB at the peak is dominated by ΔSHF, the peak occurs closer to onset than for LCEs dominated by ΔLWnet (Fig. S1a).

The distribution of LCEs by month is shown in Fig. 5d. November and December are most favorable for Atlantic LCEs, whereas Pacific LCEs are more prevalent in February and March; the latter is consistent with high blocking frequencies over the eastern Pacific in the beginning of the year (Tyrlis and Hoskins 2008). A high number of Atlantic LCEs in December and lower number in March reflect also the seasonal distribution of North Atlantic moist intrusions (Papritz et al. 2022). Additionally, considering all SEB anomaly patches over the entire period, patches are identified in greater than 60% of days within the extended winter, peaking in March with around 75% (Fig. S2a). As a comparison, during summer months, the patches are less frequent. Furthermore, the average area and intensity [as given by Eq. (3)] are largest for wintertime patches, peaking in December (Fig. S2b). These results suggest that the most intense patches are more common in December, whereas March, when the sea ice extent is at maximum, is most favorable for the occurrence of individual patches regardless of their intensity.

Finally, the peak area distribution is shown in Fig. 5e, with a median area of 1.35 × 106 km2. Even though both Atlantic and Pacific LCEs obtain similar areas at their peaks (medians of 1.37 × 106 and 1.38 × 106 km2, respectively), Atlantic LCEs are slightly smaller at the onset but increase in size during their evolution and achieve a larger extent at their decay compared to Pacific LCEs; however, these differences are statistically not significant (see Fig. S3).

Linear trends in the number of LCEs, average LCE duration, and peak area are discussed in the supplemental material (S1.1). We find rather weak trends: an increase in LCE peak area but a decreasing trend in the number of LCEs per season, especially at the beginning and end of the winter period (Figs. S4–S6), in line with smaller sea ice area at the begin of the season due to climate change.

5. Evolution of LCEs

In this section, we discuss the geographical location and the evolution of anomalies over the course of LCEs. We focus on three key stages of the life cycle evolution: onset (first day of LCE), peak (day with maximum intensity), and decay (last day of LCE).

a. Spatial distribution and evolution

Figure 6 shows the locations of onset, peak, and decay for LCEs with either Atlantic or Pacific origin. Onset tends to occur near the marginal ice zones, while peak and decay mostly occur deeper in the Arctic interior, consistent with the idea that the events are driven by advection of warm air masses from the open ocean into the high Arctic. Pacific LCEs tend to originate near the Bering Strait and mainly follow anticyclonic pathways by propagating eastward over the Beaufort Sea or farther toward the Canadian archipelago. Some of the Pacific LCEs move westward toward the eastern Siberian Sea and only a few cross the Arctic Ocean. On the other hand, Atlantic LCEs are more mobile and travel farther away from the marginal ice zone, either eastward toward the Kara and Laptev Seas or crossing the polar cap toward the Chukchi Sea. LCEs traveling across the Arctic or westward tend to be associated with a more frequent occurrence of polar cyclones, whereas those decaying in the Laptev and Kara Seas are associated with stronger blocking anomalies over the Urals (not shown).

Fig. 6.
Fig. 6.

Centroids of the patches associated with LCEs with a duration of at least 3 days of Atlantic (magenta) and Pacific (blue) origin at the onset (crosses), peak (stars), and decay (circles).

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

b. Vertical profiles

For insight into the structure of the atmospheric column collocated with LCEs, we compute composite vertical profiles by interpolating model-level data to a stack of equidistant (20 hPa) pressure levels from 1000 to 100 hPa and taking spatial averages over each patch. Statistics over a set of patches (e.g., all patches associated with the onset of an LCE with Pacific origin) are obtained by weighting the profile for each patch with the corresponding patch intensity. To compute climatological profiles, we consider the actual patches but choose a random year other than the year when the patch occurred, thereby keeping the calendar day fixed. That way, the spatial correlations and seasonal representativeness remain intact. Anomalies are defined as departures from these climatological profiles.

The resulting profiles (Fig. 7) confirm that LCEs are associated with intrusions of warm and humid air into the Arctic. Specifically, Fig. 7a reveals potential temperature anomalies in excess of 10 K in the lower troposphere. These anomalies extend throughout the troposphere but decrease rapidly near 300 hPa, where they become negative, indicative of an elevated tropopause. While anomalies for Atlantic and Pacific LCEs are similar in the lower troposphere, they are larger in the upper troposphere for Pacific LCEs. There is also a clear temporal evolution with the largest anomalies occurring at LCE peak time, followed by a pronounced decrease in the decay stage.

Fig. 7.
Fig. 7.

Vertical profiles of anomalies of (a) potential temperature (K), (b) vertical velocity (hPa h−1), (c) specific humidity (g kg−1), and (d) cloud liquid water content (mg kg−1). Black lines and gray shading indicate the mean and 10th–90th-percentile range for all patches associated with an LCE. In addition, magenta and blue lines show mean profiles for the onset (dashed), peak (solid), and decay (dotted) for LCEs with Atlantic and Pacific origin, respectively. Anomalies are computed with respect to climatology derived from randomly resampling the patches (see text for details).

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

As the air enters the Arctic, it tends to ascend along slanted isentropes as it impinges on the polar dome of cold air. Figure 7b shows ascent throughout the entire atmospheric column, with the strongest ascent during onset and decreasing throughout the LCEs’ evolution. The climatological subsidence over the Arctic is weak compared to the ascent during the LCEs (not shown). Furthermore, ascent is most intense in the lower troposphere around 800 hPa for Atlantic LCEs, whereas it is strongest in the midtroposphere for Pacific LCEs.

Warm anomalies are accompanied by strongly enhanced values of specific humidity and cloud liquid water content in the lower troposphere (Figs. 7c,d). The values peak at about 900 hPa and are slightly lower near the surface, which is in line with previous work (Woods and Caballero 2016; Naakka et al. 2019) showing that moisture transport into the Arctic is largest near 900 hPa. Profiles of cloud liquid– and ice water–content anomalies show similar behavior as in Fig. 7d, but with ice clouds dominating above 700 hPa (about 10 mg kg−1) and liquid water clouds below 800 hPa (not shown). Atlantic LCEs show higher values of specific humidity and cloud water content in the lower troposphere than do Pacific LCEs. Interestingly, however, Pacific events are associated with higher water content in the midtroposphere and upper troposphere than Atlantic ones, likely related to the more intense potential temperature anomalies there and the associated higher saturation vapor pressure.

In summary, ascent throughout the troposphere during onset and the fact that the entire tropospheric column is anomalously warm indicate that LCEs are associated with vertically deep intrusions of warm air into the Arctic, which, in the lower troposphere, are accompanied by strongly enhanced moisture content.

c. Evolution of surface fluxes and surface temperature anomalies

In our case study (section 3), the LCE’s time evolution featured radiative and turbulent fluxes of comparable importance at the onset, followed by a weakening of ΔSHF but a relatively constant contribution of LWnet. This raises the question of whether similar behavior applies generally to all LCEs with the same origin. Figure 8 summarizes the LCE time evolution in the same format as for the case study (Fig. 4) but presented as composites over onset, peak, and decay stages. When multiple patches are identified at any of the three stages, the terms are spatially averaged over all patches. Only LCEs lasting at least 3 days are considered. We composite over Atlantic and Pacific LCEs separately.

Fig. 8.
Fig. 8.

As in Fig. 4 but composited over all LCEs with origin from the (a) Atlantic and (b) Pacific sectors for onset (left set of bars), peak (middle set of bars), and decay (right set of bars).

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

At onset, typically occurring close to the sea ice edge where the warm and moist air enters the study area (× marks in Fig. 6), large positive ΔSEB is observed as a result of large and almost equal contributions from ΔLWnet and ΔSHF, with a substantial contribution also from ΔLHF (Fig. 8). Atlantic LCEs (Fig. 8a) exhibit slightly larger ΔSEB and turbulent flux anomalies than do Pacific LCEs (Fig. 8b). As LCEs move away from the marginal ice zone during their evolution (Fig. 6), ΔSEB decreases as the turbulent heat flux contributions weaken, while ΔLW increases from onset to peak and then slightly decreases in the decay stage. These changes in ΔLW are balanced by compensating changes in ΔLW, so that ΔLWnet remains roughly constant through the LCE. As a result, ΔSEB decreases steadily from onset to decay (note that intensity is maximum at the peak due to the larger areas of the identified patches). The ΔSEB differences between Atlantic and Pacific LCEs at the onset and peak are relatively small, and we conclude that the evolution over space and time for LCEs is similar for both groups, independent of the air mass origin. Interestingly, the ΔTs evolves quite differently from ΔSEB: it increases from onset to peak and stays roughly constant from peak to decay, resembling the evolution of ΔLW.

As a comparison, for LCEs with continental air mass origin (Fig. S7), the contribution to the positive ΔSEB from LWnet exceeds SHF at all life stages, particularly for LCEs with Canadian origin, and ΔSEB is also smaller compared to LCEs with maritime origin. The ΔTs has a similar evolution as for the Atlantic and Pacific LCEs, with the exception of Canadian LCEs, where the maximum obtained at the peak for other groups is gradually reached upon the decaying phase (Fig. S7a).

The results above suggest that at the onset, peak, and decay stages of the LCEs, variations of ΔSEB mainly reflect those of surface turbulent fluxes, while variations of ΔTs are mostly associated with variations of ΔLW. To further investigate these relationships across all stages of the LCEs, Fig. 9 shows scatterplots of ΔSEB and ΔTs against the various ΔSEB components for all patches belonging to the LCEs included in the composites of Fig. 8. The corresponding correlation coefficients are shown in Table 1. The results in this table show strong positive correlations between ΔSEB and the turbulent fluxes, while ΔLW, ΔLW, and ΔLWnet correlate weakly with ΔSEB. We also note that ΔLW and ΔLW anticorrelate very strongly (correlation coefficient of −0.91). These results suggest that changes in ΔLW are largely compensated by opposite-signed changes in ΔLW, so that ΔSEB (which depends on the ΔLW + ΔLW) is insensitive to variations in longwave fluxes.

Fig. 9.
Fig. 9.

Scatterplots and linear regression lines of daily patch averages of (a) ΔSEB against its components and (b) ΔTs against ΔSEB and its components (see legend). Patches from all Pacific and Atlantic LCEs with a duration of at least 3 days are included.

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

Table 1

Correlation coefficient matrix for the data shown in Fig. 9. Boldface values are statistically significant (p < 0.05) using a two-sided Welch’s t test.

Table 1

Another way to express this result is that if we compare two patches with different ΔSEB, then the difference in ΔLW between the two patches is typically compensated by a difference in ΔLW of similar magnitude and opposite sign, so that the difference in ΔSEB is mostly due to different turbulent fluxes. Since ΔLW is directly related to Ts via the Stefan–Boltzmann law, the result implies that areas where ΔLW is greatest are also those with the warmest surface temperature anomalies, while areas with the greatest ΔSEB are those with greatest turbulent flux anomalies.

To further link the evolution of ΔSEB (Fig. 8) to the geographical location of the LCEs, we select patches with anomalously high (above the 90th percentile) and low (below the 10th percentile) ΔSEB. As shown in Fig. 10, patches with very high ΔSEB are mostly near the marginal ice zone; consistently, this is also where LCEs typically initiate (Fig. 6). High values of ΔSEB are almost exclusively associated with LCEs of oceanic origin (Fig. 10a). Low ΔSEB values are found over the pack ice in the central Arctic Ocean and Beaufort and Laptev Seas (Fig. 10), collocated with LCEs at their peak (see Fig. 6). Since turbulent flux anomalies become weaker along the evolution of the LCEs, but ΔLW stays fairly uniform across the patches (Fig. 8, see also Fig. S8b), low ΔSEB patches have a greater ratio of ΔLWnet to turbulent flux anomaly (Fig. 10b). The consistency between the time evolution of ΔSEB and the LCEs’ geographical locations suggests that the correlations observed in Fig. 9 are a result of the LCEs’ spatial and temporal evolution.

Fig. 10.
Fig. 10.

As in Fig. 6, but shown for patches with anomalously high (above the 90th percentile; 72 W m−2, stars) and low (below the 10th percentile; 41 W m−2, circles) ΔSEB, colored by (a) sector and (b) the ratio of the spatially averaged ΔLWnet to the sum of ΔSHF and ΔLHF (ΔHF).

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

6. Large-scale circulation associated with LCEs

Finally, we examine the large-scale circulation patterns associated with the Atlantic and Pacific LCEs. The large-scale SLP and Z500 anomalies preceding the peak of the Atlantic LCEs display a dipole with positive anomalies over Eurasia and negative anomalies over North America and Greenland (Fig. 11a), favoring advection from the North Atlantic sector to the Arctic. The pattern persists until the events peak (Fig. 11c), but it decays thereafter and shifts geographically (Fig. 11e), no longer favoring advection from the North Atlantic.

Fig. 11.
Fig. 11.

Large-scale patterns observed during LCEs shown as daily mean composites over (a),(b) lag −3, (c),(d) lag 0, and (e),(f) lag 3 days relative to the peak for LCEs with (left) Atlantic and (right) Pacific origin (note that maps in the right column are rotated by 180° to facilitate comparison). Shown are significant geopotential height anomalies (Z500; shading, gpm) and SLP anomalies (hPa; every 3 hPa, significant in thick contours; red for positive, blue for negative anomalies; zero anomaly contour is not shown). The significance of the anomalies is performed at the one-sided 5% level using random sampling with 1000 iterations. Anomalies are significant if all positive and negative composite anomalies exceed the 5% significance level. The total number of LCEs in each composite after excluding close-in-time LCEs is given by N (see explanation in section 2f) and shown in the upper-right text boxes. The black dashed line shows the latitude line at 60°N, and the thick black line shows the Greenwich meridian.

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

For Pacific LCEs, we see a similar dipole but placed with the negative anomalies over northern Eurasia and with positive anomalies spanning Alaska and the eastern Pacific (Fig. 11b). This again creates an advection corridor, but now from the northern Pacific region into the Arctic. As for the Atlantic case, the dipole persists until the LCEs’ peak (Fig. 11d) but decays and shifts at positive lags. By lag +3 days, the negative pressure anomalies have largely vanished, and although there are still strong positive Z500 anomalies (Fig. 11f), these now sit directly over the North Pacific and prevent continued meridional advection in the Arctic. This is consistent with the westward propagation of planetary-scale ridges over Alaska preventing further moisture transport through the Bering Strait (Baggett et al. 2016). Concurrently, strongly negative Z500 anomalies emerge over the North American continent, but again, these are not conducive to continued Pacific advection toward the Arctic Basin. For both Atlantic and Pacific LCEs, the westward tilt in the vertical structure likely reflects the role of baroclinic weather systems. When taking as reference date for the compositing the onset or end of the events, the picture is time shifted but qualitatively coherent (Figs. S9 and S10).

The composite anomalies in the SLP and Z500 fields correspond to anomalies in the occurrence of atmospheric blocks and extratropical cyclones. In the buildup to the Atlantic LCEs, a dipole anomaly in cyclone frequency emerges with more frequent cyclones in the Greenland–Iceland region and a reduced frequency in the Barents Sea, the latter associated with a positive anomaly in blocking occurrence over the same region (Fig. 12a). This configuration is consistent with the SLP and Z500 anomalies in Fig. 11a. The cyclone and blocking frequency anomalies intensify and broaden up to the LCEs’ peak. At the peak, a significant positive anomaly in cyclone frequency emerges in the central Arctic, and significant positive blocking anomalies extend from northern Eurasia to the pole (Fig. 12c).

Fig. 12.
Fig. 12.

Composites of significant daily mean blocking (shading) and cyclone (contours every 0.05 from ±0.1; red for positive, blue for negative anomalies; zero not shown) frequency anomalies over (a),(b) lag −3 , (c),(d) lay 0, and (e),(f) lag 3 days relative to the peak for LCEs with (left) Atlantic and (right) Pacific origin. The robustness of the anomalies (shading for blocks and thick curves for cyclones) is performed by randomly selecting N events 1000 times within the analysis period and computing the mean frequency anomaly for each random sample. The anomalies are robust if the composite anomalies exceed the width of the random sample distribution (5th–95th percentiles). Total number of LCEs in each composite after excluding close-in-time LCEs is given by N (see explanation in section 2f) and shown in the upper-right text boxes. The black dashed line shows the latitude line at 60°N and the thick black line shows the Greenwich meridian.

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

The buildup to the Pacific LCEs also displays dipole anomalies in cyclone and blocking frequency, with more cyclones and fewer blocks in the western North Pacific and fewer cyclones and enhanced blocking in the eastern North Pacific (Fig. 12b), reflecting the SLP and Z500 anomaly dipoles shown in Fig. 11b. At lag 0, both the cyclone and blocking anomalies shift north into the Arctic Basin, although, unlike in the Atlantic case, they do not reach all the way to the pole (Fig. 12d). The anomalies for Pacific LCEs are more intense and persistent than those for Atlantic LCEs, and, at lag +3 days, there is still a large negative (positive) anomaly in cyclone (blocking) frequency over the Gulf of Alaska (Fig. 12f). Composites centered on the onset or end date of the LCEs qualitatively support the above findings (Figs. S11 and S12). Specifically, the presence of two distinct positive anomalies in cyclone frequency for the Atlantic LCEs—one in the North Atlantic and one in the Arctic Basin—emerges clearly regardless of compositing choice.

7. Discussion and conclusions

We have analyzed the characteristics and evolution of areas with large ΔSEB over Arctic sea ice in the extended winters of 1979–2020, using ERA5 data. This study complements previous analyses of wintertime Arctic warm extremes (Binder et al. 2017; Messori et al. 2018; Papritz 2020; Messori et al. 2020) and moist intrusion events (e.g., Woods et al. 2013; Woods and Caballero 2016; Fearon et al. 2021; Papritz et al. 2022). These positive ΔSEB events are climatologically important as they represent anomalous energy flux through the surface into the ice or ocean, imposing local subsurface warming, delayed ice growth, and/or ice melting (Persson et al. 2017; Hofsteenge et al. 2022). Lagrangian backward trajectories allow us to determine the origin of the air masses associated with these events and to connect them in time to form synoptic-scale LCEs.

a. Transport corridors and associated large-scale flow patterns

The majority of the LCEs are associated with inflow from either the Atlantic or Pacific sectors, whereas only a few have a continental origin. This almost equal distribution between Atlantic and Pacific events differs from previous results regarding moist intrusions, which primarily enter the Arctic from the North Atlantic (e.g., Woods et al. 2013; Binder et al. 2017; Naakka et al. 2019; Papritz et al. 2022) and lead to warm extremes in the Arctic (Messori et al. 2018). SEB events are thus not identical to events defined by moisture transport or warm extremes. There are several possible explanations for this. First, the geographical region under consideration and the winter period differ somewhat from previous studies. Second, SEB anomalies are not caused by the transition of the Arctic atmosphere to an opaque and cloudy state alone, since warm and dry conditions can also cause substantial SEB anomalies via sensible heat fluxes. In fact, Pacific events are more often associated with atmospheric blocking and have reduced cloud cover (Fig. 13b) compared to Atlantic events. Third, the superposition of episodic air mass injections with enhanced stationary wave features may compensate for the climatologically lower frequency of moist injections in the Pacific (Goss et al. 2016).

Fig. 13.
Fig. 13.

Average (a) sea ice concentration and (b) total and low cloud cover (thick edge lines for low cloud cover) for onset (left set of bars), peak (middle set of bars), and decay (right set of bars) of Atlantic (magenta) and Pacific (light blue) LCEs. Only LCEs lasting for at least 3 days are considered. The mean over all LCEs at each stage is shown by black circles.

Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0209.1

Positive potential temperature anomalies associated with LCEs extend over the whole tropospheric column and are accompanied by enhanced moisture and cloud liquid water content in the lower troposphere. This reflects their association with deep intrusions of warm and moist air into the Arctic that are strongly influenced by the large-scale circulation (Nygård et al. 2021). In line with the well-known longitudinal asymmetries of moisture transport into the Arctic, with moist intrusions occurring predominantly in the Atlantic sector east of Greenland (Woods et al. 2013; Papritz et al. 2022), Atlantic LCEs show higher values of specific humidity and cloud water content in the lower troposphere than do Pacific LCEs. We find that Atlantic LCEs are more frequent in late autumn and early winter than in late winter, consistent with higher North Atlantic cyclone activity (Papritz et al. 2022) and higher frequency of warm anomalies over the polar cap in the December–February period (Messori et al. 2018). Also, the average 5-day duration of LCEs is consistent with the average travel time of Atlantic moist intrusions crossing the Arctic Basin (Woods and Caballero 2016).

Atlantic LCEs are favored by a geopotential height anomaly dipole with its negative lobe over Greenland and positive lobe over Eurasia. This favors northward transport of moist and warm air from the North Atlantic into the Arctic Basin. A similar North Atlantic “advection corridor” was also found in previous studies (e.g., H.-S. Park et al. 2015; Binder et al. 2017; Luo et al. 2017; Messori et al. 2018; Papritz 2020; Papritz et al. 2022). Furthermore, we find this dipole to be associated with enhanced cyclone frequency southeast of Greenland and significant positive blocking anomalies over Eurasia at the peak of Atlantic LCEs, in agreement with studies showing that northward moisture transport is favored by a northward shift of the storm track (Papritz and Dunn-Sigouin 2020; Papritz et al. 2022) and by blocks over Scandinavia and the Ural Mountains (Woods et al. 2013; Liu and Barnes 2015; Gong and Luo 2017). Additionally, we find the peak of Atlantic LCEs to be associated with a significant positive anomaly in cyclone frequency in the central Arctic, highlighting the baroclinic nature of the circulation patterns and the importance of several cyclonic systems acting together in transporting midlatitude air deep into the Arctic (Messori et al. 2018; Murto et al. 2022).

A similar advection corridor is also found for events originating in the North Pacific, although with more pronounced and persistent blocking anomalies over Alaska and only slightly northward-shifted cyclone activity over the western North Pacific. Our results are in line with previous studies emphasizing the importance of a high pressure system over Alaska (Baggett et al. 2016; Nygård et al. 2021; Papritz et al. 2022) or the Beaufort Sea (Graversen et al. 2011) and the minor role of poleward transport of cyclones (Fearon et al. 2021; Papritz and Dunn-Sigouin 2020) for northward moisture transport through the Bering Strait into the Arctic. As the LCE pathways are steered by the large-scale circulation, most Pacific LCEs follow anticyclonic pathways and only a few move westward toward the eastern Siberian Sea. Pacific LCEs are more frequent in late winter, consistent with a maximum frequency of Alaskan blocks in February (Tyrlis and Hoskins 2008). We conclude that the characteristics of the Atlantic and Pacific LCEs are strongly influenced by the large-scale circulation.

b. Relationships among ΔSEB, ΔTs, ΔSHF, and ΔLW

We found that ΔSEB is greatest at the onset and then decreases, while the surface temperature anomaly ΔTs is smallest at onset and then increases over the course of the LCEs (Fig. 8). This suggests that strongly positive ΔSEB is not necessarily associated with strong surface warm anomalies, consistent with the finding that only half of previously identified extreme Arctic warm events (Messori et al. 2018; Murto et al. 2022) are associated with the LCEs identified here. Furthermore, correlation analyses show that differences in ΔSEB between different events are most strongly associated with differences in ΔSHF, while differences in ΔTs correlate most strongly with the ΔLW (Fig. 9).

The strong positive correlation between ΔLW and ΔTs is in agreement with several studies showing a dominant role of LW in Arctic warming, both in long-term trends and on synoptic time scales (e.g., Woods et al. 2013; Cullather et al. 2016; Gong et al. 2017; Lee et al. 2017; Persson et al. 2017; Kim et al. 2017). In our LCEs, ΔLW is weakest at the onset and strongest at the peak of the event, weakening slightly in the decay stage. This behavior cannot be explained by changes in lower-tropospheric temperature alone, which decreases monotonically from onset to decay (Fig. 7a). Low-level liquid clouds, however, are also important for the surface longwave radiative flux in the Arctic (e.g., Shupe and Intrieri 2004). As warmer maritime air moves into the Arctic, it cools radiatively and also ascends (Fig. 7b), enabling clouds to form. Thus, the nonmonotonic evolution of ΔLW may be explained by compensating changes in the temperature and emissivity of the lower troposphere (Svensson and Karlsson 2011), the latter due to enhanced low-level liquid water cloud content at the peak of Atlantic LCEs (Fig. 7d). Pacific LCEs exhibit smaller low-level cloud water content compared to Atlantic LCEs, which could be caused by more-pronounced blocks and topographic effects and less-intense moisture intrusions associated with Pacific LCEs, as discussed in section 7a. At the decay of Pacific LCEs, large ΔLW and ΔTs are observed, following a slight increase in low-level cloud cover (Fig. 13b) and cloud liquid water content (Fig. 7d). Nonetheless, the differences between the two groups are small. The fact that ΔTs closely tracks ΔLW means that ΔLW compensates for changes in ΔLW, leaving ΔLWnet roughly constant over the life cycle.

The largest ΔSEB values occur at the LCE onset, when ΔSHF and ΔLHF are strongest. Spatially, the onset stage occurs near the marginal ice zone, where sea ice cover is relatively low (Fig. 13a). Note that sea ice concentration is—by construction, given our event definition—high (≥0.7) throughout the LCEs. These results agree with previous work (Hofsteenge et al. 2022; Zheng et al. 2022) showing that longwave and turbulent flux anomalies contribute roughly equally to ΔSEB near the ice edge during moist advection events into the Arctic, driving negative sea ice anomalies there. Here, we also show that the turbulent flux anomalies weaken as the air mass ascends (Fig. 7b) and moves farther into the Arctic interior with larger sea ice concentration (SIC). This weakening may be related to increased ΔLW driving greater ΔTs while tropospheric temperatures cool; this combination would imply a reduced atmosphere–surface temperature difference, which could explain the weaker turbulent fluxes. Moreover, ΔTs itself may drive variations in ΔLW (Vargas Zeppetello et al. 2019). Further study would be required to better understand this aspect of our results.

Overall, our results help reconcile previous work highlighting the role of turbulent fluxes in driving sea ice reduction in the marginal ice zone (Boisvert et al. 2016; Blackport et al. 2019; Hofsteenge et al. 2022; Zheng et al. 2022; Cardinale and Rose 2022) with other work showing the key role of ΔLW in driving warm-temperature anomalies in the high Arctic (Gong et al. 2017; Lee et al. 2017; Woods and Caballero 2016). While ΔLW does play a role in driving sea ice retreat at the ice edge (D.-S. R. Park et al. 2015; Woods and Caballero 2016; Chen et al. 2018), it should not be overemphasized in comparison with turbulent fluxes.

c. Limitations and future research avenues

Our results are based on reanalysis data and as there are limited observations in the Arctic to constrain the atmospheric state, some caution is warranted. Although ERA5 represents most processes fairly realistically (Graham et al. 2019), there remain issues with excessively warm wintertime surface temperatures (Wang et al. 2019) arising from the lack of snow cover on sea ice (Batrak and Müller 2019) and poor representation of the stably stratified environment (Graham et al. 2019). Wintertime positive temperature and cloud representation biases in reanalyses further cause negative biases in the energy budget, leading to an overestimation of radiative cooling and negative turbulent flux biases (Graham et al. 2019). Conclusions based on anomalies from the local climatology and their relative changes are expected to be more reliable than absolute numbers, which should be treated with care.

Air mass origin and pathways of LCEs play a role in the strength of the resulting ΔSEB and ΔTs over sea ice. Pacific and Atlantic LCEs show little difference in terms of their surface evolution, yet the vertical structure of their temperature and moisture anomalies differs. This raises the question of how the large-scale circulation influences these differences in the vertical anomalies along the life cycles. Investigating the origin and the dominant contributions in establishing the vertically coherent positive temperature anomalies associated with the LCEs, following Papritz (2020), is a promising avenue for future research. Furthermore, our hypotheses regarding the evolution of the events could be enriched with further case studies to complete the understanding of the main processes behind the extreme anomalous SEB over sea ice.

Given the above discussion, we summarize our main conclusions as follows:

  • Wintertime high-Arctic extreme events defined as positive surface energy budget anomalies ΔSEB are associated with inflow from both the Atlantic and Pacific sectors. They are associated with vertically extended potential temperature anomalies, positive near-surface moisture and cloud water anomalies, and circulation patterns favoring northward moisture transport.

  • Variations in ΔSEB between events are statistically associated with variations in turbulent fluxes and only weakly associated with variations in surface longwave radiative fluxes. On the other hand, variations in ΔTs are strongly associated with variations in the downward longwave flux ΔLW.

  • Events with high ΔSEB are associated with positive ΔTs. Over the course of the LCEs, ΔSEB is greatest at the onset and decreases thereafter, whereas ΔTs is smallest at the onset and then increases.

Acknowledgments.

This work was funded by Knut och Alice Wallenbergs Stiftelse (Grant 2016-0024). G.M. was supported by the European Union’s H2020 research and innovation program under ERC Grant 948309 (CENÆ project). Python and the open-source package R (http://www.r-project.org/) were used for the analysis and visualization in this study. We thank three anonymous reviewers for their helpful comments and acknowledge the ECMWF for providing the ERA5 dataset.

Data availability statement.

The ERA5 data can be downloaded from the Copernicus Climate Service (https://climate.copernicus.eu/climate-reanalysis). All datasets derived from ERA5 as part of this study are available upon request.

REFERENCES

  • Alexeev, V., P. L. Langen, and J. R. Bates, 2005: Polar amplification of surface warming on an aquaplanet in “ghost forcing” experiments without sea ice feedbacks. Climate Dyn., 24, 655666, https://doi.org/10.1007/s00382-005-0018-3.

    • Search Google Scholar
    • Export Citation
  • Årthun, M., and T. Eldevik, 2016: On anomalous ocean heat transport toward the Arctic and associated climate predictability. J. Climate, 29, 689704, https://doi.org/10.1175/JCLI-D-15-0448.1.

    • Search Google Scholar
    • Export Citation
  • Baggett, C., S. Lee, and S. Feldstein, 2016: An investigation of the presence of atmospheric rivers over the North Pacific during planetary-scale wave life cycles and their role in Arctic warming. J. Atmos. Sci., 73, 43294347, https://doi.org/10.1175/JAS-D-16-0033.1.

    • Search Google Scholar
    • Export Citation
  • Batrak, Y., and M. Müller, 2019: On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice. Nat. Commun., 10, 4170, https://doi.org/10.1038/s41467-019-11975-3.

    • Search Google Scholar
    • Export Citation
  • Binder, H., M. Boettcher, C. M. Grams, H. Joos, S. Pfahl, and H. Wernli, 2017: Exceptional air mass transport and dynamical drivers of an extreme wintertime Arctic warm event. Geophys. Res. Lett., 44, 12 02812 036, https://doi.org/10.1002/2017GL075841.

    • Search Google Scholar
    • Export Citation
  • Blackport, R., J. A. Screen, K. van der Wiel, and R. Bintanja, 2019: Minimal influence of reduced Arctic sea ice on coincident cold winters in mid-latitudes. Nat. Climate Change, 9, 697704, https://doi.org/10.1038/s41558-019-0551-4.

    • Search Google Scholar
    • Export Citation
  • Boisvert, L. N., A. A. Petty, and J. C. Stroeve, 2016: The impact of the extreme winter 2015/16 Arctic cyclone on the Barents–Kara Seas. Mon. Wea. Rev., 144, 42794287, https://doi.org/10.1175/MWR-D-16-0234.1.

    • Search Google Scholar
    • Export Citation
  • Cardinale, C. J., and B. E. Rose, 2022: The Arctic surface heating efficiency of tropospheric energy flux events. J. Climate, 35, 58975913, https://doi.org/10.1175/JCLI-D-21-0852.1.

    • Search Google Scholar
    • Export Citation
  • Chen, X., D. Luo, S. B. Feldstein, and S. Lee, 2018: Impact of winter Ural blocking on Arctic sea ice: Short-time variability. J. Climate, 31, 22672282, https://doi.org/10.1175/JCLI-D-17-0194.1.

    • Search Google Scholar
    • Export Citation
  • Cohen, J., and Coauthors, 2014: Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci., 7, 627637, https://doi.org/10.1038/ngeo2234.

    • Search Google Scholar
    • Export Citation
  • Croci-Maspoli, M., C. Schwierz, and H. C. Davies, 2007: A multifaceted climatology of atmospheric blocking and its recent linear trend. J. Climate, 20, 633649, https://doi.org/10.1175/JCLI4029.1.

    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., Y.-K. Lim, L. N. Boisvert, L. Brucker, J. N. Lee, and S. M. J. Nowicki, 2016: Analysis of the warmest Arctic winter, 2015–2016. Geophys. Res. Lett., 43, 10 80810 816, https://doi.org/10.1002/2016GL071228.

    • Search Google Scholar
    • Export Citation
  • Doyle, J. G., G. Lesins, C. P. Thackray, C. Perro, G. J. Nott, T. J. Duck, R. Damoah, and J. R. Drummond, 2011: Water vapor intrusions into the High Arctic during winter. Geophys. Res. Lett., 38, L12806, https://doi.org/10.1029/2011GL047493.

    • Search Google Scholar
    • Export Citation
  • Fearon, M. G., J. D. Doyle, D. R. Ryglicki, P. M. Finocchio, and M. Sprenger, 2021: The role of cyclones in moisture transport into the Arctic. Geophys. Res. Lett., 48, e2020GL090353, https://doi.org/10.1029/2020GL090353.

    • Search Google Scholar
    • Export Citation
  • Francis, J. A., and E. Hunter, 2006: New insight into the disappearing Arctic Sea ice. Eos, Trans. Amer. Geophys. Union, 87, 509511, https://doi.org/10.1029/2006EO460001.

    • Search Google Scholar
    • Export Citation
  • Gong, T., and D. Luo, 2017: Ural blocking as an amplifier of the Arctic sea ice decline in winter. J. Climate, 30, 26392654, https://doi.org/10.1175/JCLI-D-16-0548.1.

    • Search Google Scholar
    • Export Citation
  • Gong, T., S. Feldstein, and S. Lee, 2017: The role of downward infrared radiation in the recent Arctic winter warming trend. J. Climate, 30, 49374949, https://doi.org/10.1175/JCLI-D-16-0180.1.

    • Search Google Scholar
    • Export Citation
  • Goss, M., S. B. Feldstein, and S. Lee, 2016: Stationary wave interference and its relation to tropical convection and Arctic warming. J. Climate, 29, 13691389, https://doi.org/10.1175/JCLI-D-15-0267.1.

    • Search Google Scholar
    • Export Citation
  • Graham, R. M., and Coauthors, 2019: Evaluation of six atmospheric reanalyses over Arctic sea ice from winter to early summer. J. Climate, 32, 41214143, https://doi.org/10.1175/JCLI-D-18-0643.1.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., and M. Wang, 2009: Polar amplification in a coupled climate model with locked albedo. Climate Dyn., 33, 629643, https://doi.org/10.1007/s00382-009-0535-6.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., and M. Burtu, 2016: Arctic amplification enhanced by latent energy transport of atmospheric planetary waves. Quart. J. Roy. Meteor. Soc., 142, 20462054, https://doi.org/10.1002/qj.2802.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., T. Mauritsen, S. Drijfhout, M. Tjernström, and S. Mårtensson, 2011: Warm winds from the Pacific caused extensive Arctic Sea-ice melt in summer 2007. Climate Dyn., 36, 21032112, https://doi.org/10.1007/s00382-010-0809-z.

    • Search Google Scholar
    • Export Citation
  • Hahn, L. C., K. C. Armour, M. D. Zelinka, C. M. Bitz, and A. Donohoe, 2021: Contributions to polar amplification in CMIP5 and CMIP6 models. Front. Earth Sci., 9, 710036, https://doi.org/10.3389/feart.2021.710036.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Hofsteenge, M. G., R. G. Graversen, J. H. Rydsaa, and Z. Rey, 2022: The impact of atmospheric Rossby waves and cyclones on the Arctic Sea ice variability. Climate Dyn., 59, 579594, https://doi.org/10.1007/s00382-022-06145-z.

    • Search Google Scholar
    • Export Citation
  • Kim, B.-M., and Coauthors, 2017: Major cause of unprecedented Arctic warming in January 2016: Critical role of an Atlantic windstorm. Sci. Rep., 7, 40051, https://doi.org/10.1038/srep40051.

    • Search Google Scholar
    • Export Citation
  • Lee, S., T. Gong, S. B. Feldstein, J. A. Screen, and I. Simmonds, 2017: 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., 44, 10 65410 661, https://doi.org/10.1002/2017GL075375.

    • Search Google Scholar
    • Export Citation
  • Liu, C., and E. A. Barnes, 2015: Extreme moisture transport into the Arctic linked to Rossby wave breaking. J. Geophys. Res. Atmos., 120, 37743788, https://doi.org/10.1002/2014JD022796.

    • Search Google Scholar
    • Export Citation
  • Luo, B., D. Luo, L. Wu, L. Zhong, and I. Simmonds, 2017: Atmospheric circulation patterns which promote winter Arctic sea ice decline. Environ. Res. Lett., 12, 054017, https://doi.org/10.1088/1748-9326/aa69d0.

    • Search Google Scholar
    • Export Citation
  • Luo, B., L. Wu, D. Luo, A. Dai, and I. Simmonds, 2019: The winter midlatitude-Arctic interaction: Effects of North Atlantic SST and high-latitude blocking on Arctic sea ice and Eurasian cooling. Climate Dyn., 52, 29813004, https://doi.org/10.1007/s00382-018-4301-5.

    • Search Google Scholar
    • Export Citation
  • Luo, D., Y. Xiao, Y. Yao, A. Dai, I. Simmonds, and C. L. E. Franzke, 2016: Impact of Ural blocking on winter warm Arctic–cold Eurasian anomalies. Part I: Blocking-induced amplification. J. Climate, 29, 39253947, https://doi.org/10.1175/JCLI-D-15-0611.1.

    • Search Google Scholar
    • Export Citation
  • Madonna, E., G. Hes, C. Li, C. Michel, and P. Y. F. Siew, 2020: Control of Barents Sea wintertime cyclone variability by large-scale atmospheric flow. Geophys. Res. Lett., 47, e2020GL090322, https://doi.org/10.1029/2020GL090322.

    • Search Google Scholar
    • Export Citation
  • Messori, G., C. Woods, and R. Caballero, 2018: On the drivers of wintertime temperature extremes in the high Arctic. J. Climate, 31, 15971618, https://doi.org/10.1175/JCLI-D-17-0386.1.

    • Search Google Scholar
    • Export Citation
  • Messori, G., R. Wada, and C. Woods, 2020: A spatial model for return values of warm extremes in the high Arctic. Quart. J. Roy. Meteor. Soc., 146, 38653876, https://doi.org/10.1002/qj.3877.

    • Search Google Scholar
    • Export Citation
  • Murto, S., R. Caballero, G. Svensson, and L. Papritz, 2022: Interaction between Atlantic cyclones and Eurasian atmospheric blocking drives wintertime warm extremes in the high Arctic. Wea Climate Dyn., 3, 2144, https://doi.org/10.5194/wcd-3-21-2022.

    • Search Google Scholar
    • Export Citation
  • Naakka, T., T. Nygård, T. Vihma, J. Sedlar, and R. Graversen, 2019: Atmospheric moisture transport between mid-latitudes and the Arctic: Regional, seasonal and vertical distributions. Int J. Climatol., 39, 28622879, https://doi.org/10.1002/joc.5988.

    • Search Google Scholar
    • Export Citation
  • Nygård, T., R. G. Graversen, P. Uotila, T. Naakka, and T. Vihma, 2019: Strong dependence of wintertime Arctic moisture and cloud distributions on atmospheric large-scale circulation. J. Climate, 32, 87718790, https://doi.org/10.1175/JCLI-D-19-0242.1.

    • Search Google Scholar
    • Export Citation
  • Nygård, T., M. Tjernström, and T. Naakka, 2021: Winter thermodynamic vertical structure in the Arctic atmosphere linked to large-scale circulation. Wea. Climate Dyn., 2, 12631282, https://doi.org/10.5194/wcd-2-1263-2021.

    • Search Google Scholar
    • Export Citation
  • Papritz, L., 2020: Arctic lower-tropospheric warm and cold extremes: Horizontal and vertical transport, diabatic processes, and linkage to synoptic circulation features. J. Climate, 33, 9931016, https://doi.org/10.1175/JCLI-D-19-0638.1.

    • Search Google Scholar
    • Export Citation
  • Papritz, L., and E. Dunn-Sigouin, 2020: What configuration of the atmospheric circulation drives extreme net and total moisture transport into the Arctic. Geophys. Res. Lett., 47, e2020GL089769, https://doi.org/10.1029/2020GL089769.

    • Search Google Scholar
    • Export Citation
  • Papritz, L., D. Hauswirth, and K. Hartmuth, 2022: Moisture origin, transport pathways, and driving processes of intense wintertime moisture transport into the Arctic. Wea. Climate Dyn., 3 (1), 120, https://doi.org/10.5194/wcd-3-1-2022.

    • Search Google Scholar
    • Export Citation
  • Park, D.-S. R., S. Lee, and S. B. Feldstein, 2015: Attribution of the recent winter sea ice decline over the Atlantic sector of the Arctic Ocean. J. Climate, 28, 40274033, https://doi.org/10.1175/JCLI-D-15-0042.1.

    • Search Google Scholar
    • Export Citation
  • Park, H.-S., S. Lee, S.-W. Son, S. B. Feldstein, and Y. Kosaka, 2015: The impact of poleward moisture and sensible heat flux on Arctic winter sea ice variability. J. Climate, 28, 50305040, https://doi.org/10.1175/JCLI-D-15-0074.1.

    • Search Google Scholar
    • Export Citation
  • Parmiggiani, F., 2006: Fluctuations of Terra Nova Bay polynya as observed by active (ASAR) and passive (AMSR-E) microwave radiometers. Int. J. Remote Sens., 27, 24592467, https://doi.org/10.1080/01431160600554355.

    • Search Google Scholar
    • Export Citation
  • Persson, P. O. G., M. D. Shupe, D. Perovich, and A. Solomon, 2017: Linking atmospheric synoptic transport, cloud phase, surface energy fluxes, and sea-ice growth: Observations of midwinter SHEBA conditions. Climate Dyn., 49, 13411364, https://doi.org/10.1007/s00382-016-3383-1.

    • Search Google Scholar
    • Export Citation
  • Pinto, J. G., S. Zacharias, A. H. Fink, G. C. Leckebusch, and U. Ulbrich, 2009: Factors contributing to the development of extreme North Atlantic cyclones and their relationship with the NAO. Climate Dyn., 32, 711737, https://doi.org/10.1007/s00382-008-0396-4.

    • Search Google Scholar
    • Export Citation
  • Pithan, F., and T. Mauritsen, 2014: Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci., 7, 181184, https://doi.org/10.1038/ngeo2071.

    • Search Google Scholar
    • Export Citation
  • Pithan, F., and Coauthors, 2018: Role of air-mass transformations in exchange between the Arctic and mid-latitudes. Nat. Geosci., 11, 805812, https://doi.org/10.1038/s41561-018-0234-1.

    • Search Google Scholar
    • Export Citation
  • Previdi, M., K. L. Smith, and L. M. Polvani, 2021: Arctic amplification of climate change: A review of underlying mechanisms. Environ. Res. Lett., 16, 093003, https://doi.org/10.1088/1748-9326/ac1c29.

    • Search Google Scholar
    • Export Citation
  • Schwierz, C., M. Croci-Maspoli, and H. C. Davies, 2004: Perspicacious indicators of atmospheric blocking. Geophys. Res. Lett., 31, L06125, https://doi.org/10.1029/2003GL019341.

    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2010a: Increasing fall-winter energy loss from the Arctic Ocean and its role in Arctic temperature amplification. Geophys. Res. Lett., 37, L16707, https://doi.org/10.1029/2010GL044136.

    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2010b: The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464, 13341337, https://doi.org/10.1038/nature09051.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and J. A. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264, https://doi.org/10.1007/s10584-005-9017-y.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global Planet. Change, 77, 8596, https://doi.org/10.1016/j.gloplacha.2011.03.004.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. P. Barrett, A. G. Slater, M. Steele, J. Zhang, and K. E. Trenberth, 2007a: The large-scale energy budget of the Arctic. J. Geophys. Res., 112, D11122, https://doi.org/10.1029/2006JD008230.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., M. M. Holland, and J. Stroeve, 2007b: Perspectives on the Arctic’s shrinking sea-ice cover. Science, 315, 15331536, https://doi.org/10.1126/science.1139426.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. P. Barrett, and J. Stroeve, 2012: Recent changes in tropospheric water vapor over the arctic as assessed from radiosondes and atmospheric reanalyses. J. Geophys. Res., 117, D10104, https://doi.org/10.1029/2011JD017421.

    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., and J. M. Intrieri, 2004: Cloud radiative forcing of the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. J. Climate, 17, 616628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Simmonds, I., 2015: Comparing and contrasting the behaviour of Arctic and Antarctic Sea ice over the 35 year period 1979–2013. Ann. Glaciol., 56, 1828, https://doi.org/10.3189/2015AoG69A909.

    • Search Google Scholar
    • Export Citation
  • Simmonds, I., C. Burke, and K. Keay, 2008: Arctic climate change as manifest in cyclone behavior. J. Climate, 21, 57775796, https://doi.org/10.1175/2008JCLI2366.1.

    • Search Google Scholar
    • Export Citation
  • Sorteberg, A., and J. E. Walsh, 2008: Seasonal cyclone variability at 70°N and its impact on moisture transport into the Arctic. Tellus, 60A, 570586, https://doi.org/10.1111/j.1600-0870.2008.00314.x.

    • Search Google Scholar
    • Export Citation
  • Sprenger, M., and H. Wernli, 2015: The LAGRANTO Lagrangian analysis tool–version 2.0. Geosci. Model Dev., 8, 25692586, https://doi.org/10.5194/gmd-8-2569-2015.

    • Search Google Scholar
    • Export Citation
  • Stramler, K., A. D. Del Genio, and W. B. Rossow, 2011: Synoptically driven Arctic winter states. J. Climate, 24, 17471762, https://doi.org/10.1175/2010JCLI3817.1.

    • Search Google Scholar
    • Export Citation
  • Svensson, G., and J. Karlsson, 2011: On the Arctic wintertime climate in global climate models. J. Climate, 24, 57575771, https://doi.org/10.1175/2011JCLI4012.1.

    • Search Google Scholar
    • Export Citation
  • Tyrlis, E., and B. Hoskins, 2008: Aspects of a Northern Hemisphere atmospheric blocking climatology. J. Atmos. Sci., 65, 16381652, https://doi.org/10.1175/2007JAS2337.1.

    • Search Google Scholar
    • Export Citation
  • Vargas Zeppetello, L. R., A. Donohoe, and D. S. Battisti, 2019: Does surface temperature respond to or determine downwelling longwave radiation? Geophys. Res. Lett., 46, 27812789, https://doi.org/10.1029/2019GL082220.

    • Search Google Scholar
    • Export Citation
  • Wang, C., R. M. Graham, K. Wang, S. Gerland, and M. A. Granskog, 2019: Comparison of ERA5 and ERA-interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: Effects on sea ice thermodynamics and evolution. Cryosphere, 13, 16611679, https://doi.org/10.5194/tc-13-1661-2019.

    • Search Google Scholar
    • Export Citation
  • Wernli, H., and C. Schwierz, 2006: Surface cyclones in the ERA-40 dataset (1958–2001). Part I: Novel identification method and global climatology. J. Atmos. Sci., 63, 24862507, https://doi.org/10.1175/JAS3766.1.

    • Search Google Scholar
    • Export Citation
  • Woods, C., and R. Caballero, 2016: The role of moist intrusions in winter Arctic warming and sea ice decline. J. Climate, 29, 44734485, https://doi.org/10.1175/JCLI-D-15-0773.1.

    • Search Google Scholar
    • Export Citation
  • Woods, C., R. Caballero, and G. Svensson, 2013: Large-scale circulation associated with moisture intrusions into the Arctic during winter. Geophys. Res. Lett., 40, 47174721, https://doi.org/10.1002/grl.50912.

    • Search Google Scholar
    • Export Citation
  • Zheng, C., M. Ting, Y. Wu, N. Kurtz, C. Orbe, P. Alexander, R. Seager, and M. Tedesco, 2022: Turbulent heat flux, downward longwave radiation and large-scale atmospheric circulation associated with wintertime Barents–Kara Sea extreme sea ice loss events. J. Climate, 35, 37473765, https://doi.org/10.1175/JCLI-D-21-0387.1.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • Alexeev, V., P. L. Langen, and J. R. Bates, 2005: Polar amplification of surface warming on an aquaplanet in “ghost forcing” experiments without sea ice feedbacks. Climate Dyn., 24, 655666, https://doi.org/10.1007/s00382-005-0018-3.

    • Search Google Scholar
    • Export Citation
  • Årthun, M., and T. Eldevik, 2016: On anomalous ocean heat transport toward the Arctic and associated climate predictability. J. Climate, 29, 689704, https://doi.org/10.1175/JCLI-D-15-0448.1.

    • Search Google Scholar
    • Export Citation
  • Baggett, C., S. Lee, and S. Feldstein, 2016: An investigation of the presence of atmospheric rivers over the North Pacific during planetary-scale wave life cycles and their role in Arctic warming. J. Atmos. Sci., 73, 43294347, https://doi.org/10.1175/JAS-D-16-0033.1.

    • Search Google Scholar
    • Export Citation
  • Batrak, Y., and M. Müller, 2019: On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice. Nat. Commun., 10, 4170, https://doi.org/10.1038/s41467-019-11975-3.

    • Search Google Scholar
    • Export Citation
  • Binder, H., M. Boettcher, C. M. Grams, H. Joos, S. Pfahl, and H. Wernli, 2017: Exceptional air mass transport and dynamical drivers of an extreme wintertime Arctic warm event. Geophys. Res. Lett., 44, 12 02812 036, https://doi.org/10.1002/2017GL075841.

    • Search Google Scholar
    • Export Citation
  • Blackport, R., J. A. Screen, K. van der Wiel, and R. Bintanja, 2019: Minimal influence of reduced Arctic sea ice on coincident cold winters in mid-latitudes. Nat. Climate Change, 9, 697704, https://doi.org/10.1038/s41558-019-0551-4.

    • Search Google Scholar
    • Export Citation
  • Boisvert, L. N., A. A. Petty, and J. C. Stroeve, 2016: The impact of the extreme winter 2015/16 Arctic cyclone on the Barents–Kara Seas. Mon. Wea. Rev., 144, 42794287, https://doi.org/10.1175/MWR-D-16-0234.1.

    • Search Google Scholar
    • Export Citation
  • Cardinale, C. J., and B. E. Rose, 2022: The Arctic surface heating efficiency of tropospheric energy flux events. J. Climate, 35, 58975913, https://doi.org/10.1175/JCLI-D-21-0852.1.

    • Search Google Scholar
    • Export Citation
  • Chen, X., D. Luo, S. B. Feldstein, and S. Lee, 2018: Impact of winter Ural blocking on Arctic sea ice: Short-time variability. J. Climate, 31, 22672282, https://doi.org/10.1175/JCLI-D-17-0194.1.

    • Search Google Scholar
    • Export Citation
  • Cohen, J., and Coauthors, 2014: Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci., 7, 627637, https://doi.org/10.1038/ngeo2234.

    • Search Google Scholar
    • Export Citation
  • Croci-Maspoli, M., C. Schwierz, and H. C. Davies, 2007: A multifaceted climatology of atmospheric blocking and its recent linear trend. J. Climate, 20, 633649, https://doi.org/10.1175/JCLI4029.1.

    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., Y.-K. Lim, L. N. Boisvert, L. Brucker, J. N. Lee, and S. M. J. Nowicki, 2016: Analysis of the warmest Arctic winter, 2015–2016. Geophys. Res. Lett., 43, 10 80810 816, https://doi.org/10.1002/2016GL071228.

    • Search Google Scholar
    • Export Citation
  • Doyle, J. G., G. Lesins, C. P. Thackray, C. Perro, G. J. Nott, T. J. Duck, R. Damoah, and J. R. Drummond, 2011: Water vapor intrusions into the High Arctic during winter. Geophys. Res. Lett., 38, L12806, https://doi.org/10.1029/2011GL047493.

    • Search Google Scholar
    • Export Citation
  • Fearon, M. G., J. D. Doyle, D. R. Ryglicki, P. M. Finocchio, and M. Sprenger, 2021: The role of cyclones in moisture transport into the Arctic. Geophys. Res. Lett., 48, e2020GL090353, https://doi.org/10.1029/2020GL090353.

    • Search Google Scholar
    • Export Citation
  • Francis, J. A., and E. Hunter, 2006: New insight into the disappearing Arctic Sea ice. Eos, Trans. Amer. Geophys. Union, 87, 509511, https://doi.org/10.1029/2006EO460001.

    • Search Google Scholar
    • Export Citation
  • Gong, T., and D. Luo, 2017: Ural blocking as an amplifier of the Arctic sea ice decline in winter. J. Climate, 30, 26392654, https://doi.org/10.1175/JCLI-D-16-0548.1.

    • Search Google Scholar
    • Export Citation
  • Gong, T., S. Feldstein, and S. Lee, 2017: The role of downward infrared radiation in the recent Arctic winter warming trend. J. Climate, 30, 49374949, https://doi.org/10.1175/JCLI-D-16-0180.1.

    • Search Google Scholar
    • Export Citation
  • Goss, M., S. B. Feldstein, and S. Lee, 2016: Stationary wave interference and its relation to tropical convection and Arctic warming. J. Climate, 29, 13691389, https://doi.org/10.1175/JCLI-D-15-0267.1.

    • Search Google Scholar
    • Export Citation
  • Graham, R. M., and Coauthors, 2019: Evaluation of six atmospheric reanalyses over Arctic sea ice from winter to early summer. J. Climate, 32, 41214143, https://doi.org/10.1175/JCLI-D-18-0643.1.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., and M. Wang, 2009: Polar amplification in a coupled climate model with locked albedo. Climate Dyn., 33, 629643, https://doi.org/10.1007/s00382-009-0535-6.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., and M. Burtu, 2016: Arctic amplification enhanced by latent energy transport of atmospheric planetary waves. Quart. J. Roy. Meteor. Soc., 142, 20462054, https://doi.org/10.1002/qj.2802.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., T. Mauritsen, S. Drijfhout, M. Tjernström, and S. Mårtensson, 2011: Warm winds from the Pacific caused extensive Arctic Sea-ice melt in summer 2007. Climate Dyn., 36, 21032112, https://doi.org/10.1007/s00382-010-0809-z.