Polar mesoscale cyclones (PMCs) are automatically detected and tracked over the Nordic seas using the Melbourne University algorithm applied to ERA-Interim. The novelty of this study lies in the length of the dataset (1979–2014), using PMC tracks to infer relationships to large-scale flow patterns, and elucidating the sensitivity to different selection criteria when defining PMCs and polar lows and their genesis environments. The angle between the ambient mean and thermal wind is used to distinguish two different PMC genesis environments. The forward shear environment (thermal and mean wind have the same direction) features typical baroclinic conditions with a temperature gradient at the surface and a strong jet stream at the tropopause. The reverse shear environment (thermal and mean wind have opposite directions) features an occluded cyclone with a barotropic structure throughout the entire troposphere and a low-level jet. In contrast to previous studies, PMC occurrence features neither a significant trend nor a significant link with the North Atlantic Oscillation and the Scandinavian blocking (SB), though the SB negative pattern seems to promote reverse shear PMC genesis. The sea ice extent in the Nordic seas is not associated with overall changes in PMC occurrence but influences the genesis location. Selected cold air outbreak indices and the temperature difference between the sea surface and 500 hPa (SST − T500) show no robust link with PMC occurrence, but the characteristics of forward shear PMCs and their synoptic environments are sensitive to the choice of the SST − T500 threshold.
Polar mesoscale cyclones (PMCs) are cyclonic systems with a horizontal extent less than 1000 km and a lifetime of about one day (Rasmussen and Turner 2003). They occur poleward of the polar front, over maritime areas, in both hemispheres and mainly during the cold season. The most intense PMCs, referred to as polar lows, are associated with cold air outbreaks (CAOs) and feature strong winds (e.g., Businger 1985; Harold et al. 1999a; Rasmussen and Turner 2003). The least intense systems feature neither an organized cloud signature nor a well-defined pressure minimum. Most of previous climatological studies have focused on polar lows (e.g., Wilhelmsen 1985; Ese et al. 1988; Harold et al. 1999a; Blechschmidt 2008; Bracegirdle and Gray 2008; Zahn and von Storch 2008; Noer et al. 2011; Zappa et al. 2014) and a few on PMCs (e.g., Carrasco and Bromwich 1992; Turner and Thomas 1994; Harold et al. 1999a,b; Condron et al. 2006; Irving et al. 2010). The climatologies over the Nordic seas (Greenland, Norwegian, and Barents Seas) considered rather short periods up to 12 yr (Wilhelmsen 1985; Ese et al. 1988; Harold et al. 1999a; Condron et al. 2006; Blechschmidt 2008; Bracegirdle and Gray 2008; Noer et al. 2011; Zappa et al. 2014), with the exception of Zahn and von Storch (2008), who provided polar low track density maps covering four decades using dynamically downscaled data. Businger (1985, 1987) and Chen and von Storch (2013) showed that the characteristics and identification criteria of polar lows differ between the North Atlantic and Pacific. Moreover, PMCs over the Nordic seas have a strong influence on oceanic deep convection and hence on the Atlantic meridional overturning circulation (Condron and Renfrew 2013). Therefore, we focus on the Nordic seas and use PMC tracks obtained from reanalysis data over several decades to compile a climatology and investigate the genesis environments, evolution characteristics, and linkages to large-scale circulation patterns and sea ice extent.
Climatological studies of polar lows in the Norwegian and Barents Seas have been performed by manually tracking polar lows on weather maps (Wilhelmsen 1985; Businger 1985; Ese et al. 1988) and satellite images (Forbes and Lottes 1985; Yarnal and Henderson 1989; Harold et al. 1999a; Mokhov et al. 2007; Blechschmidt 2008), or by using a combination of these two methods (Noer et al. 2011). Polar lows and PMCs have also been detected and tracked using automatic algorithms applied to reanalysis and downscaled reanalysis (Condron et al. 2006; Zahn and von Storch 2008; Chen and von Storch 2013; Zappa et al. 2014; Yanase et al. 2016). These algorithms apply several criteria to distinguish between polar lows and other cyclonic features. One commonly used criterion is low-level static stability, which is defined as the difference between the sea surface temperature (SST) and the 500-hPa temperature. Typical threshold values are 39 or 43 K, depending on the area of interest, to identify environments conducive for convection and hence polar low genesis. Another criterion is a southward propagation direction for polar lows (Zahn and von Storch 2008; Chen and von Storch 2013). However, recent studies have shown that these criteria and thresholds are problematic and potentially bias polar low climatologies toward specific polar low types and environments (Rojo et al. 2015; Terpstra et al. 2016; Yanase et al. 2016). As all types of mesoscale cyclones are relevant to assess their potential impact on the climate system, we aim to avoid these shortcomings by considering the full spectrum of mesoscale cyclones for our climatology (section 2).
As indicated above, most of the previous climatologies do not cover more than one decade, limiting the statistical robustness of track density maps as well as genesis and lysis locations. These shorter-term climatologies are also inadequate for investigating interannual variability and long-term trends in PMC occurrence. For example, Zahn and von Storch (2008) did not find any trends in polar low occurrence for the period 1948–2006, whereas Rojo et al. (2015) indicated an increasing number of polar lows during the years 2005–13. Given the limited statistical robustness and inability to investigate interannual or long-term trends, there is a need for more consistent climatologies for both PMCs and polar lows to resolve these issues. We use the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis [ERA-Interim (ERA-I)] from 1979 to 2014. However, because of the sometimes inadequate representation of polar lows in ERA-I (Zappa et al. 2014), we will focus on all types of PMCs and present their track densities together with their genesis and lysis densities (section 3).
Duncan (1978) hypothesized that polar lows can form in an environment that he coined “reverse shear,” which refers to atmospheric conditions where the mean and thermal wind in the troposphere have opposite directions. In contrast, the environment is referred to as “forward shear” when the mean wind has the same direction as the thermal wind. Using the 10-yr Sea Surface Temperature and Altimeter Synergy for Improved Forecasting of Polar Lows (STARS)1 database (Sætra et al. 2010), Terpstra et al. (2016) showed that reverse and forward shear polar low genesis environments can be objectively distinguished using the wind shear condition. In agreement with Blechschmidt (2008), they found that about 20% of polar lows in the Nordic seas form in reverse shear conditions. Furthermore, they pinpointed that the synoptic conditions for forward and reverse shear are in stark contrast, with synoptic conditions for reverse shear comprising an occluded cyclone with a low-level jet at the genesis location, whereas the synoptic conditions for forward shear resemble the typical baroclinic environment in which midlatitude cyclones develop (Terpstra et al. 2016). We extend the previous 10-yr study and present environments for PMCs forming in forward and reverse shear conditions for the ERA-I period 1979–2014. In addition, we investigate the spatial and temporal distribution as well as the evolution of PMCs in these two distinct conditions (section 4).
As PMCs are often not adequately resolved in coarser reanalyses and climate simulations, several studies used large-scale circulation patterns associated with potentially favorable environments for polar low formation as a proxy for polar low occurrence (e.g., Kolstad 2006; Claud et al. 2007; Kolstad et al. 2009). Claud et al. (2007) argued that both the negative and positive phases of the North Atlantic Oscillation (NAO− and NAO+, respectively) are environments conducive for polar low development. Using actual tracks, however, Mallet et al. (2013) demonstrated that the NAO+ was more conducive to polar low development than NAO−. Claud et al. (2007) and Mallet et al. (2013) agree that polar lows tend to form during the counter pattern of Scandinavian blocking (SB). Kolstad (2006) and Kolstad et al. (2009) associated polar low occurrence with the presence of CAOs, as result of the reduced low-level stability and potential for convective development during CAOs. However, thus far only Mallet et al. (2013) verified the correspondence of large-scale patterns with actual polar low tracks based on shorter-term climatologies, and the association of polar lows with CAO indices has not been evaluated, yet. We will use our long-term PMC track dataset to investigate the large-scale genesis environment, and the linkages between PMC occurrence and the NAO, SB, and CAO indices, and sea ice extent (section 5).
2. Data and methods
We use ERA-I generated with the ECMWF Integrated Forecast System model using a four-dimensional variational data assimilation (4D-Var) scheme (Dee et al. 2011). We used data interpolated to 0.5° × 0.5° horizontal resolution and 6-h temporal resolution for the months October–April for the period 1979–2014, resulting in 35 extended winters. Condron et al. (2006) automatically detected PMCs in the 40-yr ECMWF Re-Analysis (ERA-40; Uppala et al. 2005) and compared them to PMCs detected on satellite images. They found that the largest PMCs are well represented in ERA-40 but that the data assimilation biased the location of some PMCs. Laffineur et al. (2014) compared the representation of polar lows in ERA-I with ERA-40, showing that ERA-I features more polar lows than ERA-40. Furthermore, Zappa et al. (2014) were able to objectively track 55% of the STARS polar lows in ERA-I and showed that there are even more polar lows in ERA-I with counterparts in STARS. They did not detect these additional polar lows as a result of their chosen thresholds in vorticity and wind speed, in which the latter is known to be underestimated in ERA-I (e.g., Moore et al. 2015). Hence, ERA-I seems a suitable reanalysis dataset to objectively detect a significant fraction of PMCs and to characterize their genesis environment and evolution characteristics.
b. Polar mesoscale cyclone tracks
1) Cyclone detection and tracking algorithm
We employ the Melbourne University detection and tracking algorithm (Murray and Simmonds 1991a,b), which uses the maximum of the Laplacian of the mean sea level pressure (MSLP) to detect cyclones. We retain only smaller-scale systems (<~800-km diameter) by applying a weak smoothing of the MSLP Laplacian together with low thresholds in MSLP Laplacian maximum averaged over a small radius of 1.25° latitude.2 Once the cyclones are located they are tracked in time. The tracking combines cyclones of two consecutive time steps using the nearest-neighbor method and the most likely propagation direction determined with the previous movement and the steering velocity. A probability is calculated for all possible combinations of tracks and only the track with the largest total probability is kept (see Murray and Simmonds 1991a,b for more details). The values used in the algorithm’s namelists are shown in appendix A.
2) Selection of polar mesoscale cyclone tracks
Several studies pointed out that the criteria to select polar lows, such as the stability criterion using the difference between SST and temperature at 500 hPa, low-level wind, and restricting polar low propagation to be only southward, appear problematic (Zappa et al. 2014; Terpstra et al. 2016; Smirnova and Golubkin 2017). To avoid any a priori bias in our dataset, we keep the full range of PMCs by applying the selection criteria listed below:
minimum duration of 12 h,
minimum of MSLP along each track within 1 October–30 April from 1979 to 2014,
genesis and lysis location within (60°–85°N, 20°W–60°E),
at least 50% of the grid points in a 200-km radius around the genesis location over sea or sea ice,
Laplacian of MSLP averaged over a radius of 1.25° latitude >1 hPa per degree latitude squared for at least one time step along the track,
distance between genesis and lysis >300 km, and
tracks are discontinued when reaching land or when the distance between two consecutive points is >350 km.
We end up with 8518 tracks for the 35 extended winters, that is, 243 PMCs per extended winter on average. Even though this number might appear high, we argue it is reasonable given the selection criteria applied here. A comparison of our selected tracks with the tracks of the STARS database yields a match of about 60%, compared to 55% in Zappa et al. (2014), who used the 3-h forecast between the 6-hourly analysis times of ERA-I.
c. Forward and reverse shear conditions
We use the method based on Kolstad (2006) and Terpstra et al. (2016) to determine the angle between the mean wind and the thermal wind, where the mean wind Vm = (um, υm) is the vertically averaged geostrophic wind between 925 and 700 hPa and is defined as
where ϕ700 and ϕ925 are the geopotential at 700 and 925 hPa, respectively; f is the Coriolis parameter; a is Earth’s radius; λ is longitude; and φ is latitude. The thermal wind VT = (uT, υT) is defined between the same pressure levels,
Finally, the angle α between the two vectors is calculated as follows:
which is averaged over a radius of 200 km around the genesis point.
Following Terpstra et al. (2016), we consider the environment at the genesis time around the genesis point as forward (reverse) shear if 0° ≤ α ≤ 45°(135° ≤ α ≤ 180°). Based on these thresholds, 2002 out of 8518 PMCs (24%) form in forward shear conditions and 800 PMCs (9%) form in reverse shear conditions. These percentages are different from Terpstra et al. (2016), who found about 20% of polar lows in both categories in the STARS database. Hence, our dataset contains a larger fraction of PMCs developing in forward shear conditions compared to the polar lows in the STARS database.
Following Terpstra et al. (2016), we also investigate the relation between α and the vertical wind speed gradient between 925 and 700 hPa, where the latter is defined as
with geopotential ϕ; and , where u and υ are the zonal and meridional components of the wind, respectively; and gravity g = 9.806 65 m s−2. When the wind speed increases with altitude, the thermal wind relation states that the cold (warm) air resides to the left (right) side of the mean wind, which induces a thermal wind in the same direction as the mean wind, hence the low angle (Fig. 1). On the other hand, when the wind speed decreases with altitude, the thermal wind relation states that the cold (warm) air resides on the right (left) side of the mean wind, which induces a thermal wind in the opposite direction of the mean wind, hence the high angle. For angles around 90°, the relation between the angle and the vertical wind speed gradient is less obvious and we thus consider only the angles ≤45° and ≥135° in our study.
To test the sensitivity of our results with respect to this strict choice of categories, we also performed our analysis based on a separation at 90°. Similar to Terpstra et al. (2016), we did not find a significant difference in our results; the patterns are only slightly modified, though there is an indication of hybrid PMC types at the separation boundary (not shown).
d. Cyclone-track statistics
The mean track duration (distance traveled), ±1 standard deviation, is 38.92 ± 24.49 h (991 ± 545 km), 39.06 ± 24.719 h (984 ± 533 km), and 38.86 ± 23.47 h (972 ± 505 km) for all, forward, and reverse shear PMCs, respectively (see Fig. 2a). The mean pressure difference between the minimum and genesis pressure along the track is −5.80 ± 7.44 hPa for all PMCs, and −7.65 ± 8.50 and −3.15 ± 5.00 hPa for forward and reverse shear PMCs, respectively (Fig. 2b). Thus, on average forward shear PMCs deepen more than reverse shear PMCs, though the standard deviations are rather high.
For the 850-hPa relative vorticity, the 950-hPa wind, and the difference between the SST and the 500-hPa temperature (SST − T500), we have taken the maximum value in a 200-km radius around the cyclone center for each track point and then considered the maximum value along the track. The maximum of SST − T500 spans a wide range from 36 to 48 K (41 ± 5 K; see Fig. 2b). The maximum of relative vorticity covaries with the wind speed (Fig. 2c). The mean maximum vorticity is 1.67 ± 0.87 × 10−4, 1.43 ± 0.78 × 10−4, and 1.73 ± 0.83 × 10−4 s−1 for all, forward, and reverse shear PMCs, respectively; the mean maximum wind is 21 ± 6, 19 ± 6, and 22 ± 6 m s−1 for all, forward, and reverse shear PMCs, respectively. Therefore, PMCs developing in forward shear conditions tend to be less intense in terms of relative vorticity and wind speed than PMCs developing in reverse shear conditions. The mismatch between intensity and pressure deepening suggests that forward shear PMCs include more weak PMCs than reverse shear PMCs.
e. Rotation of data
As in Terpstra et al. (2016), the atmospheric and surface fields are rotated at the PMC location so that all PMCs have the same direction of propagation. The angle of rotation is the angle between geographical north and the direction between the first point and the second point of the track. After rotation, the field is interpolated on a regular grid with resolution 25 km × 25 km. This enables us to make composites of the synoptic environment of the PMC genesis and mature stages.
3. Climatological aspects of polar mesoscale cyclones
a. Track densities
We obtain density maps by counting the number of track, genesis, or lysis points in each 0.5° × 0.5° grid box, weighting with the area of each grid box, which decreases with increasing latitude, and smoothing the resulting field C(λ, φ) with the bell-shaped function
a = 250 km, b = 100 km, and r is the distance between the considered grid point and the cyclone center. PMC track positions are counted only once for each grid box to avoid multiple counts of cyclones staying several time steps in the same grid box. However, the density patterns do not change when counting all track positions and the amplitude is only slightly higher by less than 0.1 PMCs per extended winter per 104 km2.
The tracks are mainly over the Norwegian Sea with a maximum west of Svalbard and a secondary maximum between Svalbard and Norway (Fig. 3a), which is in agreement with previous PMC and polar low climatologies (Harold et al. 1999a; Condron et al. 2006; Bracegirdle and Gray 2008).
PMC genesis occurs close to Svalbard, along the northern Norwegian and Greenland coasts, and in the Norwegian Sea (Fig. 3b). These maxima correspond to regions with high occurrence of CAOs (Papritz and Spengler 2017). In addition, Svalbard’s topography might play a cyclogenetic role in this area (Bracegirdle and Gray 2008). The Greenland coast also features CAOs, where the position of the sea ice edge or the interaction with orography in the lee of Greenland influences the genesis area (Harold et al. 1999b). The genesis maximum close to Norway is potentially linked to cyclones forming along CAOs predominantly originating from the Fram Strait (Papritz and Spengler 2017). In contrast to Rojo et al. (2015), the genesis area does not significantly shift eastward during the winter season (not shown). Cyclolysis is rather evenly spread over the domain with a maximum in the Norwegian Sea (Fig. 3c).
PMCs forming in forward shear conditions are present over the Norwegian and Barents Seas, whereas PMCs forming in reverse shear conditions are much less prevalent over the Barents Sea but occur more often along the Norwegian coast and especially around the Lofoten Archipelago (Figs. 3d–i). In addition to genesis maxima west of Svalbard and along Greenland, forward shear genesis also has a maximum west of Novaya Zemlya (Fig. 3e), which is most likely associated with CAOs from east and north of the Barents Sea (Papritz and Spengler 2017). By comparing cyclogenesis and lysis densities, we observe that PMCs forming in forward shear conditions have a tendency to move eastward (cf. Figs. 3e and 3f). PMCs developing in reverse shear conditions mainly form in the northern Norwegian Sea and west of Svalbard and tend to travel south toward Norway (cf. Figs. 3h and 3i). The propagation directions and spatial density distribution during forward and reverse shear conditions are in agreement with the STARS database study of Terpstra et al. (2016).
b. Temporal variability
On average there are 8.00 ± 11.32 PMCs per week per extended winter and 1.87 ± 2.67 (0.75 ± 1.09) PMCs per week per extended winter forming in forward (reverse) shear conditions (Fig. 4a). The high standard deviations indicate that there is a large interannual variability in the number of PMCs (also found by Harold et al. 1999b; Zahn and von Storch 2008; Rojo et al. 2015). The trend obtained for each of the three time series is 1.3, −2.0, and 0.1 PMCs per extended winter per decade for all, forward, and reverse shear PMCs, respectively. Hence, compared to the standard deviations listed above, we do not observe a significant long-term trend in either category, which is consistent with Zahn and von Storch (2008). Even though both forward and reverse shear PMCs can occur at the same time though at different locations (as shown by the cyclogenesis densities; Figs. 3e and 3h), we observe a slightly negative correlation between PMCs forming in forward and reverse shear conditions (−0.11), which implies that there are fewer reverse shear PMCs when there are more forward shear PMCs and vice versa.
The seasonal cycle for all PMCs shows an increase in the number of PMCs per week from October to December followed by a decrease until February. A secondary maximum occurs in March followed by a decrease in April (black line in Fig. 4b). PMCs forming in forward and reverse shear conditions have a different seasonal cycle (blue and red lines, respectively, in Fig. 4b). Forward shear PMCs are most frequent in April, whereas reverse shear PMCs are most frequent in January. The minimum in February might be explained by less frequent CAOs during this month, which might be due to relatively higher pressure over Scandinavia during this particular month, as suggested by Noer et al. (2011). Note that the reduced number of days in February has been taken into account by normalizing the data by the length of the month.
c. Propagation of polar mesoscale cyclones
As in Terpstra et al. (2016), forward shear PMCs propagate mainly eastward, whereas reverse shear PMCs mainly propagate southwestward (Figs. 5a and 5d), which is in accordance with their synoptic-scale atmospheric flow (Figs. 6a–d). Forward shear PMCs travel slightly faster than reverse shear PMCs with a mean speed of 7.57 ± 2.38 m s−1 for forward shear PMCs and 6.75 ± 2.16 m s−1 for reverse shear PMCs. These propagation speeds are in agreement with Businger (1985) and Rojo et al. (2015), who also found speeds in the range of 5–13 m s−1. Moreover, Rojo et al. (2015) also mentioned that polar lows can travel in any direction but preferentially southward and southeastward.
4. Genesis and mature stage environments
a. Large-scale genesis environment
To assess the genesis environment, we averaged the 500-hPa geopotential, the potential temperature at the tropopause (Figs. 6a and 6b), the 850-hPa wind and temperature, and the sea ice concentration (Figs. 6c and 6d) over the genesis dates. Distinguishing between forward and reverse shear genesis conditions, it is evident that the two environments feature pronounced differences. At upper levels, forward shear conditions exhibit a ridge both in geopotential and potential temperature (Fig. 6a). At lower levels, there is a southwesterly flow from the North Atlantic toward central Norway, bringing warm air over the Norwegian Sea (Fig. 6c). In addition, there is a weak southward CAO in the Fram Strait diverted toward the Barents Sea.
The reverse shear environment features a large trough at upper levels over the Norwegian Sea with an incursion of cold Arctic air (Fig. 6b). At low levels, this trough is linked to a cyclonic circulation centered over the northern Norwegian Sea and a strong CAO along the east Greenland sea ice edge (Fig. 6d). The cyclonic circulation is potentially enhanced by the strong northeastward flow from the North Atlantic toward northern Europe. The sea ice extent is similar in both environments, except at the northern tip of Novaya Zemlya, suggesting that the sea ice does not play a crucial role in the location of PMC formation. This assertion will be addressed further in section 5b.
b. Synoptic-scale genesis environment
The rotated fields show similarities compared with the synoptic-scale environments (cf. Figs. 6a–d and 7). The forward shear environment features a trough–ridge pattern in 500-hPa geopotential, 850-hPa temperature, and potential temperature on 2 potential vorticity units (PVU; 1 PVU = 10−6 K kg−1 m2 s−1; Fig. 7a). The 850-hPa flow is approximately in the direction of propagation with a weak cyclonic circulation. Moreover, there is a shallow minimum in MSLP with a well-defined warm sector on the right side relative to the direction of propagation, similar to midlatitude cyclones. The reverse shear environment (Fig. 7b) is characterized by a strong cyclonic circulation (see the MSLP, and 850-hPa wind and temperature) with a minimum in potential temperature at 2 PVU above the genesis point. Thus, the characteristics of the reverse shear environment are indicative of an occluded cyclone. From 850-hPa temperature (Fig. 7) it is evident that the highest temperatures are on the right side relative to the direction of propagation in forward shear conditions and on the left side in reverse shear conditions, which is in agreement with the definition of the shear conditions.
Figure 8 displays the vertical cross sections of the wind and potential temperature in the plane perpendicular to the propagation direction at X = 0, through the center of the rotated domain. For forward shear conditions, a jet extends throughout the troposphere with a maximum at 300 hPa in conjunction with a tropopause—that is, the 2-PVU surface—sloping downward from the right to the left side relative to the direction of propagation (Fig. 8a). In addition, the isentropes show a relatively high horizontal gradient of potential temperature from 1000 to 900 hPa around Y = 0, implying that there is a baroclinic zone close to the surface (Fig. 8a). The resemblance of forward shear environments with typical midlatitude environments suggests the dominance of baroclinic PMC development.
Reverse shear conditions show a cyclonic circulation from the bottom to the top of the troposphere (Fig. 8b). In addition, there is a strong low-level jet around 925 hPa. These features reflect an occluded extratropical cyclone, which is also evident in Fig. 7b. The tropopause is almost flat with a slight height minimum above the low-level jet (Fig. 8b). The isentropes show a similarly strong baroclinic zone near the surface for both shear conditions, but midtropospheric isentropes feature a height minimum at the cyclone center (Y = 0), in contrast to forward shear conditions for which the isentropes are descending on the right-hand side (Y < 0) of the cyclone (cf. Figs. 8b and 8a). This difference is associated with the occluded warm-core cyclone present at genesis time for reverse shear conditions.
In the forward shear environment, the surface sensible heat flux is largest to the left side of the cyclone relative to the direction of propagation with a relative minimum over the center of the cyclone (Fig. 9a). The distribution of the sensible heat fluxes resembles the air–sea temperature difference (SST minus 2-m temperature), except for slightly lower fluxes in the rear–right sector as a result of lower 10-m wind there. SSTs are highest on the rear–right side of the cyclone with a minimum on the forward–left side (Fig. 9b). The surface latent heat flux is largest in the rear–left sector with higher values surrounding the cyclone, except for the rear–right sector. This structure is similar to the difference between the specific humidity at 2 m and the saturation specific humidity using SST, with relatively higher latent heat flux in the right sector as a result of higher 10-m winds (Figs. 9a and 9b).
In the reverse shear environment, the surface sensible heat flux has a relative minimum over the center of the cyclone with maximum values on the right-hand side of the cyclone (Fig. 9c). The minimum in the center is also evident in the air–sea temperature difference and in the 10-m wind. The maximum in surface sensible heat flux to the right of the cyclone is associated with higher 10-m wind collocated with the low-level jet. Compared to forward shear conditions, SSTs are rather uniform over the area (Fig. 9d). The surface latent heat flux is largest in the rear and to the right of the cyclone. This structure is similar to the difference between the specific humidity at 2 m and the saturation specific humidity using SST, with a relatively higher latent heat flux in the right sector due to higher 10-m wind (Figs. 9c and 9d).
The surface fluxes for both forward and reverse shear conditions are thus in accordance with bulk formulas for sensible and latent heat fluxes, which depend on the difference between SST and 2-m temperature and the difference between the specific humidity at 2 m and the saturation specific humidity using SST, respectively, as well as surface wind. Fluxes around the cyclone are significantly higher for reverse shear conditions compared to forward shear conditions, which is related to the higher low-level wind and the presence of a stronger CAO in reverse shear conditions (Figs. 9a and 9c).
The standard deviations of these rotated plots are around 1%–3% for all variables with a minimum at the center of the PMC and increasing radially. A t test was conducted, with the null hypothesis “forward shear composite = reverse shear composite” and assuming that the two composites are independent and normally distributed. This t test shows that FS and RS composites are statistically different almost everywhere at a significance level of 99% for all variables, except for relative vorticity at 850 hPa, which is significant only on the front side of the cyclone (not shown).
c. Mature stage environment
We define the mature stage as the time for which the MSLP reaches its lowest value during the cyclone lifetime. Another common method is to define the mature stage as the time at which the 850-hPa relative vorticity is maximum. In our dataset, 2909 (2678) PMCs have their minimum in pressure occurring after (before) the maximum of vorticity with a time difference of about 16 h. There are 2931 PMCs that have the minimum in pressure and the maximum of vorticity occurring at the same time. Given this relatively symmetric distribution, the choice of vorticity or MSLP to define the mature stage appears not overly significant. Composites of MSLP, relative vorticity at 850 hPa, and wind at 10 m for the mature stage clearly demonstrate that PMCs forming under reverse shear conditions have a higher intensity than PMCs forming in forward shear conditions (Figs. 10a and 10c).
d. Sensitivity to the SST − T500 parameter
As most previous studies used the temperature difference SST − T500 to distinguish PMCs from polar lows, we analyze the sensitivity of our results to SST − T500, based on SST − T500 at its maximum along the track. We also performed the analysis using SST − T500 at genesis time, but as both criteria basically give the same results, we present only the analysis based on the maximum value along the track. We consider three categories: SST − T500 > 46 K (high values), 40 < SST − T500 < 46 K (medium values), and SST − T500 < 40 K (low values).
The percentages of forward shear PMCs satisfying the various conditions of SST − T500 are similar for all three categories, whereas the percentage of reverse shear PMCs for higher thresholds is greater than for lower thresholds (Table 1). The PMC subset with high SST − T500 contains more polar lows listed in the STARS database than the subset with low SST − T500 (see the percentages in italics in Table 1). Note that some of the STARS polar lows develop in low SST − T500 conditions.
For forward shear mesoscale cyclones, the cyclogenesis maximum west of Svalbard (see Fig. 3e) can be explained by mesoscale cyclones with high (Fig. 11a) and medium SST − T500 (not shown), contributing with 40% and 60%, respectively. Moreover, up to 80% of the cyclogenesis maximum along the east coast of Greenland is associated with mesoscale cyclones with low SST − T500 (Fig. 11b). The cyclogenesis location of reverse shear mesoscale cyclones remains similar for all categories of SST − T500 (not shown).
The direction of propagation for forward shear PMCs is related to the choice of the SST − T500 threshold, which is also the case for reverse shear PMCs. PMCs with high SST − T500 propagate southeastward in forward shear conditions and south/southwestward in reverse shear conditions (Figs. 5b and 5e), which is in accordance with findings for polar lows (Bracegirdle and Gray 2008; Terpstra et al. 2016). On the other hand, PMCs with low SST − T500 propagate mainly northeastward (Figs. 5c and 5f), contributing to the wider distribution of propagation direction for no SST − T500 threshold (Figs. 5a and 5d).
The weak CAO observed in the Fram Strait for the large-scale genesis environment of forward shear PMCs is associated with PMC development during low SST − T500 (cf. Figs. 6e and 6f). For higher SST − T500, there is a strong CAO straight from the Arctic toward Norway (Fig. 6e), which is rather similar to Fig. 4b in Terpstra et al. (2016). Thus, the lack of significance for the forward shear composite (Fig. 6c) is due to averaging two rather distinct synoptic situations (Figs. 6e and 6f).
In general, the rotated PMC synoptic-scale genesis environment is not strongly influenced by the SST − T500 threshold, as the fields are similar and only their intensity changes. For example, irrespective of the value of SST − T500, reverse shear genesis environments always exhibit a low-level jet and forward shear genesis environments always show a baroclinic zone throughout the entire troposphere with an upper-level jet (not shown), though, as demonstrated in Figs. 6e and 6f, forward shear conditions contain rather distinct synoptic situations. Moreover, the forward shear genesis environment features a stronger surface baroclinicity for high SST − T500 (not shown), most likely explaining the stronger intensification of these cyclones (see section 2d). The patterns of the surface heat fluxes of Fig. 9 are dominated by PMCs with low SST − T500, with fluxes being stronger for higher SST − T500 (not shown).
In terms of wind and MSLP, reverse shear PMCs are stronger and deeper at their mature stage compared to forward shear PMCs for higher SST − T500 (Figs. 10b and 10d), with this difference being even more pronounced for low SST − T500 (not shown). When SST − T500 is high, the relative vorticity at 850 hPa is similar at the cyclone center for forward and reverse shear PMCs, although a bit more spatially spread in the reverse shear conditions (Figs. 10b and 10d).
e. Intense PMCs
To select intense PMCs at high latitudes, known as polar lows, we applied the following criteria and thresholds: 950-hPa wind speed >15 m s−1, SST − T500 > 43 K, and 850-hPa relative vorticity >6 10−5 s−1, which are similar to those used in previous studies about polar lows (Zahn and von Storch 2008; Zappa et al. 2014). To classify a PMC as intense, all criteria have to be fulfilled at the same time, either at the genesis time or the time when they reach their maximum within a radius of 200 km from the cyclone center. The cyclogenesis density along east Greenland decreases significantly, whereas the maxima next to Svalbard and southeast of Iceland are emphasized (not shown) for all, forward, and reverse shear conditions. During the development of intense PMCs, the 850-hPa temperature is lower over the Nordic seas, CAOs are stronger, and the surface fluxes are enhanced (not shown). Intense PMCs forming in forward and reverse shear conditions become more similar in strength, as the imposed criteria select the most intense systems (not shown). Thus, the overall characteristics of the genesis environments of intense PMCs are similar to the regular PMCs.
5. Links with atmospheric large-scale variability and Arctic sea ice extent
a. Large-scale and upper-level atmospheric structure
Compositing the wind at 300 hPa and the 500-hPa geopotential anomaly relative to the wintertime climatology (October–April from 1979 to 2014) for the different shear conditions at genesis time, we obtain geopotential anomaly dipoles that somewhat resemble the two phases of the NAO, where the negative (positive) phase corresponds to forward (reverse) shear conditions (Fig. 12). However, compared to the NAO, the patterns are shifted eastward by about 30° longitude. For forward shear conditions, the jet stream has a southwest–northeast orientation and is confined to the western Atlantic. For reverse shear conditions, the jet stream is very strong and extends straight toward the British Isles, Scandinavia, and Russia. The forward (reverse) shear conditions can also be viewed as the positive (negative) phase of SB (defined in appendix B), but they are shifted westward over the Norwegian Sea. Hence, the atmospheric state during forward and reverse shear conditions is neither directly reflecting the NAO nor the SB but rather is a mix of both. Accordingly, the averages during PMC genesis of the daily NAO index (taken from NOAA’s Climate Prediction Center3) and SB index (defined in appendix B) have mean values close to 0 with high standard deviations. Reverse shear PMC genesis, however, seems to be favored in the negative phase of the SB (see Table 2).
Thus, our analysis does not confirm the previously claimed relationship between polar low occurrence and the NAO (Claud et al. 2007). The reasons for this contrast are most likely twofold. First, our analysis includes a larger variety of PMCs, thus also mesoscale systems that are not polar lows. Second, Claud et al. (2007) investigated conditions that are conducive to polar low genesis but did not consider genesis based on actual polar low tracks. The latter reasoning is consistent when comparing our results to Mallet et al. (2013), who came to similar conclusions as presented here when using actual polar low tracks. Our findings for reverse shear PMC genesis, however, are in accordance with both Claud et al. (2007) and Mallet et al. (2013) featuring higher occurrence during the negative phase of the SB.
The mean NAO and SB indices for forward and reverse shear conditions do not have the same sign when considering a less strict angle threshold (e.g., 90°) or for the different SST − T500 categories. For high SST − T500, the mean NAO and SB indices for forward and reverse shear PMCs are always negative and the standard deviations are halved compared to Table 2 (not shown). These mean indices are in accordance with Laffineur et al. (2014), who showed a preference for polar low genesis during NAO−, and with Claud et al. (2007) and Mallet et al. (2013), who showed a preference for SB−. Given the high sensitivity of these relationships to the chosen thresholds, we argue that it is questionable to draw clear conclusions regarding the large-scale environment at genesis time.
The negative anomaly in geopotential found over the Norwegian Sea in reverse shear conditions (Fig. 12b) is displayed in several polar low climatologies (Businger 1985; Ese et al. 1988; Blechschmidt et al. 2009; Mallet et al. 2013). This climatological bias is most likely an indication that the polar lows retained in these studies mainly formed in reverse shear conditions, which could be related to their criteria chosen for polar low identification.
b. Influence of the ASIE
To investigate the link between the Arctic sea ice extent (ASIE) and the number of PMCs, we define a standardized index I(t) as
with A(t) as the mean sea ice concentration per area for each time step, which is calculated as
where λ, φ, and t are longitude, latitude, and time, respectively; N is the number of grid boxes with area a; and sic is the sea ice concentration with values between 0 and 1. The longitude and latitude span from 20°W to 60°E and from 66° to 90°N, respectively. The index is standardized by subtracting its climatological mean over the 35 extended winters () and dividing it by its standard deviation σA.
The mean value and standard deviation of the area are 4.74 ± 0.49 × 109 km2. Regressing the time series yields that the mean sea ice extent decrease per decade during the extended winter is −0.28 × 109 km2 . Hence, the decadal trend is lower than the standard deviation. The cross correlations between the mean Arctic sea ice index and the number of PMCs for each extended winter are very low with −0.16, −0.12, and 0.06 for all PMCs, and forward and reverse shear conditions, respectively.
To assess the preference of PMC occurrence with respect to sea ice extent, we average the sea ice index over the genesis dates for all PMCs and for both genesis conditions (Table 2). The mean values are very low with high standard deviations, suggesting that the Arctic sea ice extent does not play a prominent role in modulating the overall occurrence of PMCs. Forward shear conditions tend to be associated with a larger sea ice extent than average, whereas the opposite is true for reverse shear conditions. This could be linked to the seasonal cycle of forward and reverse shear PMC genesis (see Fig. 4), where forward shear PMCs occur more frequently toward the end of the extended winter when the sea ice extent is at its maximum. Furthermore, the decline of sea ice extent in the recent decades mentioned above is not reflected in the time series of PMCs occurring in the area (Fig. 4a). Correlating the detrended index with the number of PMCs during each extended winter also shows negligible negative correlations for any domain and genesis conditions (not shown).
Compiling cyclogenesis density for low (high) sea ice extent—that is, minus (plus) one standard deviation—we find that PMC genesis occurs more frequently close to Norway and in the Norwegian Sea during reduced sea ice extent conditions. On the other hand, genesis is more frequent in the Barents Sea, west of Svalbard, and along Greenland for increased sea ice extent (Fig. 13). Despite these differences in genesis location, the number of PMCs is similar for both sea ice extent conditions. The mean position of the 50% sea ice concentration does not shift much in the western part of the domain compared to the Barents Sea. The lack of correspondence between the sea ice extent and cyclogenesis is also reflected in the fact that the sea ice extent varies significantly throughout the season, while the genesis locations of PMCs remain rather unchanged (not shown).
With the Barents Sea ice concentrations projected to decrease significantly for the latter half of the twenty-first century (e.g., Overland and Wang 2007), one might be tempted to speculate about future changes in PMC occurrence based on the track density maps for low sea ice extent (Fig. 13a). Of course, using current variability patterns to infer future changes bears significant caveats, but if future changes in sea ice are not accompanied with significant changes in atmospheric and oceanic conditions compared to the present climate, then our analysis would indicate that there might be a similar overall number of PMCs in the future with track densities shifted closer to the northern Norwegian coast. This shift would be accompanied with a significant reduction in polar low genesis west of Svalbard and at the same time genesis extending into the ice-free areas of the Barents Sea.
c. Association with CAOs
We test the association of PMC occurrence with CAOs using the percentage of PMCs for which existing CAO criteria are fulfilled for at least one grid point in a radius of 200 km around the cyclone location at least once along the track. We use the CAO indices by Kolstad and Bracegirdle (2008, hereafter KB) and Papritz et al. (2015, hereafter LP), and the difference between the SST and the temperature at 500 hPa.
Table 3 indicates a weak association of CAOs with our detected PMCs. For instance, we obtain only 6.73% for KB ≥ 0.02 K hPa−1, implying that just a few of our detected PMCs occur during a CAO for this threshold. The SST − T500 criterion is the least strict criterion, with 65.33% of our polar mesoscale cyclones forming with SST − T500 ≥ 39 K, though retaining a larger fraction of reverse shear cyclones than forward shear cyclones.
Given these large sensitivities based on different CAO indices and their respective thresholds, the appropriateness to use such CAO criteria for polar low detection requires a more detailed assessment. Since reverse shear conditions feature larger SST − T500 values than forward shear conditions (Terpstra et al. 2016), one would expect an increased fraction of reverse shear polar lows detected with higher thresholds. However, among the three CAO indices and their respective thresholds, only the SST − T500 index and KB ≥ 0.0 capture a larger fraction of reverse shear than forward shear PMCs.
6. Concluding remarks
We investigated climatological aspects of PMCs using the Melbourne University cyclone detection and tracking algorithm over the Nordic seas using ERA-I, where we also separated PMC genesis environments in forward and reverse shear conditions. The length of our dataset is a great advantage compared to previous studies, which allows for a more robust statistical analysis, though one caveat of using ERA-I is its representation of mesoscale phenomena, such as polar lows, as a result of its relatively coarse resolution. Laffineur et al. (2014) showed that not all polar lows have an MSLP minimum, and Zappa et al. (2014) showed that winds associated with polar lows are underestimated. Therefore, the robustness of our results should ideally be verified using reanalyses with higher resolution, such as the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015), the Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011), the Arctic System Reanalysis (ASR; Bromwich et al. 2012), or the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; Saha et al. 2010). However, comparing ASR, ERA-I, and polar lows in a 12-km downscaled simulation (Laffineur et al. 2014), Smirnova and Golubkin (2017) suggested that higher-resolution data are not necessarily sufficient to give a better representation of polar lows and that the improved representation in ASR is possibly related to the model itself. Thus, whether the improvement in polar low representation is due to model resolution or the model itself also needs further investigation.
Consistent with previous climatologies, we found the highest densities of PMC tracks west of Svalbard and between Svalbard and Norway. In addition, we found PMCs occurring along the Greenland sea ice edge and in the Norwegian Sea. PMCs forming in forward shear conditions are present over the entire Nordic seas and propagate mainly eastward, whereas PMCs forming in reverse shear conditions occur more frequently over the Greenland and Norwegian Seas and tend to propagate southwestward. The seasonal cycle of PMC occurrence has a peak in January and a slight minimum in February followed by a secondary maximum in March. Forward (reverse) shear PMCs are most frequent in April (January). We find no long-term trend in the number of PMCs for the period 1979–2014, irrespective of the genesis environment conditions, in agreement with Zahn and von Storch (2008).
In accordance with Terpstra et al. (2016), the temperature gradient near the surface features a baroclinic zone for both shear conditions. For forward shear conditions, the baroclinic zone extends throughout the entire troposphere with a strong jet at 300 hPa on the right side relative to the direction of propagation and a weaker jet around 900 hPa. For reverse shear conditions, there is a quasi-barotropic cyclonic circulation throughout the entire troposphere widening with height and a strong and confined low-level jet at 925 hPa.
There is a synoptic-scale trough at the tropopause associated with a developing cyclone at low levels with a distinct warm sector for forward shear conditions. For reverse shear conditions, there is a cutoff at the tropopause associated with an occluded cyclone at the surface. PMCs forming in reverse shear conditions become more intense in terms of wind and vorticity during their lifetime than those forming in forward shear conditions. The higher intensity of reverse shear PMCs is in contrast to their lower deepening rates compared to forward shear PMCs. This contrast can be explained by reverse shear PMCs already featuring a lower pressure minimum at genesis time and the subset of forward shear PMCs containing more weak systems.
Fluxes are significantly higher for reverse shear PMCs compared to forward shear PMCs, which is mainly due to higher surface wind in reverse shear conditions. Forward shear PMCs tend to have higher SSTs in the rear and to the right side, while reverse shear PMCs propagate through rather uniform SSTs at genesis time.
For forward (reverse) shear conditions, there is a ridge (trough) at 500 hPa with warm (cold) air over the Norwegian Sea associated with a mild (strong) CAO over the Fram Strait. Thus, our results are in accordance with Terpstra et al. (2016), though the CAO conditions are weaker for forward shear conditions over the Norwegian Sea and the cyclonic circulation centered over northern Norway for reverse shear conditions is weaker and more centered between Svalbard and Norway (Fig. 6d). These differences are most likely explained by the fact that Terpstra et al. (2016) is based on the STARS polar low database and, compared to our dataset, they have very few cases in their composites, thereby retaining sharper structures.
The 500-hPa geopotential anomalies for PMC genesis have anomaly dipoles that are linked to neither the NAO nor SB but rather feature a mix of both patterns. Thus, in contrast to Claud et al. (2007), we did not find any significant correlation or association of PMC occurrence with neither the NAO nor SB. However, for reverse shear conditions, we found a preference for the positive (negative) phase of the NAO (SB), consistent with Claud et al. (2007) and Mallet et al. (2013).
We constructed an Arctic sea ice extent index over the region from the Greenland Sea to the Barents Sea (between 20°W and 60°E) and found no association with the overall occurrence of PMCs, irrespective of the genesis conditions. However, there is a clear shift in location of PMC occurrence (Fig. 13), with a maximum west of Svalbard in winters with extended sea ice concentrations and an indication that northern Norway experiences more PMCs during reduced sea ice extent conditions. The cyclogenesis pattern does not seem to be related to the sea ice seasonal cycle, and it is likely that other drivers are important, such as the synoptic situation leading to changes in CAO intensity and location.
With the Barents Sea ice concentrations projected to decrease significantly for the latter half of the twenty-first century, our analysis would indicate that there might be a similar overall number of PMCs in the future with track densities shifted closer to the northern Norwegian coast. This shift would be accompanied with a significant reduction of PMC genesis west of Svalbard and a northward migration into ice-free areas of the Barents Sea.
The association of existing CAO indices with all our detected PMCs is rather weak, and the diagnosed co-occurrence is only 5%–65% for our PMCs, depending on the strictness of the chosen threshold. The SST − T500 criterion includes more reverse shear PMCs for both chosen thresholds—that is, SST − T500 ≥ 43 K and SST − T500 ≥ 39 K—which is probably related to reverse shear genesis environments featuring more statically unstable conditions than forward shear environments. The SST − T500 criterion may be an issue for studies that tried to investigate polar low occurrence in a future climate (e.g., KB; Zahn and von Storch 2008), where it is not clear whether a potential reduction in reverse shear PMCs might be compensated by an increase in forward shear PMCs. Such a shift in PMC distribution might annihilate or potentially reverse previous results. Considering the potential impact of PMCs on deep convection in the ocean (Condron et al. 2008) and CAO erosion (Papritz and Pfahl 2016), it is important to note that although reverse shear PMCs are more intense, they are also less frequent. Hence, an assessment of the overall impact of PMCs should include both reverse and forward shear PMCs.
We acknowledge Kevin Keay for his great help with the Melbourne University detection and tracking algorithm. We are also grateful to ECMWF for providing the ECMWF interim reanalyses and to the Norwegian Meteorological Institute for the online availability of the STARS polar low database. We thank three anonymous reviewers, whose useful comments helped to improve this paper. This work is supported by the Research Council of Norway as part of High Impact Weather in the Arctic (Project 207875) and Marine Cold Air Outbreaks at High Latitudes (Project 262110, AT), and by the European Union’s Seventh Framework Programme for research, technological development, and demonstration under Marie Curie Grant Agreement 608695 (AT).
Namelist for Cyclone Detection and Tracking
Table A1 provides the values of the parameters for the detection and tracking namelists used by the algorithm.
Scandinavian Blocking Patterns
The SB index is based on the method by Tibaldi and Molteni (1990) using daily 500-hPa geopotential height to detect which longitudes are “blocked.” If half of the longitudes between 0° and 30°E are blocked, then this time step is considered to feature a blocking over Scandinavia. The geopotential anomaly relative to the wintertime climatology (ϕ ′ = ϕ − ) is then composited for these time steps to define our Scandinavian blocking pattern ().
Our SB index I is defined using the method of Michel and Rivière (2011),
where t represents the time step in the considered period (1 October–30 April from 1979 to 2014), which contains nt = 7429 days; P(t) is the projection of the geopotential anomaly ϕ′ on the SB geopotential anomaly ,
where λ and φ are the longitude and latitude, respectively; is the domain over which the projection is performed; and is the time mean of the projection over all time steps. We calculate the index over the North Atlantic–European domain (20°–90°N, 90°W–60°E), but the results are equivalent if the projection is performed over the entire Northern Hemisphere. As both SB phases occur frequently (≃16%), there is no bias that would affect our results.
When averaging the days for which the Scandinavian blocking index is higher (lower) than its mean plus (minus) one standard deviation, the structure of the positive (negative) phase is obtained. Since we have not found any appropriate figure in the literature, we show the two phases of the Scandinavian blocking in Fig. B1 to help interpret the large-scale structure during polar mesoscale cyclone genesis.
© 2018 American Meteorological Society.
STARS provides polar low tracks from 2002 to 2011 over the Norwegian Sea (http://polarlow.met.no/stars/).
The detection and tracking algorithm uses the unit of degree of latitude, which is about 111 km.