4. Physical and socio-economic environment
4.2 Changes in the atmosphere
4.2.3 Natural variability
Ecosystems and society are both exposed to a combination of natural variability and anthropogenic change. Arctic climate and weather exhibit considerable temporal variability, often associated with changes in mean level pressure, sea-ice cover, wind patterns, and the distribution of air masses and temperature. The natural variations are associated with different weather phenomena and are linked to the position and strength of the polar jet (polar vortex), storm tracks, the Arctic Oscillation (AO), and ocean currents (especially the Atlantic Meridional Overturning Circulation). Some of these phenomena are expected to change over the coming decades and in so doing affect the nature of the short-term fluctuations, but how they are likely to change under a warmer climate is currently unclear. This is examined in the following sections.
4.2.3.1
Feedback mechanisms
The various components of the climate system interact in different ways, often with a change in one causing a change in another, which then leads back to cause additional change in the first. This is known as a feedback, and it is these feedback mechanisms that are responsible for natural variability in the climate system and make predictions so challenging.
Although many feedbacks are well understood, the possibility of additional feedback mechanisms that are currently unknown cannot be excluded. One type of inter-connection is between sea-ice or snow and temperature, through their reflecting properties and the ability of the surface to absorb sunlight (the albedo effect). Clouds and atmospheric humidity also play a role by influencing the way light and heat are transferred. Air temperature itself also involves feedbacks to heat loss.
Pithan and Mauritsen (2014) analyzed results from state-of-the-art GCM simulations (CMIP5) and found the largest contribution to Arctic amplification was made by temperature feedbacks. Their reasoning was that more energy is radiated back to space at lower latitudes than higher latitudes when the surface warms. They concluded that the surface albedo feedback was the second largest contributor to Arctic amplification.
Graversen et al. (2014) found changes in the mean vertical temperature profile associated with a stronger greenhouse effect (the lapse-rate feedback) to make a significant contribution to the polar amplification, and their analysis showed this to account for 15% of the amplification in the Arctic. In comparison, they found melting snow and ice to account for 40% of the Arctic amplification. Another type of feedback involves changes in the vertical temperature profile, and Woods and Caballero (2016) suggested that this may be connected with southerly moisture injections as these may influence both temperature and sea ice.
Other phenomena and processes which link different parts of the Arctic climate system include storm tracks and ocean currents and their importance for moving heat around. Left to their own devices, these interconnections, which allow changes in some aspects of the climate to feed back to others, sometimes even in circles, lead to natural variability. The question as to how these different feedback processes may change in the future can be rephrased to ask more specifically: How will the character of these important natural phenomena be affected by continued global warming?
4.2.3.2
Natural hazards
Natural hazards in the Arctic result from weather phenomena such as storms (strong winds, high waves), avalanches, rockslides, floods, wildfires, and harmful effects of freezing rain and rain-on-snow events. The hazards presented here are connected with physical phenomena and processes in the atmosphere, ocean and land. Many of these phenomena may
have some connection with low-pressure systems and storm tracks. The risk associated with these events for society and ecosystems can be determined from a climatological description of a region, because climate is based on weather statistics and describes the probabilities connected to the weather events. The following sections examine the different types of hazard in the context of these extreme weather phenomena.
Synoptic activity and storm tracks
Mid-latitude storms, also referred to as synoptic storms, are associated with low surface pressure, high winds, and ocean surface waves. They may also generate heavy precipitation, and are considered weather hazards in relation to health, offshore activities, transport and infrastructure (avalanches and rockslides; Hov et al., 2013). In 2012, parts of Svalbard were covered in ice after a rain-on-snow event (Hansen et al., 2014), with severe implications for wildlife and tourism. The event itself was associated with a low-pressure system moving northwards that brought a combination of extreme warm spells and heavy precipitation, followed by sub-zero temperatures. The incident increased permafrost temperature, triggered slush avalanches, and left a significant ground-ice cover. Rain-on-snow events may become more frequent with higher temperatures in the future, which would have far-reaching implications for Arctic ecosystems and societies through the associated changes in snow-pack and permafrost properties.
On 19 December 2015, an avalanche responsible for two fatalities and the destruction of ten houses in Longyearbyen was triggered by a blizzard on the previous day with strong easterly winds that had generated a pile-up of snow on the hillside. This was connected to a low-pressure system from the Norwegian Sea, south of Iceland, that moved northeastward and combined with a temporarily stationary low-pressure system southwest of Svalbard (Figure 4.5 and 4.6). Fast icing at sea, caused by sea spray in sub-zero conditions is another winter
Christian Jaedicke
Figure 4.5 Avalanche in Longyearbyen, Svalbard on 19 December 2015.
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Figure 4.6 Development of the synoptic storm on 18 and 19 December 2015 responsible for two fatalities and considerable damage to buildings near Longyearbyen, Svalbard. Th e plots show archived forecasts from HIRLAM8 (Met Norway). Contours show mean sea-level pressure, green shading indicates wind gusts, and blue areas show the regions in which precipitation fell for a period of 24 hours.
phenomenon considered a hazard (Loeng, 2008). It becomes more severe with strong wind and higher waves, and is therefore also connected to storminess.
To know whether such storms will become more common or more severe in the Arctic in future is important for adaptation planning. However, it is difficult to predict the atmospheric circulation response to global warming owing to natural climate variability, in space and time. This natural variability generates substantial uncertainty in the model projections of future atmospheric circulation patterns, especially for the next 30 to 50 years. It should also be noted that most GCMs simulate storm tracks that are too weak and display equatorward biases in their latitude (Zappa et al., 2014), and these biases also affect their projections. At present, there is no clear signal for future changes in storm statistics.
Chang et al. (2012) analyzed a proxy for storm activity and found a reduction over the Barents area in the future for the CMIP5 results but little change using the CMIP3 results. Other model projections have also shown a decrease in cyclones over the Norwegian, Barents and Greenland seas during the cold season (Ulbrich et al., 2013; Roshydromet, 2014; Akperov et al., 2015).
Projections of North Atlantic and European cyclones from multi-model studies (CMIP5 RCP4.5 and RCP8.5) indicate a tripole pattern with decreasing cyclones in the Norwegian Sea in winter and in summer with fewer and weaker cyclones along the southern flank of the North Atlantic storm track (Zappa et al., 2013). Catto et al. (2014) analyzed climate projections based on a multi-model ensemble (CMIP5 RCP8.5) and found overall decreases in future weather front frequency, but a poleward shift in maximum frequency. Another study of extreme Arctic cyclones based on a multi-model ensemble (CMIP5 historical period) found a modest historic increase in storminess in some regions (including southeast of Iceland) compared to future projections (Vavrus, 2013). Catto et al. (2014) suggested that future changes in frontal disturbances were likely to be associated with storm tracks, and that front strength could decrease at higher latitudes due to amplified surface warming in the Arctic and a reduced temperature gradient. They found little change in storm frequency for the Barents area, but strong indications that storm intensity will decrease. The simulated storm tracks were linked to sea ice is such a way that they both influenced and were influenced by the sea ice. According to Bengtsson et al.
(2009), most models agree that a poleward shift in storm tracks is inevitable over the long-term under a warming climate, along with a general weakening of the global cyclonic activity.
In contrast, a recent study which used the dependency of storm track statistics on mean sea-level pressure to show a slight increase in the frequency of deep cyclones over the Barents Sea under a warming scenario (Benestad et al., 2016). This finding is consistent with an analysis of past trends, which suggests there has been a northward shift in the storm tracks as well as increased cyclonic activity in the Arctic in recent decades (e.g. Zhang et al., 2004; Inoue et al., 2012; Sato et al., 2014;
Rutgersson et al., 2015). Recent RCM data project an increase in the maximum daily wind speed (Figure 4.7), especially in winter, in the northeastern part of the Barents Sea including the northeastern coast of Svalbard (Dobler et al., 2016). The projections show increases in wind speed of more than 3 m/s in winter between the two blue lines on the graphic (i.e. the
Figure 4.7 Projected seasonal change in daily maximum 10-m wind speed over the Barents and Scandinavian region using the regional climate model COSMO-CLM driven by the global MPI-ESM-LR earth system model under the RCP8.5 scenario for the period 2071–2100 against a 1971–2000 baseline. The green lines indicate the northern extent of an area that is ice-free sea for at least 20% of the time in the future (dark green) and historical period (light green) (Dobler et al., 2016).
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area where sea ice shows the strongest seasonal variations). For the areas of maximum change, north and northeast of Novaya Zemlya this corresponds to a relative increase in maximum wind speed of 40%.
Polar lows
Polar lows are small (~100 km) maritime vortices with a short lifetime (several hours), accompanied by extreme weather conditions in the lower troposphere and so represent a threat to human life, coastal infrastructure and offshore activities (Figure 4.8). There are on average 13 polar low events per year in the Norwegian and Barents seas, mainly during the cold season (October to April), with a high number of events in March (Figure 4.9). The seasonal dependency of polar lows was first shown in a study at MET Norway in Tromsø from 1982 to 1985 (Lystad et al., 1986), and the same seasonality was confirmed in a more recent study for 2000 to 2009 (Noer
and Lien, 2010). Polar lows develop when cold air from the polar ice cap is forced out over the warmer waters of the North Atlantic Current such that the air is heated from below, which generates convective systems that bring storm force winds and heavy snowfall.
Polar lows pose a serious risk to shipping owing to the rapid onset of storm force winds. They are associated with weather conditions that are well outside the operating limits for aircraft and for oil and gas drilling operations. Due to their limited predictability and the rapid onset of extreme weather conditions, polar lows are a priority concern for shipping and oil exploration. A study by Zahn and von Storch (2008) using NCEP/NCAR re-analysis data shows there was little change in polar low frequency between 1948 and 2005. A mapping study by Kvammen (2014) of the actual trajectories (Rojo et al., 2015) of the polar lows recorded at MET Norway from 1999 to 2013 also shows little change in their spatial distribution
Figure 4.8 Polar low off the Finnmark coast, 23 March 2011 (MET Norway / NOAA).
0 10 20 30 40 50 60 70
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Figure 4.9 Seasonal distribution of polar lows recorded at MET Norway for the first two decades of the 21st century (note different scales on axes) (MET Norway).
prior to 2009, with peak activity off the coast of Lofoten and Vesterålen, but a recent shift eastward with more occurrences in the central Barents Sea (Figure 4.10).
According to the IPCC scenarios, the troposphere in Arctic regions is likely to warm faster than the global average, whereas sea-surface temperatures in the Arctic will rise more slowly, consistent with the concept of a shorter response time for the troposphere. The result will be reduced convective instability, which will lead to fewer polar lows in the future (Zahn and von Storch, 2010). The source area is also likely to shift slightly northward (Zahn and von Storch, 2010). This may lead to fewer polar lows affecting Norwegian coastal waters, but more in the northern and central Barents area. Observational data for the Norwegian and Barents seas show RCMs are able to reproduce the climatology of polar lows and associated extreme events reasonably well in this area (Shkolnik and Efimov, 2013). For future projections of polar low occurrence, the most useful parameters are sea-surface temperature and temperature in the mid-troposphere, since these determine the static stability, which is key to the development of polar lows. The regional flow pattern as represented by the North Atlantic Oscillation (NAO) or other similar indices can be a useful tool. There is also a connection to the planetary boundary layer (i.e. the lower part of the atmosphere between the surface and the upper layers where the air is free to flow, unrestricted by friction from the surface), because this determines the properties of the cold air outbreaks needed for polar low formation. Hence, good knowledge of ice and snow coverage in the Arctic and neighboring areas is essential for understanding polar lows.
Planetary boundary layer
The atmospheric boundary layer is a region characterized by turbulence and shallow convection, and is influenced by clouds, oceans, and the presence of sea ice. It is the medium through which the atmosphere is coupled to the oceans and a region dominated by a vertical flow of heat and moisture.
Despite an increase in the frequency of convective clouds over past decades, a shallow stably stratified boundary layers is still thought to remain frequent over the continents and northern islands in the Barents area. Through turbulent convection over the Barents Sea, heat and moisture from the ocean are mixed throughout the low- and mid-troposphere from where they are transferred via the large-scale circulation across the wider Arctic region, causing a rise in temperature and precipitation along the Arctic rim. However, the large-scale circulation is extremely sensitive to perturbations (Rossby waves) within the circulation itself. It is now recognized that warming of the Barents Sea blocks heat transport into the Eurasian continent causing widespread winter cooling (Outten et al., 2013). The most visible and important impact of the convection is connected to increasing atmospheric moisture and developing of convective clouds.
The planetary boundary layer plays a strong role in the Arctic.
A shallow planetary boundary layer is powerful magnifier of any climate forcing perturbations and anthropogenic pollution hazards. Under conditions with a shallow boundary layer, anthropogenic heat pollution (e.g. urban heat island effects) is significantly enhanced (up to 2°C in Longyearbyen, 6°C in Barrow, and 12°C in Murmansk), with potentially profound environmental implications. Theoretical studies suggest polar low events are initiated by developing boundary layer convection (Økland, 1987; van Delden et al., 2003).
Cloudiness and humidity
Clouds are associated with weather fronts, synoptic storms, sea ice and planetary boundary layer processes. Clouds are likely to be important for Arctic tourism, as is precipitation. Fog may present challenges in terms of navigation and transport, especially if the droplets are supercooled and freeze on contact with solid surfaces. The intense convection associated with some clouds may generate strong wind gusts, icing and rough surface waves. To date, there is little reliable information concerning past and future trends in cloudiness.
Figure 4.10 Kernel density estimates for two equally sized intervals of the data set. (Kvammen, 2014; Rojo et al., 2015).
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The combination of relatively high sea surface temperature and low air temperatures drives strong vertical convection in both the lower atmosphere and upper ocean. Vertical heat flux reaches an annual average of ~80 W/m2, and in winter may exceed 200 W/m2 over the open water in sea areas representative of conditions in the Barents area (Smedsrud et al., 2013). Frequent winter storms regularly increase the heat flux to 500 W/m2 or more (Ivanov et al., 2003), which is higher than observed at tropical latitudes.
Clouds require cloud condensation nuclei (aerosols) for droplets to form, and data from Arctic field studies suggest aerosol concentrations may be higher over the open sea and the ice-edge than over sea ice-covered regions (Leck and Svensson, 2015). Leck et al. (2002) identified two local aerosol sources: bubble bursting and oxidation products of dimethyl sulfide. As the Arctic climate warms, intensive convection follows the advance of open water, initiating regional and larger hemispheric impacts through teleconnections (Inoue et al., 2012; Smedsrud et al., 2013; Mori et al., 2014; Sato et al., 2014). There are some indications of more frequent convective cloud types over the past three decades (Esau and Chernokulsky, 2015), but strong interannual variability in winter cloudiness makes it difficult to identify trends (Stramler et al., 2011). Clouds are one of the most uncertain aspects of climate models (Boucher et al., 2013), and so projections are not yet possible.
Esau and Chernokulsky (2015) analyzed cloud observations recorded at stations since 1880 and found a steady increase in the frequency of convective cloud types over the past three decades. Local vertical convection with latent heat release in convective clouds is critical to the observed increase in moisture content in the mid-troposphere (2–6 km) and precipitation/snowfall in the surrounding regions (Bulygina et al., 2011; Boisvert et al., 2013; Bintanja and Selten, 2014). These processes cannot be linearly extrapolated following the prescribed scenario of Arctic warming and sea-ice retreat. Esau and Chernokulsky (2015) argued that convection over the Barents Sea develops spatially in the form of convective fields, based on the analysis of Bruemmer and Pohlman (2000), and thus is controlled by the frequency of cold air outbreak events and the size of the open water area.
As both factors are constrained, it is reasonable to suggest that the observed intensification of extreme winds and related dangerous events will peak and then decline through the 21st century. Such a reduction is seen in the regional climate projections according to the analysis of polar lows by Zahn and von Storch (2010). It should be noted that the convective fields, cold air outbreaks, and polar lows indicate a shift in extreme weather phenomena to the eastern Barents Sea where observations show they have previously been rare or absent.
The question about cloud cover changes is not clear.
On the one hand, Screen and Simmonds (2010) argued that past Arctic warming is mainly due to the decline in sea-ice extent with cloud cover playing a lesser role. However, changes in humidity may also have had some effect, and the decline in sea-ice extent is linked to the increase in humidity through the increase in open water area, and thus increased evaporation. On the other hand, higher water vapor concentrations may have enhanced the warming
observed in the lower atmosphere during summer and early autumn. Variability in cloud cover has been linked to sea-ice variability near the ice margin (Schweiger et al., 2008) and retreating sea ice may be associated with a response governed by several factors, with a decrease in low-level cloud amount and an increase in mid-level clouds (Sato et al., 2012).
Stramler et al. (2011) found conditions such as overcast or
Stramler et al. (2011) found conditions such as overcast or