Arctic amplification (AA) of global warming has been observed in both the upper and lower troposphere in the last few decades (e.g., Graversen et al., 2008; Alexeev et al., 2012; Screen et al., 2012). Chung et al. (2013) evaluated the warming in the Arctic using four different reanalysis data sets and radio-soundings for the period 1979–2011. Significant warming trends are found for all seasons in all data sets, and two times greater rates of warming are found in the lower troposphere than in the upper troposphere. The annual mean surface temperature poleward of 70°N increased by a range of 1.75±0.29 K to 2.71±0.28 K over 33 years, 4–6 times larger than the global average surface air temperature increase (Arguez et al., 2013), and the warming in winter months (December–January–February, hereafter DJF) was about twice as large as in summer months (June–July–August). With four-times present CO2 in the atmosphere, contemporary global climate models predict a surface warming of 4.3 K in the tropics (30°S–30°N) and 11.2 K in the Arctic (60°N–90°N), with a range of 3–6 K and 5–16 K, respectively (Pithan and Mauritsen, 2014). AA of climate change has been observed in geological records and is now recognised as an inherent characteristic of the global climate system (Serreze and Barry, 2011). Multiple feedback effects can contribute to AA, including albedo, lapse rate and water vapour (Cai, 2005; Winton, 2006; Taylor et al., 2013; Graversen et al., 2014; Pithan and Mauritsen, 2014). Additional warming may result from changes in arctic vegetation (Swann et al., 2010) and black carbon in snow (Flanner et al., 2007).
The arctic warming over the past decade has been accompanied by a rapid decline in sea ice concentrations in the polar region (Comiso et al., 2008; Comiso, 2012). Changes in sea ice extent impact heat fluxes between the ocean and the atmosphere (Smedrud et al., 2013), and may have contributed to the Arctic surface warming (Serreze et al., 2009; Screen and Simmonds, 2010). In model simulations, local sea ice and sea surface temperature (SST) trends explain a large portion of the observed Arctic near-surface warming, whereas remote SST changes explain the majority of observed warming aloft (Screen et al., 2012). Over longer time scales, the multidecadal oscillation of the Atlantic meridional overturning circulation (AMOC) may modulate Arctic sea ice and surface air temperature (SAT) (Chylek et al., 2009; Mahajan et al., 2011; Peings and Magnusdottir, 2014).
The vertical structure of the recent arctic warming as represented in observed data and reanalysis products has gained much deserved attention (Graversen et al., 2008; Alexeev et al., 2012; Screen et al., 2012; Chung et al., 2013). The warming aloft is not attributable to shortwave and longwave (LW) feedbacks, nor to changes in sea ice and SST within the Arctic. Graversen et al. (2008) proposed atmospheric northward energy transport (ANET) as an important mechanism for the recent arctic warming. Mid-tropospheric temperatures in the Arctic are sensitive to advection of energy into the Arctic: the 500 hPa temperature field in the Arctic was found to be positively correlated with ANET across 60°N with a lag of 5 days (Graversen et al., 2008). The correlation may be robust on the synoptic time scale, but, on longer time scales, a reduced zonal-mean meridional temperature gradient may cause a decrease of energy transport (Serreze and Barry, 2005).
Other studies have searched for changes in large-scale atmospheric circulation patterns as potential causes. Remote SST patterns might affect arctic meteorological conditions through teleconnections (Screen et al., 2012; Perlwitz et al., 2015). However, details of the dynamical processes are not known to date. In a recent study based on the ERA-Interim reanalysis product, Woods et al. (2013) found that the interannual variability of intense moisture intrusions across 70°N is strongly correlated with variability in winter-mean surface downward LW radiation and skin temperature averaged over the Arctic. The intense intrusions are caused by large-scale blocking patterns which deflect mid-latitude cyclones and their associated moisture poleward. The above studies thus emphasise the importance of understanding the variability in large-scale circulation and its impacts on energy transport into the Arctic, the surface energy budget and top-of-atmosphere (TOA) net radiative flux.
In this study, we first extend the analysis of Graversen et al. (2008) based on ERA-40 reanalysis data to NCEP/NCAR reanalysis data. We examine energy transport by planetary and transient waves through height–longitude cross-sections and by the zonal mean circulation (ZMC). Section 2 describes the data and methods. Section 3 presents the energy fluxes and trends. Section 4 discusses inter-annual variations in winter. Section 5 summarises the results and conclusions.
The total energy per unit mass of air is given by E=cvT+gz+Lq+½(u2+v2), where cv is heat capacity, T is temperature, g is gravity, z is geopotential height, L is latent heat, q is specific humidity, u is zonal wind and v is meridional wind. The energy balance in a polar cap is given by Peixoto and Oort (1992) (Eq. 13.38 with the over bars for time averaging neglected):
where m is mass, cp is heat capacity at constant pressure, v is meridional wind, x is zonal distance, p is pressure (p=psσ at a sigma-level, ps=surface pressure), FTA and FTB are downward energy fluxes at top and bottom of the atmosphere, respectively.
We calculate mass-weighted surface-TOA and surface-400 hPa northward fluxes of internal energy (denoted by vT), potential energy (vz) and latent heat (vq) using the NCEP/NCAR reanalysis data from 1979 to 2012 (Kalnay et al., 1996). The reanalysis data is archived at 6-hourly intervals, 28 vertical sigma-levels, 192 longitude bands and 94 latitude bands. The kinetic energy flux is small and is neglected. We decompose the fluxes into contributions by stationary waves (denoted by v*q*, v*T* and v*z*) and transient eddies (v'q’, v'T’ and v'z’) and by the monthly ZMC ([v][q], [v][T] and [v][z]) (Peixoto and Oort, 1992). We define transient eddies as departures from monthly mean quantities in each grid, and stationary waves as departures from zonal mean averages for each vertical level and in each month. Vertically averaged zonal mean v is subtracted from the mean v in each layer to ensure a zero net mass flux through a “wall” encircling the Arctic and to eliminate spurious results (Trenberth, 1991; Graversen, 2006). We also calculate monthly, mass-weighted variances of meridional wind (v'v’), mean positive relative vorticity (vort) and the time fraction for which vort>1.0×10−5 s−1 as measures of cyclone activity (Sorteberg and Walsh, 2008). Analyses are performed for twelve 30°-wide longitudinal sectors (Fig. 1). The time series for each cross-section are analysed for interannual variability and linear trends from 1979 to 2012. Unless otherwise noted all analyses are performed for northern winter (DJF).
In this section, we present an analysis of poleward energy fluxes. Figure 2 shows the ANET fluxes at 65.7°N and 71.4°N, which are divided by the encircled surface areas. The monthly mean fluxes (Fig. 2a) at 71.4°N are in good agreement with a previous calculation at 70°N based on a 10-year data set (Nakamura and Oort, 1988, as tabulated in Serreze and Barry, 2005). The annual cycle of ANET shows a broad peak from November to March and a minimum from May to July/August. The annual and seasonal mean ANET show large interannual variations and downward trends (Fig. 2b–f). The linear trends from 1979 to 2012 are shown in Table 1 for ANET at 65.7°N. The annual and winter trends are significant at 2 standard deviations (s.d.), while the trends in other seasons are insignificant. In comparison, calculations of ANET based on ERA-40 reanalysis data for 1979–2001 show a downward trend in winter and an upward trend in summer (Graversen, 2006; Graversen et al., 2008). The trend in ANET at 60°N explains only a fraction (e.g., 20–30% near 700 hPa) of the temperature trends in summer (Graversen et al., 2008).
A partitioning of the ANET in winter is shown in Table 2. Transport of sensible heat is much larger than latent heat and together they tend to warm the Arctic. Transport of geopotential energy is dominated by the ZMC which tends to cool the Arctic. Transport by stationary waves is comparable to that by transient eddies. The total flux at 65.7°N (2516 TW) is larger than that at 71.4°N (1545 TW) due to a net radiative cooling in between the latitudes.
Figure 3 shows the averages and linear trends of the energy flux terms by transient eddies through the cross-sections from the surface to ~400 hPa in winters from 1979 to 2012, and the same for the variances of v and for the averages of positive vorticity. The effect of transient baroclinic waves on energy transport are most pronounced below 400 hPa. The largest energy fluxes occur over the Greenland Sea (GS) (Sector 12), the Norwegian Sea (NS) (Sector 1) and the Chukchi Sea (Sector 7), where the air is warmer and contains more water vapour than over land. The land–ocean contrast is more pronounced for latent heat (Fig. 3a) than sensible heat (Fig. 3c). The sensible heat fluxes are greater than the latent heat fluxes by more than a factor of 2. The lowest fluxes are estimated for cross-sections (4 and 5) over Central Siberia and the mountainous region to the east of 120°E where the air is extremely cold and dry under persistent high pressures. More transient eddy activity occurs over the North Atlantic than other regions as indicated by the variances of v (Fig. 3g) and the mean positive local vorticities (Fig. 3i), contributing to the land-ocean contrast in eddy fluxes of energy.
Consistent decreases from 1979 to 2012 in transient eddy activities and associated energy fluxes are shown for cross-sections 3 and 4 (60°–120°E) over regions south of the Kara Sea and Taymyr Peninsula in western and central Siberia (Figs. 1 and 3). Winter blocking frequency was found to have increased for the same time period over western and central Siberia as shown by the time-series of average frequency for 40°–80°N and 60°–120°E (Barnes et al., 2014). Less significant but apparent increases of northward energy fluxes are shown in Fig. 3b, d and f for cross-sections 10–12 (270°–360°E, Fig. 1) over the regions extending from the Baffin Islands/Bay to Greenland and the GS. Transient eddy activity appears to have become stronger over the NS and the GS (sections 1 and 12, Fig. 3h and j).
In this section, we examine the interannual variability as well as the long-term trend in transient eddies in the northern Atlantic and Siberia. Figure 4 shows the winter mean positive vorticity and the time fraction of high vorticity (TFHV: vort>1.0×10−5 s−1) in the mid-troposphere (approximately 400–700 hPa) over the GS and the NS (30°W to 30°E) and over west-central Siberian (WCS) (60°E to 120°E), respectively. Over the GS and NS, the mean vort is generally above the long-term average during 2000–2012 and is lower during the earlier period (Fig. 4a). The TFHV shows large year-to-year variations but no clear trend (Fig. 4c). Over the WCS, mean vort is often above the long-term average in the early years, close to average in the middle years and below average in the later years (Fig. 4b). The TFHV also shows a general decrease from 1980 to 2012 and large interannual variations (Fig. 4d).
To demonstrate the impact of cyclone variability and trends shown in Fig. 4 on arctic climate, we calculate the winter mean precipitable water, skin temperature, upward TOA LW radiation and downward surface LW radiation north of 70°N, derived from NCEP reanalysis data (Fig. 5). These parameters show similar interannual variations and long-term trends with large correlation coefficients between each pair, except for the TOA LW flux. The skin temperature and the downward surface LW flux are also similar to those derived from the ERA-Interim reanalysis data for 1990–2010 (Woods et al., 2013), whose interannual variations were found to be associated with intense moisture intrusions to the Arctic.
Table 3 shows correlation coefficients calculated for mean positive vorticity with the Arctic mean climate parameters and for TFHV with the same parameters. Significant negative correlations (at p<0.01) are found between TFHV over the WCS and the downward surface LW flux (r=−0.73 for TFHV 400–700 hPa), precipitable water (−0.69) and skin temperature (−0.67) (compare curves in Fig. 4d and 5). The correlation between TFHV and TOA LW flux is insignificant (−0. 17). The correlation coefficients are smaller for 700–1000 hPa TFHV and for mean positive vorticity in both layers. Similar correlations are insignificant over the northern Atlantic. These correlations suggest that the cyclonic circulation (or a lack of) over the WCS may vary with and even possibly plays a role in driving winter climate in the Arctic. The causes of circulation changes over Siberia are beyond the scope of this paper.
In this study we calculate the ANET to the Arctic based on meteorological data from the NCEP/NCAR reanalysis for the period of 1979–2012. During this period the ANET decreased in all seasons, while the Arctic temperature increased and sea ice decreased. The downward trends are statistically significant for winter and annual mean fluxes, but not for other seasons. We calculated the energy fluxes, the mean variance of v and the mean positive local vorticity by cross-sections (400 hPa – surface, every 30° from 0° to 360°E) for each winter. Linear regressions of the time-series show a general increase of transient eddy activity and associated energy fluxes over the northern Atlantic, and a general decrease over the west and central Siberia. Inter-winter variations in transient eddy activity, as measured by the time fraction of high local vorticity, over the WCS are significantly anti-correlated with the Arctic climate.
Catherine Raphael drew Figure 1. Vaishali Naik, Michael Winton and Rong Zhang commented on the manuscript. Two anonymous reviewers provided constructive comments that led to significant improvements of the manuscript.
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