LETTERS PUBLISHED ONLINE: 28 APRIL 2013 | DOI: 10.1038/NCLIMATE1884
Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent Marie-Luise Kapsch*, Rune Grand Graversen and Michael Tjernström The summer sea-ice extent in the Arctic has decreased in recent decades, a feature that has become one of the most distinct signals of the continuing climate change1–4 . However, the interannual variability is large—the ice extent by the end of the summer varies by several million square kilometres from year to year5 . The underlying processes driving this year-to-year variability are not well understood. Here we demonstrate that the greenhouse effect associated with clouds and water vapour in spring is crucial for the development of the sea ice during the subsequent months. In years where the end-of-summer sea-ice extent is well below normal, a significantly enhanced transport of humid air is evident during spring into the region where the ice retreat is encountered. This enhanced transport of humid air leads to an anomalous convergence of humidity, and to an increase of the cloudiness. The increase of the cloudiness and humidity results in an enhancement of the greenhouse effect. As a result, downward long-wave radiation at the surface is larger than usual in spring, which enhances the ice melt. In addition, the increase of clouds causes an increase of the reflection of incoming solar radiation. This leads to the counterintuitive effect: for years with little sea ice in September, the downwelling short-wave radiation at the surface is smaller than usual. That is, the downwelling short-wave radiation is not responsible for the initiation of the ice anomaly but acts as an amplifying feedback once the melt is started. The sea-ice extent in the Arctic has been steadily decreasing during the satellite remote-sensing era, 1979 to present (Fig. 1a). The highest rate of retreat is found in September5 , which coincides with the month of the annual cycle that has the lowest ice extent. Factors that are believed to cause the ice retreat are, among others: changes in surface air temperature6–8 , ice circulation in response to winds/pressure patterns7–11 , and ocean currents8 , as well as changes in radiative fluxes (for example, due to changes in cloud cover)7,10,12–15 , and ocean conditions (for example, ocean warming16 ). However, large interannual variability is superimposed onto the declining trend (Fig. 1a). The year-to-year deviation of the ice extent in September relative to the trend line varies by, on average, ±0.5 × 106 km2 , but can reach 1.75 × 106 km2 , which is around 25% of the mean September extent for 1979–2010. The magnitude of the variability shows considerable regional differences: a comparison of years with an anomalously large September sea-ice extent (HIYs—high ice years) with years showing an anomalously small ice extent (LIYs—low ice years) reveals that the variability is most pronounced in the Arctic Ocean north of Siberia (Fig. 1b,c). Significant ice-concentration anomalies of ∼±30% are observed for LIYs and HIYs in this area, which is chosen as the study area for the following analyses. In 2007 and 2012—the years showing the first and second lowest Arctic ice extent since the satellite observations began—a large part of this area became entirely ice free17–19 .
What are the processes causing the year-to-year ice variability? The processes can be divided into two types, dynamical and thermodynamical. The former includes processes that transport ice away from a given location and pack it elsewhere, or export it from the Arctic to southerly latitudes where it eventually melts. The latter type includes processes causing ice melt, which are associated with alterations of the energy exchange between the ice and the ocean below or the atmosphere above. These two types of process are not independent, because weather patterns that transport the ice may also bring warm and humid air as well as warm surface water into the ice-retreat region. On the basis of correlations between lower atmospheric winds and September sea-ice extent it has been shown that 50% of the ice variability can be associated with the winds20 . Again, the linkage between the two is probably due to a combination of the direct dynamical forcing by the winds and the winds bringing warm and humid air in over the ice, which increases the energy flux to the surface10 . Here we focus on the thermodynamical component and quantify the reduction in ice extent that this component can be accounted for. Across the surface the energy balance is given by Fsrf = SWN + LWN + SH + LH
(1)
where SWN = SWSD−SWSU and LWN = LWSD−LWSU indicate net short-wave (SWN) and long-wave (LWN) radiation, defined as downward (SD) minus upward (SU) radiation, and SH and LH are the turbulent fluxes of sensible and latent heat, respectively. All terms in equation (1) are here defined positive downward. Using data from the ERA-Interim reanalysis21 we investigated this surface energy balance. The amount of long-wave radiation from the atmosphere to the surface during spring plays an important role for the September sea-ice concentration (SIC) in years that show an unusual small ice extent by the end of the summer (LIYs): during late spring (April–May) anomalies, based on detrended data, of the net long-wave radiation plus turbulent fluxes result in a significant larger-than-average energy flux to the surface over the area where the September sea-ice anomaly is encountered (Table 1, Fig. 2a and Supplementary Fig. S6). This alteration of the surface flux is linked to significant larger-than-average atmospheric content of cloud water and water vapour (Fig. 3a, Table 1 and Supplementary Fig. S7). These cloud and water-vapour anomalies lead to an enhancement of the atmospheric opacity and thus of the greenhouse effect. This causes a significant increase of long-wave radiation— but a significant decrease of short-wave radiation—downward at the surface (Fig. 2b, Table 1 and Supplementary Fig. S6). The anomaly of net long-wave radiation plus turbulent fluxes stays significantly positive throughout the late spring and summer months (April–August) and accounts for most of the enhanced energy flux to the surface during the melting season (Fig. 2a and Table 1). In mid May, when the ice anomaly begins to
Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden. *e-mail:
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LETTERS
NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1884
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Figure 1 | Arctic sea-ice extent and ice-concentration anomalies for September, 1979–2010. a, Black lines are Arctic sea-ice extent for September and the linear trend line. Red, dashed lines indicate the ±0.5 s.d. used to split the time series into years with a little (LIYs) and large (HIYs) September sea-ice extent. b,c, SIC anomalies for LIYs (b) and HIYs (c). Significant anomalies, encapsulated by the grey line (95% significance level—based on monthly averages), are mainly evident in the Arctic Ocean, off the Siberian coast. The black outline marks the study area.
appear, and the surface albedo therefore becomes anomalously low, the net short-wave-radiation anomaly becomes positive. The net short-wave radiation contributes to the enhanced energy flux to the surface during the rest of the melting season. These findings lead to the conclusion that enhanced long-wave radiation associated with positive humidity and cloud anomalies during 2
Figure 2 | Radiative and turbulent flux anomalies at the surface for LIYs. The black line shows the SIC (right-hand axis). a, The net long-wave radiation plus the turbulent fluxes (latent and sensible; red) and the net short-wave radiation (green). b, The radiative fluxes are split into their components but only downwelling long-wave (red) and short-wave (green) radiation are shown together with the latent (dark blue) and sensible (light blue) heat flux. All time series are based on daily anomalies and averaged over the area indicated by the black outline in Fig. 1b. All data are detrended before calculating the anomalies and a 30-day running mean filter is applied.
spring plays a significant role in initiating the summer ice melt, whereas short-wave-radiation anomalies act as an amplifying feedback once the melt has started. This conclusion holds also for composites of the earlier and later years of the LIYs, if 2007 is excluded from the LIY composite, for a much larger area covering most of the Arctic Ocean, and for different reanalysis data (see Supplementary Discussion). The positive anomalies of cloudiness and humidity in late spring of LIYs cannot be explained by the ice anomaly because this appears first around mid May. Instead, these cloud-water and humidity anomalies are most likely due to the variability of the atmospheric circulation: splitting the atmospheric energy-transport convergence into its dry-static and latent components reveals that over the ice-retreat area, the latent heat-transport convergence, which is essentially the convergence of water vapour, is significantly larger than average during April–May (Fig. 3b, Table 1 and Supplementary Fig. S7). The convergence of atmospheric water in spring (March–April) can be estimated roughly by taking the mean latent energy transport convergence (LTC) as 2 W m−2 (Table 1) and applying LTC × t /L, where t is set to 60 days and L is the latent heat of evaporation (2.5 × 106 J kg−1 ). Hereby it is found that the latent energy transport provides the atmosphere above the ice-retreat area with ∼4 kg m−2 of water in April–May, which is much more than the amount associated with the humidity and cloudiness anomalies (∼0.3 kg m−2 ; Table 1) during this period. The convergence of the dry-static transport during late spring of LIYs is positive but not statistically significant (Fig. 3b, Table 1 and Supplementary Fig. S7). However, during the summer season June–August, the convergence is significantly larger than average and provides the atmosphere over the ice-retreat area with an extra ∼3.5 W m−2 . This contributes to retaining the anomaly of the net long-wave radiation plus turbulent fluxes around ∼2 W m−2 , after the cloud anomaly has become small during summer (Fig. 3a).
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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1884
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Table 1 | Statistical significance of the anomalies of surface fluxes, atmospheric water contents and atmospheric energy transport convergence for LIYs. Time period
Variable
Average value
Statistical significance (%)
01 April–31 May
SIC LWN + SH + LH LWN SWN LWSD SWSD SH LH Conv. of dry-static energy Conv. of latent energy Total column water vapour Total column cloud water
−0.49% 1.73 W m−2 1.87 W m−2 −0.55 W m−2 5.76 W m−2 −4.96 W m−2 0.15 W m−2 −0.29 W m−2 −0.14 W m−2 2.18 W m−2 0.27 kg m−2 4.89 × 10−3 kg m−2
65 99 99 74 100 100 39 95 4 98 99 99
01 April– 31 Aug
SIC LWN + SH + LH LWN SWN LWSD SWSD SH LH Conv. of dry-static energy Conv. latent energy Total column water vapour Total column cloud water
−1.91% 2.01 W m−2 1.43 W m−2 0.83 W m−2 3.43 W m−2 −2.25 W m−2 0.19 W m−2 0.40 W m−2 2.06 W m−2 0.52 W m−2 0.42 kg m−2 0.97 × 10−3 kg m−2
70 99 99 85 100 95 68 89 87 45 100 47
01 June–31 Aug
SIC LWN + SH + LH LWN SWN LWSD SWSD SH LH Conv. of dry-static energy Conv. of latent energy Total column water vapour Total column cloud water
−2.84% 2.20 W m−2 1.14 W m−2 1.74 W m−2 1.87 W m−2 −0.45 W m−2 0.21 W m−2 0.85 W m−2 3.51 W m−2 −0.58 W m−2 0.52 kg m−2 −1.63 × 10−3 kg m−2
97 91 85 97 97 31 64 95 97 35 100 65
All fields are averaged over the area indicated by the black box in Fig. 1b and over selected periods. Significance is based a Monte Carlo approach (10,000 iterations; see Methods). Significance values are given by the nearest integer. LWSD/SWSD: downwelling long-wave/short-wave radiation; LWN/SWN: net long-wave/short-wave radiation; SH/LH: sensible/latent heat flux.
For LIYs, the anomaly of long-wave radiation plus turbulent fluxes prevails significantly positive during the period April–August, leading to a total energy surplus of ∼2 W m−2 over the ice-retreat area (Table 1). Further, the net short-wave radiation contributes with an additional amount of ∼1 W m−2 . As a result, the extra energy gained by the surface due to these anomalies can melt on average ∼13 cm of ice over the area. This value is found using 1Fsrf × t /Lf /ρice , where 1Fsrf = 3 W m−2 , t is set to 150 days, Lf = 334 × 103 J kg−1 is latent heat of fusion, and ρice = 900 kg m−3 is sea-ice density. Assuming an average ice thickness of 0.5–1 m by the end of the melt season the surface fluxes can melt 13–26% of the total ice amount within the study area. The assumption of the average ice thickness in this area is supported by recent observations22 and model results23 . If it is further assumed that all thickness categories are equally represented, the melted ice fraction is 7–13% (see Supplementary Fig. S8 for details), which can be compared to the ice-concentration anomaly for LIYs (Fig. 2) of around 8%. Hence, with these assumptions, the surface fluxes provide enough energy to reduce the ice concentration by the
observed amount. The results indicate that the thermodynamical processes examined here can explain the ice retreat during the LIYs. However, other processes may also contribute, for instance wind forcing11,20 and ocean currents24 . As mentioned earlier, thermodynamical and dynamical processes are probably linked, because positive anomalies of energy convergence are associated with cyclone and frontal activities and enhanced winds. For the HIYs, the case is essentially the opposite: convergence anomalies of both latent and dry-static energy are negative during late spring and summer (Supplementary Fig. S10). This is consistent with negative water-vapour and cloud anomalies that act to decrease the atmospheric opacity and weaken the greenhouse effect (Supplementary Fig. S10a). The sum of the net short-wave and long-wave radiation plus turbulent fluxes becomes negative and the energy flux to the surface is reduced by ∼3 W m−2 during April–August (Supplementary Fig. S9 and Table S1). However, most of these anomalies for HIYs are not statistically significant. An exception is the atmospheric humidity, which is significantly smaller than average, both during late spring and throughout the summer season.
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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1884
LETTERS a
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Figure 3 | Atmospheric water content and energy convergence anomalies for LIYs. a,b, Anomalies of SIC (black, right-hand axis) and net long-wave radiation plus turbulent fluxes (red) are shown together with anomalies of total column cloud water (liquid plus solid; blue) and water vapour (green) (a), as well as the convergence of dry-static (green) and latent atmospheric energy transport (blue; b).
The sea-ice anomalies have a clear but lagged effect on the atmosphere, when the autumn freeze-up sets in by mid September: in LIYs, cold air over open water induces enhanced long-wave radiation plus turbulent fluxes from the surface to the atmosphere over the ice-retreat region (Fig. 2). As a result, the atmospheric energy transport convergence is reduced (Fig. 3b). The opposite pattern of both the surface fluxes and the atmospheric transport is encountered during autumn for HIYs (Supplementary Figs S9 and S10). For years when the Arctic SIC becomes anomalously low in the end of summer, two processes seem important: an enhanced atmospheric convergence of moisture during spring over the iceretreat area, leading to an increase of the greenhouse effect, due to positive anomalies of the water vapour and cloudiness, and to an increase of the long-wave radiation to the surface; and an enhanced atmospheric convergence of dry-static energy during summer over the ice-retreat area, which acts to increase the energy-flux to the surface from long-wave radiation and sensible heat flux. When the ice anomaly begins to appear, the net short-wave radiation anomaly becomes positive. Hence, short-wave radiation is having little effect when the ice anomaly is initiated, but acts as an amplifying feedback in response to the melt. We emphasize again that although the energy transport plays a major role for the September sea ice during LIYs, wind20 and ocean-current24 anomalies may also be important.
Methods The ice extent (Fig. 1a) is obtained from the Scanning Multichannel Microwave Radiometer (SMMR; October 1979–August 1987) and the Special Sensor Microwave/Imager (SSM/I; July 1987 to present) onboard the Nimbus-7 satellite and Defense Meteorological Satellite Program, respectively. The data are provided by the National Snow and Ice Data Center25 (NSIDC). Years with an anomalous small/large September sea-ice extent are defined as years where the sea-ice extent deviates with more than ±0.5 s.d. from the linear trend line taken over 1979–2010 (Fig. 1). This definition results in 10 LIYs and 12 HIYs. The linear trend is estimated using a least-squares linear regression. Various atmospheric fields are taken from the ERA-Interim reanalysis from the European Center of Medium Range Weather Forecast21,26 (ECMWF). A reanalysis product, which is blended model results and observations, provides a high degree 4
of consistency among the variables. At present, ERA-Interim is arguably among the best data sets for the Arctic (Supplementary Section S3). For the time series of radiative and turbulent fluxes (Figs 2 and 3 and Supplementary Figs S1, S3, S9 and S10), 24-h forecast accumulations, initiated at 00 utc, are used. For all other variables 6-hourly analysis are averaged to daily values (Fig. 3 and Supplementary Figs S6 and S7). The energy transport is estimated with a 6-hour resolution on model hybrid levels and vertically integrated from the top to the bottom of the atmosphere27 . A barotropic mass-transport correction is applied28 . The dry-static and latent energy transport are defined as
¬10
Sea-ice anomaly (%)
Flux anomaly (W m¬2)
10
SIC
5
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b
LWN + LH + SH
Sea-ice anomaly (%)
Flux anomaly (W m¬2)/ water anomaly (kg m¬2)
10
1 g
1 ∂p v ·v + cp T + gz dη 2 ∂η 0 Z 1 1 ∂p Jlatent = vLq dη g 0 ∂η
Z
1
v
respectively, where g is gravity, v(u,v) is the horizontal wind with v as the northward and u as the eastward component, cp is the specific heat capacity of moist air at constant pressure, T is temperature, z is geopotential height, p is pressure, L is the specific heat of condensation, q is the specific humidity and η is the vertical hybrid coordinate used in the ERA-Interim reanalysis. All data used have a 0.5◦ × 0.5◦ horizontal resolution. Ice concentrations are also taken from ERA-Interim (except for Fig. 1a). Note that the ERA-Interim ice concentrations are neither modelled nor assimilated but taken directly from the satellite observations. Two days with obviously erroneous SICs are removed from the time series. Anomalies of all fields are estimated for the LIYs and the HIYs. As in the procedure for the sea-ice extent, these anomalies are calculated relative to the linear trend over 1979–2010 for each grid point. By estimating the anomalies relative to the linear trend rather than the mean, the focus is held on the year-to-year variability rather than the long-term change. This climatology consisting of a linear trend is referred to as average in the text. Statistical significance is tested using a Monte Carlo approach29 . At least 1,000 artificial LIY/HIY composites are randomly generated and compared to the original LIY/HIY composite. Hereby the null hypothesis that the anomaly of the original composite is not different from zero is tested. If the null hypothesis can be rejected the anomaly values of the composite are significantly different from zero. For example, the original composite is significant on a 95% level if less than 5% of the absolute values of the artificial composites are larger than the anomalies of the original composite. The advantage of the Monte Carlo method is that the data are not assumed to follow any particular statistical distribution. This is in contrast to, for example, a Student t -test, which assumes that data follow a normal distribution. Thus, robust results are expected, even for a relatively small sample size.
Received 17 September 2012; accepted 26 March 2013; published online 28 April 2013
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14. Kay, J. E., L’Ecuyer, T., Gettelman, A., Stephens, G. & O’Dell, C. The contribution of cloud and radiation anomalies to the 2007 Arctic sea ice extent minimum. Geophys. Res. Lett. 35, L08503 (2008). 15. Schweiger, A. J., Lindsay, R. W., Vavrus, S. & Francis, J. A. Relationships between Arctic sea ice and clouds during autumn. J. Clim. 21, 4799–4810 (2008). 16. Polyakov, I. V. et al. Arctic Ocean warming contributes to reduced polar ice cap. J. Phys. Oceanogr. 40, 2743–2756 (2010). 17. Maslanik, J. A. et al. A younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice loss. Geophys. Res. Lett. 34, L24501 (2007). 18. Perovich, D. K., Richter-Menge, J. A., Jones, K. F. & Light, B. Sunlight, water, and ice: Extreme Arctic sea ice melt during the summer of 2007. Geophys. Res. Lett. 35, L11501 (2008). 19. Lindsay, R. W., Zhang, J., Schweiger, A., Steele, M. & Stern, H. Arctic sea ice retreat in 2007 follows thinning trend. J. Clim. 22, 165–176 (2009). 20. Ogi, M., Yamazaki, K. & Wallace, J. M. Influence of winter and summer surface wind anomalies on summer Arctic sea ice extent. Geophys. Res. Lett. 37, L07701 (2010). 21. Dee, D. P. et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011). 22. Kwok, R. et al. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008. J. Geophys. Res. 114, C07005 (2009). 23. Zhang, J., Lindsay, R., Steele, M. & Schweiger, A. What drove the dramatic retreat of arctic sea ice during summer 2007? Geophys. Res. Lett. 35, L11505 (2008). 24. Polyakov, I. V. et al. One more step toward a warmer Arctic. Geophys. Res. Lett. 32, L17605 (2005). 25. Fetterer, F., Knowles, K., Meier, W. & Savoie, M. Sea Ice Index (National Snow and Ice Data Center, 2002, updated 2009).
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Acknowledgements This work is part of the ADSIMNOR programme, funded by a grant from the Swedish research council Formas. The ECMWF ERA-Interim reanalysis data are obtained from the ECMWF data server and the sea-ice extent from the NSIDC.
Author contributions The original idea for the paper was suggested by R.G.G. and discussed and developed by all authors. The data analysis was carried out by M-L.K., who also prepared the figures. M-L.K. and R.G.G. wrote the manuscript and M.T. provided feedback. All authors contributed to the discussion and interpretation of the results.
Additional information Supplementary information is available in the online version of the paper. Reprints and permissions information is available online at www.nature.com/reprints. Correspondence and requests for materials should be addressed to M-L.K.
Competing financial interests The authors declare no competing financial interests.
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