SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: 1.138/NCLIMATE1884 SPRINGTIME ATMOSPHERIC ENERGY TRANSPORT AND THE CONTROL OF ARCTIC SUMMER SEA-ICE EXTENT Supplementary discussion In the main text it is argued that positive anomalies of the surface fluxes are important for the ice retreat during years with a little September sea-ice extent (LIYs low ice years). A composite over the 1 LYIs shows that the anomaly of the net longwave radiation plus turbulent fluxes becomes positive during April-May, whereas the net shortwave radiation contributes to the ice melt from late May and onwards (Fig. 2a of the main text). Here we examine the robustness of these results regarding the selected composite (Supplementary Section 1) and the study area (Supplementary Section 2). Further, we discuss the quality of ERA-Interim reanalysis in the Arctic and show results of two additional reanalysis products (Supplementary Section 3). S1 Low Ice Year composite In order to examine whether the evolution of these anomalies is changing during the period of study , the surface fluxes are subdivided into two composites of the first and the last five years of the LIYs (Supplementary Fig. S1). The former composite basically represents the 198s, including 1979 and 199, whereas the latter represents the 199s and 2s. The main annual evolution pattern of the surface fluxes for both subperiods is similar to that for all LIYs: the net longwave radiation plus turbulent fluxes become positive already in spring, whereas the shortwave radiation contributes to the anomaly over the summer. By the end of the summer, however, the ice anomalies are considerably larger in the 199s-2s than in the 198s. This may be a result of the winter ice being thinner in the later decades than in the former, so that less energy is needed over the summer to create the ice anomaly. Note also that the energy flux to the atmosphere in autumn, which appears in response to the open-water anomaly, tends to be much larger for LIYs in the later decades than in the 198s. The processes leading to the extreme minimum extent of the Arctic sea ice in summer of 27 are studied extensively. Amongst others, Graversen et al. (21) showed that in 27 the convergence of atmospheric energy contributed significantly to the sea-ice melt in the area north of East Siberia where sea ice became entirely absent in September 27. To test whether the evolution of the surface fluxes during the LIYs is strongly influenced by the NATURE CLIMATE CHANGE Macmillan Publishers Limited. All rights reserved.

2 1 a) LWN+LH+SH Low Ice Years SIC 1 all LIYs Composite wo 27 First years Last years 1 b) SWN SIC 1 all LIYs Composite wo 27 First years Last years Figure S1: Radiative and turbulent flux anomalies at the surface for different LIY composites. The black line shows the sea-ice concentration for the original LIY composite (bold), the LIY composite without the year 27 (thin), the first five years of the LIY composite (dashed) and the last five years of the LIY composite (dotteddashed; minimum value out of bound: 13 %). a, displayed is the net longwave radiation plus the turbulent fluxes (red; minimum value out of bound: 16 Wm 2 ) and b, the net shortwave radiation (green) for each sub composite. All time series are based on daily anomalies and averaged over the study area. A 3-day running mean filter is applied to all time series Macmillan Publishers Limited. All rights reserved.

3 patterns during the extreme year of 27, a sub composite excluding 27 is examined (Supplementary Fig. S1). Without 27, the ice anomaly as well as the anomaly of the longwave radiation plus turbulent fluxes during summer are somewhat smaller than the composite based on all LIYs. However the main evolution pattern is similar for both composites. In summary, the conclusions drawn in the main text seem to hold separately for the 198s and for the 199s-2s, as well as when 27 is excluded from the LIY composite. S2 Study area The study area north of the Siberian coast includes the major part of the area that is vulnerable to ice variability (Fig. 1 of the main text). However the conclusions that longwave radiation plus turbulent fluxes during spring are important for initiating the ice anomaly, and that the shortwave radiation acts as an amplifying feedback are valid not only for this domain, but for most of the Arctic area (Supplementary Fig. S3). Close to the ice edge at the Atlantic side, however, in an area comprising the Greenland, Norwegian and Barents Sea as well as the Nansen Basin (Supplementary Fig. S2 blue area) others than the aforementioned processes seem to be important for the development of sea ice during the LIYs. In this region (2W to 1E; 74 to 84N) the sea-ice concentration is anomalously low already during winter. This results in a negative anomaly of net longwave radiation plus turbulent fluxes, indicating that more latent and sensible heat is transported from the open water areas to the cold winter atmosphere. In June to October the anomaly of net longwave radiation plus turbulent fluxes becomes positive in this area, due to a positive anomaly of clouds (not shown). It is a matter of further investigations which processes are driving the sea-ice anomaly in this area. For the rest of the Arctic (Supplementary Fig. S2 red and yellow area) the conclusions emphasizing the importance of the thermodynamical processes mentioned above seem to hold. Even though the anomalies are smaller for the larger area, the overall evolution of the surface fluxes over the LIYs is similar to that for the study area north of East Siberia. S3 The ERA-Interim reanalysis The results in the main text are based on the ERA-Interim reanalysis data. The European Centre for Medium-Range Weather Forecast (ECMWF) released this data set in 29. It includes several improvements relative to the former ECMWF reanalyses, ERA-1 and ERA-4: a four dimensional varia Macmillan Publishers Limited. All rights reserved.

4 13 W 13 E 4 W 4 E Figure S2: Investigation areas across the Arctic. The light green area including the areas marked in red, yellow and blue represents the entire Arctic region (7 to 8N). Further displayed are the Barents/Greenland Sea domaine (2W to 1E and 74 to 84N; blue), the study area north of the Siberian coast (1E to 1W and 74 to 84N; red), and the Arctic region excluding the Barents/Greenland Sea (1E to 2W and 74 to 84N; yellow and red area). tional data assimilation system (4DVAR) was employed, the horizontal resolution increased to T2, the model physics improved, and satellite radiances were used more extensively (see Dee and Uppala, 29; Dee et al., 211, for further information on improvements). Particularly the usage of satellite radiances is important for the study of the Arctic region where other observational data are sparse. Sea ice which is a boundary field in ERA-Interim is taken from different data sets for different periods, this may potentially lead to inconsistencies, for instance in the surface fields. Several studies showed that the ERA-Interim reanalysis is one of the most credible reanalysis available when it comes to the representation of Arctic climate. Cox et al. (212) evaluated the downwelling longwave radiation fluxes from the ERA-Interim reanalysis, the NCEP-NCAR (National Center for Environmental Prediction National Center for Atmospheric Research) reanalysis, the NCEP-DOE R2 (Department of Energy Reanalysis 2) and the NCEP-NARR (North American Regional Reanalysis) against radiation measurements conducted at Eureka, Canada, and Barrow, Alaska. Even though all reanalyses, except NCEP-NARR, show good agreement in the frequency distribution of downwelling longwave radiation for all-sky and clear-sky conditions, fluxes from ERA-Interim exhibit the lowest bias and highest correlation compared to the observations. Also the seasonal cycle, the monthly correlations and the latitudinal dependence of the downwelling longwave radiation in ERA-Interim were found to agree reasonably well with observations (Shi et al., 21; Zygmuntowska et al., 212) Macmillan Publishers Limited. All rights reserved.

5 1 a) Low Ice Years study area whole Arctic whole Arctic except 2W to 1E 2W to 1E LWN+LH+SH SIC 1 1 b) study area whole Arctic whole Arctic except 2W to 1E 2W to 1E SWN SIC 1 Figure S3: Radiative and turbulent flux anomalies at the surface over different areas in the Arctic for LIYs. The black line shows the sea-ice concentration over the entire Arctic region (7 to 8N; solid, thin), the study area north of the Siberian coast (1E to 1W and 74 to 84N; solid thick), the Arctic region excluding the Barents/Greenland Sea (1E to 2W and 74 to 84N; dashed) and the Barents/Greenland Sea only (2W to 1E and 74 to 84N; dotted-dashed; minimum value out of bound: 16 Wm 2 ). See Supplementary Fig. S2 for area definitions. a, displayed is the net longwave radiation plus the turbulent fluxes (latent and sensible; in red) and b, the net shortwave radiation (green). All time series are based on daily anomalies of LIYs and averaged over the areas and grid points over land are excluded. A 3-day running mean filter is applied to all time series. 213 Macmillan Publishers Limited. All rights reserved.

6 Jakobson et al. (212) compared air temperature, specific humidity and winds measured on a drifting ice station in the central Arctic Ocean with five reanalysis products, ERA-Interim, the Japanese JCDAS, the U.S. NCEP- CFSR, NCEP-DOE R2 and NASA-Merra, and found that even though all reanalyses include large errors, ERA-Interim performs the best in reproducing air temperature and wind speed. It has been noted, however, that ERA- Interim has some problems with near-surface fields such as the 2-m temperature and humidity (Jakobson et al., 212). The ERA-Interim surface-flux anomalies (Fig. 2 in the main text) are here compared to two other reanalysis data, from NCEP-DOE R2 (Kanamitsu et al., 22) and from JRA-2 (Onogi et al., 27). Daily estimates for the radiative fluxes from NCEP-DOE R2 are available on a T62 Gaussian grid corresponding to a resolution, whereas the JRA-2 data are obtained with a (T19) resolution and from 6-hourly output. The data are averaged over the study area (see Fig. 1 of the main text). The overall pattern of the annual evolution of the surface fluxes for JRA-2 and NCEP-DOE R2 is similar to that obtained from ERA-Interim (Supplementary Fig. S4 and S). All reanalyses show that during LIYs, the anomaly of the net longwave radiation plus turbulent fluxes is positive in late spring when the ice melt is initiated (Supplementary Fig. S4a and Sa). The net shortwave radiation does not contribute to the energy surplus at the surface during this period, but becomes positive from late May which enhances the ice melt over the summer. The anomalies from JRA-2 and ERA-Interim mainly differ with respect to their magnitudes, except for a short period during summer (June to mid July) when the anomalies of net longwave radiation plus turbulent fluxes become negative in JRA-2. In this period more solar radiation and less longwave radiation reaches the surface in JRA-2, which is associated with a negative anomaly of clouds (not shown). In NCEP-DOE R2 the anomaly of the sensible heat flux during late winter and early spring (February-June: 1.79 Wm 2 on average) is significant larger than in the other two reanalyses (JRA-2:.11 Wm 2 ; ERA-Interim: -.13 Wm 2 ). As a result, the anomaly of net longwave radiation plus turbulent fluxes stays positive throughout the whole winter in NCEP-DOE R2. In summary, the JRA-2 and NCEP-DOE R2 are consistent with ERA- Interim when it comes to the major aspects of the annual evolution of the surface fluxes during LIYs Macmillan Publishers Limited. All rights reserved.

7 1 a) Low Ice Years LWN+LH+SH SWN SIC 1 1 b) SIC LWSD SWSD SLH SSH 1 Figure S4: Radiative and turbulent flux anomalies at the surface for LIYs from JRA-2. The black line shows the sea-ice concentration (ERA-Interim reanalysis). a, displayed is the net longwave radiation plus the turbulent fluxes (latent and sensible; red) and the net shortwave radiation (green). b, the radiative fluxes are split into their components, but only downwelling longwave (red) and shortwave (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 red box in Supplementary Fig. 2. A 3-day running-mean filter is applied to all time series Macmillan Publishers Limited. All rights reserved.

8 1 a) Low Ice Years LWN+LH+SH SWN SIC 1 1 b) SIC LWSD SWSD SLH SSH 1 Figure S: Radiative and turbulent flux anomalies at the surface for LIYs from NCEP-DOE R2. The black line shows the sea-ice concentration (ERA-Interim reanalysis). a, displayed is the net longwave radiation plus the turbulent fluxes (latent and sensible; in red) and the net shortwave radiation (green). b, the radiative fluxes are split into their components but only downwelling longwave (red) and shortwave (green) radiation are shown together with the latent (dark blue) and sensible (light blue) heat flux. All time series are based on daily anomalies of LIYs and averaged over the area indicated by the red box in Supplementary Fig. 2. A 3-day runningmean filter is applied to all time series Macmillan Publishers Limited. All rights reserved.

9 Supplementary Figures and Tables Figure S6: Surface radiation anomalies during late spring for LIYs. a, net and, b, downwelling longwave radiation anomalies averaged over April and May. c, and, d, as a and b, respectively, but for shortwave radiation. Gray lines encapsulate values that are significantly different from zero on the 9% level. The black box marks the study area Macmillan Publishers Limited. All rights reserved.

10 Figure S7: Anomalies of atmospheric water content and energy convergence during late spring for LIYs. As Supplementary Fig. S6 but for, a, the dry-static and, b, the latent energy-transport convergence as well as the, c, total column cloud water (liquid plus solid) and, d, the water vapor. A 7 running-mean filter over the longitude and latitude is applied to the energy-transport convergence Macmillan Publishers Limited. All rights reserved.

11 Figure S8: An illustration of the relation between ice-volume and ice-area reduction in a region where all thickness categories are equally represented, and where the average thickness is 1 m. a, assume that all thickness categories represent areas of the same size c. b, then the total ice area can be divided into equally sized portions which have their mean ice thicknesses within the dotted boxes. As the bin size of the thickness categories goes to zero, the dependence of the ice thickness on area can be described by a straight line (solid line). Now, say that the ice melts everywhere with a given amount (dashed line). Since the two triangles constituted by the straight lines and the axis are similar, the fractional change of the ice thickness where it is most thick is equal to the fractional change of the total ice area. Accordingly, if 13 cm of ice melts everywhere and the ice is up to 2 m thick, the resulting ice-area reduction is 13 cm/2 cm = 6. %. Model results indicate that the end-of-summer probability density of the ice thickness in general decreases with increasing thickness (Komuro and Suzuki, 212). This indicates that the above assumptions may lead to an underestimation of the ice-cover change for the given volume change Macmillan Publishers Limited. All rights reserved.

12 1 a) High Ice Years 1 LWN+LH+SH SWN SIC 1 b) 1 SIC LWSD SWSD LH SH Figure S9: Radiative and turbulent flux anomalies at the surface for years when the September ice extent is larger than usual (HIYs high ice years). The black line shows the sea-ice concentration. a, displayed is the net longwave radiation plus the turbulent fluxes (latent and sensible; in red) and the net shortwave radiation (green). b, the radiative fluxes are split into their components but only downwelling longwave (red) and shortwave (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 study area. A 3-day running mean filter is applied to all time series Macmillan Publishers Limited. All rights reserved.

13 1 a) LWN+LH+SH High Ice Years SIC 1 / Water anomaly in kgm 2 Water vapor [1 1 ] Cloud water [1 3 ] 1 b) LWN+LH+SH SIC 1 Conv. of latent energy Conv. of dry static energy Figure S1: Atmospheric water-content and energy-convergence anomalies for HIYs. As Supplementary Fig. 9 but displaying anomalies of sea-ice concentration (black) and net longwave radiation plus turbulent fluxes (red) together with, a, anomalies of total column cloud water (liquid plus solid; blue) and water vapor (green), as well as the, b, convergence of dry-static (green) and latent atmospheric energy transport (blue) Macmillan Publishers Limited. All rights reserved.

14 Table S1: Statistical significance of the anomalies of surface fluxes, atmospheric water contents and atmospheric energy transport convergence for HIYs. All fields are averaged over the area indicated by the black box in Supplementary Fig. S6 and over selected periods. Significance is based on a Monte Carlo approach (1, iterations). Gray shadings emphasize that values are statistically different from zero on a 9% significance level. Significance values are given by the nearest integer. Time Variable Average value Statistical period significance [%] April 1 SIC.1 % 64 to LWN+SH+LH -.4 Wm 2 1 May 31 LWN -.3 Wm 2 64 SWN -.19 Wm 2 3 LWSD Wm 2 87 SWSD 1.94 Wm 2 74 SSH -.11 Wm 2 31 SLH.24 Wm 2 94 Conv. of dry-static energy -2.7 Wm 2 77 Conv. of latent energy -.98 Wm 2 71 Total column water vapor -.18 kgm 2 9 Total column cloud water kgm 2 66 April 1 SIC 2.26% 68 to LWN+SH+LH Wm 2 93 Aug 31 LWN -.78 Wm 2 9 SWN Wm 2 99 LWSD -2.1 Wm 2 9 SWSD 1.3 Wm 2 79 SSH -.24 Wm 2 86 SLH -.31 Wm 2 8 Conv. of dry-static energy Wm 2 8 Conv. of latent energy -.73 Wm 2 67 Total column water vapor -.3 kgm Total column cloud water kgm 2 33 June 1 SIC 3.42 % 1 to LWN+SH+LH -1.9 Wm 2 91 Aug 31 LWN -.91 Wm 2 81 SWN Wm 2 1 LWSD -1.2 Wm 2 94 SWSD.9 Wm 2 64 SSH -.33 Wm 2 91 SLH -.67 Wm 2 92 Conv. of dry-static energy -.76 Wm 2 41 Conv. of latent energy -.6 Wm 2 39 Total column water vapor -.4 kgm 2 1 Total column cloud water kgm 2 12 SIC: Sea-ice concentration; LWSD/SWSD: Downwelling longwave/shortwave radiation; LWN/SWN: Net longwave/shortwave radiation; SH/LH: sensible/latent heat flux 213 Macmillan Publishers Limited. All rights reserved.

15 References 1. Graversen, R. G., T. Mauritsen, S. Drijfhout, M. Tjernström and S. Mårtensson. Warm winds from the Pacific caused extensive Arctic sea-ice melt in summer 27. Clim. Dyn. 36, (211). 2. Dee, D. P., and S. Uppala. Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Quart. J. R. Meteorol. Soc. 13, (29). 3. Dee, D.P. et al.. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 397 (211). 4. Cox, C. J., V. P. Walden and P. M. Rowe. A comparison of the atmospheric conditions at Eureka, Canada, and Barrow, Alaska (2628). J. Geophys. Res. 117, D1224 (212).. Shi, X., M. Wild, and D. P. Lettenmaier. Surface radiative fluxes over the pan- Arctic land region: Variability and trends. J. Geophys. Res. 11, D2214 (21). 6. Zygmuntowska, M., T. Mauritsen, J. Quaas and L. Kaleschke. Arctic Clouds and Surface Radiation a critical comparison of satellite retrievals and the ERA- Interim reanalysis. Atmos. Chem. Phys. 12, (212). 7. Jakobson, E., T. Vihma, T. Palo, L. Jakobson, H. Keernik, and J. Jaagus. Validation of atmospheric reanalyses over the central Arctic Ocean. Geophys. Res. Lett. 39, L182 (212). 8. Kanamitsu, M., W. Ebisuzaki, J. Woollen, S-K Yang, J.J. Hnilo, M. Fiorino, and G. L. Potter. NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Amer. Met. Soc. 83, (22). 9. Onogi, K. et al.. The JRA-2 Reanalysis. J. meteor. Soc. Japan 8, (27). 1. Komuro, Y. and T. Suzuki. Impact of sub grid-scale ice thickness distribution on hear flux on and through sea ice. Ocean Modelling in press, (212). Acknowledgements NCEP-DOE R2 data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, and JRA-2 data by the Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry. Both data sets are from the Research Data Archive (RDA) which is maintained by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR). NCAR is sponsored by the National Science Foundation (NSF). The original data are available from the RDA ( in dataset number 91. (NCEP-DOE R2) and 62. (JRA-2) Macmillan Publishers Limited. All rights reserved.