1. Introduction
As the solar insolation in high latitudes rapidly weakens in late fall, sea ice over the Arctic Ocean gradually thickens and extends farther southward until it reaches its maximum extent in early March. The processes that drive sea ice variability during the Arctic winter have not received as much attention as those for the summer, when ice–albedo feedback is thought to play an important role. However, Arctic warming has been most rapid during the winter (Bekryaev et al. 2010). Since downward infrared radiation (IR) is an important warming agent in the Arctic (Walsh and Chapman 1998; Uttal et al. 2002; Winton 2006), it is natural to ask whether fluctuations in downward IR can induce changes in the sea ice. Wintertime sea ice fluctuations have generally been attributed to wind-induced ice motion (Fang and Wallace 1994; Rigor et al. 2002; Rigor and Wallace 2004; Kwok et al. 2005; Sorteberg and Kvingedal 2006; Liptak and Strong 2014) and temperature anomalies (Deser et al. 2000), but increased downward IR in Arctic winter was also proposed as a factor for changing sea ice thickness (Francis and Hunter 2006). In particular, recent studies have found that poleward moisture fluxes into the Arctic associated with anomalous wintertime large-scale circulations increase downward IR at the surface (Lee et al. 2011; Yoo et al. 2012a; Woods et al. 2013). While one may wonder if an increase in downward IR can reduce sea ice thickness during the Arctic winter, we will show with model calculations that the relatively thin sea ice over the Barents, Kara, and Laptev Seas (see Fig. S1 in the online supplemental material) readily decreases for typical positive downward IR anomalies.
In this study, we investigate the increase in wintertime downward IR and the associated sea ice melting (or suppression of sea ice growth). As we will show, Arctic downward IR fluctuates on a weekly time scale. We use the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI; Dee et al. 2011) for the horizontal moisture flux, downward IR, and surface heat fluxes. Arctic station-based observations indicate that reanalysis data capture the observed day-to-day variability of downward IR reasonably well (Morcrette 2002). To analyze the impact of winter downward IR, with minimal impact by solar radiation, data from December to early March are analyzed. The relationships among the moisture flux, downward IR, and sea ice are analyzed using lagged composite analyses where individual events are chosen based on the occurrence of anomalously strong downward IR anomalies averaged over the domain from 70° to 90°N (see section 2). To corroborate the physical relationship between downward IR and sea ice identified in our analysis, we also examine the output from a coupled climate model.
2. Data and methods
a. Data
The ERAI dataset is 6-hourly on 16 pressure levels between 1000 and 30 hPa, with a horizontal resolution of 1.5° × 1.5°. To cross-check our findings from the ERAI, we also analyzed data from the Japanese 25-year Reanalysis Project (JRA-25; Onogi et al. 2007) produced by the Japan Meteorological Agency and the Central Research Institute of Electric Power Industry, Japan. The JRA-25 global model has a spectral resolution of T106 and 40 vertical layers with the top at 0.4 hPa. Arctic sea ice concentration data are from the U.S. National Snow and Ice Data Center (NSIDC) where the NASA Team algorithm (Swift and Cavalieri 1985) was used. This algorithm estimates sea ice concentration from satellite-derived passive microwave brightness temperatures. These data are provided on a polar stereographic grid with 25 km × 25 km resolution. We regridded these data onto a regular 1.0° × 1.0° grid. The NSIDC sea ice concentration data are available every two days from the year 1979 to 1989, and then daily since 1990. Because we are focusing on daily variations of sea ice, we used the data that begin in 1990. For Arctic sea ice thickness, the coupled Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS; Zhang and Rothrock 2003) is used. PIOMAS consists of a 12-category thickness and enthalpy distribution sea ice model coupled with the Parallel Ocean Program (POP) ocean model (Smith et al. 2010).
To identify the signal of tropical convection, we use daily-mean outgoing longwave radiation (OLR) data from the National Oceanic and Atmospheric Administration (NOAA); satellite-based data are interpolated onto a 2.5° × 2.5° grid (Liebmann and Smith 1996). Both for the reanalysis and NSDIC sea ice concentration data, we used boreal winter (from 1 December to 10 March) data for the 23-yr period spanning December 1990–March 2012. Unlike the conventional definition of winter (December–February), our definition of Arctic winter extends to 10 March. This is mainly because Arctic sea ice cover reaches its maximum extent in early March, after which the intensifying solar radiation starts to rapidly melt the sea ice edge.
To test the ideas gained from the observational analysis, we also examined output from the coupled climate model GFDL CM3.0 (Donner et al. 2011) that was developed at the NOAA/Geophysical Fluid Dynamics Laboratory (GFDL) and has participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This model uses a finite-volume dynamical core with a horizontal resolution of approximately 200 km. The sea ice component of CM3.0 is the GFDL Sea Ice Simulator (SIS), which is the same sea ice model used in CM2.1. The SIS is a dynamical model with three vertical layers, one for snow and two for sea ice, and five ice thickness categories. In this study, we analyze a transient twentieth- to twenty-first-century simulation forced by historical and representative concentration pathway 4.5 (RCP4.5) forcing. Here, we examine the first ensemble member (r1). For the model output, we take advantage of the availability of the data over a longer time period: from the year 1990 to 2030.
b. Methods
1) Lagged composite analysis
Composite calculations are performed in order to investigate the impact of downward IR on the intraseasonal variability of sea ice concentration during the Arctic winter. First, the seasonal cycle is removed at each grid point; the seasonal cycle is defined as a 30-day moving average of the 23-yr mean for each calendar day. The deseasonalized downward IR is zonally and meridionally averaged from 70° to 90°N (cosine weighting is applied to each latitude) to obtain an Arctic-mean downward IR. This daily downward IR time series is then detrended for each calendar day (from day 1 to day 365). A downward IR event is defined as a time period when the downward IR value exceeds 1.0 standard deviation (σ) for three or more consecutive days. If the beginning of an event occurs within 7 days of the end of the preceding event, then the latter event is discarded. This procedure identifies 36 events during 1 December–10 March over the 23 years (1990–2012). Lag zero is defined as the onset day (i.e., the first day of the event). These lag-zero days are used to construct composites of all variables considered in this study. Prior to generating the composites, a 3-day moving average is applied to filter out noise associated with day-to-day fluctuations. The same procedure is also applied to the GFDL CM3.0 output to identify the effect of downward IR events on sea ice thickness changes.
2) A toy model
3. Results
Figure 1 (middle) shows that the Arctic downward IR strengthens rapidly over several days, and this is preceded by a poleward moisture flux (vectors in the left column) from both the Norwegian and Bering Seas, with the moisture flux in the former region being more pronounced. This relative timing suggests that anomalously strong downward IR may be caused by poleward moisture transport. The vertically integrated moisture flux convergence, multiplied by the latent heat of vaporization Lυ (=2.26 × 106 J kg−1), compares reasonably well with the downward IR in spatial structure if the former is compared with the latter with a 2-day lead time. Both the moisture flux convergence (multiplied by Lυ) and the downward IR range up to 40 W m−2. Because Arctic clouds have a relatively low amount of liquid water in winter, an increase in cloud liquid water can strengthen the downward IR (Gorodetskaya and Tremblay 2008; Chen et al. 2006). Therefore, poleward moisture fluxes can have a large impact on downward IR. Moreover, poleward moisture fluxes are accompanied by a poleward sensible heat flux (e.g., warm air advection from the south), further strengthening the downward IR (Skific and Francis 2013). Figure 2 shows that the relationship between the poleward moisture flux and downward IR is not just limited to the composite events, but also holds within the entire 1990–2012 wintertime period at a correlation of 0.53. The lag correlations demonstrate that the moisture flux leads the downward IR by 1–3 days.
Composites of the anomalous (left) moisture flux, (middle) downward IR, and (right) sea ice concentration for (from top to bottom) lag −4, −2, 0, and +4 days. In the left column, vectors show the vertically integrated moisture flux (kg m s−1) and the shading is the estimated latent heat release (W m−2). For the moisture flux convergence and downward IR, statistically significant (Student’s t test; p < 0.05) values are shaded. The black contours in the right column indicate the regions where the sea ice reduction is statistically significant (Student’s t test; p < 0.05).
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
Composites of the anomalous (left) moisture flux, (middle) downward IR, and (right) sea ice concentration for (from top to bottom) lag −4, −2, 0, and +4 days. In the left column, vectors show the vertically integrated moisture flux (kg m s−1) and the shading is the estimated latent heat release (W m−2). For the moisture flux convergence and downward IR, statistically significant (Student’s t test; p < 0.05) values are shaded. The black contours in the right column indicate the regions where the sea ice reduction is statistically significant (Student’s t test; p < 0.05).
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
Composites of the anomalous (left) moisture flux, (middle) downward IR, and (right) sea ice concentration for (from top to bottom) lag −4, −2, 0, and +4 days. In the left column, vectors show the vertically integrated moisture flux (kg m s−1) and the shading is the estimated latent heat release (W m−2). For the moisture flux convergence and downward IR, statistically significant (Student’s t test; p < 0.05) values are shaded. The black contours in the right column indicate the regions where the sea ice reduction is statistically significant (Student’s t test; p < 0.05).
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
(a) Scatterplot between the zonal-mean poleward moisture flux at the Arctic Circle (abscissa; averaged between 70° and 75°N) and the Arctic-mean downward IR (ordinate; averaged between 70° and 90°N), with the former leading the latter by 2 days. (b) Lag correlations between the poleward moisture flux and downward IR. Negative lags indicate moisture flux leading downward IR. Both for (a) and (b), the winter days (1 Dec–10 Mar) from 1990 to 2012 are used, and the deseasonalized daily poleward moisture flux and downward IR are smoothed with a 3-day moving average. (c),(d) As in (a),(b), but for JRA-25.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
(a) Scatterplot between the zonal-mean poleward moisture flux at the Arctic Circle (abscissa; averaged between 70° and 75°N) and the Arctic-mean downward IR (ordinate; averaged between 70° and 90°N), with the former leading the latter by 2 days. (b) Lag correlations between the poleward moisture flux and downward IR. Negative lags indicate moisture flux leading downward IR. Both for (a) and (b), the winter days (1 Dec–10 Mar) from 1990 to 2012 are used, and the deseasonalized daily poleward moisture flux and downward IR are smoothed with a 3-day moving average. (c),(d) As in (a),(b), but for JRA-25.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
(a) Scatterplot between the zonal-mean poleward moisture flux at the Arctic Circle (abscissa; averaged between 70° and 75°N) and the Arctic-mean downward IR (ordinate; averaged between 70° and 90°N), with the former leading the latter by 2 days. (b) Lag correlations between the poleward moisture flux and downward IR. Negative lags indicate moisture flux leading downward IR. Both for (a) and (b), the winter days (1 Dec–10 Mar) from 1990 to 2012 are used, and the deseasonalized daily poleward moisture flux and downward IR are smoothed with a 3-day moving average. (c),(d) As in (a),(b), but for JRA-25.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
Downward IR events are followed by anomalously low sea ice concentration (SIC), especially over the Greenland, Barents, and Kara Seas [Fig. 1 (right)]. Over a 6-day period, the SIC in these regions decreases by 10%, an amount comparable to or slightly smaller than one standard deviation of the corresponding daily SIC time series [Fig. S2 (left panel) in the online supplemental material]. This daily variation of SIC is only about 50%–60% of the interannual variability of SIC, which is defined as one standard deviation of the December through February (DJF)-mean SIC [Fig. S2 (right panel) in the online supplemental material]. However, if strong downward IR events occur multiple times in a single winter season, because of their cumulative effect the seasonal change would be larger than that caused by an individual event. Therefore, it is possible that downward IR events are an important contributor for interannual SIC variability.
We further explore the relationship between downward IR and SIC by calculating the sea ice thickness change caused by the observed downward IR anomaly. The changes in sea ice thickness associated with the anomalous downward IR [
(a) Changes in sea ice thickness (cm) associated with winter downward IR events, calculated with (1). (b) The sensitivity of sea ice thickness changes to the initial sea ice thickness for the cumulative downward IR forcing of 24 MJ m−2.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
(a) Changes in sea ice thickness (cm) associated with winter downward IR events, calculated with (1). (b) The sensitivity of sea ice thickness changes to the initial sea ice thickness for the cumulative downward IR forcing of 24 MJ m−2.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
(a) Changes in sea ice thickness (cm) associated with winter downward IR events, calculated with (1). (b) The sensitivity of sea ice thickness changes to the initial sea ice thickness for the cumulative downward IR forcing of 24 MJ m−2.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
A climate model simulation (see section 2a) supports the above observation-based estimation. Figure 4 presents model analysis that was constructed following the same procedure used to produce Fig. 1. Comparing these two figures, it can be seen that the simulated downward IR events are in good agreement with those from the reanalysis in spatial pattern and magnitude; the downward IR anomaly maximum is close to 40 W m−2, and occurs over the Barents–Kara Seas [Fig. 4 (left)]. At the end of these strong downward IR events, specifically at day +8, the simulated sea ice thins by 5–6 cm over a wide area of the Arctic Ocean, including the Barents–Kara, Greenland, and Chukchi Seas [Fig. 4 (middle)]. The magnitude of these model-simulated sea ice thickness changes is comparable to the observation-based sea ice thickness changes calculated from (1). Over the marginal sea ice zone, especially over the Barents Sea [Fig. 4 (right)], the simulated SIC pattern during the downward IR events resembles the sea ice thickness anomaly pattern, suggesting that sea ice thinning driven by downward IR anomalies contributes to the SIC decline. Toward the interior of the Arctic Ocean where sea ice is relatively thick, the thinning would have no consequence on SIC, and this is borne out in lag day +8 anomalies.
GFDL CM3.0: Composites of the anomalous (left) downward IR (W m−2), (middle) sea ice thickness (cm), and (right) sea ice concentration (%) for (from top to bottom) lag −2, 0, +2, and +8 days. The black contours in the middle and right columns indicate the regions where the sea ice changes are statistically significant (Student’s t test; p < 0.05).
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
GFDL CM3.0: Composites of the anomalous (left) downward IR (W m−2), (middle) sea ice thickness (cm), and (right) sea ice concentration (%) for (from top to bottom) lag −2, 0, +2, and +8 days. The black contours in the middle and right columns indicate the regions where the sea ice changes are statistically significant (Student’s t test; p < 0.05).
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
GFDL CM3.0: Composites of the anomalous (left) downward IR (W m−2), (middle) sea ice thickness (cm), and (right) sea ice concentration (%) for (from top to bottom) lag −2, 0, +2, and +8 days. The black contours in the middle and right columns indicate the regions where the sea ice changes are statistically significant (Student’s t test; p < 0.05).
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
Figure 5 shows the total thickness change (Fig. 5a) and the ice drift–induced thickness change (Fig. 5b). As can be seen, the total and ice drift–induced cumulative thickness changes remain similar until lag day 0 (first column). The time evolution of anomalous sea ice thickness averaged over the Atlantic sector (cf. the red and black curves in Fig. 5c) indicates that the rapid decrease in sea ice thickness during the early stage of the downward IR events (until day +2) is dictated by ice drift. The ice drift–induced thickness change then rapidly diminishes at about day +3 as the southerly winds weaken. However, sea ice thickness continues to decrease. Based on the results presented in Fig. 3a, it is likely that beyond day +3 the majority of the subsequent sea ice decline is driven by the increased downward IR. Over most of the Arctic Ocean, the total sea ice thickness steadily declines through to day +16. This contrasts with the ice drift–induced thickness change, which exhibits a dipole anomaly pattern. These differences are clearly seen when the total and ice drift–induced thickness changes are averaged over the Arctic Ocean (Fig. 5d). While sea ice thickness continuously decreases until day +16 (black curve), by this time the ice drift effect on sea ice thickness is small (red curve). These findings indicate that the ice drift is effective in decreasing sea ice thickness regionally, such as over the Atlantic sector. However, for the entire Arctic Ocean, during the downward IR events, the effect of thickness changes due to ice drift appears to be more limited.
GFDL CM3.0: Composites of the (a) anomalous sea ice thickness (cm) and (b) thickness changes by ice drift (cm) for (left) lag 0, (middle) lag +8, and (right) lag +16 days. Thickness changes (cm) by sea ice drift as a function of time, averaged over (c) the Atlantic sector (0°–60°E) and (d) the entire Arctic (0°–360°). The left and middle columns of (a) are identical to the second and fourth rows of Fig. 4.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
GFDL CM3.0: Composites of the (a) anomalous sea ice thickness (cm) and (b) thickness changes by ice drift (cm) for (left) lag 0, (middle) lag +8, and (right) lag +16 days. Thickness changes (cm) by sea ice drift as a function of time, averaged over (c) the Atlantic sector (0°–60°E) and (d) the entire Arctic (0°–360°). The left and middle columns of (a) are identical to the second and fourth rows of Fig. 4.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
GFDL CM3.0: Composites of the (a) anomalous sea ice thickness (cm) and (b) thickness changes by ice drift (cm) for (left) lag 0, (middle) lag +8, and (right) lag +16 days. Thickness changes (cm) by sea ice drift as a function of time, averaged over (c) the Atlantic sector (0°–60°E) and (d) the entire Arctic (0°–360°). The left and middle columns of (a) are identical to the second and fourth rows of Fig. 4.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
The above findings suggest that an increase in the poleward moisture flux during the winter, realized through an enhanced moisture flux convergence and downward IR on intraseasonal time scales, leads to a substantial reduction in Arctic sea ice thickness. Recent studies consistently indicate that Arctic warming is preceded by poleward propagating planetary-scale Rossby waves that are often triggered by tropical convection (Lee et al. 2011; Yoo et al. 2012b; Ding et al. 2014). Indeed, as Fig. 6 shows, the downward IR events are preceded several days earlier by enhanced convection [as indicated by negative anomalies in OLR] over the tropical Indian–western Pacific Ocean and a few days later by a Rossby wave train over the North Pacific and North America. This result is consistent with a modeling study (Yoo et al. 2012b) that shows a similar arching wave train in the transient solution of a global circulation model, when initialized with warm pool convective heating superimposed on a climatological DJF background flow. By lag day −3, a cyclonic circulation is firmly established over the Canadian Arctic Archipelago and Greenland. With the accompanying anticyclonic circulation to its east, the circulation can bring warm, moist air poleward toward the Barents and Kara Seas, as shown in Fig. 1. A recent observational study (Henderson et al. 2014) also found a linkage between tropical convection (associated with the MJO) and Arctic sea ice, which they attributed to wind-driven sea ice motion.
(left) Composites of the anomalous outgoing longwave radiation (W m−2) for (from top to bottom) lag −14, −10, −6, and −2 days. (right) Anomalous 250-hPa streamfunction (106 m2 s−1) for (from top to bottom) lag −11, −7, −3, and +1 days. Statistically significant (Student’s t test; p < 0.05) values are hatched.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
(left) Composites of the anomalous outgoing longwave radiation (W m−2) for (from top to bottom) lag −14, −10, −6, and −2 days. (right) Anomalous 250-hPa streamfunction (106 m2 s−1) for (from top to bottom) lag −11, −7, −3, and +1 days. Statistically significant (Student’s t test; p < 0.05) values are hatched.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
(left) Composites of the anomalous outgoing longwave radiation (W m−2) for (from top to bottom) lag −14, −10, −6, and −2 days. (right) Anomalous 250-hPa streamfunction (106 m2 s−1) for (from top to bottom) lag −11, −7, −3, and +1 days. Statistically significant (Student’s t test; p < 0.05) values are hatched.
Citation: Journal of Climate 28, 13; 10.1175/JCLI-D-15-0074.1
4. Discussion
As the thickness of the Arctic sea ice declines over the long term, (1) indicates that the downward IR forcing can be effective at thinning the sea ice. In future climate change, Arctic sea ice is projected to become even thinner (Holland et al. 2006). Therefore, the winter weather perturbations and the associated downward IR are likely to be even more influential in changing sea ice thickness over wider areas of the Arctic Ocean. Also downward IR events are accompanied by wind-driven sea ice motion and, as the thinning continues, this effect on sea ice variability is also expected to become more potent. In addition, since long-term changes in the frequency of intraseasonal time scale processes have been shown to account for interdecadal variability in the atmospheric circulation (Johnson et al. 2008; Lee and Feldstein 2013), it is possible that an enhanced understanding of processes such as those investigated in this study can help to improve our understanding of the interdecadal changes in sea ice.
Acknowledgments
We thank the editor and anonymous reviewers for their constructive comments. HSP is supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Knowledge Economy of Korea. SWS’s work is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A1006530). SL is supported by Seoul National University, South Korea. SBF is supported by NSF Grants AGS-1036858 and AGS-1401220, USA. YK is supported by the Japanese Ministry of Environment through the Environment Research and Technology Development Fund A-1201 and by the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research 25287120.
REFERENCES
Bekryaev, R. V., I. V. Polyakov, and V. A. Alexeev, 2010: Role of polar amplification in long-term surface air temperature variations and modern Arctic warming. J. Climate, 23, 3888–3906, doi:10.1175/2010JCLI3297.1.
Chen, Y., F. Aires, J. A. Francis, and J. R. Miller, 2006: Observed relationships between Arctic longwave cloud forcing and cloud parameters using a neural network. J. Climate, 19, 4087–4104, doi:10.1175/JCLI3839.1.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
Deser, C., J. E. Walsh, and M. S. Timlin, 2000: Arctic sea ice variability in the context of recent atmospheric circulation trends. J. Climate, 13, 617–633, doi:10.1175/1520-0442(2000)013<0617:ASIVIT>2.0.CO;2.
Ding, Q., J. M. Wallace, D. S. Battisti, E. J. Steig, A. E. Gallant, H.-J. Kim, and L. Geng, 2014: Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland. Nature, 509, 209–212, doi:10.1038/nature13260.
Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL Global Coupled Model CM3. J. Climate, 24, 3484–3519, doi:10.1175/2011JCLI3955.1.
Eisenman, I., 2012: Factors controlling the bifurcation structure of sea ice retreat. J. Geophys. Res., 117, D01111, doi:10.1029/2011JD016164.
Fang, Z., and J. M. Wallace, 1994: Arctic sea ice variability on a timescale of weeks: Its relation to atmospheric forcing. J. Climate, 7, 1897–1913, doi:10.1175/1520-0442(1994)007<1897:ASIVOA>2.0.CO;2.
Francis, J. A., and E. Hunter, 2006: New insight into the disappearing Arctic sea ice. Eos, Trans. Amer. Geophys. Union, 87, 509–511, doi:10.1029/2006EO460001.
Gorodetskaya, I. V., and L. B. Tremblay, 2008: Arctic cloud properties and radiative forcing from observations and their role in sea ice decline predicted by the NCAR CCSM3 model during the 21st century. Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications, Geophys. Monogr., Vol. 180, Amer. Geophys. Union, 213–268.
Hansen, E., S. Gerland, M. A. Granskog, O. Pavlova, A. H. H. Renner, J. Haapala, T. B. Løyning, and M. Tschudi, 2013: Thinning of Arctic sea ice observed in Fram Strait: 1990–2011. J. Geophys. Res. Oceans, 118, 5202–5221, doi:10.1002/jgrc.20393.
Henderson, G. R., B. S. Barrett, and D. M. Lafleur, 2014: Arctic sea ice and the Madden–Julian oscillation (MJO). Climate Dyn., 43, 2185–2196, doi:10.1007/s00382-013-2043-y.
Holland, M. M., C. M. Bitz, and B. Tremblay, 2006: Future abrupt reductions in the summer Arctic sea ice. Geophys. Res. Lett., 33, L23503, doi:10.1029/2006GL028024.
Johnson, N. C., S. B. Feldstein, and L. B. Tremblay, 2008: The continuum of Northern Hemisphere teleconnection patterns and a description of the NAO shift with the use of self-organizing maps. J. Climate, 21, 6354–6371, doi:10.1175/2008JCLI2380.1.
Kwok, R., W. Maslowski, and S. W. Laxon, 2005: On large outflows of Arctic sea ice into the Barents Sea. Geophys. Res. Lett., 32, L22503, doi:10.1029/2005GL024485.
Lee, S., and S. B. Feldstein, 2013: Detecting ozone- and greenhouse gas–driven wind trends with observational data. Science, 339, 563–567, doi:10.1126/science.1225154.
Lee, S., T. T. Gong, N. C. Johnson, S. B. Feldstein, and D. Pollard, 2011: On the possible link between tropical convection and the Northern Hemisphere Arctic surface air temperature change between 1958 and 2001. J. Climate, 24, 4350–4367, doi:10.1175/2011JCLI4003.1.
Liebmann, B., and C. a. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275–1277.
Liptak, J., and C. Strong, 2014: The winter atmospheric response to sea ice anomalies in the Barents Sea. J. Climate, 27, 914–924, doi:10.1175/JCLI-D-13-00186.1.
Morcrette, J.-J., 2002: The surface downward longwave radiation in the ECMWF forecast system. J. Climate, 15, 1875–1892, doi:10.1175/1520-0442(2002)015<1875:TSDLRI>2.0.CO;2.
Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85, 369–432, doi:10.2151/jmsj.85.369.
Renner, A. H. H., S. Gerland, C. Haas, G. Spreen, J. F. Beckers, E. Hansen, M. Nicolaus, and H. Goodwin, 2014: Evidence of Arctic sea ice thinning from direct observations. Geophys. Res. Lett., 41, 5029–5036, doi:10.1002/2014GL060369.
Rigor, I. G., and J. M. Wallace, 2004: Variations in the age of Arctic sea-ice and summer sea-ice extent. Geophys. Res. Lett., 31, L09401, doi:10.1029/2004GL019492.
Rigor, I. G., J. M. Wallace, and R. L. Colony, 2002: Response of sea ice to the Arctic Oscillation. J. Climate, 15, 2648–2663, doi:10.1175/1520-0442(2002)015<2648:ROSITT>2.0.CO;2.
Skific, N., and J. A. Francis, 2013: Drivers of projected change in Arctic moist static energy transport. J. Geophys. Res. Atmos., 118, 2748–2761, doi:10.1002/jgrd.50292.
Smith, R., and Coauthors, 2010: The Parallel Ocean Program (POP) reference manual. Los Alamos National Laboratory Tech. Rep. LAUR-10-01853, 140 pp.
Sorteberg, A., and B. Kvingedal, 2006: Atmospheric forcing on the Barents Sea winter ice extent. J. Climate, 19, 4772–4784, doi:10.1175/JCLI3885.1.
Swift, C. T., and D. J. Cavalieri, 1985: Passive microwave remote sensing for sea ice research. Eos, Trans. Amer. Geophys. Union, 66, 1210–1212, doi:10.1029/EO066i049p01210.
Thorndike, A. S., 1992: A toy model linking atmospheric thermal radiation and sea ice growth. J. Geophys. Res., 97, 9401–9410, doi:10.1029/92JC00695.
Uttal, T., and Coauthors, 2002: Surface heat budget of the Arctic Ocean. Bull. Amer. Meteor. Soc., 83, 255–276, doi:10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2.
Walsh, J. E., and W. L. Chapman, 1998: Arctic cloud–radiation–temperature associations in observational data and atmospheric reanalyses. J. Climate, 11, 3030–3045, doi:10.1175/1520-0442(1998)011<3030:ACRTAI>2.0.CO;2.
Winton, M., 2006: Amplified Arctic climate change: What does surface albedo feedback have to do with it? Geophys. Res. Lett., 33, L03701, doi:10.1029/2005GL025244.
Woods, C., R. Caballero, and G. Svensson, 2013: Large-scale circulation associated with moisture intrusions into the Arctic during winter. Geophys. Res. Lett., 40, 4717–4721, doi:10.1002/grl.50912.
Yoo, C., S. Lee, and S. B. Feldstein, 2012a: Mechanisms of extratropical surface air temperature change in response to the Madden–Julian oscillation. J. Climate, 25, 5777–5790, doi:10.1175/JCLI-D-11-00566.1.
Yoo, C., S. Lee, and S. B. Feldstein, 2012b: Arctic response to an MJO-like tropical heating in an idealized GCM. J. Atmos. Sci., 69, 2379–2393, doi:10.1175/JAS-D-11-0261.1.
Zhang, J., and D. A. Rothrock, 2003: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon. Wea. Rev., 131, 845–861, doi:10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2.
