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    (a) The interannual variation of summer (JJA) sea ice area (black dotted line; km2) averaged over the Eurasian sector of the Arctic Ocean (70°–90°N, 0°–180°E) from 1991 to 2013. The thick blue line indicates the second-order polynomial fit for the decadal declining trend of sea ice area. DJF climatological-mean sea ice thickness (m) averaged (b) from 1979 to 1990 and (c) from 1991 to 2013. The sea ice thickness data are from PIOMAS.

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    The interannual variance (i.e., one standard deviation) of downward radiation (longwave plus shortwave; W m−2) at the surface during (a) December-February (DJF), (b) March–May (MAM), (c) June-August (JJA), and (d) September–November (SON). Blue contours indicate sea ice margins, defined as areas where sea ice concentration is 40%.

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    The anomalous (a) sea ice concentration (%) and (b) downward longwave radiation at the surface (shadings; W m−2) in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. In (b), statistically significant (p < 0.05) values of downward longwave radiation are hatched.

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    Changes in sea ice thickness (cm) by anomalous downward longwave radiation in DJF for the years of small summer (JJA) sea ice area. The thickness changes are calculated from the equation by Eisenman (2012), specifically for the thermodynamic thickness of (a) = 0.4 m and (b) = 0.7 m.

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    The anomalous (a) vertically integrated moisture flux (vectors; kg m s−1) with moisture flux convergence (shadings; W m−2), (b) column-integrated water vapor (shadings: g m−2) with tropospheric-mean (1000–400 hPa) temperature (contours; K), and (c) surface heat fluxes (sensible plus latent; W m−2; positive if upward) in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. In (a) and (b), absolute values larger than 3.0 W m−2 and 150 g m−2, respectively, are mostly significant (p < 0.05). In (c), statistically significant (p < 0.05) values of surface heat fluxes are hatched.

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    The interannual correlation between the anomalous winter (DJF) downward longwave radiation at the surface (abscissa; averaged between 70°–90°N and 0°–180°E) and the anomalous (a) spring (MAM) and (b) summer (JJA) sea ice area (ordinate; averaged between 70°–90°N and 0°–180°E). Corresponding correlation coefficient (r) is indicated in each panel. The absolute value of the correlation coefficient, |r|, greater than 0.42, is statistically significant (p < 0.05).

  • View in gallery

    GFDL CM3: the anomalous (a) sea ice concentration (%), (b) downward longwave radiation at the surface (shadings; W m−2), and (c) sea ice thickness change (cm) in the (left) preceding DJF, (middle) MAM and (right) concurrent JJA for the years of small summer (JJA) sea ice area. In (b) and (c), statistically significant (p < 0.05) values are hatched. This analysis is based on the period of 1980–2040.

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    GFDL CM3: anomalous sea ice thickness change (cm) associated with ice drift in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. Absolute values greater than 4 cm are mostly significant (p < 0.05).

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    GFDL CM3: the anomalous (a) SSTs (K) and (b) potential temperature (averaged between 0 and 300 m) in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. Both in (a) and (b), absolute values larger than 0.2 K are mostly significant (p < 0.05).

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    GFDL CM3: Time evolution of the anomalous ocean potential temperature (K) averaged over the (a) Barents Sea (70°–80°N, 20°–60°E), (b) Kara Sea (70°–80°N, 60°–100°E), and (c) Laptev Sea (70°–80°N, 100°–140°E), preceding the years of small summer (JJA) sea ice area. Abscissa is time (monthly mean) and ordinate is ocean depth (m).

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The Impact of Arctic Winter Infrared Radiation on Early Summer Sea Ice

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  • 1 Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea
  • | 2 School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea , and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
  • | 3 Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
  • | 4 School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
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Abstract

The Arctic summer sea ice area has been rapidly decreasing in recent decades. In addition to this trend, substantial interannual variability is present, as is highlighted by the recovery in sea ice area in 2013 following the record minimum in 2012. This interannual variability of the Arctic summer sea ice area has been attributed to the springtime weather disturbances. Here, by utilizing reanalysis- and satellite-based sea ice data, this study shows that summers with unusually small sea ice area are preceded by winters with anomalously strong downward longwave radiation over the Eurasian sector of the Arctic Ocean. This anomalous wintertime radiative forcing at the surface is up to 10–15 W m−2, which is about twice as strong than that during the spring. During the same winters, the poleward moisture and warm-air intrusions into the Eurasian sector of the Arctic Ocean are anomalously strong and the resulting moisture convergence field closely resembles positive anomalies in column-integrated water vapor and tropospheric temperature.

Climate model simulations support the above-mentioned findings and further show that the anomalously strong wintertime radiative forcing can decrease sea ice thickness over wide areas of the Arctic Ocean, especially over the Eurasian sector. During the winters preceding the anomalously small summer sea ice area, the upper ocean of the model is anomalously warm over the Barents Sea, indicating that the upper-ocean heat content contributes to winter sea ice thinning. Finally, mass divergence by ice drift in the preceding winter and spring contributes to the thinning of sea ice over the East Siberian and Chukchi Seas, where radiative forcing and upper-ocean heat content anomalies are relatively weak.

Corresponding author address: Hyo-Seok Park, Korea Institute of Geoscience and Mineral Resources, 124 Gwahak-ro 124, Yuseong-gu, 312 D1 Building, Daejeon 305-350, South Korea. E-mail: hspark@kigam.re.kr

Abstract

The Arctic summer sea ice area has been rapidly decreasing in recent decades. In addition to this trend, substantial interannual variability is present, as is highlighted by the recovery in sea ice area in 2013 following the record minimum in 2012. This interannual variability of the Arctic summer sea ice area has been attributed to the springtime weather disturbances. Here, by utilizing reanalysis- and satellite-based sea ice data, this study shows that summers with unusually small sea ice area are preceded by winters with anomalously strong downward longwave radiation over the Eurasian sector of the Arctic Ocean. This anomalous wintertime radiative forcing at the surface is up to 10–15 W m−2, which is about twice as strong than that during the spring. During the same winters, the poleward moisture and warm-air intrusions into the Eurasian sector of the Arctic Ocean are anomalously strong and the resulting moisture convergence field closely resembles positive anomalies in column-integrated water vapor and tropospheric temperature.

Climate model simulations support the above-mentioned findings and further show that the anomalously strong wintertime radiative forcing can decrease sea ice thickness over wide areas of the Arctic Ocean, especially over the Eurasian sector. During the winters preceding the anomalously small summer sea ice area, the upper ocean of the model is anomalously warm over the Barents Sea, indicating that the upper-ocean heat content contributes to winter sea ice thinning. Finally, mass divergence by ice drift in the preceding winter and spring contributes to the thinning of sea ice over the East Siberian and Chukchi Seas, where radiative forcing and upper-ocean heat content anomalies are relatively weak.

Corresponding author address: Hyo-Seok Park, Korea Institute of Geoscience and Mineral Resources, 124 Gwahak-ro 124, Yuseong-gu, 312 D1 Building, Daejeon 305-350, South Korea. E-mail: hspark@kigam.re.kr

1. Introduction

As the sunlight in high latitudes weakens in the fall, Arctic sea ice thickens and extends southward until it reaches its maximum extent in late February. This seasonal march shows large interannual variations presumably due to vigorous wintertime atmospheric and oceanic circulations. For example, the interannual variability of ocean heat flux convergence over the Barents Sea is large and is correlated with sea ice concentration in the winter (Årthun et al. 2012). Moreover, it has been known that changes in surface winds associated with atmospheric low-frequency variability [e.g., the Arctic Oscillation (AO)] can redistribute sea ice cover via wind-induced ice motion (Rigor et al. 2002; Rigor and Wallace 2004).

Surface wind changes are also accompanied by poleward heat and moisture flux into the Arctic, which can lead to an increase in downward longwave radiation (DLW) at the surface (Lee et al. 2011; Yoo et al. 2012; Skific and Francis 2013; Woods et al. 2013). An increase in precipitable water and cloud liquid water is particularly efficient in strengthening DLW in the Arctic winter (Ghatak and Miller 2013). Groves and Francis (2002) analyzed satellite and global reanalysis data and found notable poleward moisture transport from the midlatitudes. Because DLW is strongly coupled to surface temperature during the winter (Walsh and Chapman 1998; Lee et al. 2011; Yoo et al. 2012; Woods et al. 2013), it is possible that an increase in DLW can influence sea ice concentration. These dynamic and thermodynamic effects of atmospheric circulation might provide new initial conditions of Arctic sea ice at the beginning of each year. The altered sea ice condition in the winter may further affect the subsequent seasonal evolution of sea ice extent because the thinner the ice, the more susceptible it is to melting, regardless of whether the melting is caused by continued DLW forcing in the spring (Francis et al. 2005) and summer (Francis and Hunter 2006), by breaking of sea ice by storms (Kohout et al. 2014), or by ice-albedo feedback in the early summer (Eisenman et al. 2007).

It was postulated that DLW forcing, associated with a poleward moisture transport into the Arctic during the winter season, can impact sea ice during the subsequent summer season (Lee 2014). However, this hypothesis has not yet been tested. There are previous studies that focused on the effect of spring sea ice initial condition on the seasonal evolution of sea ice extent. An idealized modeling study indicates that late spring weather perturbations can help shrink sea ice extent by promoting ice-albedo feedback (Bitz et al. 1996). With observational analysis, Kapsch et al. (2013) showed that interannual variability in summer sea ice extent over the East Siberian and Laptev Seas is influenced by the springtime moisture flux into the region and the associated DLW. Perhaps because of these spring weather disturbances, Blanchard-Wrigglesworth et al. (2011) found that the decorrelation time scale of the observed sea ice extent time series is about 3 months when the lag-0 month is January. However, since the entire Arctic-mean sea ice extent was used in that study, over relatively sensitive regions of the Arctic Ocean, the decorrelation time scale could be longer than 3 months.

In this study, we show that the interannual variability of wintertime DLW is much stronger than that during the spring and that it plays a key role in the seasonal evolution of sea ice area over the Eurasian sector of the Arctic Ocean. While one may wonder if sea ice can be affected by DLW during the cold Arctic winter, sea ice thickness over the Eurasian sector of the Arctic Ocean is typically less than 1.2 m over the Barents–Kara Seas and less than 1.5 m over the Laptev Sea since 1991 (Fig. 1c). As we will show, such thin sea ice is more sensitive to DLW anomalies than thick sea ice in the central and Pacific sectors of the Arctic Ocean. The rest of this paper is organized as follows: Sections 2 and 3 describe the data sources and methods used in this study, respectively. In section 4, we evaluate the effect of wintertime radiative forcings on early summer sea ice using the reanalysis- and satellite-based sea ice concentration data. In section 5, we delve into the findings from section 4 using coupled global climate model (GCM) outputs, by evaluating the impact of DLW, sea ice drift, and upper-ocean heat content on the model’s sea ice thinning and sea ice concentration.

Fig. 1.
Fig. 1.

(a) The interannual variation of summer (JJA) sea ice area (black dotted line; km2) averaged over the Eurasian sector of the Arctic Ocean (70°–90°N, 0°–180°E) from 1991 to 2013. The thick blue line indicates the second-order polynomial fit for the decadal declining trend of sea ice area. DJF climatological-mean sea ice thickness (m) averaged (b) from 1979 to 1990 and (c) from 1991 to 2013. The sea ice thickness data are from PIOMAS.

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

2. Data

We use the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) dataset (Dee et al. 2011) for the surface radiation, surface heat fluxes (both sensible and latent), tropospheric temperature, vertically integrated moisture flux, and moisture flux convergence. The data are 6 hourly on 16 pressure levels between 1000 and 30 hPa, with a horizontal resolution of 1.5° × 1.5°. A recent study showed that surface radiation data of ERA-Interim compares reasonably well with in situ measurements (Zib et al. 2012). Arctic sea ice concentration data are from the 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. 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). This sea ice thickness data are used to estimate the response of sea ice thickness to anomalous DLW forcing (see section 3b).

In this study, the “sea-ice area” is defined as the total area covered by sea ice and this is calculated by multiplying the sea ice concentration by the area of each grid cell. This definition is slightly different from the “sea-ice extent,” which is generally defined as the area where the sea ice concentration is higher than 15%. The interannual correlation coefficient between the Arctic sea ice area and sea ice extent is higher than 0.9. Therefore, the results we present are not sensitive to the definition.

For the data analysis, we used data for the 23-yr period spanning from September 1991 to August 2013. The Arctic sea ice has been thinning since 1990 and the thinning trend has been particularly rapid since 2003 (Hansen et al. 2013; Renner et al. 2014). The wintertime [December–February (DJF)] sea ice thickness from PIOMAS before 1990 (Fig. 1b) and after 1990 (Fig. 1c) clearly shows that the Arctic sea ice has experienced substantial thinning. Over most of the Barents–Kara Seas, the mean thickness is less than 1.2 m during the last 23 years. The mean sea ice thickness over the Laptev and East Siberian Seas is thicker than over the Barents–Kara Seas, but it is still less than 1.5 and 1.8 m, respectively. While PIOMAS simulates the Arctic sea ice thickness within a reasonable range, the model is known to generally overestimate the thickness of measured sea ice thinner than 2 m (Johnson et al. 2012; Schweiger et al. 2011). Therefore, the actual sea ice thickness over the Eurasian sector (0°–180°E; Barents–Kara–Laptev–East Siberian Seas) is likely to be even thinner than what is shown in Fig. 1c. This raises the possibility that sea ice over the Eastern Hemisphere of the Arctic seas is susceptible to destruction by both mechanical and thermodynamic forcings.

As a way to test the idea gained from the observational analysis, we also examine the outputs from a coupled climate model that was developed at the Geophysical Fluid Dynamics Laboratory (GFDL). It is the Climate Model, version 3 (CM3; Donner et al. 2011) that has participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This model uses a finite-volume dynamical core with horizontal resolution of approximately 200 km. The sea ice component of CM3 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 snow and two ice, and five ice thickness categories. The Arctic sea ice thickness in CM2.1 is known to be thinner than that in other CMIP3 climate models, but this thin bias is substantially improved in CM3 (Griffies et al. 2011). Specifically, the pattern of ice thickness in CM3 is in good agreement with the observation, primarily because of an increased albedo (more realistic) and improvements in the simulation of Arctic sea level pressure by the atmospheric model (Griffies et al. 2011). In this study, we analyze transient twentieth–twenty-first-century simulations forced by historical and representative concentration pathway 4.5 (RCP4.5) forcing. The CM3 simulations archived for CMIP5 provide five ensemble members for historical simulations (from r1 to r5) and three ensemble members for RCP4.5 (r1, r3, and r5). Here, we examine all of the three ensemble members (r1, r3, and r5) that share both historical and RCP4.5 simulations. For the model outputs, we take advantage of the availability of the data over a longer time period: from the year 1980 to 2040 (61 yr).

3. Methods

To address the question of whether winter DLW and sea ice concentration are linked to summer sea ice area over the Eurasian sector of the Arctic Ocean, we utilize composite analyses with respect to the June–August (JJA) sea ice area anomalies over the Eurasian sector of the Arctic Ocean (from 70° to 90°N and from 0° to 180°E), where sea ice is relatively thin (Fig. 1c). To evaluate how DLW anomalies influence sea ice thickness, a toy model for sea ice thickness change is also used.

a. Composite analysis

The small sea ice area years are defined as those years with a below-0.5σ anomaly threshold, where σ is the standard deviation of the JJA-mean sea ice area, calculated using sea ice concentration over the Eurasian sector. Prior to calculating the standard deviation, the seasonal cycle of each variable is removed at each grid point; the seasonal cycle is defined as the monthly long-term average of 22 yr (from 1991–92 to 2012–13). Multidecadal trends are then removed from monthly mean data by fitting the data to a second-order polynomial. The DLW and surface heat fluxes over the Arctic also exhibit a slightly increasing trend and these trends are once again removed by the second-order polynomial fit. Figure 1a shows the interannual variability of JJA sea ice area over the Eurasian sector and the polynomial fit. The small sea ice area summers are found to occur in 1995, 2000, 2005, 2006, 2007, and 2012, whereas the large sea ice area summers take place in 1996, 1998, 2003, 2004, 2008, 2009, and 2013. A statistical significance test is performed for each seasonally averaged variable with a Monte Carlo method. Specifically, composites were performed 1000 times with randomly selected subsamples from the 22 seasonal-mean values. Based on the distribution of the resulting composites, 95% confidence levels were calculated. The same procedures are applied to the outputs from GFDL CM3. These procedures are applied to each of the three ensemble members before averaging.

b. A simple model for sea ice thickness change

To estimate the sea ice thickness change associated with DLW anomalies, we follow Eisenman (2012) and express the changes in sea ice thickness as a function of DLW,
e1
where is the sea ice thickness change, is the anomalous DLW (W m−2), is the density of sea ice, and is the latent heat of fusion for sea ice. The dimensionless quantity is a measure of efficiency: , where is the thermodynamic scale thickness, a measure of effectiveness in ice thickness changes (Eisenman 2012), and is the initial sea ice thickness. For this calculation, we assume that the ocean heat flux convergence is constant and that solar radiation is negligible. For , we used the climatological-mean sea ice thickness of PIOMAS, averaged from 1991 to 2013 (see Fig. 1c). As an example, for (Eisenman 2012), if then . This means that for 30-cm-thick ice, about 70% of the DLW energy is used for changing ice volume (as opposed to increasing surface temperature). The term γ represents the tendency that thin ice grows/melts more rapidly than thick ice and is estimated as 70 cm (Eisenman 2012), but the exact value for γ is dependent on the feedback effect associated with DLW. In the absence of feedback, γ can be estimated by linearizing the Stefan–Boltzmann constant near the freezing point (Thorndike 1992). In this case, γ = 44 cm, which is smaller than that of Eisenman (2012)

4. Results

In winter, the Arctic Ocean is mostly covered with sea ice, especially over the Pacific sector. Over the Atlantic sector, the relatively warm ocean current pushes sea ice edges farther north, maintaining the Norwegian Sea almost ice free (Bitz et al. 2005). Figure 2 illustrates that the interannual variability of surface radiative forcing is largest in winter, especially over the Atlantic Ocean and the western Eurasian sector of the Arctic Ocean. Here, downward radiative forcing at the surface (DRF) is defined as the sum of the downward longwave and shortwave radiations at the surface: DRF = DLW + DSW. The interannual variability of winter DRF, which is dominated by DLW in the Arctic, is almost 3 times stronger than that in other seasons. We suggest two possible reasons for this seasonality. First, because Arctic clouds have a relatively low amount of liquid water in winter, an increase in cloud liquid water can effectively strengthen DLW, whereas clouds are often saturated with liquid water in late spring and summer (Gorodetskaya and Tremblay 2008; Chen et al. 2006). Therefore, DLW would be most sensitive during the winter to sensible heat and moisture intrusions into the Arctic (Ghatak and Miller 2013). Second, planetary-scale waves (teleconnections) and synoptic-scale weather systems (e.g., blockings) that cause moisture fluctuations in the Arctic (Yoo et al. 2012; Woods et al. 2013) are most energetic during the winter.

Fig. 2.
Fig. 2.

The interannual variance (i.e., one standard deviation) of downward radiation (longwave plus shortwave; W m−2) at the surface during (a) December-February (DJF), (b) March–May (MAM), (c) June-August (JJA), and (d) September–November (SON). Blue contours indicate sea ice margins, defined as areas where sea ice concentration is 40%.

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

While the interannual variability of spring DLW is smaller than during the winter, a small increase in spring DLW can accelerate sea ice melting and can contribute to record minimum in the Arctic summer sea ice extent (Graversen et al. 2011; Kapsch et al. 2013). In the Arctic summer, DSW is known to be more sensitive to the changes in cloud cover than DLW (Kay et al. 2008). Presumably because of this DSW effect, the interannual variability of summer DRF is generally large around the Arctic Circle (Fig. 2c).

a. DLW and sea ice area anomalies

Figure 3 presents sea ice concentration and DLW anomalies in the preceding winter and spring for the years of anomalously low summer sea ice area over the Eurasian sector of the Arctic Ocean. It can be seen that the Barents Sea is the key region for the wintertime variability of sea ice concentration. This variability has been to some extent explained by wind-induced sea ice drift (Sorteberg and Kvingedal 2006; Strong et al. 2009; Tremblay 2001) and oceanic heat flux convergence (Årthun et al. 2012). However, as mentioned earlier, relatively little attention has been given to the role of winter DLW on Arctic sea ice.

Fig. 3.
Fig. 3.

The anomalous (a) sea ice concentration (%) and (b) downward longwave radiation at the surface (shadings; W m−2) in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. In (b), statistically significant (p < 0.05) values of downward longwave radiation are hatched.

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

In the absence of solar radiation, DLW plays a dominant role in the surface energy budget in Arctic winter (Gorodetskaya and Tremblay 2008). The left column of Fig. 3b shows that DLW is anomalously strong over the Eurasian sector of the Arctic Ocean in the winter preceding anomalously low sea ice area in the summer. The DLW anomaly averaged over DJF ranges up to 10–15 W m−2 and the spatial pattern of the DLW anomaly is similar to the interannual variability of winter DLW (Fig. 2a). Here it is important to note that winter is the season when sea ice tends to grow and the DLW anomaly cannot be interpreted as a steady forcing that continuously decreases sea ice thickness. Instead, the anomalously strong DLW is a transient forcing associated with teleconnections (Lee et al. 2011), affecting sea ice thickness on intraseasonal time scales.

To better quantify the effect of DLW on sea ice thickness, we used Eq. (1), an approximation for sea ice thickness change based on the toy model of Thorndike (1992). The calculated anomalous sea ice thickness associated with the anomalous DLW is presented in Fig. 4. While the spatial pattern of the anomalous sea ice thickness is generally consistent with the DLW anomalies, the response of sea ice thickness is particularly large along the Eurasian coasts of the Arctic Ocean, where the mean sea ice thickness is much thinner than the Canadian coasts. Over the Laptev Sea, there is almost no change in sea ice concentration in the preceding winter (Fig. 3a) despite the anomalously strong DLW forcing that ranges up to 10 W m−2 (Fig. 3b). Figure 4 suggests that the strong wintertime DLW can decrease ice thickness over the Laptev Sea by 5–10 cm, which can be an important precondition for the reduction of sea ice concentration in the subsequent seasons.

Fig. 4.
Fig. 4.

Changes in sea ice thickness (cm) by anomalous downward longwave radiation in DJF for the years of small summer (JJA) sea ice area. The thickness changes are calculated from the equation by Eisenman (2012), specifically for the thermodynamic thickness of (a) = 0.4 m and (b) = 0.7 m.

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

In the spring, the anomalous DLW forcing is up to 4–8 W m−2 (middle panel of Fig. 3b) and its maximum value is about 40% of the wintertime DLW anomalies. In addition, the areas covered by the positive DLW anomalies are smaller than that during the winter. As was stated earlier, Arctic winter is the season when the poleward moisture flux associated with the southwesterlies can effectively increase DLW. In the late spring and summer, the poleward moisture flux becomes much less efficient in strengthening DLW because Arctic clouds have a large amount of liquid water and the associated effect on radiative transfer is more or less saturated (Gorodetskaya and Tremblay 2008; Chen et al. 2006).

Although the spring DLW anomalies are less than half of the winter DLW anomalies, the spring DLW might still be important because of shortwave radiation that can accelerate the ice melting. Over the Pacific sector of the Arctic Ocean, where the DJF sea ice is generally thicker than 1.8 m, the spring DLW was suggested as a key factor leading to the summer sea ice minimum extent (Graversen et al. 2011; Persson 2012; Kapsch et al. 2013). Because of shortwave radiation in the spring, it is difficult to quantify the effect of DLW forcing on sea ice volume reduction using Eq. (1). In section 5, utilizing GCM outputs, we show that the winter DLW is as important as the spring DLW on the seasonal evolution of sea ice area over the Eurasian sector of the Arctic Ocean.

b. Poleward moisture flux

Is the anomalously strong DLW in the winters driven by energy transport outside of the Arctic? Fig. 5a shows that during the winters that precede the summers of anomalously low sea ice area, there is an enhanced moisture intrusion into the Arctic Ocean, especially over the Barents–Kara Seas. Here, the latent heat of vaporization Lυ (=2.26 × 106 J kg−1) is multiplied by the vertically integrated moisture flux convergence. This quantity corresponds to the latent heat release under the assumption that all of the water vapor condenses into liquid water. The enhanced moisture flux convergence leads to increased column-integrated water vapor (shadings in Fig. 5b). While the spatial pattern of moisture convergence and column-integrated water vapor is similar to the DLW anomaly, the energy of moisture convergence is about 2–8 W m−2, which is approximately half of the DLW anomaly. Additional sensible heat flux convergence can help explain the deficit. In fact, contours in Fig. 5b show that in the winter, the moisture flux convergence is accompanied by anomalously warm tropospheric air, up to 1.5 K. In addition, because DLW is strongly coupled to surface temperature (Walsh and Chapman 1998), the local feedback between surface temperature and DLW might also strengthen the DLW anomalies.

Fig. 5.
Fig. 5.

The anomalous (a) vertically integrated moisture flux (vectors; kg m s−1) with moisture flux convergence (shadings; W m−2), (b) column-integrated water vapor (shadings: g m−2) with tropospheric-mean (1000–400 hPa) temperature (contours; K), and (c) surface heat fluxes (sensible plus latent; W m−2; positive if upward) in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. In (a) and (b), absolute values larger than 3.0 W m−2 and 150 g m−2, respectively, are mostly significant (p < 0.05). In (c), statistically significant (p < 0.05) values of surface heat fluxes are hatched.

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

Surface heat fluxes, the sum of surface latent and sensible heat fluxes, are generally small throughout the seasons (Fig. 5c). Surface heat fluxes in the winters preceding anomalously small summer sea ice area are strongly downward over the open ocean of the Norwegian and western Barents Seas. It is worth noting that this is the region where DLW is anomalously strong. There is no evidence that the increase in DLW is driven by evaporation from the open Arctic Ocean for the time period examined here. Instead, the finding here raises the possibility that the increase in DLW contributes to the warming of the Norwegian and western Barents Seas.

c. Potential predictability

The abovementioned finding, that the interannual variability of Arctic surface radiative forcing is largest during winter, and that it can influence sea ice area in the following summer, provides potential seasonal predictability of Arctic sea ice area. The relationship between the winter DLW and summer sea ice area is not just limited to the selected years. Figure 6 shows that the relationship holds within the 1991–2013 time period at a correlation of −0.72 and −0.53 with the spring and summer sea ice area, respectively. Consistent with this relationship, a recent observational study indicates that the dramatic recovery of sea ice area in the summer of 2013 is associated with a decrease in Arctic cloud cover in the preceding winter (Liu and Key 2014). However, as shown in Liu and Key (2014), the Arctic winter DLW anomalies are accompanied by the wind-induced sea ice drift that can also contribute to the statistical relationship presented in Fig. 6. In section 5b, we seek to better quantify the role of wind-induced sea ice drift on ice thickness by examining a GCM output.

Fig. 6.
Fig. 6.

The interannual correlation between the anomalous winter (DJF) downward longwave radiation at the surface (abscissa; averaged between 70°–90°N and 0°–180°E) and the anomalous (a) spring (MAM) and (b) summer (JJA) sea ice area (ordinate; averaged between 70°–90°N and 0°–180°E). Corresponding correlation coefficient (r) is indicated in each panel. The absolute value of the correlation coefficient, |r|, greater than 0.42, is statistically significant (p < 0.05).

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

The relationship between winter DLW and summer sea ice area is much weaker over the Pacific and western Arctic Oceans (not shown). This is likely because the winter sea ice over the western Arctic Ocean is much thicker than the Eurasian sector (Fig. 1). Also, there are notable Arctic weather perturbations in the spring and summer over the western Arctic Ocean (e.g., the Chukchi and Beaufort Seas), such as the development of the Arctic dipole mode (Wu et al. 2006) and surface anticyclones (Ogi and Wallace 2012). Such weather activities may substantially disturb the winter sea ice initial conditions, such as the anomalous sea ice thickness and area. There are several statistical forecast models for the seasonal evolution of sea ice area (Barnett 1980; Drobot et al. 2006; Lindsay et al. 2008). This study proposes that the wintertime atmospheric circulation and the associated DLW can be used for improving the statistical forecast models of Arctic spring and early summer sea ice area. Because Arctic sea ice thickness is going to be thinner under future global warming scenarios, the winter weather activity is likely to be more influential on the seasonal evolution of sea ice.

5. Climate model simulations

The observational record for satellite-based sea ice concentration is somewhat short, especially because we examined the period since 1991 when the DJF sea ice thickness is notably thinner than previous decades. Utilizing long-term climate model simulations, we examine the relationship between the preceding winter DLW and the summer sea ice minimum more quantitatively. Because sea ice thickness is an output variable in the GFDL CM3, the effect of wintertime DLW on sea ice thickness can be quantified without relying on the estimation based on Eq. (1). In addition, the effect of wind-driven ice drift on the redistribution of sea ice thickness can be explicitly evaluated.

a. DLW and sea ice thickness

Consistent with observation and reanalysis data (Fig. 3), CM3 outputs indicate that the years of anomalously low JJA sea ice area are preceded by anomalously strong DLW over the Eurasian sector of the Arctic Ocean (Figs. 7a and 7b). The spatial pattern and the magnitude of simulated DLW anomalies in the winter and spring are in agreement with the reanalysis. Specifically, the DLW anomalies are strongest in the winter, up to 10–12 W m−2, and the spring DLW anomalies are about 50%–60% of the winter DLW anomalies. The seasonal evolution of sea ice concentration anomalies is also similar to the observation. The anomalously low sea ice concentration over the Barents–Kara Seas in the winter is followed by the substantial decrease in sea ice concentration over the Laptev and East Siberian Seas.

Fig. 7.
Fig. 7.

GFDL CM3: the anomalous (a) sea ice concentration (%), (b) downward longwave radiation at the surface (shadings; W m−2), and (c) sea ice thickness change (cm) in the (left) preceding DJF, (middle) MAM and (right) concurrent JJA for the years of small summer (JJA) sea ice area. In (b) and (c), statistically significant (p < 0.05) values are hatched. This analysis is based on the period of 1980–2040.

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

To estimate the sea ice thickness changes during these years, the sea ice thickness change anomaly, , a deviation from the climatological-mean thickness change rate, is calculated and integrated over the period of 3 months: . The resulting indicates that the sea ice thickness decreases over the Laptev and East Siberian Seas by about 10 cm (Fig. 7c), which is comparable to the estimated sea ice thickness changes based on reanalysis (Fig. 4). This result supports the main finding described in the previous section that anomalously strong DLW in the winter can decrease sea ice thickness without substantially changing sea ice concentration.

A surprising finding is that for the selected years, the winter sea ice thickness change is comparable to that of the spring and early summer (Fig. 7). In the Barents–Kara Seas, the greatest thinning occurs during the winter. The spring DLW anomalies are about 50%–60% of the winter DLW anomalies, but net sea ice thickness change is comparable to that of the winter, probably because of the shortwave radiation that is absent during the winter. It has been documented that the spring DLW anomalies can have a critical impact on the summer sea ice minimum (Kapsch et al. 2013). Our analysis suggests that the winter DLW anomaly is another influential factor for summer sea ice, especially over the Eurasian sector. In particular, both the reanalysis and CM3 indicate that the winter DLW anomaly covers much wider areas of Arctic Ocean than the spring DLW anomaly, possibly because of the relatively strong atmospheric circulation in the winter.

In the summer, the thinning is accelerated () over the Laptev and East Siberian Seas (red colors in the third column of Fig. 7c). However, there is no apparent longwave radiative forcing over these regions. Therefore, it is probable that shortwave radiation plays an important role for the thinning of sea ice. Over the Barents–Kara Seas, however, is positive (blue colors in the right column of Fig. 7c) even though the sea ice concentration in the same region is anomalously low. This CM3 model solution is difficult to explain and requires further investigation.

b. Wind-induced sea ice drift

Sea ice drift associated with surface winds and upper-ocean currents is an important factor that affects the mean sea ice thickness in a grid. Indeed, in the winter of 2012–13, sea ice thickness redistributed by ice drift over the Pacific sector of the Arctic Ocean was almost comparable to the thermal effect associated with cloud radiative forcing at the surface (Liu and Key 2014). The GFDL CM3 provides sea ice mass flux as an output variable. Utilizing this output, the time rate of change in thickness caused by ice drift can be estimated as , where and denote zonal and meridional ice drift velocity, respectively. Figure 8 shows that the years of anomalously low JJA sea ice area are preceded by anomalously large sea ice transport out of the East Siberian and Chukchi Seas in the winter and spring. The resulting thickness change ranges up to 10 cm, which is comparable to the total ice thickness changes (Fig. 7c). This indicates that mass divergence by ice drift is a factor for the thinning of sea ice over the East Siberian and Chukchi Seas, where positive DLW anomalies are relatively weak. However, in these regions the spatial pattern of the mass divergence by ice drift is localized in some areas, whereas the total ice thickness changes are nearly uniform. The positive DLW anomalies still play a nontrivial role in suppressing the growth of sea ice over the East Siberian and Chukchi Seas during the winter and spring, and the persistence of this thickness anomaly into summer can lead to reduced summer sea ice thickness and area.

Fig. 8.
Fig. 8.

GFDL CM3: anomalous sea ice thickness change (cm) associated with ice drift in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. Absolute values greater than 4 cm are mostly significant (p < 0.05).

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

Over the Barents Sea, where the positive DLW anomalies are most prominent, there are both mass flux divergence and convergence with similar magnitude. This result indicates that the seasonal-mean ice mass transport is not simply dependent on the strength of southerlies. Therefore, the relationship between the southerly wind speed and the sea ice concentration over the Barents Sea (e.g., Kwok et al. 2005; Sorteberg and Kvingedal 2006; Liptak and Strong 2014) may not hold for interannual time scales.

c. Upper-ocean temperature

The upper-ocean heat content is often suggested as the primary factor influencing the decadal variability of sea ice area, such as the rapid decline in the Arctic summer sea ice area during the last decade (Stroeve et al. 2007). The interannual variability of sea ice area is also to some extent correlated with the ocean heat flux convergence in a climate model (Årthun et al. 2012). The CM3 outputs indicate that the years of anomalously small sea ice area in JJA are preceded by higher sea surface temperature (SST) over the Barents Sea in the winter and spring (Fig. 9a). Similar to sea ice concentration anomalies, the warm SST anomalies over the Barents Sea in the winter progress farther east into the Laptev and East Siberian Seas in the following summer. The upper-ocean potential temperature anomalies, averaged from the surface to 300 m, coincide with the SST anomalies in the winter and spring, especially over the Barents Sea (Fig. 9b).

Fig. 9.
Fig. 9.

GFDL CM3: the anomalous (a) SSTs (K) and (b) potential temperature (averaged between 0 and 300 m) in the (left) preceding DJF, (middle) MAM, and (right) concurrent JJA for the years of small summer (JJA) sea ice area. Both in (a) and (b), absolute values larger than 0.2 K are mostly significant (p < 0.05).

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

The seasonal evolution of the vertical temperature profile shows that while the warm SST anomalies are associated with the increased upper-ocean heat content, the largest ocean heat anomaly occurs at the surface (Fig. 10a). During the winters that precede the low JJA sea ice area, the subsurface heat anomaly extends to the Kara Sea (Fig. 10b), but SST remains close to its mean value, and notable SST anomalies do not emerge until early summer. Similar seasonal development in SST anomalies can be seen in the Laptev Sea, but in this region there are no subsurface upper-ocean heat anomalies (Fig. 10c). In the Barents and Kara Seas, the relatively high upper-ocean heat content in DJF may affect the seasonal evolution of SSTs and help reduce sea ice. However, in the Laptev Sea, upper-ocean heat content anomalies are confined to the top 50 m (Fig. 10c) and to the coastal areas (Fig. 9b), suggesting that the warm surface water in the summer is an outcome of the decreased sea ice concentration.

Fig. 10.
Fig. 10.

GFDL CM3: Time evolution of the anomalous ocean potential temperature (K) averaged over the (a) Barents Sea (70°–80°N, 20°–60°E), (b) Kara Sea (70°–80°N, 60°–100°E), and (c) Laptev Sea (70°–80°N, 100°–140°E), preceding the years of small summer (JJA) sea ice area. Abscissa is time (monthly mean) and ordinate is ocean depth (m).

Citation: Journal of Climate 28, 15; 10.1175/JCLI-D-14-00773.1

6. Summary and discussion

The dark and cold Arctic winter has been regarded as a season when sea ice variability is constrained by the Arctic Ocean condition established during the preceding summer (Stroeve et al. 2012). In this study, we demonstrated that the winter can be a critical season affecting the subsequent summer’s Arctic sea ice thickness and area. In particular, relatively thin sea ice over the Eurasian sector of the Arctic Ocean is sensitive to winter DLW anomalies. In both observation-based data and climate model simulations, summers with anomalously low sea ice area are preceded by winters of significantly enhanced DLW. The DLW heating can thin winter sea ice by up to 10–20 cm, and its spatial pattern of anomalous DLW closely matches with the regions of sea ice thinning.

At least for the data examined here, and for the interannual time scale, there is no evidence that local evaporation substantially contributes to the DLW. In fact, over the western Barents Sea, where sea ice concentration is low (hence, more open water), the anomalously strong DLW coincides with downward surface heat flux anomalies. This indicates that the upper ocean is being warmed by the anomalous DLW and the suppression of surface evaporation. However, the climate model (CM3) shows that during the winters preceding the summers of anomalously low sea ice area, the upper-ocean (down to 300 m) heat content in the Barents and western Kara Seas are higher than average, suggesting that ocean heat flux convergence may also play an important role in warming surface waters in these regions. Consistent with previous studies, the model simulation also suggests that the wind-induced sea ice drift in the preceding winter and spring play a nontrivial role in leading to the small JJA sea ice area. Specifically, the wind-induced sea ice drift contributes to decreasing sea ice thickness over the Laptev and East Siberian Seas, where sea ice concentration is close to 100% in winter.

This study shows that an increase in wintertime DLW acts to thin sea ice over the Eurasian sector of the Arctic Ocean. The DLW increase is associated with anomalously warm tropospheric air and an enhanced poleward moisture flux that can increase Arctic cloud liquid water and cloud cover. The substantial reduction of wintertime sea ice thickness can affect the sea ice area in the following summer presumably because thin ice is more susceptible to breaking and melting. Therefore, we conclude that the significant correlation that we found between the winter DLW and the sea ice area during the subsequent summer is not a mere association but instead represents a process-based causality. Northern winter is the season when atmospheric circulations are most energetic. Recent studies consistently indicate that the Arctic winter DLW is associated with poleward sensible heat and moisture flux from the midlatitudes (Lee et al. 2011; Yoo et al. 2012; Woods et al. 2013). This study shows that understanding winter circulation can be critical for predicting the early summer sea ice area over the Eurasian sector of the Arctic Ocean and that it provides a new avenue to enhance our understanding of the Arctic sea ice variability. In future climate change, Arctic sea ice is projected to become thinner (Holland et al. 2006). Therefore, the winter weather perturbations and the associated DLW are likely to be even more influential in changing sea ice concentration over wider areas of the Arctic Ocean.

Acknowledgments

We thank three anonymous reviewers, whose insightful and constructive comments helped improve the manuscript. 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. SL is supported by Seoul National University and by U.S. National Science Foundation Grant AGS-1139970. YK is supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan through Grant-in-Aid for Young Scientists 15H05466, by the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research 25287120 and by the Japanese Ministry of Environment through the Environment Research and Technology Development Fund 2-1503. SWS and SWK are supported by the Polar Academic Program (PAP) of the Korea Polar Research Institute (KOPRI) and by the Korean Ministry of Environment through the Climate Change Correspondence Program.

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