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    (a) September Arctic sea ice area (106 km2) for the control simulation (blue) and the 800-yr perturbation simulation. (b) As in (a), but for the first 50 years of the eight realizations of the transient perturbation ensemble (thin black), the transient perturbation ensemble average (thick black), and a representative 50-yr segment (years 101–150 of the analysis period) for the control simulation (blue). (c) As in (a), but for the AMOC, defined as the maximum of the North Atlantic meridional streamfunction (Sv; 1 Sv ≡ 106 m3 s−1) between depths of 500 and 1800 m, for the control (blue) and perturbed (black) simulations.

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    (a) The climatological seasonal cycle of the control simulation (blue) and the perturbation simulation during the equilibrium period (solid black) and transient period (dashed black) for Arctic sea ice area (106 km2). (b) As in (a), but for Arctic-averaged thickness of sea ice that is present (m).

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    (a) The climatological seasonal cycle of the equilibrium response for the net surface sensible, latent longwave, and shortwave (multiplied by 0.1) heat flux (W m−2) averaged over the Arctic Ocean (65°–90°N and less than 50% land). (b) As in (a), but for temperature response averaged over the Arctic Ocean, North America, and Eurasia (°C). Only latitudes between 30° and 60°N were used for the spatial averages over North America and Eurasia.

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    (a) The zonal average equilibrium response during SON for temperature (°C). (b) As in (a), but for zonal wind (m s−1).

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    Probability density functions for the control (blue) and equilibrium perturbation (black) simulations of daily averaged SON 2-m temperature anomalies (°C) for a single grid cell over (a) the Arctic Ocean (75°N,175°W), (b) the Arctic coast (69°N, 134°W), and (c) the continental interior (50°N, 110°W). The climatological temperature was subtracted from each day to remove the mean warming response.

  • View in gallery

    The equilibrium response of SON 2-m temperature standard deviation (°C) for (a) interannual and (b) subseasonal time scales.

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    The equilibrium response of meridional SON 2-m temperature gradient [°C (103 km)−1].

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    Seasonal cycle of the equilibrium response in standard deviation of (a) 2-m temperature (°C), (b) SLP (hPa), and (c) Z500 (m) over the Arctic Ocean (blue), North America (green), and Eurasia (red). The standard deviation is calculated at each grid point before averaging.

  • View in gallery

    The equilibrium response in planetary wave amplitude (% change) in all four seasons and the first 10 wavenumbers, as well as the total amplitude (T) for (left) AM and (right) AZ. The isopleths used for AM are 5400, 5500, 5700, and 5600 m for winter (JFM), spring (AMJ), summer (JAS), and fall (OND), respectively. The black dots indicate a 95% statistical significance using a Student’s t test on the seasonal averages of the daily amplitudes.

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    The power spectra for SON (a) Arctic Ocean 2-m temperature (65°–90°N), (c) land 2-m temperature (30°–60°N), and (e) 500-hPa geopotential height (30°–60°N) for the control simulation (blue) and the perturbed simulation (black). The spectra are displayed as the variance (°C2) as a function of period (days). (b),(d),(f) The corresponding percent change in power spectral variance between the control simulation and perturbed simulations are in.

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    The DJF 2-m temperature difference (°C) between the eight perturbation simulations of years 1–50 and the control integration.

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    As in Fig. 11, but for SLP (hPa).

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    The (left) transient and (right) equilibrium response for DJF (a),(b) 2-m temperature (°C) and (c),(d) SLP (hPa).

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The Transient and Equilibrium Climate Response to Rapid Summertime Sea Ice Loss in CCSM4

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  • 1 Department of Physics, University of Toronto, Toronto, Ontario, Canada
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Abstract

The impact that disappearing Arctic sea ice will have on the atmospheric circulation and weather variability remains uncertain. In this study, results are presented from a sea ice perturbation experiment using the coupled Community Climate System Model, version 4 (CCSM4). By decreasing the albedo of the sea ice, the impact of an ice-free summertime Arctic on the coupled ocean–atmosphere system is isolated in an idealized but energetically self-consistent way. The multicentury equilibrium response is examined, as well as the transient response in an initial condition ensemble. The perturbation drives pronounced year-round sea ice thinning, Arctic warming, Arctic amplification, and moderate global warming. Even in the almost complete absence of summertime sea ice, the atmospheric general circulation response is very weak and the transient response is small compared to the internal variability. Surface temperature variability is reduced on all time scales over most of the middle and high latitudes with a 50% reduction in the standard deviation of temperature over the Arctic Ocean. The reduction is attributed to decreased temperature gradients and increased maritime influence once the sea ice melts. This reduced variability extends weakly into the variability of the midlatitude and free tropospheric geopotential height (less than 10% reduction in the standard deviation). Consistently, eddy geopotential height variability is found to decrease while geopotential isopleth meandering, which reflects Arctic amplified warming, increases moderately. The sign of these changes is consistent with recent observations, but the size of these changes is relatively small.

Corresponding author address: Russell Blackport, Department of Physics, University of Toronto, 60 St George St., Toronto, ON M5A 1A7, Canada. E-mail: blackport@atmosp.physics.utoronto.ca

Abstract

The impact that disappearing Arctic sea ice will have on the atmospheric circulation and weather variability remains uncertain. In this study, results are presented from a sea ice perturbation experiment using the coupled Community Climate System Model, version 4 (CCSM4). By decreasing the albedo of the sea ice, the impact of an ice-free summertime Arctic on the coupled ocean–atmosphere system is isolated in an idealized but energetically self-consistent way. The multicentury equilibrium response is examined, as well as the transient response in an initial condition ensemble. The perturbation drives pronounced year-round sea ice thinning, Arctic warming, Arctic amplification, and moderate global warming. Even in the almost complete absence of summertime sea ice, the atmospheric general circulation response is very weak and the transient response is small compared to the internal variability. Surface temperature variability is reduced on all time scales over most of the middle and high latitudes with a 50% reduction in the standard deviation of temperature over the Arctic Ocean. The reduction is attributed to decreased temperature gradients and increased maritime influence once the sea ice melts. This reduced variability extends weakly into the variability of the midlatitude and free tropospheric geopotential height (less than 10% reduction in the standard deviation). Consistently, eddy geopotential height variability is found to decrease while geopotential isopleth meandering, which reflects Arctic amplified warming, increases moderately. The sign of these changes is consistent with recent observations, but the size of these changes is relatively small.

Corresponding author address: Russell Blackport, Department of Physics, University of Toronto, 60 St George St., Toronto, ON M5A 1A7, Canada. E-mail: blackport@atmosp.physics.utoronto.ca

1. Introduction

To isolate the role that observed and projected anthropogenic Arctic sea ice loss (Stroeve et al. 2012; Overland and Wang 2013; Liu et al. 2013; Snape and Forster 2014) has on global climate and circulation, several studies have carried out forced climate simulations that capture the surface perturbations associated with ice melt, sea ice extremes, or observed trends (e.g., Magnusdottir et al. 2004; Deser et al. 2004, 2010; Screen et al. 2014; Peings and Magnusdottir 2014; Kim et al. 2014). To probe circulation responses, an atmospheric general circulation model (AGCM) is typically forced with altered sea ice concentration (SIC) and sea surface temperature (SST). This drives an atmospheric response that consists of a thermally direct local component and a teleconnected indirect component that is less predictable and robust (Deser et al. 2004). Common local responses to sea ice loss include warming and an increase of sensible and latent heat into the atmosphere (e.g., Deser et al. 2004, 2010; Screen et al. 2014; Peings and Magnusdottir 2014). Several studies emphasize remote circulation changes that project onto modes of variability, particularly the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and northern annular mode (NAM). NAM responses to sea ice loss are not robust and can encompass shifts to either the negative or the positive phase of the mode, with the negative response being more common (Newson 1973; Alexander et al. 2004; Deser et al. 2004, 2010; Liu et al. 2012; Peings and Magnusdottir 2014; Screen et al. 2014). Other studies have focused on the atmospheric response to regionally confined sea ice anomalies. These can be used, for example, to connect sea ice loss in the Barents and Kara Seas to a wintertime cold temperature response in Eurasia in both observations and models (Honda et al. 2009; Petoukhov and Semenov 2010; Mori et al. 2014; Feldstein and Lee 2014; Nakamura et al. 2015; Kug et al. 2015). Whether this connection is observed is complicated by a lack of statistically significant signals (Hopsch et al. 2012) and by issues about causality (King et al. 2015; Sorokina et al. 2015). In addition, other modeling studies do not show this connection (e.g., Deser et al. 2010; Screen et al. 2013, 2014).

The inconsistency among these studies could be caused by model differences or by differences in the magnitude and spatial extent of the sea ice and related SST forcing. These can lead to differences in the circulation response, since several dynamical processes might be simultaneously involved in linking sea ice loss and the associated warming with atmospheric circulation, including wave–mean flow interactions, changes to the stationary and quasi-stationary waves, and couplings of the waves with stratospheric conditions. In addition, internal climate variability combined with a relatively weak circulation response may lead to relationships that are not statistically significant if too few years are used in model experiments (Screen et al. 2014). This lack of agreement, as well as a relatively short observational record, means that proposed impacts of Arctic change, including impacts on storm track variability, the jet stream, and weather extremes (e.g., Francis and Vavrus 2012, 2015; Barnes 2013; Screen and Simmonds 2013; Cohen et al. 2014; Screen 2014; Coumou et al. 2014, 2015; Schneider et al. 2015; Barnes and Screen 2015), are challenging to assess theoretically.

In this report we examine robust aspects of the climate system’s adjustment to rapid sea ice loss in a relatively simple perturbation framework. We use a coupled ocean–atmosphere–sea ice–land model that allows all components of the physical climate system to adjust to sea ice perturbations in an energetically self-consistent way. In our experiment, rapid sea ice loss is forced by suddenly reducing sea ice albedo. This method is similar to that of Scinocca et al. (2009), whose focus was on the stratospheric ozone response to sea ice melt. As in Deser et al. (2015), in this coupled model setup, the direct radiative response first drives sea ice loss and Arctic surface warming, which then spreads through adjustment of ocean temperatures, in the presence of water vapor and other feedbacks, into the midlatitudes and tropics. Deser et al.’s approach complements ours: they study the coupled response to longwave radiation–forced sea ice loss associated with greenhouse forcing. Besides driving sea ice loss in summer, this maintains the projected anthropogenic global warming effect over the Arctic, leading to strong sea ice losses from longwave forcing all year. Our study attempts to isolate the forcing more precisely in order to answer the climate dynamical question of what might happen to the climate system if summertime sea ice were lost in the Arctic in isolation from anthropogenic forcing. Because our forcing is relatively weak compared to Deser et al. (2015), we need multicentury simulations and sampling to obtain statistically robust results. We also perform a number of shorter perturbation simulations to examine the initial adjustment due to sea ice loss, which allows us to analyze the climate response to rapid sea ice loss in the presence of internal variability. Another similar study is that of Cvijanovic and Caldeira (2015), who compared the CO2-induced response with active and suppressed sea ice. Our simulations use a simpler approach, as we are looking at only the sea ice response without CO2-induced warming. We also use a full dynamical ocean model instead of a slab ocean model. We describe the response, the robustness and variability of the response, and the response of atmospheric variability itself, to a sea ice perturbation.

We organize the paper as follows: in section 2, we describe the model and the experiment design. In section 3, we present an overview of the equilibrium response to sea ice loss, the response of atmospheric variability, and the transient adjustment to equilibrium. We summarize and discuss our results in section 4.

2. Experiment design

We use the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 4 (CCSM4) (Gent et al. 2011), configured as part of NCAR’s contribution to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The atmospheric component is the Community Atmospheric Model, version 4 (CAM4), with a nominal horizontal resolution of approximately 1° and a vertical grid of 26 levels up to 3 hPa. The ocean model is the Parallel Ocean Program, version 2 (POP2), which also has a nominal 1° horizontal resolution horizontal resolution and 60 vertical levels. The land and sea ice model are the Community Land Model, version 4 (CLM4), and the Community Ice Code, version 4 (CICE4), which use the same grids as the atmosphere and ocean respectively.

We carry out a control simulation of CCSM4 that uses constant, year 2000 levels of greenhouse gases and aerosol forcing. To obtain this simulation, we branch off of a CMIP5 historical simulation at year 2000, allow the resulting simulation to drift to a new equilibrium over the subsequent 270 years, and integrate the model for another 680 years to use as an analysis period. To drive sea ice loss for our perturbation experiments, we instantaneously alter three parameters in the sea ice model code (r_ice = −6.0, r_snw = −6.0, and r_pnd = −6.0); these changes reduce the albedo of the sea ice, snow on top of the sea ice, and the ice that forms on top of melt ponds. The exact amount that the albedo is reduced depends on the time of year and location, but on average there is a reduction of approximately 20%–50% throughout the year (not shown). These changes increase the amount of shortwave radiation absorbed, thus directly driving ice and snow-on-ice melt (see Fig. 3 for change of shortwave flux into the ocean). This change is applied globally and thus impacts both Antarctic and Arctic sea ice; the focus of this study will be on the Northern Hemisphere extratropical response, which presumably is dominated by the Arctic sea ice loss perturbation; the Southern Hemisphere response will be the subject of a separate study. We note that in Deser et al. (2015) the Southern Hemisphere middle-to-high-latitude response to Arctic sea ice loss was relatively weak even though the Arctic forcing was generally stronger than ours; thus, we expect that the Northern Hemisphere middle-to-high-latitude response discussed here will be fairly independent of Antarctic forcing.

We perform several perturbation realizations that are branched off the control. They are initialized 50 years apart to ensure statistical independence, but are otherwise identical. The first perturbation experiment is extended to 800 years to examine the equilibrium response. This length allows long-term oceanic adjustment to the change in sea ice (section 3) to take place. We also examine the transient adjustment of the first 50 years of seven additional realizations, for a total of an eight-member 50-yr ensemble, to the instantaneous change in sea ice albedo. To compare the sea ice albedo response to a standard greenhouse warming scenario, we also include in this study a brief comparison of these results with the ensemble mean of a five-member ensemble of the representative concentration pathway 8.5 (RCP8.5) scenario of CCSM4 (Meehl et al. 2012; Vavrus et al. 2012), which is available online at http://www.cesm.ucar.edu/experiments/.

3. Results

a. Overview of the response to sea ice loss

Figure 1a shows the time series of September Arctic sea ice area (SIA) for the control and the 800-yr sea ice perturbation simulation. We use the SIA, which is the total area covered by sea ice (i.e., the area of the grid cells weighted by the SIC), instead of the sea ice extent (SIE), which is the total area with greater the 15% SIC. The SIA in the year 2000 forced control simulation averages just under 4 × 106 km2, which is lower than observed in every year up to and including 2006, and is greater than observed every year since 2007.There is less sea ice in our control simulation than in year 2000 observations, which can in part be attributed to the adjustment to radiative equilibrium under constant year 2000 forcing in the simulation (adjustment not shown). For comparison, in the projected greenhouse warming of the CCSM4 RCP8.5 ensemble mean (see section 2), annual and September–November (SON) mean SIA is closest to our equilibrated control simulation in the 10-yr period 2032–41. The September SIA of the low albedo simulation equilibrates at just under 0.5 × 106 km2, which CMIP5 model projections suggest will happen sometime in the middle of this century (e.g., Snape and Forster 2014). In the case of the CCSM4 RCP8.5 scenario ensemble, ice-free conditions occur later than the multimodel mean with SIA reaching below 0.5 × 106 km2 in the 10-yr period 2072–81, whereas comparable SIA in SON and the annual mean occurs in the 10-yr period 2067–76. Thus our sea ice albedo perturbation simulations drive the approximate equivalent of three to four decades of summertime sea ice loss under ongoing projected anthropogenic greenhouse warming.

Fig. 1.
Fig. 1.

(a) September Arctic sea ice area (106 km2) for the control simulation (blue) and the 800-yr perturbation simulation. (b) As in (a), but for the first 50 years of the eight realizations of the transient perturbation ensemble (thin black), the transient perturbation ensemble average (thick black), and a representative 50-yr segment (years 101–150 of the analysis period) for the control simulation (blue). (c) As in (a), but for the AMOC, defined as the maximum of the North Atlantic meridional streamfunction (Sv; 1 Sv ≡ 106 m3 s−1) between depths of 500 and 1800 m, for the control (blue) and perturbed (black) simulations.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

The time series of the September SIA for the first 50 years of the eight perturbation simulations, along with the ensemble average and a sample of 50 years from the control simulation, are shown in Fig. 1b. In each of the eight perturbation simulations, most of the ice melts within the first few years after the change in albedo is made, with the ensemble mean SIA dropping below 1 × 106 km2 by year 3. But not all aspects of the adjustment are as rapid: the Atlantic meridional overturning circulation (AMOC) (Fig. 1c) undergoes several centennial-scale oscillations before recovering to a strength approaching that of the control simulation strength after 400 years. For this reason, years 400–800 of the perturbation simulation will be defined as an equilibrium period and years 1–50 of the eight perturbation simulations will be taken to represent the sea ice loss onset period.

Figure 2a shows the seasonal cycle of the Arctic SIA for the control simulation and the equilibrium and transient periods in the perturbation simulations. The largest changes occur during late summer and early fall due to the ice–albedo feedback, with only small changes occurring throughout the rest of the year. Unlike the changes in SIA, the sea ice thickness (SIT) (which in the case of the perturbation simulation corresponds to the smaller area of ice that has not melted) shows less seasonality with a relatively constant reduction in thickness (24%–38%) throughout the whole year (Fig. 2b). Thus SIT appears to respond strongly to the ocean–atmosphere warming throughout the year. Most of the response in SIA and SIT occurs within the first 50 years of the albedo change (cf. dashed black to solid black curves), although there is ongoing reduction in both quantities in the adjustment to equilibrium. Sea ice albedo perturbations, which act most strongly in the summer season, do not capture the year-round reductions in SIA expected under long-term greenhouse warming (Deser et al. 2015). Nevertheless, SIT is reduced throughout the entire year, suggesting that summertime sea ice loss can potentially strongly influence the atmosphere outside the summer season.

Fig. 2.
Fig. 2.

(a) The climatological seasonal cycle of the control simulation (blue) and the perturbation simulation during the equilibrium period (solid black) and transient period (dashed black) for Arctic sea ice area (106 km2). (b) As in (a), but for Arctic-averaged thickness of sea ice that is present (m).

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

The atmospheric response to sea ice loss is communicated through a response in surface temperature and surface energy flux. The seasonal cycle of the surface sensible heat, latent heat, and longwave and shortwave radiative flux response for the Arctic Ocean (65°–90°N and less than 50% land) are shown in Fig. 3a. A positive response corresponds to an increase in upward heat flux into the atmosphere while a negative response corresponds to a net increased heat flux into the ocean/ice. The response is dominated by the negative shortwave response (note that the line for the shortwave response in Fig. 3a is multiplied by 0.1), which peaks in spring and summer, and is a direct result of the albedo perturbation made in the sea ice model. The largest sensible and latent heat flux response, however, occurs in late fall and winter when the temperature difference between the ocean and the atmosphere above it is large. This is qualitatively similar to Fig. 3 in Screen et al. (2013), who forced two different atmospheric models with prescribed, observed sea ice losses. Thus important aspects of the energy budget response are consistent between prescribed sea ice integrations with observed sea ice loss and our forced albedo-driven response. The principal difference between the prescribed simulations and the forced albedo-driven response in the coupled simulations is that in the latter the increased heat flux continues longer into spring. This could be linked to reductions in SIT captured in the coupled model response: thinned ice allows heat to be conducted more easily between the ocean and atmosphere, even with little to no change in SIC. It also reflects the additional warming of the ocean (not shown) through a general warming response to sea ice loss.

Fig. 3.
Fig. 3.

(a) The climatological seasonal cycle of the equilibrium response for the net surface sensible, latent longwave, and shortwave (multiplied by 0.1) heat flux (W m−2) averaged over the Arctic Ocean (65°–90°N and less than 50% land). (b) As in (a), but for temperature response averaged over the Arctic Ocean, North America, and Eurasia (°C). Only latitudes between 30° and 60°N were used for the spatial averages over North America and Eurasia.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

The seasonal cycle of the 2-m temperature response averaged over the Arctic Ocean, North America, and Eurasia is shown in Fig. 3b. The Arctic Ocean temperature is averaged over the same region as Fig. 3a and for the North America and Eurasia temperatures only the land area between 30° and 60°N is used. The Arctic ocean temperature warming follows the sensible and latent heat flux response in Fig. 3a, with a peak in November of over 5°C along with an approximately 3°C response during spring. Over North America and Eurasia there are small increases in temperature throughout the whole year, with the largest changes occurring during November, December, and January over North America, which sees an increase of 1.0°–1.5°C averaged over the continent.

By construction, our experiment yields a highly Arctic amplified response with a weak global warming: the Arctic warms 2.4°C and the Northern Hemisphere warms 0.6°C in the annual mean. Arctic amplification expressed as the ratio of Arctic to Northern Hemisphere warming is 2.4/0.6 = 4.0. (For SON, the values are 3.5°C for the Arctic and 0.7°C for the Northern Hemisphere, and the ratio is 3.5/0.7 = 5.0 for the Arctic amplification.). The Arctic and Northern Hemisphere annual mean warming between the RCP8.5 periods 2067–78 and 2032–41 mentioned above are 3.4° and 1.6°C, respectively, with an Arctic amplification of 3.4/1.6 ≈ 2.1 (For SON, the values are 4.4°C for the Arctic and 1.8°C for the Northern Hemisphere, and the ratio is 4.4/1.8 ≈ 2.4 for the Arctic amplification.) Thus the sea ice albedo perturbation and resulting summertime sea ice loss drives approximately three-quarters of projected Arctic anthropogenic greenhouse warming over a 30–40-yr period. This warming is much more strongly confined to the Arctic, with stronger Arctic amplification than expected under greenhouse warming.

The zonal mean equilibrium temperature response for SON is shown as a latitude–pressure cross section in Fig. 4a. The largest temperature response is, as expected from the Arctic amplification ratio, confined to the Arctic. It is also confined to the lowest levels in the atmosphere, below 850 hPa, consistent with Screen et al. (2012). The lack of penetration of the response above 850 hPa leads to a relatively weak wind response (discussed next). The global warming effect of the sea ice loss is linked to a warming of the tropical upper troposphere of about 0.5°C, which is part of a tropospheric global warming response of 0.2°–0.6°C.

Fig. 4.
Fig. 4.

(a) The zonal average equilibrium response during SON for temperature (°C). (b) As in (a), but for zonal wind (m s−1).

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

The equilibrium zonal mean wind response (Fig. 4b) is very weak with only a small (less than 0.3 m s−1) decrease in the westerly winds between 60° and 80°N and a positive (westerly) response in the tropical upper troposphere and stratosphere. These changes are consistent with thermal wind balance, with the slight weakening of the westerlies reflecting very modest reductions in baroclinicity above 850 hPa. The zonal mean response in the ensemble mean of the transient phase (averaged up to year 50) is similar (not shown); that is, there is no pronounced difference in the zonal winds during the transient adjustment phase. Shorter segments of the integrations exhibit stronger zonal wind changes, especially in the Arctic stratosphere (not shown), but these average out as the length of the averaging period is increased to several centuries. The increase in the meridional temperature gradient in the upper troposphere at lower latitudes (10°–30°N) coincides with a positive anomaly in the zonal wind, which reflects the moderate global warming simultaneously induced by Arctic and Antarctic sea ice loss in these integrations.

The zonal mean wind and temperature for winter [December–February (DJF)] are similar to SON, with a smaller temperature response that is still confined to below 850 hPa, and a very small decrease in the zonally averaged westerly wind speeds (not shown). The size of the response in winter compared to fall that we see in our simulations is smaller than what it would be for future projections of anthropogenic greenhouse warming (Deser et al. 2015). Nevertheless, it is striking that the notable wintertime surface warming driven by our sea ice albedo perturbation is not accompanied by a stronger midlatitude circulation response.

It has been proposed that Arctic warming may lead to a weakened and more meandering jet stream, which in turn might be associated with increases in extreme weather events (e.g., Francis and Vavrus 2012). In this experiment, we have tried to isolate that part of Arctic warming associated with summertime sea ice loss and found a relatively weak jet stream response. Specifically, in this model, an almost complete induced loss of summer sea ice induces 3°–4°C of SON Arctic warming, which is comparable to several decades of global warming, and a reduction in midlatitude westerly strength of less than 0.3 m s−1 in SON. The sensitivity of the winds in the extratropical zonal mean jet stream is thus less than 0.1 m s−1 °C−1 of summertime Arctic surface warming. In other seasons, the response is of similar magnitude. In the next subsection, we investigate in more detail the character of the response of geopotential height meandering and other aspects of variability to sea ice loss. We at this point caution that the results found here may very well be model dependent, and will return to this point in the discussion (section 4).

b. Response of atmospheric variability to sea ice loss

We now investigate how the melting of sea ice in the Arctic affects variability and measures of jet meandering. We use 200 years of daily averaged data from the control integration and the equilibrium portion of the perturbation integration to examine the response of atmospheric variability. Beginning with 2-m temperature, we calculate a daily climatology based on 200 years of data, then remove this from the each day of the 200-year period to create a 200-yr anomaly time series. In this way we remove the mean warming response. Figure 5 shows a probability density function of daily anomalous 2-m temperatures during SON for points representative of the Arctic Ocean (75°N, 175°W; Fig. 5a), the Arctic coast in northern Canada (69°N, 134°W; Fig. 5b), and the North American continental interior in Saskatchewan (50°N, 110°W; Fig. 5c). There is a very pronounced narrowing of the temperature distribution in the perturbation simulation for the point over the Arctic Ocean. The standard deviation decreases by over 60% from 5.1°C in the control simulation to 2.0°C in the perturbation simulation. The temperature extremes are not nearly as large in the perturbation simulation, because the wide tail of the control distribution disappears. The reduction in the standard deviation for the Canadian Arctic coastal point (reduced 11%, from 5.4° to 4.8°C) and the Saskatchewan point (reduced 8%, from 5.4° to 5.0°C) are more modest than the Arctic Ocean point, but are, given our large sample, very statistically significant using a Levene test for equal variance.

Fig. 5.
Fig. 5.

Probability density functions for the control (blue) and equilibrium perturbation (black) simulations of daily averaged SON 2-m temperature anomalies (°C) for a single grid cell over (a) the Arctic Ocean (75°N,175°W), (b) the Arctic coast (69°N, 134°W), and (c) the continental interior (50°N, 110°W). The climatological temperature was subtracted from each day to remove the mean warming response.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

To separately map changes to interannual and subseasonal variability, we decompose the 2-m temperature anomalies on a given day represented in Fig. 5 (i.e., the SON daily 2-m temperature values with the climatology removed) into that year’s seasonal SON mean plus that day’s departure from that year’s seasonal SON mean. It can be shown that by construction the total SON variance similarly decomposes into the interannual variance of the 200 SON seasonal means plus the subseasonal variance for SON. This is done at each grid point and the difference in the standard deviation (the square root of the variance) between the two simulations measures the responses of the variability at that point. A map of the response of interannual (Fig. 6a) and subseasonal (Fig. 6b) standard deviation shows that the reduced variability shown at the grid points in Fig. 5 extends throughout the middle and high latitudes. Both the interannual and subseasonal time scales contribute to the reduction in temperature variability. The reduction in the interannual temperature variability is largest in the southern parts of the Arctic Ocean, while there is actually a small increase near the North Pole. There is little change over land with the exception of over northern Russia. The spatial structure of the subseasonal response in the 2-m temperature standard deviation is different, with a large reduction over the whole Arctic Ocean as well as smaller changes over the middle and high latitudes over Russia and North America.

Fig. 6.
Fig. 6.

The equilibrium response of SON 2-m temperature standard deviation (°C) for (a) interannual and (b) subseasonal time scales.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

There are a number of mechanisms that potentially contribute to the decrease in temperature variability seen in Figs. 5 and 6. One idea that has been explored by Screen (2014) and Schneider et al. (2015) is that Arctic amplification will weaken surface temperature gradients in the middle and high latitudes and even without any change in the atmosphere dynamics this will reduce the temperature variability on synoptic time scales. The meridional temperature gradient response (Fig. 7) indicates that this likely contributes to the reduction in variability in some regions, as the temperature gradients do weaken, particularly in the regions between 70° and 80°N. However, there are regions where the temperature gradient does not change, yet there is still a reduction of temperature variability. For example, north of 80°N there is little change in temperature gradient, yet on subseasonal time scales, there are still large reductions in variability.

Fig. 7.
Fig. 7.

The equilibrium response of meridional SON 2-m temperature gradient [°C (103 km)−1].

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

Much of the reduction in temperature variability over the Arctic that we see in our simulations is more simply explained as a direct result of decreasing the SIC and SIT. The ocean has a larger heat capacity than sea ice, which will tend to make the temperature less variable over the ocean than over sea ice. We find strong gradients in 2-m temperature variability across the ice margin in both the control and perturbed simulations (not shown); the contrast in surface properties appears to exert a stronger control on temperature variability than background temperature gradients. This suggests that the temperature variability will decrease over regions that have lost sea ice. Consistently, the reduction of sea ice thickness that occurs throughout the year in our simulations allows more heat transfer and stronger atmosphere–ocean coupling, which also tends to reduce temperature variability.

We note that unlike for the subseasonal response the patterns of temperature gradient and interannual temperature variability response somewhat resemble each other (Figs. 6a and 7). There is no obvious dynamical reason why interannual temperature variability should be connected to local temperature gradients; this coincidence likely has more to do with both fields being strongly influenced by the spatial redistribution of the melting sea ice and its variability. In particular, we note the following:

  • Regarding variability, regions that go from partially covered ice to ice free see a reduction of SIC variability, while regions that go from completely ice covered to partially ice covered see an increase of SIC variability (not shown). To the extent that these SIC variability changes are coupled to temperature, this might explain the slight increase in interannual variability in temperature poleward of the Canadian Arctic Archipelago in Fig. 6a, and might contribute to the reduction in interannual variability in the western Arctic Ocean. By contrast, the reduction of subseasonal variability has a wider spatial extent, and reflects a general reduction in temperature variability in an environment that has become more maritime and less continental.
  • Regarding temperature gradients, the reduction in temperature gradients reflects enhanced Arctic amplification where sea ice is lost, whereas at the pole the temperature response over the remaining sea ice is quite weak and so is the temperature gradient response.

Other factors contributing to the change in variability over the Arctic Ocean and continents could include dynamical changes in the atmosphere, changes to clouds and precipitation associated with Arctic warming, and the advection of temperature variance. In addition, decreasing the difference in albedo between the ice and the ocean as we have done in these experiments weakens the ice–albedo feedback, which could also reduce the temperature variability, particularly on interannual time scales.

The seasonal cycle of the response of the variability in 2-m temperature for different regions is shown in Fig. 8a. We calculate this by averaging the change in standard deviation over the Arctic Ocean, North America, and Eurasia for each month. The standard deviation here includes both intraseasonal and interannual variability. The seasonal cycle of the change in variability in temperature for each of the three regions resembles the negative of the seasonal cycle of temperature in Fig. 3b. Over the Arctic Ocean, the main differences are the timing of peaks of reduction of variability which occur one month early in fall and one month later in spring compared to the temperature. In July, there is a slight increase in 2-m temperature variability over the Arctic Ocean, which represents an exception to the general tendency of reduced variability. This increase might relate to an increase in the temperature gradient in the perturbation simulation over the Arctic Ocean in July. There are small reductions in temperature variability over land throughout most of the year, with the biggest changes being over North America in the winter. The change in temperature variability over the continents is likely linked to the weakening of temperature gradients as discussed in Screen (2014) and Schneider et al. (2015) and possibly advection of the less variable air from higher latitudes.

Fig. 8.
Fig. 8.

Seasonal cycle of the equilibrium response in standard deviation of (a) 2-m temperature (°C), (b) SLP (hPa), and (c) Z500 (m) over the Arctic Ocean (blue), North America (green), and Eurasia (red). The standard deviation is calculated at each grid point before averaging.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

The responses of sea level pressure (SLP) variability (Fig. 8b) and 500-hPa geopotential height (Z500) variability (Fig. 8c) are relatively small in amplitude compared to the background variability (e.g., changes in springtime Arctic temperature, SLP, and Z500 variability are on the order of 30%, 3%, and 5%, respectively, of the control simulation variability). The spatial structure of the responses (not shown) is much noisier than that seen in temperature (Fig. 6); generally speaking there is a reduction at each point of subseasonal variability of Z500 and a mixture of increases and decreases in interannual variability. As for temperature, for SLP and Z500 there is a decrease in variability in winter and spring particularly over the Arctic Ocean. In July, when the temperature variability increases slightly, there is an increase in SLP and Z500 variability. By the hypsometric equation, Z500 variability is influenced barotropically by surface pressure variability and baroclinically by tropospheric temperature variability (see, e.g., Mudryk and Kushner 2011). Features of the change in Z500 variability seem to reflect a combination of changes in SLP and temperature variability.

Next we investigate whether there is any change in the amplitude of planetary waves as has been discussed in observations by Screen and Simmonds (2013), mainly in the context of the discussions about expected changes to midlatitude variability under Arctic amplification (e.g., Francis and Vavrus 2012; Barnes 2013). As in in Screen and Simmonds (2013) we use two complementary measures of the amplitude of the waves. The first, meridional amplitude AM, measures the meridional amplitude of an individual Z500 isopleth (line of constant geopotential height). As in Screen and Simmonds (2013) we use the 5400-, 5500-, 5700-, and 5600-m isopleths for winter [January–March (JFM)], spring [April–June (AMJ)], summer [July–September (JAS)], and fall [October–December (OND)], respectively. The second method, zonal amplitude AZ, measures the amplitude of the depth and height of the troughs and ridges of the Z500 along the 45°N latitude circle. We use a Fourier decomposition to calculate the amplitude of wavenumbers 1–10 and the total amplitude around the whole Northern Hemisphere for each day. For a more detailed description of the methods see Screen and Simmonds (2013).

Figure 9 shows the percentage change in amplitude for AM (left) and AZ (right) for wavenumbers 1–10 and the total for seasons defined in Screen and Simmonds (2013), with a black dot indicating 95% statistical significance using a Student’s t test on the monthly means of the daily averaged amplitudes. Figure 9 is similar to Fig. 2 in Screen and Simmonds (2013), except that they calculate a normalized trend instead of a response based on equilibrated epochs of our simulations. There is generally an increase in AM and a decrease in AZ in response to sea ice loss. The largest changes in AM are in AMJ when all wavenumbers show a significant increase in amplitude and the total amplitude increases by 6% in Fig. 9 or 0.2–0.3 standard deviations of the control run variability (not shown). This is relatively small compared to the 0.3–0.4 standard deviation per decade trend in the recent reanalysis record from Screen and Simmonds (2013). JAS and OND also show smaller but statistically significant increases in the total amplitude of 2%–3%. A significant increase is also found in JFM wavenumber 1, but the JFM total response is not significant. The only season that has a statistically significant total response in AZ is JFM with a decrease in amplitude of 1.5%. The other seasons all have a decrease in amplitude of varying degrees in intensity. The only season and wavenumber in which both methods show a statistically significant response of the same sign is JFM wavenumber 1, which shows an increase of 5%–6% in AM and AZ.

Fig. 9.
Fig. 9.

The equilibrium response in planetary wave amplitude (% change) in all four seasons and the first 10 wavenumbers, as well as the total amplitude (T) for (left) AM and (right) AZ. The isopleths used for AM are 5400, 5500, 5700, and 5600 m for winter (JFM), spring (AMJ), summer (JAS), and fall (OND), respectively. The black dots indicate a 95% statistical significance using a Student’s t test on the seasonal averages of the daily amplitudes.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

Qualitatively, the general increase in AM and decrease in AZ was also seen by Screen and Simmonds (2013) in their analysis of the observed trends. Screen and Simmonds (2013) argue that this behavior is in part expected: since more warming is observed at high latitudes, Z500 isopleths shift more poleward at high latitudes than at lower latitudes, increasing AM. This same Arctic amplification effect is pronounced in our integrations. On the other hand, AZ is measured at only one latitude, so it is not directly affected in a predictable way by Arctic amplification. Our results are consistent with this explanation as well. We see a larger contrast between the two methods than found by Screen and Simmonds (2013), and the seasonal and scale dependence of the results are also quite different. Apart from model inaccuracies, such differences are to be expected because observations do not reflect the idealized sea ice loss perturbation being imposed here and because we have the luxury of a multicentury sampling period versus the approximately 30 years of observations used by Screen and Simmonds (2013). The decrease in AZ seen in our study may in part be due to the decrease in temperature variability and its associated impact on the Z500 variability (Fig. 8).

A number of studies have suggested that sea ice loss and associated Arctic amplification would lead to a slower circulation and more persistent weather in the midlatitudes, which could decrease temperature variability on short time scales, but increase it on longer time scales (Francis and Vavrus 2012; Coumou et al. 2015). To investigate the temporal dependence of the change in variability seen in Figs. 5, 6, and 8 in more detail we calculate the temporal power spectra (displayed as the variance as a function of period) for SON 2-m temperature over the Arctic Ocean (Fig. 10a), land temperature (Fig. 10c), and Z500 (Fig. 10e) for both the control and perturbed simulations. These are calculated by averaging the SON power spectra for each of the 200 years and over all grid points. For the land temperature and Z500 only grid points between 30° and 60°N are used to capture the midlatitudes. Figures 10b,d,f show the corresponding percent change in variance between the control and perturbed simulations. Over the Arctic Ocean, the power spectrum of the control simulation is very similar to what is seen over land, but it decreases by more than 50% at all periods. The changes over midlatitude land are much smaller, but there is a decrease in variability across almost all periods, including all periods greater than 10 days. This decrease in temperature variability on all time scales is seen over both North America and Eurasia (not shown). The changes in the Z500 power spectra show a decrease in variability at periods less than 5 days and longer than 30 days, with periods in between these values showing neither a robust increase nor a robust decrease. Similar results are seen in DJF, but with smaller magnitude changes over the Arctic Ocean and bigger changes over land in both 2-m temperature and Z500 (not shown).

Fig. 10.
Fig. 10.

The power spectra for SON (a) Arctic Ocean 2-m temperature (65°–90°N), (c) land 2-m temperature (30°–60°N), and (e) 500-hPa geopotential height (30°–60°N) for the control simulation (blue) and the perturbed simulation (black). The spectra are displayed as the variance (°C2) as a function of period (days). (b),(d),(f) The corresponding percent change in power spectral variance between the control simulation and perturbed simulations are in.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

In summary, for this CCSM4 sea ice perturbation experiment, summertime Arctic sea ice loss in isolation generally drives a reduction of point (spatially local) variability of the surface temperature, surface circulation, and midtropospheric circulation on nearly all times scales. In July, however, summertime Arctic sea ice loss drives a moderate increase in circulation variability. Given that this occurs during the season of maximum observed Arctic cyclone activity (Serreze and Barrett 2008) and there is evidence that Arctic amplification has changed the midlatitude circulation variability during summer in observations (Coumou et al. 2014, 2015), further analysis of this change will be an interesting subject of future study. Summertime sea ice loss drives an increase in the meridional excursion of isopleths, which can be attributed in part to the strong Arctic amplification built into our simulations, but a general decrease in eddy amplitude, which is consistent with the point variability changes. This suggests that sea ice loss might be directly responsible for some of the observed trends in these two measures of wave amplitude. One exception is that sea ice loss drives an increase in wavenumber-1 eddy amplitude and meridional excursion in JFM, which also merits future investigation.

c. Transient response

We now turn our focus to the robustness of the transient response to the sea ice loss during the first 50 years after the change to the sea ice was made. This is of interest because it can qualitatively be related to the global response to rapid sea ice loss that is currently observed. Figure 11 shows the DJF 2-m temperature difference between the perturbation and control integrations for all eight realizations over the first 50 years. (The ensemble mean of this is shown in Fig. 13a.) All realizations in Fig. 11 show an Arctic warming as well as warming over Hudson Bay and North America. Over Eurasia, the changes are more variable: the sign, magnitude, location, and spatial extent of the change vary considerably between each realization. For example, relatively strong Eurasian cooling is seen in realization 3, peaking around 1.5°C in the central part of southern Russia. Realizations 1, 2, and 8 also have a cooling in eastern Asia of less than 1°C, while realizations 5 and 6 have a cooling farther to the west, north of the Black and Caspian Seas. Realization 4, however, shows a 0.5°–1°C warming over much of the northern part of Eurasia where many of the other realizations saw a cooling. The 2-m temperature warming over North America is more consistent between realizations; however, the amount of warming and the spatial pattern is variable. For example, the warming in realizations 4, 5, and 6 is more confined to eastern North America while the other realizations have warming over the western part of the continent as well.

Fig. 11.
Fig. 11.

The DJF 2-m temperature difference (°C) between the eight perturbation simulations of years 1–50 and the control integration.

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

The SLP difference between the perturbation and control integrations for all eight realizations over the first 50 years is shown in Fig. 12. Similar to the 2-m temperature, there is significant variability between realizations. For example, the largest difference is in realization 3, which has a strong positive SLP anomaly of about 4 hPa over the northern coast of Eurasia and a low pressure anomaly over the northern Pacific Ocean. Realization 4 has an opposite low pressure anomaly of about 2 hPa over the Arctic Ocean and a smaller high pressure anomaly over the northern Pacific Ocean. The other realizations all have a high pressure anomaly somewhere near the Eurasia Arctic coast and a low pressure anomaly over the Pacific; however, the size and location vary between them.

Fig. 12.
Fig. 12.

As in Fig. 11, but for SLP (hPa).

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

Many of the differences in the 2-m temperature changes in Fig. 11 can be understood in the context of the SLP changes in Fig. 12. For example, the strong high pressure anomaly in realization 3 is associated with anomalous anticyclonic winds that advect the colder air from the Arctic Ocean and Siberia, creating a large cold anomaly. This can be applied to realizations 1, 2, and 8; however, in these realizations, the anomalous SLP gradients are weaker, resulting in a smaller cold anomaly. Realization 4 has a low anomaly over the Arctic Ocean, so the cyclonic winds advect warmer air from west to east, resulting in warmer temperatures. Similar reasoning can be applied to explain the different temperature anomalies in the other realizations and over North America.

Figure 13 shows the DJF 2-m temperature and SLP transient response averaged over all eight realizations compared to the equilibrium response. The biggest difference in the temperature is that there is more warming over Eurasia in the equilibrium response that reflects a general warming of the ocean temperatures in response to sea ice loss (not shown). There is a slight cooling (0.3°C) over Eurasia in the transient response that is below the smallest contour level. It was not possible to find any 50-yr periods during the equilibrium perturbation simulation period for which Eurasia was as cold as was found in any of the transient responses in any of the simulations. This suggests that although the difference between the transient and equilibrium responses in 2-m temperature over Eurasia is small and there is a considerable amount of internal variability, the initial reduction in warming over Eurasia is a robust part of the response. The differences between the transient and equilibrium response in SLP are consistent with the 2-m temperature fields, as the only difference is the high pressure anomaly located over the coast of northern Europe and Russia that advectively cools Eurasia during the transient period. In both the time periods, once sufficient averaging is done, the SLP response is very weak, especially in the equilibrium response where the only place that has a sizeable response is the 1–2-hPa SLP reduction in the North Pacific.

Fig. 13.
Fig. 13.

The (left) transient and (right) equilibrium response for DJF (a),(b) 2-m temperature (°C) and (c),(d) SLP (hPa).

Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-15-0284.1

In this experiment neither of the robust features of the DJF SLP response—the cyclonic North Pacific response in both transient and equilibrium periods and the anticyclonic eastern Arctic Ocean response in the transient period—resemble hemispheric modes of variability such as the North Atlantic Oscillation or the northern annular mode, as has been suggested by the studies mentioned in the introduction. The connection between reduced sea ice (particularly over the Barents and Kara Seas), SLP, and temperatures over Eurasia has been explored in both observations and models in many other studies. The proposed mechanisms include a thermally generated Rossby wave that enhances cold air advection from the Arctic to East Asia (Honda et al. 2009), changing cyclone tracks through a reduction in baroclinicity near the Barents Sea (Inoue et al. 2012), and weakening of the zonal winds that bring warm air from the North Atlantic (Petoukhov and Semenov 2010). The transient winter 2-m temperature response that we find is consistent with these results even though we do not see the same pattern in every realization. Some studies have suggested that sea ice loss in the Barents and Kara Seas generate upward propagating Rossby waves that weaken the stratospheric polar vortex, which can then propagate downward, producing a negative NAM response at the surface and cold temperatures over land (Kim et al. 2014; Feldstein and Lee 2014; King et al. 2015). We do not see a robust stratospheric response in our simulations that survives multicentury averaging, so our simulations are not consistent with these explanations.

4. Summary and discussion

We have examined the equilibrium and transient responses to sea ice albedo-induced summertime sea ice loss in a coupled ocean–atmosphere–sea ice–land model. Although the impact of the albedo perturbation is strongest in late summer, sea ice thickness is reduced and Arctic Ocean warming (not shown in our figures) occurs in all seasons. The seasonal cycle of the Arctic heat flux and temperature equilibrium response are similar to those found in prescribed SST experiments (e.g., Deser et al. 2010; Screen et al. 2013) with the largest response being in the fall and winter and lagging the changes in sea ice area. However, the coupling appears to enhance the springtime sensible and latent heat flux response.

The zonal mean warming, apart from a minor global warming response (Deser et al. 2015), is mainly confined to the lowest part of the atmosphere, consistent with Screen et al. (2012). This coincides with very little change in the zonal mean winds: we see an easterly response per degree of Arctic warming of magnitude less than 0.1 m s−1 °C−1. The amount of near-surface Arctic warming in our integrations is comparable to several decades of greenhouse warming in the RCP8.5 scenario, and is more strongly Arctic amplified. Therefore, judging from this model, if a large reduction in jet stream strength associated with summertime sea ice loss and associated Arctic amplification is hypothesized, it needs to come from some other source than direct forcing by summertime sea ice loss. For example, the Arctic midtroposphere is expected to warm for reasons not related to sea ice (Screen et al. 2012; Laliberté and Kushner 2013, 2014), and this will likely have more of an impact on the jet stream.

We find a large reduction in temperature variability throughout the middle and high latitudes in all seasons and at all time scales. The biggest changes are seen over the Arctic Ocean in fall where the standard deviation of the 2-m temperature is decreased by more than half. This reduction is consistent with Screen (2014) and Schneider et al. (2015), who attribute it to decreased temperature gradients. Although the change in temperature gradients certainly contributes to the decrease in temperature variability, we have proposed that the difference in heat capacity between ice and ocean also plays a large role. This decrease in temperature variability that we see in our experiments implies that cold extremes will become less likely as a result of summertime sea ice loss, in agreement with Screen et al. (2015). Conversely, if an increase in cold extremes is hypothesized under global warming, mechanisms will need to be found that counteract the simple effects of weakened temperature gradients and increased ocean–atmosphere coupling associated with summertime sea ice loss. We also find a decrease in SLP and Z500 variability throughout most of the year and over most regions although the changes were found to be smaller than in temperature. The change in tropospheric planetary wave amplitudes were shown to be dependent on the metric used, with a general increase in AM and a decrease in AZ qualitatively similar to what was found in the analysis of observed trends in Screen and Simmonds (2013), apart from a robust increase in wavenumber-1 amplitude in both AM and AZ, which is currently under investigation and is not to our knowledge detected in the observations. The increase in AM is explained as an impact of Arctic amplification induced by the simulation, and the reduction in AZ is consistent with the reduction of geopotential variability at each point. The similarity suggests that summertime sea ice loss might have a role in driving changes in variability qualitatively similar to observations. However, the changes are small compared to those observed (0.3 standard deviation reduction in response to sea ice loss in our simulations versus 0.3–0.4 standard deviation per decade in observations), suggesting a limited role for summertime sea ice loss in driving recent observed changes.

We have investigated the transient adjustment to the sea ice albedo perturbation in the presence of internal variability. We find that the wintertime SLP and 2-m temperature response to rapid sea ice loss in the first 50 years is different to that equilibrium response, with a high pressure anomaly response over the Eurasian coast that is not seen after it adjustment to equilibrium. This high pressure anomaly is associated with a cold temperature response over Eurasia as a result of colder air being advected from the Arctic. Although the ensemble-mean transient response was found to be different from the equilibrium response, there is considerable spread in the location, magnitude, and even the sign among realizations. The total amount of sea ice reduction in each of these realizations was very similar and there is no correlation between any regional differences in SIC and the response patterns (not shown), so internal atmospheric variability is likely the main reason why we see such large differences. The large samples required to detect a robust responses reinforces the point that attributing observed changes in circulation to sea ice loss will remain an ongoing challenge.

We conclude with a note of caution concerning the robustness of these results. For example, sea ice loss experiments with different models have shown more significant changes in the strength of the jet stream (e.g., Peings and Magnusdottir 2014). One way that model difference could lead to different responses to sea ice loss is through how the stratosphere is represented. It has been shown that CAM4, the atmosphere model used in CCSM4, produced a weaker tropospheric circulation response compared to a version of the same model with a better-resolved stratosphere (Sun et al. 2015) and that, more broadly, intermodel spread in stratospheric wind response contributes to the intermodel spread in Arctic circulation change in CMIP5 climate change experiments (Manzini et al. 2014). In addition, there is some evidence that the climate model response to climate variability is too weak compared to observations, particularly in the North Atlantic (Gastineau et al. 2013; Eade et al. 2014), so it is possible that could also be the case in our simulations in response to sea ice loss. Multimodel comparisons are required to better assess the extent to which changes in midlatitude winds can be separately attributed to Arctic sea ice loss and midtropospheric Arctic warming from the global warming process (Screen et al. 2012; Laliberté and Kushner 2013, 2014).

Acknowledgments

This work is supported by the Canadian Sea Ice and Snow Evolution Network (CanSISE), which is funded by the Natural Science and Engineering Research Council of Canada (NSERC) under the Climate Change and Atmospheric Research (CCAR) program. We thank the National Center for Atmospheric Research (NCAR) for providing the CCSM4 code and RCP8.5 simulation data. Simulations original to this study were performed on SciNet, which is part of Compute Canada. We thank Lawrence Mudryk for helping with model simulations and three anonymous reviewers for their useful comments and suggestions.

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