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    Schematic of the numerical experiments performed in this study, including the spinup phase, the ensemble initial conditions, and the sensitivity experiments.

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    Initial condition of the Antarctic sea ice (a) concentration (%) and (b) thickness (m) extreme fields.

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    Time series of the globally averaged (a) sea surface temperature (GSST, °C) and (b) sea surface salinity (GSSS, psu) showing the raw data (black) and the filtered 1- and 10-yr coupled-stage integration data (red and blue, respectively) of the spinup.

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    Differences of Antarctic sea ice (a) concentration (%) and (b) thickness (m) from the LAYERMAX and LAYERCTL experiments (black lines) and the SLABMAX and SLABCTL experiments (red lines) for years 1–10 with phases of the sea ice differences indicated.

  • View in gallery

    Hovmöller longitude–time diagrams of differences of Antarctic sea ice thickness (m) (monthly average from 60° to 80°S) for (a) LAYERMAX minus LAYERCTL and (b) SLABMAX minus SLABCTL.

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    Latitude vs height (hPa) cross section of the (top)–(bottom) seasonal differences of zonal mean temperature (°C) between the (a)–(d) LAYERMAX and LAYERCTL and (e)–(h) SLABMAX and SLABCTL experiments for the Pacific sector. Yellow lines indicate differences statistically significant at the 95% confidence level (p < 0.05). The color legend runs from −1° to +1°C in 0.2°C increments.

  • View in gallery

    As in Fig. 6, but for the Atlantic sector.

  • View in gallery

    As in Fig. 6, but for the Indian Ocean sector.

  • View in gallery

    As in Fig. 6, but for zonal mean wind (m s−1).

  • View in gallery

    As in Fig. 9, but for the Atlantic sector.

  • View in gallery

    As in Fig. 9, but for the Indian Ocean sector.

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    Mean sea level pressure differences (hPa) from (a)–(d) LAYERMAX minus LAYERCTL and (e)–(h) SLABMAX minus SLABCTL during (top)–(bottom) the spring (SON), summer (DJF), autumn (MAM), and winter (JJA). Yellow lines indicate differences statistically significant at the 95% confidence level (p < 0.05).

  • View in gallery

    Ocean temperature monthly average (°C, 50°–80°S) as a function of depth (0–500 m) and time. (left) LAYERMAX minus LAYERCTL differences in the (a) Pacific, (b) Atlantic, and (c) Indian Ocean sector. (right) As in (left), but for SLABMAX minus SLABCTL.

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    As in Fig. 13, but for salinity (psu).

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    Mean sea surface temperature (SST) for both simulated climates during the summer (DJF). (top) Differences (°C) of the LAYERMAX and LAYERCTL experiments in years (a) 1–4, (b) 5–8, and (c) 9 and 10. (d)–(f) As in (a)–(c), but for SLABMAX and SLABCTL.

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    As in Fig.15, but for salinity (SSS, psu).

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    Schematic of the changes in the atmosphere and ocean in response to the Antarctic sea ice increasing.

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The Influence of Sea Ice Dynamics on the Climate Sensitivity and Memory to Increased Antarctic Sea Ice

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  • 1 Center for Weather Forecast and Climate Studies (CPTEC), National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil
  • 2 Earth Observation General Coordination (OBT), National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil
  • 3 Department of Meteorology, University of Reading, Reading, Berkshire, United Kingdom
  • 4 Agricultural Engineering Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
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Abstract

The study analyzes the sensitivity and memory of the Southern Hemisphere coupled climate system to increased Antarctic sea ice (ASI), taking into account the persistence of the sea ice maxima in the current climate. The mechanisms involved in restoring the climate balance under two sets of experiments, which differ in regard to their sea ice models, are discussed. The experiments are perturbed with extremes of ASI and integrated for 10 yr in a large 30-member ensemble. The results show that an ASI maximum is able to persist for ~4 yr in the current climate, followed by a negative sea ice phase. The sea ice insulating effect during the positive phase reduces heat fluxes south of 60°S, while at the same time these are intensified at the sea ice edge. The increased air stability over the sea ice field strengthens the polar cell while the baroclinicity increases at midlatitudes. The mean sea level pressure is reduced (increased) over high latitudes (midlatitudes), typical of the southern annular mode (SAM) positive phase. The Southern Ocean (SO) becomes colder and fresher as the sea ice melts mainly through sea ice lateral melting, the consequence of which is an increase in the ocean stability by buoyancy and mixing changes. The climate sensitivity is triggered by the sea ice insulating process and the resulting freshwater pulse (fast response), while the climate equilibrium is restored by the heat stored in the SO subsurface layers (long response). It is concluded that the time needed for the ASI anomaly to be dissipated and/or melted is shortened by the sea ice dynamical processes.

Corresponding author address: Claudia K. Parise, National Institute for Space Research (INPE), Av. dos Astronaltas, 1758, Jardim da Granja, São José dos Campos, São Paulo 12227-010, Brazil. E-mail: claudiakparise@gmail.com

Abstract

The study analyzes the sensitivity and memory of the Southern Hemisphere coupled climate system to increased Antarctic sea ice (ASI), taking into account the persistence of the sea ice maxima in the current climate. The mechanisms involved in restoring the climate balance under two sets of experiments, which differ in regard to their sea ice models, are discussed. The experiments are perturbed with extremes of ASI and integrated for 10 yr in a large 30-member ensemble. The results show that an ASI maximum is able to persist for ~4 yr in the current climate, followed by a negative sea ice phase. The sea ice insulating effect during the positive phase reduces heat fluxes south of 60°S, while at the same time these are intensified at the sea ice edge. The increased air stability over the sea ice field strengthens the polar cell while the baroclinicity increases at midlatitudes. The mean sea level pressure is reduced (increased) over high latitudes (midlatitudes), typical of the southern annular mode (SAM) positive phase. The Southern Ocean (SO) becomes colder and fresher as the sea ice melts mainly through sea ice lateral melting, the consequence of which is an increase in the ocean stability by buoyancy and mixing changes. The climate sensitivity is triggered by the sea ice insulating process and the resulting freshwater pulse (fast response), while the climate equilibrium is restored by the heat stored in the SO subsurface layers (long response). It is concluded that the time needed for the ASI anomaly to be dissipated and/or melted is shortened by the sea ice dynamical processes.

Corresponding author address: Claudia K. Parise, National Institute for Space Research (INPE), Av. dos Astronaltas, 1758, Jardim da Granja, São José dos Campos, São Paulo 12227-010, Brazil. E-mail: claudiakparise@gmail.com

1. Introduction

Over the last three decades (since 1979), satellite records and modeling studies have shown a substantial decline in both area and volume of the Arctic sea ice (Cavalieri et al. 1999; Zwally et al. 2002; Comiso and Nishio 2008; Kwok and Rothrock 2009; Stroeve et al. 2012), with a minimum record of 3.41 × 106 km2 in September 2012 (Laxon et al. 2013). The ongoing sea ice decrease in the Arctic is considered to be associated with the broad phenomenon of global warming (e.g., Turner and Overland 2009). Conversely, Antarctic sea ice (ASI) has been increasing at a statistically significant rate of 1.9% ± 1.3% decade−1 (Cavalieri et al. 1999; Turner and Overland 2009; Turner et al. 2009; Holland and Kwok 2012; Bintanja et al. 2013), with the two last records in September 2013 (19.77 × 106 km2) and October 2014 (20.11 × 106 km2) (NOAA/NCDC 2014). This is considered a paradox in the face of current climate warming (e.g., Zhang 2007).

Several mechanisms have been suggested for the increasing ASI, including ice shelf melting (Bintanja et al. 2013) and ozone depletion (Turner et al. 2009; Polvani et al. 2011). The accelerated basal melting of Antarctic ice shelves, resulting from the subsurface ocean warming, works as an input of cold and fresh meltwater to the upper ocean layers (from the surface to 100-m depth) (Bintanja et al. 2013). The ice shelf meltwater has a lower density compared to the more saline surrounding water and hence accumulates in the top ocean layer. The warmer water at the subsurface (from 100- to 300-m depth) is exported from lower latitudes toward the Southern Ocean (SO) where it emerges as warm Circumpolar Deep Water (CDW) (Martinson et al. 2008; Thoma et al. 2008). The SO then exhibits a remarkable signature of the warming in the deeper layers accompanied by a cooling trend in the upper 100 m (Price et al. 2008), which provides favorable conditions for the ongoing ASI expansion.

Other studies show that the influence of Antarctic ozone depletion on surface climate strongly resembles the principal pattern of large-scale Southern Hemisphere (SH) climate variability, the southern annular mode (SAM) (Thompson and Solomon 2002; Thompson et al. 2011) and that the ozone-driven SAM can be related to the ASI trends. However, while some authors show that ozone depletion may generate sea surface temperature (SST) cooling around Antarctica and sea ice expansion (e.g., Goosse et al. 2009), others suggest that ozone depletion may drive a warming of the SO and sea ice loss (Sigmond and Fyfe 2010; Bitz and Polvani 2012; Smith et al. 2012; Sigmond and Fyfe 2014). Recently, Ferreira et al. (2015) proposed a key mechanism to explain the inconsistency regarding the ocean and sea ice response to ozone depletion in the SH. The SST and sea ice response to the wind perturbation in the SO was treated as a two-time-scale problem, which consists of a rapid cooling followed by slow but persistent warming. The fast response is similar to the interannual signature of the SAM, with a cooling where the surface wind increases [south of the Antarctic Circumpolar Current (ACC)] and a warming where surface westerly winds weaken (around 35°S). According to the authors, this short response is mediated by mixed layer dynamics primarily driven by anomalous Ekman advection and by air–sea heat interactions that provide a damping effect. The slow response, in turn, is due to interior ocean dynamics. The northward Ekman flow at 70°–50°S drives upwelling south of the ACC, which brings warm water to the surface. On long (multiyear) time scales, this warmth is entrained into the mixed layer and counteracts the initial SST cooling. Eventually, the SST response to ozone depletion is a widespread warming of the SO.

The other suggested mechanisms for the increasing ASI are the increased northward advection by katabatic winds (Harangozo 2006; Holland and Kwok 2012), changes in rain and snow precipitation (Liu and Curry 2010), increasing greenhouse gases (Thompson et al. 2011), and the westerly wind intensification (Zhang 2014).

Studies in the Arctic have shown that knowledge of the sea ice persistence has improved its predictability (Blanchard-Wrigglesworth et al. 2011). Consequently, the improved predictability of opening of the northwest and northeast passages between the North Atlantic and North Pacific has offered faster and cheaper shipping (Lemke et al. 1980; Bitz et al. 1996; Hassol 2004; Lindsay et al. 2008). Even so, there has been little work regarding the persistence of sea ice in the SH. The ASI season duration, determined by the time elapsed between the day of ice advance and the day of ice retreat, has been assessed in some studies (Stammerjohn et al. 2008; Massom et al. 2013). According to these authors, the day of advance is the time when the sea ice concentration in a given pixel first exceeds 15% (taken to approximate the ice edge) for at least 5 days, while the day of retreat is the time when concentration remains below 15% until the end of the given sea ice year. Based on this definition, the outer pack ice at East Antarctica has short duration periods whereas the sea ice close to the coast persists for much longer periods (Massom et al. 2013). Also, in general the advance occurs much more slowly than retreat and earlier (later) advance largely relates to longer (shorter) duration. Massom et al. (2013) showed that the Antarctic Peninsula–Bellingshausen Sea sector (western Ross Sea sector) has presented a shortening (lengthening) sea ice season duration of 3 days per year. The possibility of forecasting ASI anomalies derived from observations was explored by Chen and Yuan (2004). Using a technique combining multivariate empirical orthogonal function analysis and linear Markov prediction the authors found that the dominant modes of the Antarctic climate variability are predictable up to one year in advance. From climate simulations, Holland et al. (2013) found that the initialized ice and ocean state provides predictive capability of the ice edge location around Antarctica for the first months of integration. These studies show that the present ASI state may influence its state in the future, which may make it predictable.

The ongoing expansion of the SH sea ice has increased the demand for a greater understanding of its impacts on the climate. In addition, knowledge of the period over which the increase of ASI is able to persist in the current climate has not previously been studied, nor has the climate memory related to the current sea ice changes.

The present study aims to explore the impact of increasing ASI on the mean state of the current climate, taking into account the persistence time of an extreme of sea ice concentration and thickness under current climate conditions. In addition to the sensitivity and memory of the mean climate to ASI expansion, the study evaluates the mechanism responsible for restoring the climate equilibrium state. We address these issues by examining how long an ASI maximum is able to persist under current climate conditions and what is the role of sea ice dynamics on the dissipation of the maximum sea ice (concentration and thickness) perturbation. The hypothesis is that correct simulation of sea ice dynamics strengthens the dissipation of the sea ice maximum by enhancing the ocean–sea ice–atmosphere interaction. This is explored through an ensemble modeling framework with perturbed sea ice initial conditions in two sets of experiments that differ in regard to their sea ice models.

This paper continues in section 2 by describing the climate model used, the spinup phase, and data as well as the sensitivity experiments and ensemble framework. The results are discussed in section 3, which includes an assessment of the model spinup, followed by an analysis of the persistence of the ASI maxima in the current climate and an evaluation of the current mean climate sensitivity to increased ASI. This latter analysis provides a separate perspective on the oceanic and the atmospheric responses to sea ice extremes. Finally, the summary and conclusions are found in section 4.

2. Climate model, data, and experiment design

a. Climate model

The model used in the present study is the Coupled Climate Model, version 2.1 (CM2.1), developed at the Geophysical Fluid Dynamics Laboratory (GFDL). This model has previously been used to run a suite of climate change experiments for the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) standing out among the climate models with one of the best performances at simulating the atmospheric physics and dynamics (Reichler and Kim 2008a,b) and the SO mean climate (Knutson et al. 2006; Russell et al. 2006; Sloyan and Kamenkovich 2007). The CM2.1 model is able to simulate the climate system from weather to climate change scales without using flux adjustment to maintain a stable climate. It is composed of four separate components—an atmospheric model, a land model, an ocean model, and a sea ice model—that interact with each other through the Flexible Modeling System (FMS) coupling system. The coupler computes and passes the fluxes between the model components and does all the necessary regridding once each component receives inputs and provides outputs on its own grid. The FMS contains two loops which define the coupling frequency of the sea ice with the atmosphere (fast inner loop that computes the ice temperatures and surface fluxes) and with the ocean (slow outer loop that computes temperature, albedo, and roughness). The fast time scale is determined by vertical mixing in the atmosphere.

A brief description of the four components of the CM2.1 model together with the implementation used in the present study is given below while further information on the physics and formulation of the coupled model can be found in Delworth et al. (2006), Gnanadesikan et al. (2006), Wittenberg et al. (2006), and Stouffer et al. (2006).

The atmospheric component of CM2.1 is known as AM2.1 and derives from previous models developed at GFDL (Hamilton et al. 1995; Stern and Miyakoda 1995; Delworth et al. 2002). The AM2.1 uses a finite-volume dynamical core with a terrain-following Lagrangian discretization (Lin 2004) that substantially reduces the extratropical wind stress and temperature biases found in earlier versions. The atmospheric model is configured at a horizontal resolution of 2.5° longitude and 2° latitude with 24 vertical hybrid sigma-pressure levels with sigma surfaces near the ground and pressure surfaces above 250 hPa. The planetary boundary layer (below 1.5 km) is represented by nine levels to support the boundary layer turbulence scheme. The lowest model level is 30 m above the surface. Aloft the resolution is coarser with approximately 2-km resolution in the upper troposphere. The stratosphere is represented by five levels, with the top level at 3 hPa.

The land component of CM2.1 is known as LM2.1 and is based on the Land Dynamics Model (LaD) of Milly and Shmakin (2002). The land model uses the same horizontal grid as the atmospheric model (i.e., 2.5° longitude and 2° latitude) with 18 soil temperature levels to 6-m total depth. The land model parameters vary spatially as a function of the mapped vegetation and soil types, although they are temporally invariant. The model does not represent subgrid-scale heterogeneity once it prescribes a single dominant vegetation type for each grid cell (Burke et al. 2000). Energy is stored as sensible heat in the 18 soil layers and as (fixed) latent heat of fusion in snowpack and in all soil layers, except the top layer.

The ocean component of CM2.1 is known as the Modular Ocean Model (MOM), which owes its genesis to the work of Bryan (1969) and Cox (1984). The present study uses the last public version of this model (MOM5.0.2) (Griffies 2012). The model uses a horizontal tripolar grid (Murray 1996) with poles over Eurasia, North America, and Antarctica. South of 65°N a spherical or latitude–longitude grid is used with a single pole over Antarctica. In the Arctic region, the grid consists of two poles over Siberia and Canada to avoid polar filtering over the Arctic. According to Griffies et al. (2005) the switch between the spherical and bipolar grids in the Arctic introduces a discontinuity in the derivative of the meridional grid spacing at 65°N, although it does not affect the fields (e.g., tracers, velocity, surface height) simulated on this grid. The ocean model is configured with a horizontal grid of 1° latitude and 1° longitude, this latter getting progressively finer toward the equator where it reaches 0.33° resolution. In the vertical, it uses 50 z-coordinate levels with 22 of them (10-m thickness each) uniformly spaced from 220-m depth to the surface. Below this depth, the grid box thickness increases gradually to a value of 366.6 m in the deepest parts of the ocean, with a maximum depth of 5500 m.

The CM2.1 model’s sea ice component has been continuously improved by the GFDL and is currently known as the Sea Ice Simulator (SIS; Winton 2000). However, in the present study two versions of this sea ice model are considered, which are used in the sensitivity experiments to study the importance of sea ice dynamics.

The simpler sea ice model version derives from a slab model developed by Bryan (1969) and a zero-layer sea ice model of Semtner (1976). Its single thickness covers a grid box, where the sea ice does not coexist with an ice-free fraction. There is also no internal sensible and latent heat storage (i.e., the sea ice has zero heat capacity) nor any detailed physics involving brine formation. The heat flux at the sea ice bottom is approximated by a vertical heat diffusion process (Maykut and Untersteiner 1971) in which the temperature at the sea ice base is the seawater freezing point temperature (−1.9°C) and the temperature at the sea ice top is the surface air temperature. Thus, the growth and melting of sea ice are parameterized through a linear energy balance profile between the top and bottom surfaces of the sea ice (Semtner 1976). Any snow that falls on top of the sea ice is immediately converted to sea ice. The thermal insulating capacity of snow is neglected and the surface albedo does not depend explicitly on snow properties (Bryan 1969). The sea ice albedo is parameterized to artificially account for leads, snow cover, and melt ponds, depending on both the surface temperature and ice thickness (Broccoli and Manabe 1987). Nevertheless, the sea ice dynamics present in the slab sea ice model uses cavitating fluid (CF) physics (Flato and Hibler 1992), which treats the sea ice floes and leads as a continuum (Bitz et al. 2012). This means that the model neglects the compression and shear stress and assumes that there are no lateral interactions.

In the slab model, the sea ice is advected by the ocean currents. This is a dynamical process known as free drift. However, when the ASI reaches a thickness threshold of 2 m there is no more sea ice motion. As the slab model does not represent the rheological processes, such as internal stress forces, the sea ice does not deform. This prevents the sea ice grows continually in regions of convergence (Manabe et al. 1991).

The more sophisticated sea ice model is a multilayer model that considers thermodynamic, dynamic, and rheological processes of the sea ice. The sea ice model solves the ice rheology through the elastic–viscous–plastic (EVP) technique (Hunke and Dukowicz 1997), which computes the ice deformation internal forces as a function of the wind and ocean stresses, Coriolis force, sea slope (or tilt effects), and internal ice pressure. Thus, the sea ice in this model is able to move even when ice becomes thick since it is allowed to deform. The model has three vertical layers, one of snow and two of ice, distributed in five thickness categories including open water areas (Semtner 1976). The sea ice thickness is described by a probability density function whose time evolution depends on both thermodynamic and dynamic processes. Growth and melt processes may shift the ice between the categories or create new thin ice, while deformation tends to break up thin ice and raft it or pile it up into ridges, altering its probability distribution (Bitz et al. 2001; Holland et al. 2001, 2006). Winton (2000) reformulated the Setmner’s three-layer scheme adding a variable heat capacity to the upper sea ice layer to represent brine pockets. The brine content is completely determined by the upper ice temperature and the (predetermined) ice salinity. The rejection rate of the brine content is given by the rate of ice growth times the salinity difference between the sea ice (S = 5 psu) and seawater. The heat capacity of the snow layer is typically small relative to that of the ice.

Therefore, the treatment of the brine content and the sea ice rheology are among the features that distinguish the simple and sophisticated sea ice models (Bitz et al. 2012). Both sea ice models used here are configured at the same horizontal grid resolution as the ocean model (i.e., 1° longitude and 1° latitude).

b. Spinup phase and data

An important step in running a climate model is to spin up the model toward an equilibrium condition equivalent to a real climate. Normally spinning up a coupled climate model requires a very long simulation to bring the ocean component to equilibrium. To shortcut this procedure, the climate model spinup is carried out in two stages where an uncoupled approach based on observations is initially taken to generate the initial conditions for a coupled model (Fig. 1).

Fig. 1.
Fig. 1.

Schematic of the numerical experiments performed in this study, including the spinup phase, the ensemble initial conditions, and the sensitivity experiments.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

The first stage of the spinup consists of a short (1 yr) simulation with only the ocean–sea ice model, that is, without coupling to the atmosphere (uncoupled stage) (Fig. 1). This stage provides a certain time for the model to adjust to the input data (derived from observation), before allowing the atmospheric feedbacks. This part of the simulation is forced with the CORE-NYF atmospheric conditions (Griffies et al. 2009). Normal year forcing (NYF) consists of single annual cycles of all the data needed to force an ocean–sea ice model. NYF is constructed so that it can be repeated continually without initiating spurious transients while still retaining seasonal and propagating synoptic (weather) variability. These data are representative of climatological conditions over decades and can be applied repeatedly for as many years of model integration as necessary (Large and Yeager 2004). This dataset has a horizontal grid resolution of 1.91° latitude and 1.87° longitude (T62 spectral resolution) and includes 6-hourly atmospheric data of 10-m air temperature, specific humidity, air density, and zonal and meridional wind and sea level pressure from the National Centers for Environmental Prediction (NCEP) reanalysis (Kalnay et al. 1996), GPCP and CMAP monthly varying precipitation (Huffman et al. 1997; Xie and Arkin 1997), and annual climatology of continental runoff (Large and Yeager 2004). The initial conditions of ocean temperature and salinity are obtained from the World Ocean Atlas dataset (Boyer et al. 2009), which consists of monthly climatologies of ocean temperature (Locarnini et al. 2010) and salinity (Antonov et al. 2010) provided by the World Data Center for Oceanography (WDC) and the National Oceanographic Data Center (NODC) of the National Oceanic and Atmospheric Administration (NOAA). The climatological monthly mean values have a horizontal grid resolution of 1° in latitude and 1° in longitude at standard depth levels from the surface to a maximum depth of 5500 m, which cover the period from 1955 to 2006. The ocean model is initialized with zero velocity (from rest) (Griffies et al. 2009). The SST initial conditions derive from a monthly climatology (from 1971 to 2000) presented by Hurrell et al. (2008), which merged both Met Office Hadley Centre (HadISST, version 1) (Rayner et al. 2003) and NOAA Optimum Interpolation Sea Surface Temperature (OISST, version 2) datasets (Reynolds et al. 2002). The Antarctic and Arctic sea ice fields are taken from a monthly climatology comprising the period from 1978 to 2004 provided by the National Snow and Ice Data Center (NSIDC). This dataset is based on passive microwave observations from both Nimbus-7 SMMR and SSM/I that were subsequently converted to sea ice concentration (Cavalieri et al. 1997, 1999).

The second stage of the spinup consists of a long (30 yr) fully coupled integration where the coupling to the atmospheric and land models is allowed (coupled stage) (Fig. 1). The ocean and sea ice fields are initialized from the short (1 yr) ocean and sea ice model integration (the uncoupled stage).

The initial conditions for the atmospheric model (e.g., zonal and meridional winds, air temperature, sea level pressure, and precipitation) are taken from a 17-yr Atmospheric Model Intercomparison Project (AMIP) integration of the GFDL atmosphere–land model in which SST and sea ice concentration are prescribed based on observed monthly means for the period from January 1979 through February 1996 (Taylor et al. 2000). The radiative conditions are prescribed and representative of the year 1990 (Fig. 1). They include the well-mixed greenhouse gases (CO2, CH4, N4O, and the halocarbons CFC-11, CFC-12, HCFC-22, and CFC-113), tropospheric and stratospheric ozone (Randel and Wu 1999; Fortuin and Kelder 1998, respectively), shortwave and longwave radiation (Freidenreich and Ramaswamy 1999; Schwarzkopf and Ramaswamy 1999, respectively), and a three-dimensional distribution of natural (sea salt and dust) and anthropogenic (black carbon, organic carbon, and sulfate) aerosols. These latter derive from outputs of a chemical transport model, the Model for Ozone and Related Chemical Tracers (MOZART) (Horowitz et al. 2003), that uses input emissions based on Olivier et al. (1996) and Cooke et al. (1999).

From the spinup, the fully coupled climate model is integrated a further 10 yr forward to generate the initial conditions for the ensembles, discussed in the following section.

c. Ensemble framework and sensitivity experiments

To answer the question of how the persistence of extreme sea ice conditions impacts on the climate system an ensemble modeling framework is employed. The initial conditions for the ensemble experiments are derived from a control integration of 10 yr forward from the spinup, where the climate conditions for each July–September (JAS) are used (Fig. 1). This procedure saves the climate conditions as close as possible to the period of the seasonal ASI maximum (September) and also includes the interannual variability of the climate by saving the JAS period for the first year, second year, third year, and so on. These 30 (10 yr × 3 months; middle–late winter and early spring) climate conditions are used as initial conditions for the ensemble experiments. Except for the ASI maximum fields, the ensemble initial conditions are the same for all experiments.

In the present study, two sets of sensitivity experiments are carried out that differ in regard to their sea ice models (Fig. 1). In the first group the sea ice is simulated by the slab sea ice model (hereinafter referred to as SLAB) whereas in the other group the sea ice is simulated by the multilayer model (hereinafter referred to as LAYER). Each group is initialized with a maximum (max) and a climatological (ctl) condition of ASI concentration and thickness. The four experiments—LAYERCTL, LAYERMAX, SLABCTL, and SLABMAX—are integrated in a 30-member ensemble for a period of 10 yr each without any flux adjustment. The initial conditions for the ctl ensemble experiments are those derived from the restarts of the 10-yr integration.

The LAYERMAX and SLABMAX experiments are designed to investigate the impact of a maximum ASI condition, which is done by comparing with their respective control simulations. Both LAYERCTL and SLABCTL represent a climatological state, free of sea ice perturbation and representative of the current climate, although different sea ice models result in different representations of the current climate (Parise 2014). So, by knowing the differences between the sea ice models it is possible to determine the role of sea ice dynamics during the dissipation of an ASI maximum.

The concentration field provides the area whereas the thickness provides the volume of sea ice and they are treated separately in both the slab and multilayer sea ice models. So, in order to analyze the impacts of increasing ASI it is needed to provide the maximum of both fields for the model. The maximum sea ice concentration for each grid point is computed from the Met Office Hadley Center dataset (Rayner et al. 2003), from the period of 1870–2008. Given the long time coverage of this dataset, the sea ice concentration field with maximum values represents an extreme condition of ASI and is used to initialize the sea ice area around Antarctica (Fig. 2a). The maximum sea ice thickness is computed for each grid point from a monthly climatology provided by the GFDL, which covers the period from 1979 to 1996 (Taylor et al. 2000) (Fig. 2b). This can damp an extreme thickness value by representing a climatological maximum, but differences around 0.6 m between the max and ctl experiments (section 3b) reflect a positive anomaly of sea ice thickness.

Fig. 2.
Fig. 2.

Initial condition of the Antarctic sea ice (a) concentration (%) and (b) thickness (m) extreme fields.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

3. Results

The results in this study are based on statistics of the 30-member ensemble, with the focus on the seasonal [i.e., September–November (SON; spring in the SH), December–February (DJF; summer), March–May (MAM; autumn), and June–August (JJA; winter)] and interannual time scales. Although the ensemble integrations have been initialized in June, the first June and July were not included in the seasonal analyses. Thus, the first season to be analyzed is the spring (SON).

a. Analysis of the model spinup

The model spinup is evaluated through time series of important fields within the time scale of this study, such as the globally averaged sea surface temperature (GSST) (Fig. 3a) and the globally averaged sea surface salinity (GSSS) (Fig. 3b), which are based on the 30-yr coupled stage integration (Fig. 1).

Fig. 3.
Fig. 3.

Time series of the globally averaged (a) sea surface temperature (GSST, °C) and (b) sea surface salinity (GSSS, psu) showing the raw data (black) and the filtered 1- and 10-yr coupled-stage integration data (red and blue, respectively) of the spinup.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

During the first year of simulation, the GSST increases from 16.5° to 18°C (~1.5°C in 12.5 months) and then varies seasonally between 17.6° and 19.2°C (Fig. 3a, black line). The initial adjustment of the GSST is even more evident following 1-yr low-pass filtering (Fig. 3a, red line). However, the 10-yr band filtering (Fig. 3a, blue line) indicates that during the first 10 yr of model integration (i.e., until month 120) the GSST increases monotonically until it reaches 18.4°C. During the second decade (from months 120 to 240) the GSST shows a slight increase (up to 18.5°C) followed by a small reduction (18.3°C). Finally, during the last decade (from months 240 to 360) the GSST is maintained at 18.35°C until the end of the simulation (Fig. 3a). The GSSS in turn decreases from 34.8 to 34.5 psu during the first ~20.6 yr (month 310) when it appears to have reached a near-equilibrium-to-equilibrium state through the last 50 months (Fig. 3b, both black and blue lines).

The time series analysis of these surface ocean fields shows that the climate has come to an almost equilibrium condition at the end of the 30-yr integration, sufficient for the seasonal and interannual time scales studied here.

b. Persistence of the Antarctic sea ice maximum in the current climate

The persistence of the sea ice maximum under current climate conditions represents the time needed for the max minus ctl sea ice differences to decrease to near-zero values. The analysis of monthly averages of ASI shows that the sea ice thickness max minus ctl differences (Fig. 4b) are larger than that of concentration (Fig. 4a), suggesting that the changes on the sea ice volume must play a larger impact on the climate than those on the sea ice area.

Fig. 4.
Fig. 4.

Differences of Antarctic sea ice (a) concentration (%) and (b) thickness (m) from the LAYERMAX and LAYERCTL experiments (black lines) and the SLABMAX and SLABCTL experiments (red lines) for years 1–10 with phases of the sea ice differences indicated.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

The results show that the largest sea ice concentration and thickness differences between the max and ctl experiments occur during the first 4 yr of the ensemble integrations (Fig. 4). From the fifth to eighth year, the sea ice budget is nearly zero (Fig. 4). During the ninth and tenth years a weak negative phase of the sea ice differences is observed (Fig. 4). The decline in sea ice persistence is not linear throughout the years, showing two peaks of positive sea ice differences every year (in summer and winter) in both groups of sensitivity experiments. The sea ice differences are larger for the LAYER (black lines) than the SLAB experiments (red lines), although they last longer for these latter (Fig. 4). This feature is associated with warmer subsurface temperatures beneath the sea ice in the LAYER experiment [see section 3c(2)].

Even under the same climate conditions, the sea ice field in the SLABCTL is larger than in the LAYERCTL experiments throughout the seasons, both near the Antarctic coast (summer and autumn) and over the midlatitudes (spring and winter) (Parise 2014). So, as the slab model in its climatological state sustains more sea ice than the multilayer sea ice model, the imposed positive extreme condition of ASI causes a minor impact in the SLAB experiments (once the max minus ctl sea ice differences are smaller) compared to the LAYER experiments (Fig. 4).

Based on Hovmöller longitude–time diagrams we observe that in both simulated climates the maximum ASI concentration and thickness exhibit different persistence times according to the sectors (Figs. 5a,b). Climatologically, the sea ice variability around Antarctica is spatially heterogeneous (e.g., Raphael and Hobbs 2014), with opposite trends between some sectors (e.g., Turner et al. 2014). Among the causes for this heterogeneity are the acting of the surface winds (e.g., Holland and Kwok 2012), the thermodynamic and dynamic sea ice processes (Holland et al. 2005), and the interaction with the SO (Hall and Visbeck 2002; Holland et al. 2005). In the present study where the persistence of a sea ice maximum is analyzed, a feature common for all sectors is that positive sea ice differences (Fig. 5, green shading) are followed by negative sea ice differences (Fig. 5, blue shading).

Fig. 5.
Fig. 5.

Hovmöller longitude–time diagrams of differences of Antarctic sea ice thickness (m) (monthly average from 60° to 80°S) for (a) LAYERMAX minus LAYERCTL and (b) SLABMAX minus SLABCTL.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

In the LAYER climate, the sea ice thickness differences are positive up to the fourth year in the Pacific Ocean sector, up to the sixth year in the Atlantic Ocean sector, and a bit longer (middle of the seventh year) in the Indian Ocean sector (Fig. 5a). After 4 yr of the LAYER model integration, negative sea ice anomalies are observed over the Pacific sector while positive sea ice anomalies dominate over the Atlantic sector. This out-of-phase pattern is more intense from years 5 to 7 of simulation (Fig. 5a). The Pacific sector presents the shortest persistence since it is the first to melt the sea ice maximum initial condition. For the Pacific sector the sea ice positive phase (years 1–4) lasts for the same amount of time (i.e., 4 yr) as the negative phase (years 4–8), while in the last 2 yr (years 9 and 10) the differences are small (Fig. 5a). The earlier dissipation of the sea ice perturbation in the Pacific sector may be related to the strong tropical–extratropical teleconnection between El Niño–Southern Oscillation (ENSO) events and the Antarctic dipole (ADP) discussed in Yuan and Martinson (2001) and Renwick (2002). The ADP is characterized by an out-of-phase relationship between sea ice and air surface temperature anomalies in the South Pacific and South Atlantic triggered by the ENSO forcing and persisting for three to four seasons (Yuan 2004). Here, from the fifth to the eighth year of the LAYER ensemble integrations, the SST increases (reduces) over the Pacific (Atlantic) sector [see section 3c(2)]. In the slab sea ice model’s experiments, because of the longer persistence of the ASI maximum (cf. the Pacific sea ice persistence in Fig. 4a to that in Fig. 4b), positive SST anomalies are only observed later, during the ninth and tenth years [see section 3c(2)]. This implies that the sea ice dynamics acting in the LAYER simulations may be accelerating the Pacific tropical–extratropical teleconnections through the strengthening of the ADP mode. Yuan (2004) concluded that for ENSO warm events there are simultaneously higher (lower) temperatures and less (more) sea ice in the Pacific (Atlantic) center of the dipole. In the LAYER climate, at the same time that negative SST anomalies are observed over the SO during the first 4 yr, positive SST anomalies are observed over the tropical region, especially over the Atlantic and eastern Pacific [see section 3c(2)]. So, the higher SSTs in the LAYER simulations resemble a positive phase of ENSO and may explain the shorter persistence of the sea ice maximum in the Pacific sector.

In the SLAB climate, the sea ice thickness differences are positive up to the sixth year in the Indian Ocean sector and up to the eighth year in the Pacific (at 150°–60°W in the Bellingshausen–Amundsen Seas and at 150°W–150°E in the Ross Sea) and Atlantic sectors (at the eastern Weddell Sea) (Fig. 5b). Because of the longer persistence of the ASI in the slab sea ice model’s climate it is not possible to observe the same reverse signal in the sea ice differences as in the LAYER climate, at least not until the end of the 10 yr simulated here.

Climatologically, over the Pacific sector of the SO between the Antarctic Peninsula and the Ross Sea there is a quasi-stationary low pressure center, the Amundsen–Bellingshausen Sea low (ASL), which influences the climate of West Antarctica by controlling the meridional component of the large-scale atmospheric circulation (e.g., Turner et al. 2009, 2013; Hosking et al. 2013). The lower (higher) pressure over the ASL leads to onshore (offshore) advection of warm maritime (cold continental) air (Vaughan et al. 1999; Massom et al. 2008; Schneider et al. 2012). According to Turner et al. (1997) large (small) sea ice extent on the western side of the peninsula is observed in years of weak (strong) northerly flow. Intense and persistent northerly airflow across the West Antarctica peninsula region results from a simultaneous low MSLP anomaly at 130°W and high MSLP anomaly at 60°W (Massom et al. 2008), [as can be observed in section 3c(1)]. For the LAYER experiments it is observed a deepening of the ASL in the winter [see section 3c(1)], indicating that enhanced northerly wind anomalies may carry warm maritime air toward the Antarctic coast and contribute to reduce the sea ice extent there. On the other hand, when the sea ice dynamics are neglected (i.e., in the SLAB climate), the sea ice extremes last longer in the Amundsen–Bellingshausen Seas (Fig. 5b), mostly because the slab sea ice does not respond to the atmospheric and oceanic dynamics when it becomes thick. Thus, the sea ice–atmosphere interactions enhance the dissipation of ASI extremes in the Pacific sector, a mechanism primarily driven by the ASL intensification.

c. Current mean climate sensitivity to increased Antarctic sea ice

The sensitivity of the SH atmosphere and ocean to the ASI extremes is evaluated separately. The changes in the atmosphere are examined in the sensitivity of air temperature, mean zonal wind, and mean sea level pressure while those in the ocean are based on analyses of the upper ocean temperature and salinity fields.

1) Atmospheric changes

The response of the SH atmosphere to the sea ice changes is based on the 30-member ensemble average for the first 3 yr of simulations. This shorter period of analyses is justified by the larger sensitivity of the ocean during the initial period [section 3c(2)]. Also, the max minus ctl sea ice differences are larger during this period (Fig. 4), which results in stronger impact to the atmosphere. Finally, because of the smaller heat capacity of the atmosphere compared to the ocean (White and Walker 1974) it is expected that the atmosphere has a shorter memory of the sea ice maximum initial condition.

The results show that the positive ASI extremes cause changes to the air temperature, zonal mean wind, and sea level pressure, whose patterns of response differ seasonally (SON, DJF, MAM, and JJA) and sectorially (Pacific, Atlantic, and Indian Ocean) for both sets of experiments. In general, larger sensitivity is observed for the LAYER experiments, mainly due to their larger sea ice differences compared to the SLAB experiments (Fig. 4).

The addition of ASI in the current climate causes a general cooling (~−1°C) of the SH atmosphere, which in some cases extends from the surface to the upper (~250 hPa) levels of troposphere (Figs. 68). For all sectors the air cooling is stronger in spring (SON), mostly because the spring is the closest season to the start date (June) of the experiments, when the integrations experience the ASI maxima. Hence, the air temperature responses for the LAYER and SLAB experiments are very similar in this season, especially for the Pacific (Figs. 6a,e) and Atlantic (Figs. 7a,e) sectors. In a climate condition of increased freshwater flow over the SO, Aiken and England (2008) found air temperature anomalies of −0.3°C, especially over the sea ice edge.

Fig. 6.
Fig. 6.

Latitude vs height (hPa) cross section of the (top)–(bottom) seasonal differences of zonal mean temperature (°C) between the (a)–(d) LAYERMAX and LAYERCTL and (e)–(h) SLABMAX and SLABCTL experiments for the Pacific sector. Yellow lines indicate differences statistically significant at the 95% confidence level (p < 0.05). The color legend runs from −1° to +1°C in 0.2°C increments.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

Fig. 7.
Fig. 7.

As in Fig. 6, but for the Atlantic sector.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

Fig. 8.
Fig. 8.

As in Fig. 6, but for the Indian Ocean sector.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

In spring (SON), except for the Indian Ocean sector in the LAYER experiments, significant positive differences (~1°C) of air temperature are observed at the upper levels (above ~200 hPa) for all sectors in both experiments (Figs. 6a,e, 7a,e, and 8e). The warm anomalies extend from Antarctic latitudes toward ~50°S and are also observed in winter for both the Pacific (Fig. 6d) and Indian Ocean (Fig. 8d) sectors. In the SLAB experiments these anomalies are also statistically significant in summer (Figs. 6f, 7f, and 8f), and in the case of Pacific significant changes are also found in autumn and winter (Figs. 6g,h). This upper-level warming may result from troposphere–stratosphere interactions, such as changes related to the ozone. Ozone generates heat in the stratosphere, by absorbing both the sun’s ultraviolet radiation and the upwelling infrared radiation from the lower troposphere (Mohanakumar 2008). Here, the positive extreme initial condition of sea ice has increased the albedo (not shown), so that the ozone absorbs more upwelling radiation.

During the summer (DJF), autumn (MAM), and winter (JJA), the ASI extremes continue to cool the atmosphere. There is a clear equatorward displacement of the cold anomalies in Atlantic compared to Pacific sector for both experiments. While the cold anomalies are larger over the high latitudes (south of 60°S) in the Pacific sector (Fig. 6) they move toward the midlatitudes (~50°S) in the Atlantic sector (Fig. 7). In the Indian Ocean sector, there are also significant changes at midlatitudes (Fig. 8).

The impact of increased ASI on the zonal mean temperature triggers changes in the pattern of zonal mean wind (Figs. 911). In general, there are positive differences of the zonal mean wind from the high to midlatitudes that represent a strengthening of the westerly winds. At the same time, there are negative differences of the zonal mean wind from the middle to low latitudes that represent a weakening of the westerly winds. The larger changes in the zonal mean wind are observed in spring (Figs. 9a,e, 10a,e, and 11a,e), the same as for the air temperature, in accordance with the thermal wind balance.

Fig. 9.
Fig. 9.

As in Fig. 6, but for zonal mean wind (m s−1).

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

Fig. 10.
Fig. 10.

As in Fig. 9, but for the Atlantic sector.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

Fig. 11.
Fig. 11.

As in Fig. 9, but for the Indian Ocean sector.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

For the Pacific sector, the positive wind anomalies in the LAYER experiments are observed at 60°S in spring (Fig. 9a), at 45°S in summer (Fig. 9b), and at 55°S in autumn (Fig. 9c) and winter (Fig. 9d), being stronger in the latter. These changes indicate that, except for summer, there is a polar jet intensification (~1.2 m s−1 stronger) around its climatological position and a strengthening of the polar cell as a whole. The positive wind anomalies in the SLAB experiments are observed at 70°S in spring (Fig. 9e), from 80° to 70°S in summer (Fig. 9f), at 50°S in autumn (Fig. 9g), and at 35°S in winter (Fig. 9h), being weaker in the latter. These changes show that there is a poleward shift of the polar jet in spring and summer and a polar jet intensification in its climatological position in autumn. The atmospheric response in winter is less significant and exceptionally the opposite (i.e., weaker westerly wind south of 60°S and stronger westerly wind around 45°S).

For the Atlantic sector, the positive wind anomalies in the LAYER experiments are found at 50°S in spring (Fig. 10a), from 75° to 55°S in autumn (Fig. 10c), and south of 65°S in winter (Fig. 10d), being stronger in the latter. In summer there are no statistically significant changes (Fig. 10b). In the SLAB experiments, the positive wind anomalies are found from 50° to 65°S in spring (Fig. 10e), and south of 65°S in summer (Fig. 10f) and autumn (Fig. 10g), being the latter concentrated at the upper levels. In winter an opposite pattern is observed, although the changes are not significant (Fig. 10h).

For the Indian Ocean sector, the positive wind anomalies in the LAYER experiments are found from 75° to 65°S in spring (Fig. 11a), at 55°S in autumn (Fig. 11c), and south of 70°S in winter (Fig. 11d). In summer the changes are not statistically significant (Fig. 11b). In the SLAB experiments, large positive wind differences are found from 70° to 50°S in spring (Fig. 11e), and then smaller changes are found south of 70°S in summer (Fig. 11f) and autumn (Fig. 11g). In winter, the changes are not significant (Fig. 11h).

For all sectors, the larger horizontal wind shear occurs during the winter in the LAYER experiments and during the spring in the SLAB experiments. For both Atlantic and Indian Ocean sectors, the wind changes in the LAYER experiments do not persist during the summer. On the other hand, in the SLAB experiments they persist in summer and reverse the signal of response in winter. The northernmost latitude of the positive mean zonal wind differences at low levels is observed for the Pacific sector during winter in both experiments (Figs. 9d,h).

The ASI extremes also generate changes to the SH mean sea level pressure (MSLP), which are also shown based on the differences between the max and ctl ensemble experiments. The results show that, on average, the MSLP decreases over the high latitudes (~3 hPa) and increases over the midlatitudes (~1.5 hPa) in both sets of experiments (Fig. 12).

Fig. 12.
Fig. 12.

Mean sea level pressure differences (hPa) from (a)–(d) LAYERMAX minus LAYERCTL and (e)–(h) SLABMAX minus SLABCTL during (top)–(bottom) the spring (SON), summer (DJF), autumn (MAM), and winter (JJA). Yellow lines indicate differences statistically significant at the 95% confidence level (p < 0.05).

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

In the LAYER experiments the MSLP response to increased ASI is more significant in autumn (MAM; Fig. 12c) and winter (JJA; Fig. 12d) whereas in the SLAB experiments the largest changes are found in spring (SON; Fig. 12e) and summer (DJF; Fig. 12f). The nearly annular pattern with low pressure anomalies centered on the South Pole and a ring of high pressure anomalies at the midlatitudes is observed in all seasons of both sets of experiments, except in the SLAB experiments during the winter, when differences of opposite sign are observed (Fig. 12h).

The changes to the MSLP as well as to the atmospheric general circulation cells found in the present study are typical of a SAM positive phase. The SAM is the principal mode of variability of the SH extratropical circulation, whose structure and variability depend on the interactions between the internal atmospheric dynamics with eddy momentum fluxes, through the jet stream and storm tracks variability (Limpasuvan and Hartmann 1999, 2000). The results presented here corroborate studies based on modeling (e.g., Hall and Visbeck 2002) and observational (e.g., Carvalho et al. 2005) data, where the polar jet strengthens (weakens) and the subtropical jet weakens (strengthens) in positive (negative) SAM events.

2) Oceanic changes

The SO sensitivity analysis is based on the 30-member ensemble average over the 10 yr of simulation. The monthly averages of the ocean temperature and salinity (from 50°S to 80°S) as a function of depth (0–500 m) and time are shown in Figs. 13 and 14, respectively. The results show that the ASI extremes in concentration and thickness generate negative temperature and salinity anomalies in the SO, in both sets of experiments.

Fig. 13.
Fig. 13.

Ocean temperature monthly average (°C, 50°–80°S) as a function of depth (0–500 m) and time. (left) LAYERMAX minus LAYERCTL differences in the (a) Pacific, (b) Atlantic, and (c) Indian Ocean sector. (right) As in (left), but for SLABMAX minus SLABCTL.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

Fig. 14.
Fig. 14.

As in Fig. 13, but for salinity (psu).

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

Initially, in the LAYER experiments the SO becomes on average 0.9°C colder and 0.25 psu fresher (Figs. 13a–c and 14a–c) whereas in the SLAB experiments, the magnitude of anomalies are smaller (i.e., 0.6°C colder and 0.15 psu fresher; Figs. 13d–f and 14d–f). For both experiments, the temperature and salinity changes reach their peaks in summer (DJF) and extend to the autumn (MAM), that is, during the melting seasons.

This colder and fresher water at the surface ocean layers (0–100 m) is associated with a relatively warmer (0.25°C) and saltier (0.0025 psu) water in the subsurface ocean layers (below 100-m depth), a pattern that varies according to the experiments (LAYER and SLAB) and sectors (Pacific, Atlantic, and Indian Ocean). This subsurface water is formed from the insulating effect generated by the sea ice cover, which prevents the heat transfer from the ocean to the atmosphere. Because of the enhanced buoyancy, the cold and fresh meltwater is stored in the surface ocean, which results in a relatively stratified ocean. So, the sea ice meltwater also diminishes the heat exchanges at the ocean–atmosphere interface by providing less warmth to the SH atmosphere, as observed in other studies (e.g., Richardson et al. 2005; Aiken and England 2008; Ferreira et al. 2015). According to Bitz et al. (2006) and Aiken and England (2008), a stratified ocean has its deep convection and mixing suppressed, which also favors a warming in the deeper layers. Also, the freshwater forcing associated with ASI loss may reduce the annually averaged production of the Antarctic Bottom Water (AABW) (Aiken and England 2008).

After ~4 yr of integration, the coupled climate system loses its memory to the initial conditions and the positive feedback is no longer able to maintain the positive anomaly of sea ice, which tends to be dissipated and the climate directed to an equilibrium restoring condition.

The period in which the ocean responds to the sea ice initial conditions (referred to as climate memory) is determined by the presence of negative temperature anomalies at high and extratropical latitudes. When positive temperature anomalies reach the ocean surface means that the cold meltwater is largely dissipated. In general, the climate memory is ~8 yr in the LAYER climate and ~10 yr in the SLAB climate, although it depends on the sector. Although the negative temperature and salinity differences are larger when the sea ice dynamics are present, they last longer when the sea ice dynamics are neglected (e.g., Figs. 13b and 13e). The climate memory to the sea ice initial condition persists even though the sea ice field had returned to its climatological state in ~4 yr.

The longer impact on the SO temperature in the SLAB climate (i.e., longer climate memory) compared to the LAYER climate highlights the importance of the sea ice dynamics at restoring the heat and mass balance. This demonstrates that models running with a misrepresentation of sea ice dynamics will very likely fail in representing ocean mixed layer processes. According to Ferreira et al. (2015), the sea ice positive feedback is the main cause for the sea ice to persist from one year to the other. So, as the misrepresentation of sea ice dynamics increases the persistence of the ASI extremes it might influence the sea ice positive feedback by delaying the onset of sea ice melting. Sectorially, in both simulated climates the longest climate response to the sea ice extremes occurs in the Atlantic (Figs. 13b,e and 14b,e), followed by the Pacific sector (Figs. 13a,d and 14a,d). The Indian Ocean sector shows the smallest sensitivity to the maximum sea ice (Figs. 13c,f and 14c,f), which is in accordance with its smaller sea ice budget between the max and ctl ensemble experiments.

The SST and sea surface salinity (SSS) differences between the max and ctl ensemble experiments are analyzed for three different periods: from the first to fourth years, from the fifth to eighth years, and the ninth and tenth years of model integration (Figs. 15 and 16). This split in time is based on the persistence of the ASI extremes in the current climate, that is, looking at the phases of positive (years 1–4), near-zero (years 5–8), and negative (years 9 and 10) sea ice differences (Fig. 4). Although the duration of these phases is longer for the SLAB experiments, the analysis of SST and SSS response is performed for the same period for comparison purposes.

Fig. 15.
Fig. 15.

Mean sea surface temperature (SST) for both simulated climates during the summer (DJF). (top) Differences (°C) of the LAYERMAX and LAYERCTL experiments in years (a) 1–4, (b) 5–8, and (c) 9 and 10. (d)–(f) As in (a)–(c), but for SLABMAX and SLABCTL.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

Fig. 16.
Fig. 16.

As in Fig.15, but for salinity (SSS, psu).

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

During the positive sea ice phase (years 1–4), the maximum ASI extremes cause a surface cooling (~−1°C) and freshening (~−0.5 psu) over the SO in both simulations, with a larger impact in the LAYER climate (e.g., Figs. 15a and 15d). Because of the larger max − ctl sea ice budget and the more intense sea ice–ocean–atmosphere interactions in the LAYER experiments, the impact on the climate is likewise stronger, resulting in a larger freshening of the ocean surface as soon as the sea ice melts. Here the sea ice dynamics have the effect of shortening the persistence time of the sea ice extremes under current climate conditions.

In the nearly zero sea ice phase (years 5–8), the negative SST and SSS differences decrease over the high latitudes at the same time as they spread equatorward (Figs. 15b,e and 16b,e). Instead, positive SST differences (~0.1°C) are observed in the Pacific sector, over the high latitudes in the LAYER climate [e.g., in summer (DJF); Fig. 15b] and over the midlatitudes in the SLAB climate [e.g., in summer (DJF); Fig. 15e]. Warming of similar magnitude is observed over the equatorial region in both climates, but it is stronger in the LAYER experiments (e.g., Fig. 15b). Positive SSS differences are observed in the LAYER climate over the subtropical Indian Ocean sector and eastern and western Pacific as well as around the Antarctic continent facing to southern Australia (Fig. 16b). In the SLAB climate, however, the SSS changes are very small (Fig. 16e).

During the negative sea ice phase (years 9 and 10), the SST increases over the extratropical and high latitudes, with visible changes between the sets of experiments. In the LAYER climate the positive SST differences become larger over the SO, covering the Atlantic, Indian Ocean, and western Pacific sectors (Fig. 15c). In the SLAB climate the larger SST changes occur in the eastern-central Pacific over the subtropical latitudes as well as throughout the SO (Fig. 15f). The positive SSS differences also increase in this final period of simulation for both sets of experiments, especially over the regions of subtropical region and around the Antarctic continent (Figs. 16c,f).

As the cold and fresh meltwater drifts northward, toward the extratropical (years 5–8) and tropical (years 9 and 10) latitudes, the SO surface temperature becomes warmer (0.2°C) by the enhanced upwelling of the subsurface water. This occurs because the increased surface westerly winds south of 60°S [details in section 3c(1)] trigger an enhanced divergent flow that advects cold water northward. This dynamical mechanism is discussed in Ferreira et al. (2015)’s study, which shows that stronger westerly winds increase the divergent flow (Ekman drift) away from the Antarctic continent, resulting in upwelling near the Antarctic continent (at the Antarctic divergence) and downwelling at the midlatitudes (at the Antarctic convergence).

In short, a positive extreme of ASI causes a strong initial cooling followed by a small warming of the SO. By insulating effects, the presence of sea ice on the surface warms the subsurface ocean. As the sea ice melts and the resulting cold and freshwater is advected equatorward this heat is released. So, the warming of the surface ocean in the last two years of simulations (years 9 and 10) is the main cause for the sea ice negative phase.

4. Summary and conclusions

Despite the current global warming, the sea ice around Antarctica has been showing a small but significant increase in the last three decades. There are some hypotheses to explain this climate paradox but there is no knowledge about the period over which this increasing condition of ASI is able to persist in the current climate, or about the climate memory to the sea ice extremes. The present study analyzes the persistence of ASI positive extremes in the coupled climate system, the sensitivity and memory of the current climate to these maxima, and finally the mechanism involved in restoring the climate equilibrium. Moreover, the study has considered the hypothesis that sea ice dynamics accelerate the dissipation of the sea ice maxima.

To answer these questions, the study has used a coupled climate model to run two sets of sensitivity experiments in a 30-member ensemble in each, which differ in regard to the sea ice model type used. The models were integrated for a period of 10 yr to study the seasonal and interannual variability of the SH climate. The sensitivity experiments were designed to explore the impacts caused exclusively by increased ASI so other variables of the coupled climate system (e.g., temperature, salinity, currents, and winds) were kept unchanged (i.e., climatological). Since both simulated climates are relatively warmer than a “real” climate experiencing an ASI increasing phase, this makes the sea ice maxima melt and/or dissipate as the coupled climate model integrates. As a consequence, the melting of a large sea ice anomaly results in a net freshening of the surface ocean, akin to a meltwater pulse rather than an anomalously strong sea ice production. Hence, the climate sensitivity to both the ASI extremes and the melt-resulting SO freshening has been studied here.

Our results show that a maximum of ASI is able to persist for ~4 yr under current climate conditions (positive sea ice phase). During the following 4 yr of the model integration the sea ice maximum field is similar to its climatology (near-zero-to-zero sea ice phase). Then, in the last 2 yr of simulation a negative sea ice phase is observed. However, we found the persistence of ASI maxima varies with the sector (Pacific, Atlantic, and Indian Ocean) and the sea ice model type (slab and multilayer sea ice models).

During the sea ice positive phase, the increased ASI cools (−1°C) the SH atmosphere from low levels to midlevels by insulating effects, with significant changes moving toward the lower latitudes (until ~20°S) as the model integrates. The fast response of the SH climate resembles the typical pattern of the SAM positive phase, with a cooling where the surface wind increases (south of ~50°S) and a warming where surface westerly winds weaken (from 45° to 20°S). Notwithstanding, the atmospheric response has strong seasonal (SON, DJF, MAM, and JJA) and regional (Pacific, Atlantic, and Indian Ocean) dependences. In some seasons and sectors, there is a poleward shift and strengthening of the polar jet and a northward expansion and weakening of the subtropical jet. In other cases, the cooling of the atmosphere over the polar and extratropical latitudes is accompanied by a warming from 80° to 50°S at upper levels (related to troposphere–stratosphere interactions) and from 70° to 60°S at lower levels (related to the sea ice pack opening). Furthermore, the local changes of atmospheric circulation extend toward the middle and upper tropospheric levels so that the inclusion of sea ice dynamics has the potential of triggering a remote climate signal by influencing important drivers of the climate variability, such as ENSO, the Antarctic dipole, and ASL.

As the sea ice melts, an input of cold and fresh meltwater into the ocean mixed layer (from the surface to 100-m depth) is observed in the SO. The sea ice–ocean interactions occur more intensely at the sea ice edge (mainly through sea ice lateral melting) and have the consequence of reducing the oceanic vertical mixing. Eventually, enhanced buoyancy results in a relatively stratified ocean. The cold and fresh water stored in the upper ocean layers as a climate memory (~8 yr) diminishes the potential of the ocean to lose heat to the atmosphere. So, in addition to the sea ice coverage, the colder surface ocean also impacts the overlying atmosphere by providing less warmth. Hence, the larger the input of sea ice meltwater into the SO, the stronger the impact on the climate. The increased surface westerly winds south of ~60°S enhance the divergent flow (Ekman drift), which advects the freshwater northward, leading to enhanced upwelling of the relatively warmer subsurface water (0.2°C).

The major changes caused by the increased ASI in the SH atmosphere and ocean are highlighted in a schematic (Fig. 17), where the numbers are used to show the sequential changes. The mechanisms dictating how the climate equilibrium is restored are suggested to be as follows. 1) The imposed sea ice maxima reduce the heat fluxes south of ~60°S (i.e., under the perturbed sea ice field) at the same time as 2) these are intensified at the sea ice edge. 3) The increased air stability over the sea ice strengthens the polar cell while the baroclinicity increases at midlatitudes. 4) The mean sea level pressure is reduced (increased) over the high (middle) latitudes, resembling the typical pattern of the SAM positive phase. 5) The westerly winds are intensified south of ~50°S and weakened from 45° to 20°S. 6) The polar jet intensifies and shifts poleward whereas the subtropical jet weakens and expands toward the equator. 7) The stronger westerly winds strengthen the ACC and, consequently, the Ekman drift northward. 8) The anomalous Ekman advection at the Antarctic divergence enhances the upwelling south of the ACC, bringing the relatively warmer subsurface water to the surface.

Fig. 17.
Fig. 17.

Schematic of the changes in the atmosphere and ocean in response to the Antarctic sea ice increasing.

Citation: Journal of Climate 28, 24; 10.1175/JCLI-D-14-00748.1

The first six steps of the proposed mechanism configure a fast response of the current climate to ASI extremes, which is mediated by the presence of increased ASI and its interactions with the ocean (through increased heat fluxes at the sea ice edge and enhanced lateral sea ice melting) and atmosphere (through reduced heat fluxes under the sea ice field and increased albedo). Steps 7 and 8 refer to the slow response of the mean climate, which starts after the sea ice extremes have been melted and remains until the end of the simulations. The heat stored in the subsurface ocean layers works to dissipate the climate signal generated by the increased ASI and leads the climate toward a near-equilibrium-to-equilibrium state.

Thus, the two-time-scale mechanism is triggered by sea ice insulating processes (fast response) and restored by the heat stored in the subsurface ocean layers (long response). As the fast response depends on the persistence of the sea ice extremes under certain climate conditions this must also influence the timing of the long response. In addition, the sea ice dynamics have the role of shortening the persistence time of ASI extremes since it represents better the ocean mixed layer processes that determine how long an ASI perturbation is able to persist in the current climate.

Finally, we conclude that the sea ice insulating effect is the most important thermodynamic process for both the high sensitivity of the atmosphere and the subsurface warm water formation whereas the stronger westerly winds and the consequent enhanced northward Ekman drift are the main dynamical processes for the restoring of climate equilibrium. Last, there are other factors in the coupled climate system, such changes to the storm tracks and planetary waves that might complement this mechanism. These are being analyzed to be included in future publications.

Acknowledgments

The authors acknowledge the GFDL Coupled Climate Model Development Team for providing a public version of the model and for all technical support, especially Stephen Griffies and Seth Underwood for their comments while setting up the model experiments. The first author acknowledges the scholarship from CNPq for the Project No. 140843/2011-6. The second author acknowledges support from CNPq, as a contribution for the PQ (CNPq) Project No. 304633/2012-7. This study is also a contribution to the Projects Atlantic Carbon Experiment (ACEx-CNPq) 558108/2009-1 and Marine Sciences research grant 1992/2014 (CAPES).

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