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  • View in gallery

    Climatological areal extent of Antarctic sea ice, summed over grid points having fractional ice concentration of at least 0.15, as a function of month (tick marks indicate the midpoint of each month). The solid line shows the median extent and the dashed lines indicate the 10th and 90th percentile of the observed distribution. All three curves have been smoothed with a seven-point binomial filter.

  • View in gallery

    Long-term mean ice concentration for pentads of (top left) minimum extent, (top right) ice growth (midway between minimum and maximum), (bottom left) maximum extent, and (bottom right) ice decay (midway between maximum and minimum). The dates in each are the middle day of the respective pentads. Text in (top left) indicates the approximate location of areas referred to in the text: Ross Sea (RS), McMurdo Sound (MS), Bellingshausen–Amundsen Sea (BA), and the Weddell Sea (WS). See text for more details.

  • View in gallery

    The leading three EOFs of sea ice concentration anomaly for the four sea ice “seasons”: minimum extent (pentads 6–14), growth (pentads 28–36), maximum extent (49–57), and decay (pentads 64–72). Plots are regression maps, indicating mean concentration anomalies for a +1 standard deviation in the associated amplitude time series. The contour interval is 0.025 (nondimensional, fractional concentration) with the zero contour omitted. Red contours indicate positive ice concentration anomalies and blue contours indicate negative anomalies. The percentage of variance accounted for by each EOF is printed in the top right of each panel.

  • View in gallery

    Summary statistics for the leading two modes of lagged MCA analyses between pentad sea ice concentration and H500 anomalies. (left) Pentads 28–36 (16 May–29 June, growth period) and (right) pentads 49–57 (29 August–12 October, maximum extent period). The first mode is in black and the second in gray. Negative lags indicate the H500 field leading while positive lags indicate the sea ice field leading. The statistics are temporal correlation (rt) between amplitude time series, squared covariance fraction (SCF), and the percentage of the Frobenius norm (Ck) accounted for by each mode (Bretherton et al. 1992).

  • View in gallery

    Leading two sets of patterns and time series from an MCA between pentad H500 and sea ice concentration anomalies, for pentads 49–57 (ice maximum), where the H500 field leads by 5 days. (top) Homogeneous regression maps for the H500 patterns and (middle) heterogeneous regression maps for sea ice concentration. All maps show average pattern amplitude for a +1 standard deviation excursion of the associated H500 time series. For H500, the contour interval is 10 m and is 0.02 for sea ice concentration. Positive contours are red, negative contours are blue, and the zero contour has been omitted. (bottom) The time series of the H500 (blue) and sea ice concentration (red) patterns.

  • View in gallery

    As in Fig. 5, but for pentads 28–36 (ice growth period), where the H500 field leads by 4 days.

  • View in gallery

    Composite average: (top) anomalous meridional temperature advection (υ′T′; units: K m s−1) and (bottom) anomalous sea ice motion for the (left) top and (right) bottom quartiles of the amplitude time series of the leading sea ice pattern from Fig. 5 (MCA during ice maximum). In (top), the contour interval is 2 K m s−1, red (blue) shows positive (negative). In (bottom), the longest arrows represent an ice speed of 4 cm s−1 (arrows shown at every second grid point, for clarity). The black lines show the outer contours of the MCA pattern of Fig. 5, negative (ice decrease) shown as dashed and positive (increase) as solid.

  • View in gallery

    As in Fig. 7, but for the amplitude time series of the leading sea ice pattern from Fig. 6 (MCA during ice growth).

  • View in gallery

    SIC cluster means for two clusters during the maximum ice period. Positive contours are red, negative contours are blue, and the zero contour has been omitted. The contour interval is 0.02 (fractional ice concentration anomaly). The numbers in the top right of each panel indicate the frequency of occurrence of each cluster.

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Atmospheric Forcing of Antarctic Sea Ice on Intraseasonal Time Scales

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Abstract

Intraseasonal relationships between Antarctic sea ice and atmospheric circulation have been investigated using a 29-yr record of pentad-mean Antarctic sea ice concentration and Southern Hemisphere 500-hPa height fields. Analyses were carried out for four sea ice seasons: minimum extent, growth, maximum extent, and decay. Interannual variability was removed from both datasets to focus on intraseasonal variations. Patterns of sea ice variability and linkages to the atmospheric circulation varied markedly with season. The strongest and most coherent relationships were evident during the maximum ice extent period and to a lesser degree during the growth period. At those times of year, the strongest relationships were associated with atmospheric circulation anomalies leading sea ice anomalies by 4 or 5 days, suggesting that variations in the atmospheric circulation force changes in the sea ice field. Ice decreases are generally found in regions of poleward flow and ice increases are found in regions of equatorward flow. Mechanisms appear to be related both to thermal advection and to mechanical forcing, with the relative importance of each varying in space and in time. During the period of maximum ice extent, the leading pattern from a maximum covariance analysis between 500-hPa height and sea ice concentration accounted for 38% of the squared covariance between fields, and the associated time series were correlated at 0.74. The leading patterns of variability exhibit clear zonal wavenumber 3 signatures and appear to be largely a result of internal variability in the extratropical circulation.

Corresponding author address: Dr. J. A. Renwick, NIWA, Private Bag 14-901, Wellington 6021, New Zealand. E-mail: james.renwick@niwa.co.nz

Abstract

Intraseasonal relationships between Antarctic sea ice and atmospheric circulation have been investigated using a 29-yr record of pentad-mean Antarctic sea ice concentration and Southern Hemisphere 500-hPa height fields. Analyses were carried out for four sea ice seasons: minimum extent, growth, maximum extent, and decay. Interannual variability was removed from both datasets to focus on intraseasonal variations. Patterns of sea ice variability and linkages to the atmospheric circulation varied markedly with season. The strongest and most coherent relationships were evident during the maximum ice extent period and to a lesser degree during the growth period. At those times of year, the strongest relationships were associated with atmospheric circulation anomalies leading sea ice anomalies by 4 or 5 days, suggesting that variations in the atmospheric circulation force changes in the sea ice field. Ice decreases are generally found in regions of poleward flow and ice increases are found in regions of equatorward flow. Mechanisms appear to be related both to thermal advection and to mechanical forcing, with the relative importance of each varying in space and in time. During the period of maximum ice extent, the leading pattern from a maximum covariance analysis between 500-hPa height and sea ice concentration accounted for 38% of the squared covariance between fields, and the associated time series were correlated at 0.74. The leading patterns of variability exhibit clear zonal wavenumber 3 signatures and appear to be largely a result of internal variability in the extratropical circulation.

Corresponding author address: Dr. J. A. Renwick, NIWA, Private Bag 14-901, Wellington 6021, New Zealand. E-mail: james.renwick@niwa.co.nz

1. Introduction

The annual cycle of Antarctic sea ice extent is one of the largest seasonal variations on Earth. The maximum areal extent varies by a factor of 5, from ~4 million km2 in late summer to ~19 million km2 in late winter, effectively doubling the ice-covered area of Antarctica from minimum to maximum extent (Thomas and Dieckmann 2003; Wadhams 2000).

Moreover, sea ice in the Antarctic grows around the perimeter of a continent, unconstrained in the equatorward direction by any continental boundaries. It is thereby highly susceptible to modulation by the atmospheric circulation, and has in recent decades been observed to be gradually increasing in overall extent (Turner et al. 2009; Zhang 2007), but with significant regional variability. The increasing trend is in marked contrast to the significant loss of sea ice in the Arctic (e.g., Comiso 2002; Lemke et al. 2007). Understanding interannual variability and long-term trends in Antarctic sea ice extent is a critically important component of understanding variability and change in high southern latitude climate and oceanography.

The increasing trend in Antarctic sea ice extent may be related to the trend toward the positive polarity of the southern annular mode (SAM), as proposed by Hall and Visbeck (2002), although recent work has cast doubt on how or even if this mechanism operates (Sigmond and Fyfe 2010) and has suggested that ocean heat transports may play a greater role (Spence et al. 2010; Zhang 2007). Rather than being zonally uniform, trends in extent show a marked pattern of regional contrast, with increases over the Ross Sea region and decreases (up to ~6% decade−1) farther east over the Bellingshausen–Amundsen Sea region and near the Antarctic Peninsula (Cavalieri and Parkinson 2008; Lefebvre and Goosse 2008; Turner et al. 2009). Such contrasts appear to be related to atmospheric forcing associated with the nonannular nature of the SAM (Turner et al. 2009), and to El Niño–Southern Oscillation (ENSO)-related teleconnections over the southern Pacific Ocean (Renwick 2002; Stammerjohn et al. 2008; Yuan 2004). It therefore seems likely that future trends in sea ice extent will be modulated by changes in atmospheric circulation at high latitudes and teleconnections to the tropics, as well as by direct thermal forcing associated with greenhouse gas emissions.

Previous work on coupling between atmospheric circulation and Antarctic sea ice (e.g., Lefebvre and Goosse 2008; Renwick 2002; Turner et al. 2009; Yuan 2004) has concentrated largely on the monthly or seasonal time scale. On those time scales, strong contemporary relationships are apparent, although there is some evidence of variations in the atmospheric circulation leading those in sea ice (Yuan and Li 2008). Around the weekly time scale, early work by Cavalieri and Parkinson (1981) showed evidence of wind forcing of Antarctic sea ice. Stammerjohn et al. (2003) and Harangozo (2004) demonstrated, using daily data, that northerly (southerly) wind anomalies were associated with sea ice retreat (advance), and suggested that ice compaction, ridging, and rafting were the main mechanisms responsible. In the Arctic, Matthewman and Magnusdottir (2011) recently found that anomalous circulation associated with the western Pacific (WP) pattern forced changes in North Pacific sea ice most strongly at a 1-week lag. The sea ice anomaly was also observed to feed back positively on the atmosphere, extending the lifetime of the WP pattern anomaly.

The motivation for the research described here was to investigate the nature of large-scale submonthly linkages between Antarctic sea ice and the overlying atmospheric circulation, and in particular to determine lead–lag relationships at shorter time scales. A secondary goal was to investigate the nature of Antarctic sea ice growth and decay through the seasonal cycle. A better understanding of the nature of the coupling between the atmospheric circulation and the sea ice field will help to inform studies of future changes in sea ice concentration and extent.

The paper is set out as follows. First, datasets are described, and the statistical and data analysis approaches are outlined. The general behavior and climatology of Antarctic sea ice is presented in section 3. Section 4 deals with relationships between sea ice and atmospheric circulation on the weekly time scale. The final section summarizes the method and results, and discusses the implications of the results for understanding sea ice trends and for global modeling.

2. Data and methodology

a. Sea ice data

The sea ice data used here are fractional concentrations derived from the National Aeronautics and Space Administration (NASA) Team algorithm “Final” dataset, based upon Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) passive microwave data (Cavalieri et al. 1996) and were obtained from the U.S. National Snow and Ice data Center (NSIDC; http://nsidc.org/). The period analyzed was from October 1978 to December 2007 inclusive. The data are available daily from 9 July 1987 and every 2 days prior to that date. The raw data are defined on a polar stereographic grid with mean grid spacing of around 25 km (Cavalieri et al. 1996).

The analysis presented here concentrates on large-scale variability on time scales of approximately a week. For computational efficiency and to emphasize large-scale features, the raw sea ice concentration data were interpolated onto a coarser polar stereographic grid, with a horizontal spacing of approximately 150 km in the region of sea ice (60°–70°S). To facilitate subsequent filtering and time averaging, the data were linearly interpolated in time prior to 9 July 1987, resulting in a daily time series for the whole period of record.

Sea ice motion vectors were also analyzed to investigate the mechanisms behind observed atmosphere–ice linkages. The ice motion fields were also taken from NSIDC (http://nsidc.org/data/docs/daac/nsidc0116_icemotion.gd.html) and were processed in exactly the same way as the sea ice concentration fields.

b. Atmospheric circulation data

Once-daily geopotential height fields (at 1200 UTC) were extracted from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) pressure-level reanalysis dataset for 500- and 1000-hPa levels. The results presented here make use of only 500-hPa heights, but the 1000-hPa data produce very similar results. The 500-hPa level was preferred as it lies completely above the surface of the Antarctic continent. For analysis of atmospheric heat transports, daily reanalysis 500-hPa temperature and meridional wind fields were also extracted for the period of the sea ice record. All reanalysis data were interpolated from their initial 2.5° × 2.5° latitude–longitude grid onto a Southern Hemisphere polar stereographic grid extending to 20°S with mean grid spacing around 600 km.

Some use was also made of outgoing longwave radiation (OLR) data as a proxy for tropical convection. Daily interpolated OLR data for the period matching the sea ice data (1979–2007) were obtained from the U.S. National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ERSL) website (http://www.esrl.noaa.gov/psd/data/gridded/data.interp_OLR.html; Liebmann and Smith 1996). The data are available on a 2.5° × 2.5° latitude–longitude global grid. Only the tropical region (30°S–30°N) was retained for use here. Use was also made of the daily Real-time Multivariate MJO (Madden–Julian oscillation) indices (RMM1 and RMM2; Wheeler and Hendon 2004) available from the Centre for Australian Weather and Climate Research website (http://www.cawcr.gov.au/staff/mwheeler/maproom/RMM/RMM1RMM2.74toRealtime.txt), again for the period of the sea ice data.

c. Methods

Analyses were carried out on 5-day (pentad) means of sea ice and height data. The pentad definitions are fixed from year to year by incorporating leap days into the relevant pentad. Many of the analyses shown here were also applied to unfiltered daily data and to 3-day means, with little qualitative change in results. For the results presented here, interannual variability was removed by subtracting the annual mean field from each dataset for each year of the analysis period.

Calculation of climatological mean fields was handled differently for the sea ice and the reanalysis datasets. For the reanalysis geopotential height, wind, and temperature data, a daily climatology was defined by harmonic analysis, retaining the first two Fourier components (12- and 6-month cycles), as used elsewhere (e.g., Renwick 1998, 2005). Daily anomalies were taken as differences from the daily climatology.

Since the sea ice field varies very strongly in space through the seasonal cycle, and there is no sea ice in most grid cells for at least part of the year, a climatology was calculated through averaging and smoothing, rather than by harmonic analysis. Sea ice concentration and ice motion fields were averaged separately in each pentad, over the 29-yr period, 1979–2007. The 29-yr means were then smoothed in time at each grid point using five passes of a 1–2–1 filter, to define the climatology. Sea ice concentration and ice motion anomalies were taken as differences from the climatological mean fields.

Sea ice and atmospheric circulation fields were analyzed using a combination of empirical orthogonal function (EOF; Wilks 1995) analysis and maximum covariance analysis (MCA; e.g., Mo 2003). EOF analysis identifies spatial patterns in a single dataset whose amplitude time series exhibit maximal variance, while MCA identifies pairs of spatial patterns in two datasets whose amplitude time series exhibit maximal covariance.

MCA between 500-hPa height (H500) anomalies and sea ice concentration (SIC) anomalies was carried out using pentad means of both fields. Before averaging, the H500 fields were offset in time compared to the SIC pentads in steps of 1 day, with offsets ranging from up to 10 days earlier (H500 leads SIC) to 10 days later (SIC leads H500). Results were analyzed to identify the time offset of maximal coupling (covariance) and hence the optimal lead/lag interval between the H500 and SIC fields.

Sea ice concentration fields were also analyzed using a cluster analysis, as in Kidson (2000) and Renwick (2005). Clusters were defined based on the root-mean-square (rms) difference between spatially normalized patterns of pentad mean sea ice concentration anomalies. At each step, a merge was performed using the two clusters whose means were separated by the smallest rms difference. Following that, the convergent k-means approach was used (MacQueen 1967) to iteratively reassign cluster membership and to recalculate cluster means. The method starts from a random set of one-member seed clusters (50 in this case) and converges to a limit of one cluster. The final stopping point for cluster selection was made visually, based on consistency across a series of 20 trials starting from different seeds.

3. Sea ice climatology

The seasonal cycle of Southern Hemisphere total sea ice extent (summing the area of pixels where the estimated sea ice concentration is at least 15%) is illustrated in Fig. 1. In terms of total extent, there is a minimum of around 4.0 × 106 km2 during pentad 10 (15–19 February) and a maximum of around 19.1 × 106 km2 during pentad 54 (23–27 September). The loss of ice through spring and summer is faster on average than the growth period in autumn and winter, as first reported by Gordon (1981) who noted the role of the ocean circulation in defining the form of the seasonal cycle, with 29 pentads from the time of maximum extent to the time of minimum extent, but 44 pentads from minimum to maximum extent. There is little interannual variability in the amplitude of the annual cycle of sea ice extent, as indicated by the dashed lines in Fig. 1 (10 and 90 percentiles of the extent distribution). For the 29 years examined here, the maximum extent varied only by ±3% around the mean maximum, while the minimum extent varied between 85% and 122% of the mean minimum. In terms of timing, there was most variability in maximum extent, occurring anywhere between pentads 49 and 57. Minimum extent was more regular, occurring between pentads 10 and 13.

Fig. 1.
Fig. 1.

Climatological areal extent of Antarctic sea ice, summed over grid points having fractional ice concentration of at least 0.15, as a function of month (tick marks indicate the midpoint of each month). The solid line shows the median extent and the dashed lines indicate the 10th and 90th percentile of the observed distribution. All three curves have been smoothed with a seven-point binomial filter.

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

Average concentrations are shown in Fig. 2 for four key pentads, the minimum pentad 10 (15–19 February), the maximum pentad 54 (23–27 September), and two transition pentads: midgrowth pentad 32 (5–9 June) and middecay pentad 68 (2–6 December). The timing of average ice growth and decay is similar in different parts of the hemisphere, but the largest amplitude is in the Atlantic (Weddell Sea) sector. The existence of major polynyas is a significant feature of the growth process in the Weddell Sea (Zwally et al. 2002).

Fig. 2.
Fig. 2.

Long-term mean ice concentration for pentads of (top left) minimum extent, (top right) ice growth (midway between minimum and maximum), (bottom left) maximum extent, and (bottom right) ice decay (midway between maximum and minimum). The dates in each are the middle day of the respective pentads. Text in (top left) indicates the approximate location of areas referred to in the text: Ross Sea (RS), McMurdo Sound (MS), Bellingshausen–Amundsen Sea (BA), and the Weddell Sea (WS). See text for more details.

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

The “sea ice year” was split into four distinct periods for the purposes of EOF analysis and subsequent MCA experiments (see below). Four nonoverlapping periods of nine pentads (45 days) each were defined to represent the summer minimum period (pentads 6–14, 26 January–11 March), the growth period (pentads 28–36, 16 May–29 June), the winter maximum period (pentads 49–57, 29 August–12 October), and the decay period (pentads 64–72, 12 November–26 December). A window of nine pentads was chosen as a compromise between maximizing temporal degrees of freedom and keeping the time windows short enough to capture distinct phases of the seasonal cycle.

The three leading EOFs (Wilks 1995) for each of the four key times of year (minimum, growth phase, maximum, and decay phase) are shown in Fig. 3. Quite different features are evident at different times of year, related in part to the very different spatial extent of sea ice through the year. At the summer minimum, sea ice variability is restricted to eastern Weddell Sea and the Pacific coastal region, especially near McMurdo Sound (see Fig. 2 for an indication of place names and geographical locations). The leading two EOFs during the growth and peak periods exhibit zonal wavenumber 3 patterns, maximizing over the Pacific sector. It is notable that at the maximum period, the leading sea ice EOF is much more prominent than at other times of year, accounting for 13% of the total variance during that period, compared to 7%–8% at other times. The growth and decay period EOFs show evidence of north–south dipoles, and also retain some evidence of zonal wave 3 (and 2) structures.

Fig. 3.
Fig. 3.

The leading three EOFs of sea ice concentration anomaly for the four sea ice “seasons”: minimum extent (pentads 6–14), growth (pentads 28–36), maximum extent (49–57), and decay (pentads 64–72). Plots are regression maps, indicating mean concentration anomalies for a +1 standard deviation in the associated amplitude time series. The contour interval is 0.025 (nondimensional, fractional concentration) with the zero contour omitted. Red contours indicate positive ice concentration anomalies and blue contours indicate negative anomalies. The percentage of variance accounted for by each EOF is printed in the top right of each panel.

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

4. Sea ice–circulation relationships

a. Coupled analysis

Relationships between SIC variability and the atmospheric circulation (H500) were investigated through a series of lagged MCA experiments. MCA was carried out separately for each of the four periods of the sea ice year described above and illustrated in the EOF analysis of Fig. 3. As described above, MCA was carried out for time lags of between −10 days (H500 leads SIC by 10 days) and +10 days (SIC leads H500 by 10 days).

Analyses were carried out both including and excluding interannual variability. The removal of interannual variability was achieved by removing the annual mean SIC and H500 anomaly for each year from each pentad in that year. Results where interannual variability was included echoed the seasonal-scale results of Renwick (2002) and others very strongly, but are not presented here. Removal of interannual variability effectively filtered out the ENSO-related signals known to force Antarctic sea ice extent to emphasize shorter time-scale relationships.

Summary statistics from the series of lagged MCA experiments are plotted in Fig. 4 for the growth and maximum extent periods of the seasonal cycle. The top panels show the correlation between the time series of the leading MCA patterns in each field, while the lower two panels show statistics related to the fraction of squared covariance accounted for by each of the leading patterns. In the correlation plots, there is a broad peak for negative lags (H500 leads SIC), with maxima for the first MCA pattern pairs at 4 days in the ice growth phase and 5 days in the ice maximum phase. For the second MCA pattern pair, the maximum correlation is at a 2-day shorter lag in each case, possibly indicating that the second pattern is more transient in space and/or time than the first. Note that the time series correlations are statistically significant at the 99% level for all lags, positive and negative. The covariance fraction statistics show broader peaks with generally higher values for negative lags (H500 leads) than for positive lags (SIC leads).

Fig. 4.
Fig. 4.

Summary statistics for the leading two modes of lagged MCA analyses between pentad sea ice concentration and H500 anomalies. (left) Pentads 28–36 (16 May–29 June, growth period) and (right) pentads 49–57 (29 August–12 October, maximum extent period). The first mode is in black and the second in gray. Negative lags indicate the H500 field leading while positive lags indicate the sea ice field leading. The statistics are temporal correlation (rt) between amplitude time series, squared covariance fraction (SCF), and the percentage of the Frobenius norm (Ck) accounted for by each mode (Bretherton et al. 1992).

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

For both the decay period and the minimum ice period, there was no preferred lag between H500 and SIC in terms of the summary statistics shown in Fig. 4. The lack of an optimal lag during the decay period (late spring/early summer) may be because atmospheric forcing is transient at that time of year, or operates on time scales longer than the nine-pentad window used here, or that the linear analysis used here does not properly capture the interactions between circulation and sea ice at that time. At ice minimum, the Antarctic SIC field is strongly topographically constrained and is perhaps less susceptible to atmospheric forcing. Reasons behind the lack of an optimal lag at those times of year are the subject of further study.

The two leading MCA pattern pairs for the ice maximum phase (pentads 49–57, 29 August to 12 October), with H500 leading by 5 days, are shown in Fig. 5. The first pattern pair shows a “South Pacific” wave train with approximately zonal wavenumber 3 spatial scale, with its amplitude very strongly concentrated across the South Pacific, associated with a pattern of ice growth and decay with most of its amplitude from the date line to the Antarctic Peninsula. The time series of the two patterns are well correlated (r = 0.74) at the 5-day lag, and account for nearly 40% of the squared covariance between the fields. SIC decreases lie under regions where the anomalous H500 flow is northerly (poleward) and increases lie under regions of southerly (equatorward) flow.

Fig. 5.
Fig. 5.

Leading two sets of patterns and time series from an MCA between pentad H500 and sea ice concentration anomalies, for pentads 49–57 (ice maximum), where the H500 field leads by 5 days. (top) Homogeneous regression maps for the H500 patterns and (middle) heterogeneous regression maps for sea ice concentration. All maps show average pattern amplitude for a +1 standard deviation excursion of the associated H500 time series. For H500, the contour interval is 10 m and is 0.02 for sea ice concentration. Positive contours are red, negative contours are blue, and the zero contour has been omitted. (bottom) The time series of the H500 (blue) and sea ice concentration (red) patterns.

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

The second pattern in Fig. 5 also shows a zonal wavenumber 3 pattern in the H500 field, but is more zonally symmetric than the first pattern and exhibits some elements of the SAM (same-sign anomalies at high latitudes). Again, negative SIC anomalies generally underlie regions of anomalous poleward flow at 500 hPa, while positive SIC anomalies underlie regions of anomalous equatorward flow.

For the ice growth period (pentads 28–36, 16 May to 29 June), a zonal wavenumber 3 pattern is again evident in the H500 field of the leading pattern (Fig. 6), lying from southwest of New Zealand to the South Atlantic Ocean. Here, results are shown for the H500 field leading by 4 days. The time series of the H500 and SIC patterns are again well correlated (r = 0.78), but only 30% of the squared covariance is accounted for by the leading pattern pair. Relationships between anomalous meridional flow at 500 hPa and SIC variations appear to be similar to those seen in the ice maximum phase, although close inspection reveals that the match is not as good as for the ice maximum period.

Fig. 6.
Fig. 6.

As in Fig. 5, but for pentads 28–36 (ice growth period), where the H500 field leads by 4 days.

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

A series of lagged composite and correlation analyses of the H500 field (not shown) revealed that the leading H500 pattern during the growth period tends to propagate eastward, while the leading pattern at ice maximum is quasi-stationary. This perhaps explains the weaker relationship seen during the growth phase, and the less exact match between meridional flow and SIC anomalies. It may be useful in future work to analyze propagating signals in both the H500 and SIC fields.

During both the growth and maximum ice periods, the leading SIC patterns seen in the lagged MCA are similar in form to the leading EOFs of the SIC field (for the matching periods), suggesting that much of the coherent variability in Antarctic sea ice concentration at these times of year is attributable to atmospheric wave activity. The time delay between the development of the anomalous atmospheric circulation and the maximum sea ice response is 4–5 days.

A potential explanation of these results is that wave trains in the H500 field induce, through thermal (anomalous atmospheric heat flux) forcing, regions of SIC growth and decay that maximize on average a few days later. It is also likely that the atmospheric influence includes a component of mechanical forcing (Harangozo 2004). Another possible factor is the role of ocean wave energy, which can break the ice at the sea ice edge, influencing ice extent by enhancing melt rates in summer and growth rates in winter (Dumont et al. 2011).

To investigate the mechanisms responsible, a series of composites of anomalous sea ice motion vectors and midtropospheric meridional thermal advection (υ′T′ at 500 hPa calculated from temperature and meridional wind anomalies) were calculated. Fields were averaged over the dates associated with the upper and lower quartiles of the amplitude time series of the leading anomalous sea ice concentration patterns shown in Figs. 5 and 6. The resulting composite averages show the anomalous meridional thermal advection and sea ice motion associated with the positive (upper quartile) and negative (lower quartile) polarities of the sea ice concentration patterns.

Figure 7 shows the result for the ice maximum period, based on the leading sea ice pattern and time series shown in Fig. 5. The regions of reduced sea ice (central Pacific in the positive phase and Bellingshausen Sea/Antarctic Peninsula in the negative phase) are associated with coherent regions of negative (poleward) thermal advection. Areas of increased sea ice are only weakly associated with positive (equatorward) thermal advection. The anomalous ice motion vectors show that the region over the Bellingshausen Sea is associated with ice increase when the motion is equatorward and ice decrease when the motion is poleward. The region over the central Pacific is associated more with zonal motion, eastward when ice concentration is reduced, and westward when it is increased.

Fig. 7.
Fig. 7.

Composite average: (top) anomalous meridional temperature advection (υ′T′; units: K m s−1) and (bottom) anomalous sea ice motion for the (left) top and (right) bottom quartiles of the amplitude time series of the leading sea ice pattern from Fig. 5 (MCA during ice maximum). In (top), the contour interval is 2 K m s−1, red (blue) shows positive (negative). In (bottom), the longest arrows represent an ice speed of 4 cm s−1 (arrows shown at every second grid point, for clarity). The black lines show the outer contours of the MCA pattern of Fig. 5, negative (ice decrease) shown as dashed and positive (increase) as solid.

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

Composites for the ice growth period are show in Fig. 8. Again, regions of reduced sea ice are associated with coherent regions of negative (poleward) thermal advection while areas of increased sea ice are only weakly associated with positive (equatorward) thermal advection. During the growth season, the region over the Weddell Sea is associated with ice increase when the motion is equatorward and ice decrease when the motion is poleward (as was seen west of the Antarctic Peninsula during the period of maximum ice extent). Over the Pacific, areas of anomalous sea ice concentration are associated with relatively weak sea ice motion anomalies, which are largely zonal.

Fig. 8.
Fig. 8.

As in Fig. 7, but for the amplitude time series of the leading sea ice pattern from Fig. 6 (MCA during ice growth).

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

In summary, both thermal (thermal advection) and mechanical (ice motion) influences appear to contribute to the leading patterns of sea ice concentration variability found here. The picture is quite complex: anomalous poleward advection of warm air appears to contribute to reductions in sea ice, while anomalous sea ice motion (both meridional and zonal) appears to contribute to both increases and decreases in sea ice concentrations regionally. The details of such interactions are the subject of further research.

For the ice decay and ice minimum periods, relationships were weaker but still statistically significant (squared covariance fraction around 20%–25%, time series correlations around 0.6). The leading H500 patterns (not shown) were a combination of the SAM and a wave train across the South Pacific. The leading SIC patterns accounted for only around 5% of the sea ice variability and did not resemble the leading SIC EOFs for those times of year. This suggests that atmospheric circulation variability is not the dominant influence on weekly-scale SIC variability at those times of year. What does most strongly modulate sea ice variability during the decay and minimum periods is worth further study.

b. Sea ice clustering

The MCA approach is inherently linear, giving equal weight to positive or negative excursions of the patterns shown in Figs. 5 and 6. To assess the linearity of H500-SIC relationships, a cluster analysis was carried out on the SIC fields, followed by a series of lagged composites of the H500 field based on the dates of occurrence of each of the SIC clusters (as described in section 2c).

Consistent (reproducible between trials) clusters were found only during the maximum ice period, and then only for the leading pair of clusters. Stopping at two clusters produced virtually the same result for each of the 20 trials, while stopping at 3–6 clusters produced a similar leading pair in almost all trials, with subsequent patterns varying in form between trials. The two most reproducible cluster means (allowing the clustering algorithm to proceed to stopping at two clusters) are shown in Fig. 9. The patterns shown in Fig. 9 come from trial 9 of the clustering procedure, which is typical of the set of trials in terms of relative frequency of occurrence of the two clusters.

Fig. 9.
Fig. 9.

SIC cluster means for two clusters during the maximum ice period. Positive contours are red, negative contours are blue, and the zero contour has been omitted. The contour interval is 0.02 (fractional ice concentration anomaly). The numbers in the top right of each panel indicate the frequency of occurrence of each cluster.

Citation: Journal of Climate 25, 17; 10.1175/JCLI-D-11-00423.1

The leading two clusters are almost perfectly negatively correlated in space and have very similar spatial patterns to the first MCA-based SIC pattern (Fig. 5). This suggests that these patterns of SIC anomalies may be considered to vary linearly and are forced in a linear fashion by the associated atmospheric circulation anomaly pattern shown in Fig. 5. As a check on the latter point, lagged composites of the H500 anomaly field were generated, based on the dates of occurrence of each of the clusters (not shown). The H500 composites were also strongly negatively correlated in space and were very similar in form to the top-left panel of Fig. 5.

When the clustering was stopped at four clusters, the leading (most frequent) pair were again as in Fig. 9, while the subsequent pair were not strongly negatively correlated in space, nor were they always similar in form to the second MCA pattern of Fig. 5. Such a result (and the general lack of stability of the cluster analysis results in other seasons) suggests that beyond the leading MCA pattern of Fig. 5, atmosphere–SIC interactions are somewhat nonlinear and may not be fixed spatially. That is not to say that the patterns in Fig. 6, and the second MCA pattern in Fig. 5, are not physically meaningful, rather that they represent an average response that in nature exhibits significant variability in space and that is not equally frequently represented in both positive and negative polarities.

c. Relationship with tropical variability

Tropical heating anomalies associated with ENSO and the MJO are known to be related on the monthly-seasonal time scale to Rossby wave propagation across the southern Pacific (Mo and Higgins 1998; Renwick and Revell 1999), and to the SAM (L’Heureux and Thompson 2006). The weekly time scale analyzed here is likely to be too short, and too dominated by extratropical synoptic-scale variability, to see any clear links to the tropics. Time series of the H500 patterns shown in Figs. 5 and 6 were, however, correlated against tropical OLR anomalies and with MJO indices RMM1 and RMM2 (Wheeler and Hendon 2004), at a range of time lags. As expected, results were generally statistically insignificant, suggesting both that tropical teleconnections are not consistently evident on such short time scales and that internal variability is a significant component of the circulation patterns shown here.

5. Discussion and summary

The Antarctic sea ice field varies very strongly in spatial extent through the year, with distinct patterns of variability in different seasons. Submonthly time-scale linkages with the atmospheric circulation are strongest near the time of maximum sea ice extent (August–October, approximately), where anomalous meridional flow is associated with variations in sea ice concentration (SIC), especially across the Pacific and southern Atlantic Oceans. Such relationships are also evident during the ice growth period (May–July, approximately). At both times of year, the patterns of atmospheric circulation that are most strongly linked to SIC variations are associated with a wave train across the southern Pacific Ocean with a horizontal scale equivalent to zonal wavenumber 3. The lack of such linkages between SIC and atmospheric circulation during the decay and summer seasons is interesting and deserving of further research.

While seasonal time-scale linkages between atmospheric circulation and sea ice (e.g., Renwick 2002; Yuan and Li 2008) do not show consistent evidence of lead–lag relationships, on the weekly scale investigated here there is a clear indication that the atmospheric circulation tends to lead the SIC field, by around 4 or 5 days on average. The linkage between the atmospheric circulation and the sea ice field appears related to thermal and to mechanical forcings, but relationships are not straightforward. Poleward flow (warm advection) is most clearly associated a few days later with decreased SIC, but equatorward flow (cold advection) is not strongly related to increased SIC, at least at the large spatial scales considered here. Movement of the sea ice field, driven by the anomalous circulation pattern, appears to play a role over the Bellingshausen and Weddell Seas, but is less obviously related to SIC variations over the central Pacific. The details of how these mechanisms affect the sea ice field are the subject of further research.

Based on the analysis presented here, there is little indication that the key SIC-related atmospheric circulation patterns are forced by tropical heating anomalies, which suggests that they may instead be generated by internal variability in the midlatitude circulation. This is in contrast to the situation on the seasonal time scale, where there is a clear link between ENSO, the atmospheric circulation across the South Pacific, and the Antarctic sea ice field (Renwick 2002; Yuan 2004). It may be possible that links to tropical intraseasonal variability would be more evident after filtering out synoptic-scale activity, and this is a topic of ongoing research.

Variability and trends in Antarctic sea ice depend upon the nature of atmospheric and oceanic forcing on times scales from weeks to decades. The results presented here suggest that to properly characterize future variability in Antarctic sea ice concentration and extent, climate models need to properly simulate changes in circulation variability on all time scales.

Acknowledgments

The sea ice data were kindly provided by NSIDC in Boulder, Colorado; OLR data and the NCEP–NCAR reanalyses were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado. JR is grateful to Mike Wallace and Todd Mitchell for initial ideas about the analyses presented here and to Mike Williams, Craig Stevens, and especially Tim Haskell for the opportunity to observe Antarctic sea ice first-hand in McMurdo Sound. Funding for this research was provided by the New Zealand Foundation for Research, Science and Technology through Contract C01X0701.

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