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Dominant Covarying Climate Signals in the Southern Ocean and Antarctic Sea Ice Influence during the Last Three Decades D. CERRONE Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Naples, Italy
G. FUSCO Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Naples, and Consorzio Nazionale Interuniversitario per le Scienze del Mare, Rome, Italy
I. SIMMONDS School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia
G. AULICINO Dipartimento di Scienze della Vita e dell’Ambiente, Università Politecnica delle Marche, Ancona, Italy
G. BUDILLON Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Naples, Italy (Manuscript received 9 June 2016, in final form 17 January 2017) ABSTRACT A composite dataset (comprising geopotential height, sea surface temperature, zonal and meridional surface winds, precipitation, cloud cover, surface air temperature, latent plus sensible heat fluxes, and sea ice concentration) has been investigated with the aim of revealing the dominant time scales of variability from 1982 to 2013. Three covarying climate signals associated with variations in the sea ice distribution around Antarctica have been detected through the application of the multiple-taper method with singular value decomposition (MTM-SVD). Features of the established patterns of variation over the Southern Hemisphere extratropics have been identified in each of these three climate signals in the form of coupled or individual oscillations. The climate patterns considered here are the southern annular mode (SAM), the Pacific–South American (PSA) teleconnection, the semiannual oscillation (SAO), and the zonal wavenumber-3 (ZW3) mode. It is shown that most of the sea ice temporal variance is concentrated at the quasi-triennial scale resulting from the constructive superposition of the PSA and ZW3 patterns. In addition, the combination of the SAM and SAO patterns is found to promote the interannual sea ice variations underlying a general change in the Southern Ocean atmospheric and oceanic circulations. These two modes of variability are also found to be consistent with the occurrence of the positive SAM/negative PSA (SAM1/PSA2) or negative SAM/positive PSA (SAM2/PSA1) combinations, which could have favored the cooling of the sub-Antarctic region and important changes in the Antarctic sea ice distribution since 2000.
1. Introduction Ocean–atmosphere coupling is a leading factor influencing cryosphere variability in the Southern Ocean (SO). Sea ice is an important active component of the global climate system, and its spatial distribution and
Corresponding author e-mail: D. Cerrone, dario.cerrone@ uniparthenope.it; G. Fusco,
[email protected]
temporal variability are strongly conditioned by the characteristic patterns of variations in the atmospheric and oceanic circulation over the Southern Hemisphere (SH) extratropics on a wide range of time scales. The atmospheric circulation exhibits a large number of modes of variability that are often associated with remote ‘‘teleconnections’’ (Simmonds and King 2004), these being persistent and preferred large-scale low-frequency patterns of intercorrelated circulation anomalies within
DOI: 10.1175/JCLI-D-16-0439.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
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the atmosphere, typically lasting weeks or months. These patterns affect weather and circulation systems, generally occurring at regional scales but also spanning ocean basins and continents (Wallace and Gutzler 1981; Mo and Livezey 1986; Barnston and Livezey 1987; Bretherton et al. 1992; Simmonds et al. 2005). This study investigates the dominant time scales of variability and covariability in a composite dataset (comprising geopotential height, sea surface temperature, zonal and meridional surface winds, precipitation, cloud cover, surface air temperature, latent plus sensible heat fluxes, and sea ice concentration) over 1982–2013, through the application of the multiple-taper method with singular value decomposition (MTM-SVD). The aim is to detect the leading climate signals that are associated with variations in the spatial and temporal sea ice distribution around Antarctica, and diagnose their generating mechanisms, characteristics, and local effects over the SO. We will relate these signals to four key SH climate modes of variability, namely (i) the southern annular mode (SAM), (ii) the regional-scale El Niño– Southern Oscillation (ENSO) teleconnection pattern [the Pacific–South American (PSA) pattern], (iii) the semiannual oscillation (SAO), and (iv) a quasi-standing and zonal wavenumber-3 (ZW3) pattern. These patterns are formed by a range of dynamic and thermodynamic processes with distinct spatial structures and temporal characteristics. Theoretical and observational considerations suggest that these climate modes influence variations in the sea ice distribution around Antarctica, modulating its growth and decay from seasonal to longer time scales (de Magalhães Neto et al. 2012; Teleti and Luis 2016; Kohyama and Hartmann 2016). Recent observations indicate that the total Antarctic sea ice extent has increased in recent decades, reaching regional maxima in the period over which satellite measures are available (Simmonds 2015). The cause of expansion of sea ice in a time of global warming is currently the topic of much scientific debate. Nonetheless, there are compelling reasons to believe that the recent trends in the total sea ice result from variations in the low-frequency variability experienced by the leading circulation patterns diagnosed here. The following analysis focusses on sea ice concentration (SIC). It would have been of value to include sea ice thickness in our analysis. However, although a number of satellitederived approaches exist for estimating this parameter (Tamura et al. 2007; Aulicino et al. 2014), most of them are either still under validation or limited to a certain sea ice thickness range, type, and/or region and so are not suitable to be included in our study. Our overall aim is to investigate the relationships between
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the climate modes and sea ice, and their variations over time.
2. Key relevant modes of SH atmospheric variability To set a context for our analysis we provide a brief overview of the four modes of atmospheric variability of interest here, namely the SAM, PSA, SAO, and ZW3 patterns.
a. The SAM The SAM is a large-scale low-frequency pattern related to the variation in the strength of the midlatitude westerlies (Thompson and Solomon 2002). This signal can also be seen in terms of large-scale anomalies in near-surface pressure, characterized by anomalies of opposite sign in the midlatitude and sub-Antarctic regions. This mode can be identified throughout the year in the troposphere (Thompson and Wallace 2000) and it is maintained by a positive feedback between eddy activity in the mid- and high latitudes and zonal mean flow (Lorenz and Hartmann 2001; Rashid and Simmonds 2004). Hall and Visbeck (2002) showed that the positive SAM phase leads to anomalous northward sea ice transport resulting in thinner sea ice near the coast and thicker ice near the edge, and vice versa in negative phases of the SAM. Lefebvre et al. (2004) argued that the sea ice responds to SAM oscillations as of out-ofphase concentrations in the Ross and Weddell Seas, rather than a zonally symmetric response. Liu et al. (2004) pointed out that the observed SAM index trend could not explain the total trend in Antarctic sea ice of the Amundsen–Bellingshausen and Weddell Seas, and Yuan and Li (2008) remarked that although the influence of SAM on sea ice is fairly uniform around Antarctica, it is somewhat more important in the south Indian Ocean and also plays a less important role on the interannual scale.
b. ENSO and PSA pattern ENSO is a dominant global-scale climate pattern, originating from ocean–air interactions in the tropical Pacific (Turner 2004). The two opposing phases of ENSO are usually referred to in terms of SST anomaly in the equatorial eastern Pacific: the warm event or El Niño and the cold event or La Niña. ENSO affects the climate at local scale (Kiladis and Diaz 1989; Bell and Halpert 1998; Bell et al. 1999) in the midlatitudes (Ropelewski and Halpert 1986, 1989; van Loon and Shea 1987; Hoerling et al. 2001, 2004) and high latitudes (Cullather et al. 1996; Bromwich et al. 2000; Genthon and Cosme 2003; Fogt and Bromwich 2006). The ENSO
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teleconnection to southern high latitudes, known as the PSA pattern (Mo and Higgins 1998; Irving and Simmonds 2016), manifests as a standing wave train of anomalies that extends southeastward through to the Amundsen and Bellingshausen Seas, crosses the Antarctic Peninsula, and extends into the southwestern Atlantic Ocean. The PSA structure is consistent with the Rossby wave response to equatorial anomalous heating (Hoskins and Karoly 1981), ranging from daily (Mo and Ghil 1987) to interseasonal (Mo and Higgins 1998), interannual (Kidson 1988a,b), and interdecadal time scales (Garreaud and Battisti 1999). Several studies have investigated the relationship between the ENSO phenomenon and the sea ice fields around Antarctica (Chiu 1983; Carleton 1989; Simmonds and Jacka 1995; White and Peterson 1996; Yuan et al. 1996; Smith et al. 1996; Ledley and Huang 1997; Carleton et al. 1998; Yuan and Martinson 2000, 2001; Harangozo 2000; Kwok and Comiso 2002; Martinson and Iannuzzi 2003). The sub-Antarctic branch of the PSA assumes a strong out-of-phase sea level pressure (SLP) relationship between the Amundsen–Bellingshausen Seas and the Weddell Sea, the ‘‘Antarctic dipole,’’ which impacts regional surface temperature and sea ice anomalies (Yuan and Martinson 2001). In a typical El Niño, the Amundsen Sea low is weaker and the Weddell Sea low stronger than normal, whereas the reverse holds during La Niña. The dipole can persist for 3–4 seasons.
c. The SAO The SAO is a coupled ocean–atmosphere mode of variability that is directly linked to the seasonal cycle and is manifest in the mid-to-high latitudes in the SH extratropics (van Loon 1967; Meehl 1991; Simmonds and Jones 1998; Walland and Simmonds 1999). This twice-yearly cycle in zonal pressure and/or height in the sub-Antarctic coastal region is associated with the ‘‘coreless winter’’ (van Loon 1967). The SAO of the meridional temperature gradient in the high southern latitudes results from the differing annual cycles of the temperatures over the Antarctic continent and the midlatitude oceans. The SAO is closely related to the latitude location of the circumpolar trough, which undergoes contraction and strengthening from June to September and from December to March, and expansion and weakening from March to June and from September to December. The atmospheric convergence related to the SAO influences the seasonal advance and retreat of the ice favoring its slow northward advance in fall and winter and its fast retreat in spring and summer (Enomoto and Ohmura 1990). The SAO also influences the open water areas within the sea ice pack (Watkins and Simmonds 1999).
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d. Zonal wavenumber 3 The ZW3 pattern exhibits its greatest amplitudes in the 458–558S belt, and varies on a wide spectrum of time scales, from daily (Mo and Ghil 1987; Kidson 1988a,b) to interseasonal (Mo and White 1985, hereinafter MW85; Irving and Simmonds 2015) and interannual (Karoly et al. 1989). It is characterized by geographically preferred locations in the atmosphere’s pressure–height field exhibiting three alternating low and high centers of action in the Indian, Pacific, and Atlantic Ocean sectors. Yuan et al. (1999) showed that the three southerly wind branches associated with this pattern coincide with three northward maximum extent of sea ice edge during late winter 1996, suggesting its role in influencing the sea ice edge at synoptic time scales. The phase of ZW3 influences the preferred locations for cyclogenesis in the open ocean north of the ice cover (Yuan and Li 2008) and the subsequent eastward translation of these impacts on sea ice ‘‘downstream’’ (Godfred-Spenning and Simmonds 1996). In a similar vein Raphael (2004) pointed out that the pattern also plays an important role in setting up meridional transports (warm air to the south in some sectors and cold air to the north in others) that influence the SIC distribution. In this study the main features of these leading circulation patterns in the SH are examined to understand their role in modulating the sea ice distribution from regional to large spatial scales. The interdecadal changes affecting the ocean–atmosphere–ice coupled system at middle and high southern latitudes are also investigated here to relate their variability to the trends observed in SIC. This paper is laid out as follows. Data sources and methods of analysis are described in section 3. The dominant atmospheric signals over the SO and their impacts on sea ice are presented in section 4. The spatial structures of the key covariance modes are examined in section 5. Interdecadal variations in sea ice are investigated in section 6, and a summary and conclusions are presented in section 7.
3. Data and methods a. Gridded datasets We use the NCEP–NCAR reanalysis (Kalnay et al. 1996) data, and from it extract monthly mean values of 500- and 850-hPa geopotential height (Z500 and Z850), total air column precipitable water (PRWAT), meridional wind stress (MWS) and zonal wind stress (ZWS) at 10 m above the surface, surface air temperature (SAT) at 2 m above the surface, surface latent plus sensible heat fluxes (HF), and cloud cover (CLCO). The first three fields are available on a 2.58 longitude–latitude global
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grid, while the others are on the model spectral T62 resolution with Gaussian grid of 192 3 94. Reanalysis products are classified according to how much the variable is influenced by observed data as opposed to the characteristics of the assimilating model. Accordingly, Z850 and Z500 data are classified as the reanalysis class A (‘‘strongly influenced by observed data’’); MWS, ZWS, SAT, and PRWAT as class B (‘‘model has a very strong influence on the analysis value’’); and HF and CLCO as class C (‘‘derived solely from the model fields’’) (Kalnay et al. 1996). This suggests that care should be taken in the interpretation of heat flux and CLCO results. Optimum interpolation (OI) version-2 data (Reynolds et al. 2002) of sea surface temperature (SST) and SIC provided by the National Oceanic and Atmospheric Administration (NOAA) have also been included in this study. The OI analysis is produced on a 18 longitude– latitude grid using in situ and satellite SST plus SST simulated by sea ice cover. The sea ice field shows the approximate monthly average of the ice concentration values stored as the percentage of covered area. Simulated SST corresponds to a value of 21.88C (i.e., the freezing point of seawater) with a salinity of 34 practical salinity units (psu), in grid boxes where the SIC is at least 90%. The region of analysis is from 308S to the South Pole, and all records span the 32-yr period from January 1982 to December 2013. Monthly mean standardized anomalies are computed prior to the analysis by subtracting the climatological monthly means from the original data. This procedure removes the seasonal cycle from the data.
b. Analysis techniques We conduct part of our analysis with the MTMSVD, a powerful multivariate signal detection technique developed by Mann and Park (1999). This approach combines the spectral MTM (Thomson 1982) with a principal component analysis (PCA) using SVD to identify statistically significant narrowband oscillations that are correlated among a large number of time series (Mann and Park 1994, 1996; Tourre et al. 1999; Venegas and Mysak 2000; Venegas 2003). The MTM is a nonparametric procedure designed to reduce the variance of spectral estimates by using a small set of orthogonal tapers (see, e.g., Venegas 2003). A set of independent spectral estimates is computed by premultiplying each grid point time series by these tapers, which are constructed to minimize the ‘‘spectral leakage’’ associated with the finite length of the dataset. In this application the choice of k 5 3 orthogonal tapers with bandwidth parameter p 5 2 offers a good compromise between the required frequency resolution and stability (in terms of variance properties) of the spectral
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estimate. At each frequency the spectral estimates for the time series have been formed into a matrix on which an SVD is then performed. Finally, a local fractional variance (LFV) spectrum is constructed by plotting the percentage of variance explained by the first (i.e., dominant) mode at each frequency (Mann and Park 1999). The leading time scales of variability are then identified as the frequencies showing the largest significant peaks in the LFV spectrum. A significant frequency is selected from the LFV spectrum and then the first spatial and spectral singular vectors of the SVD decomposition are used to reconstruct the spatial and temporal patterns of the dominant signal within the chosen frequency band. In the presentations below we display these anomaly patterns as sequences of consecutive snapshots spanning one-half of an average oscillation of the corresponding period. The significance levels for the LFV spectra have been determined through bootstrap resampling calculations. This resampling, as a function of frequency and mode, destroys any time-domain structure and keeps spatial structure intact in order to estimate a null distribution of the LFV parameter for spatially colored noise in the absence of signal (Mann and Park 1999). This approach is applied to all relevant variables in this investigation. Prior to the application of MTM-SVD analysis, the SST and SIC data have been interpolated to 2.58 3 2.58 grids, the same resolution as the NCEP–NCAR reanalysis dataset. Subsequently, resampling analyses have been performed for further reducing all the reanalysis data to the regular 58 3 58 grids involved in the calculation of LFV spectra. Note that the spatial and the temporal patterns of the dominant signals have been reconstructed using 2.58 3 2.58 grids to retain their high spatial resolution.
4. Dominant signals in the Southern Ocean and impacts on sea ice The application of the MTM-SVD analysis to the nine variables under consideration allows us to isolate the dominant signals modulating the sea ice field. The LFV spectrum of the joint extratropical anomaly fields (Fig. 1) shows coherent significant variability ranging from around annual/quasi-biennial (1–0.52 cpy) to interannual (0.33–0.19 cpy) time scales. Five highly significant (.99% confidence level) signals have been detected at around 1.4-yr (0.70 cpy), 1.5-yr (0.66 cpy), 2-yr (0.49 cpy), 2.7-yr (0.37 cpy), and 4-yr (0.25 cpy) periods. As our aim is to investigate variability ranging from biennial to interannual scales, only the signals detected at 2-, 2.7-, and 4-yr periods have been subject to further investigation. The reconstructed spatial patterns
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FIG. 1. (a) LFV spectrum of the joint Z850, SST, SIC, SAT, ZWS, MWS, HF, PRWAT, and CLCO anomalies. (b) LFV spectrum for SIC anomalies. (c) LFV spectrum for the joint SIC and Z850 anomalies. (d) LFV spectrum for the joint SIC and SAT anomalies. (e) LFV spectrum for the joint SIC and PRWAT anomalies. All the spectra are based on the 32-yr period 1982–2013 and the region of analysis is from 308S to the South Pole. Dotted, dashed, and dashed–dotted lines indicate the 90%, 95%, and 99% significance levels respectively and are obtained through a bootstrap resampling technique.
at these three periods display different structures, and we consider each separately. Note that the frequency resolution of the LFV spectrum varies between 1/(NDt) and p/(NDt), where N 5 384 samples (32 years 3 12 months) is the length of the time series, Dt 5 1/12 is the sampling interval, and p 5 2 is the bandwidth parameter of the tapers, thus corresponding to oscillations
with periods between 0.03 and 0.06 cpy. We point out that the three signals account for a large fraction of covariance in their respective frequency bands. The 2- and 2.7-yr signals explain 51% of the covariance in both the biennial and quasi-triennial bands, and the 4-yr signal explains 52% of the covariance in the interannual band.
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We have also undertaken similar MTM-SVD analyses for each of the geophysical variables considered. We then calculate the LFV spectra combining Z850, SST, and SIC anomalies with the other variables. These fields are treated in pairs to reveal how they influence, or are influenced by, the other atmospheric and oceanic variables. Individual LFV spectra have shown that the 2-yr signal is significant at 99% level for the Z850 anomalies, as is the 2.7-yr signal for the SST, CLCO, PRWAT, and SIC anomalies. As for the 4-yr signal, it is significant (p , 0.01) only for the SST anomalies (Table 1). The SIC does not display significant signals at either 2- or 4-yr periods (Fig. 1b), indicating that the modulations at 2.7 yr are key to its behavior. The combined LFV spectra have shown that the Z850 anomalies are strongly linked to ZWS, MWS, SAT, HF, CLCO, PRWAT, and SIC anomalies only at 2 yr, for which a significant signal at the 99% level is found for all combinations (Table 2) (note that the coupled Z850– SIC signal accounts for the greatest covariance of 72%). By contrast, no strong relationships between the Z850 anomalies and the remaining fields are identified at either 2.7- or 4-yr periods. The LFV spectra obtained from the combination of the SST anomalies and other parameters reveal no highly significant relationships at 2 yr. Conversely, the SSTs strongly covary with ZWS, HF, PRWAT, SIC, and CLCO at 2.7 yr (Table 2). Highly significant relationships between SST and ZWS, MWS, SAT, HF, PRWAT, CLCO, and SIC anomalies are also found at 4 yr. The LFV spectra combining SIC anomalies with the other fields indicate a significant association only with the Z850 at 2 yr (Fig. 1c). This confirms the previously observed strong relationship between the SIC variability and the tropospheric pressure pattern. Robust relationships are also observed between SIC and CLCO, SST, ZWS, and PRWAT anomalies at 2.7 yr, and between SIC and SST, HF, and SAT anomalies at 4 yr (Table 2). In particular, the coupled SIC–PRWAT 2.7-yr and SIC–SAT 4-yr signals (Figs. 1d,e) explain 57% and 58% of the covariance, respectively, suggesting that SAT and PRWAT play the strongest role in modulating the sea ice at these time scales. In summary, the SIC modulation is most marked on the quasi-triennial scale concomitant with variations observed in CLCO, SST, ZWS, and PRWAT anomalies. A different picture emerges on the biennial and interannual time scales, when only Z850, SAT, HF, and SST anomalies affect the SIC in a statistically significant fashion. The finding that the SST anomalies affect the SIC field only at 2.7 and 4 yr indicates that sea ice grows and decays through different dynamic and thermodynamic mechanisms in each detected signal. In the 2-yr
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signal, the pressure pattern induces variations in ZWS, MWS, SAT, HF, CLCO, and PRWAT anomalies that modulate SIC. According to our analyses, both the growth and melt of sea ice comes about mainly through air–sea interactions, and that these are not significantly associated directly with SST anomalies. On the contrary, in the 2.7- and 4-yr signals the pressure field does not play a prominent role in modulating SIC. This means that in these two signals the growth and melt of sea ice is mainly affected by the interaction between SST anomalies and the atmospheric fields. As stated above, the PRWAT field is strongly involved in the 2.7-yr signal while SAT anomalies play a leading role at the 4-yr period (Table 1). These findings are consistent with the hypothesis that variations in precipitation (Meehl et al. 2005; Zhang et al. 2007), pressure/geopotential height (Trenberth and Smith 2005), and surface air temperatures (Steig et al. 2009) over the SH and Antarctica have been important drivers of the sea ice variations observed in recent decades.
5. Spatial structures of the key covariance modes To appreciate the physical mechanisms involved in SIC modulation at the three periodicities, the spatial patterns for the relevant fields have been reconstructed at 2-, 2.7-, and 4-yr periods. Guided by the results presented in Table 2, the spatial patterns for Z850 and SIC anomalies at 2 yr; for Z850, PRWAT, SST, and SIC anomalies at 2.7 yr; and for Z850, SIC, SAT, and SST anomalies at a 4-yr period have been so reconstructed.
a. Biennial sea ice modulation (SIC 2 yr) The reconstructed spatial patterns of the Z850 and SIC anomalies associated with the 2-yr signal (Fig. 2) are presented as a sequence of four consecutive snapshots at phases 08, 458, 908, and 1358 over the first half of their 2-yr cycle. Note that these snapshots are separated by 3 months, and that the second half of the cycle exhibits opposite spatial structure. The Z850 anomalies show a quasi-standing ZW3 pattern in the midlatitudes and a superimposed zonally uniform pattern intersecting the Antarctic plateau. The latter is the SAM, and it is readily apparent in most of these phases. The three high pressure centers (HPCs) associated with ZW3 show interesting shifts during the cycle. The Atlantic and Indian Ocean HPCs experience a westward shift along the phase sequence, while a small eastward shift is exhibited by the HPC in the Pacific sector. These findings are consistent with a 158 easterly phase shift from fall to winter and back in spring (MW85; van Loon and Rogers 1984). Note that the SAM is characterized by three meridional branches of anomalies, which extend toward
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TABLE 1. Percentage of variance detected at 2 yr ( f 5 0.49 cpy), 2.7 yr ( f 5 0.37 cpy), and 4 yr ( f 5 0.25 cpy) in the individual LFV spectra performed for Z850, SST, SIC, HF, ZWS, SAT, PRWAT, and CLCO anomalies. Signals that did not attain the 90% confidence level are not included in the table. The significance levels of 99% are indicated by boldface numbers, 95% by italic numbers, and 90% by regular font. The period covered by the analysis is 1982–2013, and significance levels have been obtained through a bootstrap resampling technique. Geophysical variables
Signals
Z850
SST
SIC
HF
ZWS
SAT
PRWAT
CLCO
77 — —
— 55 60
— 57 —
— — 49
55 55 —
— — 60
53 59 52
— 49 48
2 yr ( f 5 0.49 cpy) 2.7 yr ( f 5 0.37 cpy) 4 yr ( f 5 0.25 cpy)
the midlatitudes (phase 458) showing an opposite polarity to the ZW3. This suggests that the SAM and ZW3 patterns are strictly linked in the 2-yr signal and cannot be considered separately. We comment that the variability observed across the Pacific and Atlantic sectors resemble, respectively, the tropically forced PSA teleconnection pattern and the Atlantic pattern identified by Simpkins et al. (2014). To quantitatively test this perception we make use of the PSA index of Yuan and Li (2008): 1 PSA index 5 [H1(508S, 458W) 1 H2(458S, 1708W) 3 2 H3(67.58S, 1208W)], where geopotential anomalies H1, H2, and H3 are those in the southwestern Atlantic, east of New Zealand, and in the Amundsen Sea, respectively. The PSA index is calculated for the four phases of the reconstructed 2-yr Z850 anomalies shown in Fig. 2a. The corresponding values of the index are 0.1555, 0.3752, 0.3751, and 0.1553, respectively. The magnitude of these values establishes the presence of the PSA pattern at the 2-yr period, particularly in phases 458 and 908. (We comment that
very similar results are obtained when the index is calculated using Z500 and SLP. This is to be expected as the relevant anomalies have a barotropic structure.) These results are consistent with the SAM–PSA–ZW3 pattern superposition found by Yuan and Li (2008) using SVD analysis, and the Hilbert transform approach of Irving and Simmonds (2016). Yuan and Li (2008) identified the leading coupled mode between SLP and SIC winter anomalies as accounting for 50%–60% of the total squared covariance for all seasons, while in this study the Z850–SIC coupled mode accounts for about 72% of total covariance in the biennial band for all calendar months.
AIR–SEA ICE RELATIONSHIPS The presence of the PSA pattern at the 2-yr period indicates a connection with tropical Pacific variability. We indicated earlier that previous literature suggested that the ice field around Antarctica linearly covaries with ENSO phases, showing that sea ice oscillations contain quasi-biennial and quasi-quadrennial periodicities associated with ENSO variations (Gloersen 1995). Yuan and Martinson (2000) also found periodicities of 2 and 5 years in sea ice extent around Antarctica and strong connections between sea ice anomalies in the
TABLE 2. Percentage of variance detected at 2 yr ( f 5 0.49 cpy), 2.7 yr ( f 5 0.37 cpy), and 4 yr ( f 5 0.25 cpy) in the LFV spectra performed combining Z850, SST, and SIC anomalies with the remaining geophysical fields considered in the study (see text). Combinations in pairs indicate how strong the relationship is between the investigated fields at the three frequencies analyzed. Signals that did not attain the 90% confidence level are not included in the table. The significance levels of 99% are indicated by boldface numbers, 95% by italic numbers, and 90% by regular font. The period covered by the analysis is 1982–2013 and significance levels have been obtained through a bootstrap resampling technique. Geophysical variables
Signals
2 yr ( f 5 0.49 cpy) 2.7 yr ( f 5 0.37 cpy) 4 yr ( f 5 0.25 cpy)
Z850 SST SIC Z850 SST SIC Z850 SST SIC
Z850
SST
SIC
HF
MWS
ZWS
SAT
PRWAT
CLCO
— — — — — — — — —
62 — — — — — — — —
72 — — — 54 — — 57 —
66 44 — — 51 46 57 55 47
65 — — — 49 — 57 55 47
67 49 52 — 52 54 — 55 —
65 — — — 54 53 — 59 58
67 — 50 60 56 57 — 56 50
64 — — 55 51 50 56 54 47
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FIG. 2. Spatial patterns of the reconstructed (a) Z850 and (b) SIC anomalies associated with the 2-yr signal. The patterns are presented as a sequence of four consecutive snapshots that represent (top)–(bottom) the phases 08, 458, 908, and 1358 of an average 2-yr period cycle. Phases 1808, 2258, 2708, and 3158 correspond to the same patterns with opposite sign (not shown). The time interval between snapshots is around 3 months. White-to-red (blue) colors indicate positive (negative) anomalies, with contours at intervals of 0.12.
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TABLE 3. Correlation coefficients of the main relationships between the geophysical fields in the 2-, 2.7-, and 4-yr signals. Only correlations for the first variable lagging by up 2 months and leading by up 6 months the second variable are displayed. The significance levels of 99% are indicated by boldface numbers and 95% by italic numbers. The box without a value is for the correlation that did not attain the 90% confidence level. Time series for each field are based on the average anomaly value associated with the oscillating global pattern (308–908S). Periods
Relationships
22
21
0
1
2
3
4
5
6
2 yr 2.7 yr
Z850–SIC Z850–SIC PRWAT–SST PRWAT–SIC Z850–SST SIC–SST SIC–SST SIC–SAT SST–SAT Z850–SAT Z850–SST
0.28 0.66 — 20.71 20.13 20.12 20.73 20.24 0.64 0.72 0.35
0.39 0.72 0.23 20.78 20.29 20.28 20.77 20.27 0.64 0.73 0.38
0.49 0.76 0.40 20.82 20.45 20.43 20.79 20.30 0.64 0.73 0.40
0.55 0.77 0.56 20.83 20.59 20.57 20.81 20.32 0.63 0.71 0.42
0.59 0.75 0.70 20.81 20.70 20.69 20.81 20.34 0.60 0.68 0.43
0.58 0.70 0.81 20.76 20.79 20.79 20.80 20.35 0.56 0.63 0.44
0.54 0.63 0.89 20.69 20.85 20.85 20.77 20.36 0.52 0.58 0.44
0.46 0.54 0.94 20.59 20.88 20.89 20.74 20.36 0.46 0.52 0.43
0.35 0.43 0.95 20.47 20.87 20.90 20.69 20.36 0.40 0.46 0.42
4 yr
Amundsen, Bellingshausen, and Weddell Seas and extrapolar climate. We remark that, although PSA features are found at 2 yr, the signal essentially is capturing the biennial component of the SAM. The asymmetries characterizing the zonal pressures across the subAntarctic are responsible for generating out-of-phase meridional atmospheric flows that locally affect the Antarctic ice edge (Pezza et al. 2012). The geostrophic relation implies warmer northerly winds on the western side of the HPCs inducing negative SIC anomalies around the SO, as these winds locally affect the SAT field, which in turn impacts the thermal contrast between the ocean surface and the lower troposphere. Opposite processes affect the eastern side of the HPCs and induce positive SIC anomalies. The strongest modulation of SIC anomalies is observed when a positive ZW3 pattern occurs in conjunction with a positive SAM pattern, and vice versa. In particular, the SIC shows positive anomalies where the geostrophic southerlies are located over the Bellingshausen–Amundsen Seas and the eastern Indian Ocean and western Pacific sectors. We observe that the strongest SIC oscillation is captured in phase 458, when the global pattern shows the minimum value of the anomaly. Contemporaneous with this variability, the Z850 pattern exhibits lower troposphere structure associated with that of the SIC. These considerations indicate that the 458 phases of SIC and Z850 anomalies capture the superposition between positive SAM and ZW3 pattern oscillations at the midlatitudes at 2 yr (Fig. 2). The opposite is apparent when the sign of the anomalies is reversed (not shown). The cross-correlation analysis (Table 3) shows that the maximum correlation (r 5 0.59) between the Z850 and SIC anomalies occurs when the former leads the latter by 2 months. This is consistent with the atmospheric forcing provided by SAM and ZW3 patterns (Yuan and Li 2008). We comment that the fact that the
global Z850 anomaly pattern positively oscillates when the zonally uniform sign of anomaly over Antarctica is positive underlies the leading influence of SAM on 2-yr SIC variability, in comparison to the influence of the ZW3 pattern at the midlatitudes (Fig. 2). In summary, positive SIC anomalies are seen when three low pressure centers (LPCs) over the midlatitude oceans accompany higher pressures over Antarctica and vice versa.
b. Quasi-triennial sea ice modulation (SIC 2.7 yr) The reconstructed spatial patterns for Z850, PRWAT, SST, and SIC anomalies associated with the 2.7-yr signal are presented in Fig. 3. The phase sequences of these fields reveal the presence of wave-2 and wave-3 patterns around Antarctica. No SAM influences across the SO are observed at this frequency. At phase 08, the SST and Z850 patterns show three positive and three negative anomalies around the hemisphere located in the Indian, Atlantic, and Pacific Ocean sectors, and one can see their presence extends from the sub-Antarctic to the midlatitudes (Figs. 3a,b). As expected from the arguments presented above, positive SST anomalies are located on the western side of three quasi-standing HPCs. The SIC responds to these thermal variations with negative anomalies (Fig. 3d). Figure 3c shows that this warm and humid air from the midlatitudes is associated with increased cloudiness and positive PRWAT anomalies. Similar reasoning can be applied to explain the opposite associations on the eastern side of the HPCs. The sequence of the plots for phases 458, 908, and 1358 highlights the eastward propagation of both the Z850 and SST patterns (Figs. 3a and 3b, respectively). This is driven, in part, by ocean–atmosphere feedback mechanisms that appear at different times and places. First, the quasi-standing HPCs force the underlying upper-ocean layers that passively respond showing positive SST anomalies on their westward side concomitant with
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FIG. 3. Spatial patterns of the reconstructed (a) Z850, (b) SST, (c) PRWAT, and (d) SIC anomalies associated with the 2.7-yr signal. The patterns are presented as a sequence of four consecutive snapshots that describe (top)–(bottom) phases 08, 458, 908, and 1358of an average 2.7-yr period cycle. Phases 1808, 2258, 2708, and 3158 correspond to the same patterns with opposite sign (not shown). The time interval between snapshots is around 4 months. White-to-red (blue) colors indicate positive (negative) anomalies, with contours at intervals of 0.12. White color indicates no anomaly.
positive PRWAT anomalies. Then these positive SST anomalies, transported eastward by the Antarctic Circumpolar Current (ACC) at the midlatitudes, cross locations where HPCs were previously located. As a result, the lower troposphere responds to the oceanic forcing, showing a change in pressure from high to low as the SST anomalies propagate eastward. These pressure variations also result in a change in PRWAT anomalies (Fig. 3c), which show positive anomalies concomitant with the tropospheric pattern modification from higher
to lower pressures. These feedback mechanisms are clearly observed in the Indian and Pacific Ocean sectors while their dynamics appear somewhat different in the Atlantic sector (Fig. 3).
1) DIPOLAR SEA ICE CONCENTRATION PATTERNS IN THE WEDDELL AND ROSS SEA SECTORS A noteworthy feature of the SIC variability is that both the Weddell and Ross–Amundsen–Bellingshausen Sea sectors exhibit meridional dipolar patterns in sea
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ice, most apparent in phases 08 and 1358 (Fig. 3d) (in addition to their exhibiting the well-known longitudinal dipole). We comment that this behavior is not present in either the 2- or 4-yr modes of covariability. These structures are due to the action of thermally different zonal winds that are induced locally by the pressure centers located in the open ocean north of the ice cover. In the western Weddell Sea sector cold and dry easterly winds affect the coastal region, whereas warm and humid northerly winds blow over the open ocean in the midlatitudes of the sector. These features occur because the LPC (the Amundsen–Bellingshausen Seas low; Fogt et al. 2012) in the southeastern Pacific sector, which profoundly affects the Bellingshausen–Amundsen Sea sector and the Antarctic Peninsula, induces warm and humid advection on its northern and eastern sides and, at the same time, draws colder and dry coastal easterly winds on its southern side as it approaches the Antarctic Peninsula. These coastal winds affect the ice edge resulting in negative SST and positive SIC anomalies (phase 08; Figs. 3b and 3d, respectively). The reverse occurs over the open ocean in the midlatitudes of the Weddell Sea sector, where positive SST anomalies, induced by the warmer northerly winds, are found in association with negative SIC anomalies. As a consequence, there is a meridional dipole in SIC. At phase 08 another important feature is observed in the eastern Weddell Sea sector around 08–158E. Here, the strong cold and dry southerly winds associated with the LPC centered between the eastern Weddell Sea and the western Indian Ocean sectors. These winds are also responsible for generating open water areas (polynyas) over which, as a result of their massive heat loss, large amounts of sea ice are produced (Jacobs and Comiso 1989; Van Woert 1999; Fusco et al. 2002, 2009; Tamura et al. 2008; Rusciano et al. 2013). Some of this ice is transported toward the midlatitudes and enters the eastward flowing ACC, while part is pushed back toward the west by the westward-directed coastal currents and enters the clockwise Weddell Gyre circulation. The negative SST anomalies, concomitant with positive SIC anomalies, are transported in this circulation and lead to a gradual cooling of the entire sector and favoring the development of a strong HPC. This is confirmed by the large negative PRWAT anomaly that amplifies over the Weddell Sea (phases 458 and 908; Fig. 3c). The opposite chain of events occurs when an anomalous anticyclone approaches the Antarctic Peninsula from the west (phase 1358; Fig. 3). The asymmetric pressure center developing in the South Atlantic sector is located farther to the north than in the Pacific and Indian Ocean sectors, resulting in stronger zonal than meridional atmospheric flows in the Weddell Sea sector. These considerations imply that SIC in the
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Weddell Sea is modulated by two different contributions, namely the zonal winds affecting the coastal area and the meridional advection affecting the open ocean region. Similar but opposite processes modulate SIC anomalies in the Ross Sea sector, as can be appreciated from the sequences in Fig. 3. This opposite behavior results in meridional dipole patterns of SIC anomalies, which have polarities different from those in the Weddell Sea sector.
2) POSITIVE AIR–SEA ICE FEEDBACK IN THE WEDDELL SEA As previously mentioned, the 2.7-yr signal exhibits ocean–atmosphere coupling processes around Antarctica involving PRWAT, SIC, and SST anomalies. As distinct from the Indian and Pacific Ocean sectors, where negative feedbacks link the lower troposphere with the upper ocean, in the South Atlantic dynamical mechanisms have a different character and are predominantly in the form of a positive feedback. At phase 08 the entire Weddell Sea sector is affected by a large wave of higher PRWAT anomalies that are directly linked to warmer and humid northerly winds impinging on the Antarctic Peninsula and the western Weddell Sea sector (Fig. 3c). These winds develop on the eastern side of the LPC located in the southeastern Pacific sector resulting in positive SST anomalies, which in turn influence heat fluxes in the open ocean area of the Weddell Sea in the midlatitudes (Figs. 3a,b). Here, the strong variability of the ACC flow, associated with mesoscale eddies spawning (Cotroneo et al. 2013), plays a crucial role in advecting the SST anomalies to the east and north, shifting them away from the Weddell Sea sector. This feature is unique because the Antarctic Peninsula represents an obstacle to the zonal flow. Simultaneously, negative SST anomalies are generated by cold and dry southerly winds on the western side of the LPC in the western Indian Ocean sector (Figs. 3a,b). These negative SST anomalies, which result in positive SIC anomalies, are advected westward by the coastal current, which plays a crucial role in incorporating the anomalies into the clockwise gyre circulation (phase 458; Figs. 3b,d). Here negative SST anomalies are located under a weak anticyclonic lower troposphere circulation that extends into the Weddell Sea sector. The associated low-level cooling stabilizes the atmospheric column and strengthens the high pressure system. The combination of positive SIC anomalies and the clear sky above results in strong heat loss and a further cooling of the surrounding air. This feature also affects SST anomalies, which become more negative (phases 908 and 1358; Fig. 3). This positive feedback results in higher pressure conditions that develop over the entire Weddell Sea sector.
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Distinct from the feedback processes affecting the Indian and Pacific Ocean sectors, in the Atlantic sector negative SST anomalies move westward to lie beneath the HPC and, in turn, reinforce the lower troposphere pressure pattern. Hence, the air–sea ice coupling in the Weddell Sea differs from its counterparts affecting the Indian and Pacific Ocean sectors, because in this case a strengthening between the fields is observed. We argue that the geographical position of the Antarctic Peninsula plays a crucial role in favoring the positive feedbacks affecting the Weddell Sea.
3) NEGATIVE FEEDBACK PROCESSES AROUND ANTARCTICA Lagged cross-correlation analysis (Table 3) shows that the greatest correlation magnitude between PRWAT and SIC anomalies is achieved when the former leads the latter by 1 month (r 5 20.83). A similar, but positive, relationship appears when Z850 leads the SIC anomalies by 1 month (r 5 0.77), demonstrating that the pressure pattern (and hence the winds) profoundly influences SIC variability. In particular, the positive correlation indicates that positive SIC anomalies are observed on the western side of the three LPCs in the midlatitudes. A negative correlation (r 5 20.88) is also found when the Z850 leads the SST by 5 months (Table 3), a circumstance that can be explained in terms of the advective arguments presented above. We also note that a strong positive correlation (r 5 0.89) is found when the SST leads the Z850 anomalies by 11 months (not shown), indicating a change in the polarity of the pressure anomalies (i.e., from low to high pressure). These air– sea interaction mechanisms are a little faster than, but consistent with, the findings of Venegas (2003), who showed that the SLP leads SST by 10 months in a ZW3 signal detected at around 3.3-yr period. She also showed the presence of a reversed SLP–SST relationship at 1-yr lag occurring in the signal. Here, the time lag through which the pressure pattern changes polarity is comparable also with the 1.25-yr lag found by Cai et al. (1999) using CSIRO model data. We also note that a strong positive correlation (r 5 0.95) is found when the PRWAT leads the SST and a negative correlation (r 5 20.90) is observed when the SIC leads the SST, both by 6 months (Table 3). Reasoning from arguments similar to those presented above regarding the temperature and moisture advection, one can appreciate that negative PRWAT anomalies impact SIC variability, and thence induce a lagged response in SST anomalies. The fact that the atmospheric forcing in the 2.7-yr signal is faster than the oceanic counterpart suggests the interaction with more than one of the climate modes. We note that the SST and PRWAT anomalies intensify
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in the Pacific sector during the eastward propagation (phases 458 and 908), whereas the circumpolar variability evolves from a ZW3 (phase 08) to a wave-2 pattern (phases 458 and 908) around Antarctica (Fig. 3). These observations indicate that the ENSO teleconnection pattern plays a role in the signal, which is essentially capturing ZW3 variability. The fact that the Z850 leads the SST by 5 months supports this hypothesis, as this lag is compatible with the 3–6-month lagged SST response to the ENSO signal (Klein et al. 1999). It is also in accord with the fact that the SST anomalies in the Pacific and Atlantic sectors resemble the PSA wave train. The PSA pattern can be seen as superimposed on the ZW3 pattern at the midlatitudes, giving rise to a constructive interference between these two climate modes (i.e., the resulting anomaly comes from the sum of the two anomalous contributions having the same sign). The feedbacks of SIC to SST anomalies described for Ross and Weddell Sea sectors provide further evidence of the likely influence of the ENSO teleconnection on the 2.7-yr signal. In the Ross Sea sector the SSTs involved in the PSA teleconnection have a quasi-contemporary oscillatory relationship with the SIC, while the SSTs have about a 1.3-yr lag oscillation relationship with Weddell Sea SIC. The magnitude of this latter lag is very similar to that found by Xie et al. (1994), who correlated central tropical Pacific SST and sea ice in the Weddell and Ross Sea sectors. In summary, we remark that the 08 phases of the Z850 and SIC patterns (Figs. 3a and 3d, respectively) are capturing the combination between a positive ZW3 oscillation at the midlatitudes and a positive PSA pattern across the southern Pacific and Atlantic sectors at 2.7 yr. The opposite occurs when the sign of the anomalies is reversed.
c. Interannual sea ice modulation (SIC 4 yr) The reconstructed spatial patterns for Z850, SST, SAT, and SIC anomalies associated with the 4-yr signal (Fig. 4) reveal a combination of eastward-propagating wave-1 and wave-2 patterns. In particular, the SAT anomalies show a leading wave-2 pattern around Antarctica with highest values in the southeastern Pacific, Atlantic, and western Indian Ocean sectors (Fig. 4c). The SST anomalies display a similar alternation between wave-1 and wave-2 patterns, with somewhat faster propagation in the Pacific and Indian Ocean sectors (Fig. 4b). The Z850 reconstructed patterns at phases 08 and 458 (Fig. 4a) display a strong SAM signature, in association with a ZW3 pattern in the midlatitudes. The variability features identified between the southwestern Pacific and Atlantic sectors are suggestive of a negative PSA-like teleconnection pattern in this 4-yr signal. The SAM
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FIG. 4. Spatial patterns of the reconstructed (a) Z850, (b) SST, (c) SAT, and (d) SIC anomalies associated with the 4-yr signal. The patterns are presented as a sequence of four consecutive snapshots that describe (top)–(bottom) phases 08, 458, 908, and 1358of an average 4-yr period cycle. Phases 1808, 2258, 2708, and 3158 correspond to the same patterns with opposite sign (not shown). The time interval between snapshots is around 6 months. White-to-red (blue) colors indicate positive (negative) anomalies, with contours at intervals of 0.12 for Z850 and SIC and 0.15 for SST and SAT. White color indicates no anomaly.
exhibits a meridional bulging of positive Z850 anomalies in the southeastern Pacific sector that are out of phase with the LPCs located in the southwestern Atlantic and southwestern Pacific sectors. At phase 908, the Z850 pattern clearly shows a belt of positive anomalies extending from the subtropics into the central Pacific (indicative of the transmission of the ENSO signal) and an LPC located to the south of New Zealand that is linked to the ZW3 (Fig. 4a). In phase 1358 the LPC moves eastward to the central Pacific and deepens. At the same time, the
ZW3 pattern in the midlatitudes reverses polarity and manifests as three HPCs in the South Atlantic, western Indian, and western Pacific Ocean sectors (Fig. 4a). This variability indicates the presence of a positive PSA pattern extending from the southern Pacific to the Atlantic sectors, which arises from the combination with the underlying negative ZW3 pattern. We also note that the reconstructed patterns partially resemble SAO features as the SST anomalies show stronger variations in the Indian and Pacific Ocean sectors (Fig. 4b).
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Although the covarying SAT–SIC mode accounts for 58% of total covariance in the interannual band (Table 2), SIC anomalies do not seem to play an important role in conditioning SAT variability. Note that although both SST and SAT anomalies explain 60% of the variance in the interannual band (Table 1), the former is more highly significant (99% vs 95%). This higher level could mean that the SIC pattern is more strongly conditioned by the SST at that periodicity. The revealed association of Z850 and SIC in phase 458 of these (Figs. 4a and 4d, respectively) demonstrates the combination of a negative SAM (SAM2) oscillation, a positive PSA (PSA1) pattern across the southern Pacific and Atlantic sectors, and a negative ZW3 pattern at the midlatitudes. The opposite is found when the sign of the anomalies is reversed. Cross-correlation analyses show that SST and SAT anomalies in this 4-yr period are maximally linked at 0 months lag (r 5 0.64), pointing to these two fields being phase locked (Table 3). This means that positive SAT locally reduces the thermal difference between the lower troposphere and the upper-ocean layer, resulting in reduced heat lost to the atmosphere and consequently in the warming of SST. A positive correlation is also found between the Z850 and SAT anomalies at 0 months lag (r 5 0.73), indicating near-simultaneous mutual modulation of these fields. Positive correlations are found when the Z850 leads the SST, and these achieve their maximum (r 5 0.44) when the lead is 3–4 months. The smaller correlations and the lagged response confirm that the pressure pattern plays a more influential role in modulating SAT anomalies than it does for SST anomalies. As another insight into the physical process operating in the 4-yr signal, we note that a strong correlation (r 5 20.81) is found when the SIC leads the SST by 2 months and a weaker correlation (r 5 20.36) is observed when the SIC leads the SAT anomalies by 5 months (Table 3). Thus, the SIC seems to play a dual role, cooling first the adjacent SST and then the air above. A realistic scenario in which we may see the net effect of these connections is that the 4-yr signal represents a winter mode of variability in which SIC anomalies influence the extratropical SST more than it does the SAT. An implication of this is that the 4-yr signal describes a combination between the lowfrequency SAM and PSA teleconnection patterns. The PSA promotes the SAT variability around the SO and, in turn, marginally affects the SST variability.
6. Interdecadal sea ice variations As a final aspect of our investigation we undertake a regression analysis in which we regress the unnormalized and unfiltered Z500 field at each SH grid point onto
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each of the three reconstructed SIC signals. This allows us to present a picture of the direct association between the midtropospheric circulation and the sea ice. The regression pattern associated with the SIC 2-yr signal (Fig. 5a) clearly indicates that the Antarctic sea ice grows under the combined influences of negative SAM and ZW3 patterns, as previously observed. It is worth noticing that there is little evidence of tropical influence in the pattern. By contrast, the Z500 regression pattern associated with the SIC 2.7-yr series (Fig. 5b) illustrates the combined impact of negative ZW3 and PSA oscillating patterns over the sea ice in a manner which promotes sea ice growth around Antarctica. Tropical influences can be identified in the regression pattern, in particular over the tropical western Indian Ocean. The structure of the Z500 regression pattern on the SIC 4-yr signal (Fig. 5c) is somewhat different to the two presented above. It shows bands of negative anomalies across the sub-Antarctic and positive anomalies in opposition in the midlatitudes. This meridional pressure dipole is associated with the promotion of sea ice growth, especially in the Pacific sector, and reflects SAO features contained in the 4-yr signal. For this period also, tropical influences of ENSO can be detected, in that the geopotential height anomalies over the southeastern Pacific sector and tropical western Indian Ocean exhibit the same sign. It is of especial interest to note, however, that the likely influences, including ENSO, on the SIC 4-yr signal are quite different from those in the SIC 2.7-yr signal. These results are consistent with recent analyses of coupled model experiments (e.g., Kostov et al. 2017; Holland et al. 2017) showing that the response of SIC and SST to the SAM variations depends strongly on the time scale being considered. To cast further light on the interpretation of the results presented above, in Fig. 6 we show the reconstructed SIC time series for the 2-, 2.7-, and 4-yr signals. The 2-yr signal shows large oscillations during the intervals 1983–88, 1995–99, and 2006–12, exhibiting the largest amplitudes during the second and the third of these subperiods; and the 2.7-yr period shows substantial variations during the 1983–87 and 1993–2012 periods, exhibiting largest amplitudes in the mid-1990s and in the mid-to-late 2000s. In contrast, the 4-yr signal exhibits major oscillations only since the early 2000s, thus indicating a change in circulation that could have conditioned SIC variability starting from this decade. This behavior seems to result from the PSA1/SAM2 [negative PSA/positive SAM (PSA2/SAM1)] combination (Fogt et al. 2011) emerging in the 4-yr signal. This combination is in contrast to the relationship between the SAM and PSA patterns recognized in the 2-yr signal, where negative
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FIG. 5. Spatial patterns generated by regressing the unnormalized and unfiltered 500-hPa geopotential height upon the temporal coefficients of the normalized SIC (a) 2-, (b) 2.7-, and (c) 4-yr signals. White-to-red (blue) colors indicate positive (negative) anomalies, with contours at interval of 1.2 m per unit of the temporal coefficient of each signal.
(positive) phase-locked oscillations of SAM and PSA occurred.
7. Summary and conclusions We have identified the dominant time scales of variability over the SO in a composite dataset (Z850, SAT, ZWS, MWS, CLCO, PRWAT, SST, HF, and SIC) through the application of the MTM-SVD technique. Three leading climate signals (at 2-, 2.7-, and 4-yr periods) from biennial to interannual time scales have been examined to understand how the Antarctic sea ice distribution has been modulated during the last three decades (1982–2013). The dominant SH circulation features, namely the SAM, SAO, PSA, and ZW3 patterns, have been detected in each of the three periodicities in the form of coupled and individual oscillations. More specifically, the 2-yr signal showed the
superposition of the positive phases of SAM, ZW3, and PSA patterns, while the 2.7-yr signal exhibited the combination between positive oscillations of the ZW3 and PSA patterns. The 4-yr signal reveals a superposition of positive oscillations of SAM and ZW3 patterns accompanied by the negative phase of the PSA pattern. The opposite relationships between these patterns are observed when the sign of anomalies in the three signals is reversed. Moreover, influences of SAO have been detected in the 4-yr signal, because the SST signal has shown a wave-1 pattern consistent with the circumpolar character of the SAO mode. While the 2- and 2.7-yr signals have shown important variations over the entire period, the 4-yr signal has experienced large variations only since the early 2000s. This finding is consistent with a general change in the SO circulation that has exerted an influence on SIC over the last decade (Simmonds 2015). In part, the
FIG. 6. Reconstructed SIC anomalies time series for the 2- (solid blue line), 2.7- (dashed black line), and 4-yr (dashed red line) signals.
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increase in the 4-yr signal since the early 2000s is a consequence of the opposite PSA and SAM phaselocked combination (Fogt et al. 2011), highlighting a more frequent occurrence of positive SAM phases in association with contemporaneous weaker influences of the ENSO teleconnection over the SO. The opposite SAM–PSA phase relationship detected in the 4-yr signal seems to have favored a marked cooling of the sub-Antarctic climate. This cooling, in turn, could represent the key factor driving the increased Antarctic sea ice extent since 2000. Acknowledgments. This study was performed in the framework of ‘‘Interocean Exchange of Antarctic Intermediate Water in the Southern Hemisphere South of South Africa’’ and of ‘‘Coastal Ecosystem Functioning in a Changing Antarctic Ocean’’ projects as part of the Italian National Program for Research in Antarctica (PNRA). Part of this research was made possible by an Australian Research Council grant (DP160101997) to Simmonds. Data used in this study are available from NOAA/OAR/ESRL PSD, Boulder, Colorado (online at http://www.esrl.noaa.gov/psd/). REFERENCES Aulicino, G., G. Fusco, S. Kern, and G. Budillon, 2014: Estimation of sea-ice thickness in Ross and Weddell Seas from SSM/I brightness temperatures. IEEE Trans. Geosci. Remote Sens., 52, 4122–4140, doi:10.1109/TGRS.2013.2279799. Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality, and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 1083–1126, doi:10.1175/ 1520-0493(1987)115,1083:CSAPOL.2.0.CO;2. Bell, G. D. and M. S. Halpert, 1998: Climate assessment for 1997. Bull. Amer. Meteor. Soc., 79, S1–S50, doi:10.1175/ 1520-0477(1998)079,1014:CAF.2.0.CO;2. ——, ——, V. E. Kousky, M. E. Gelman, C. F. Ropelewski, A. V. Douglas, and R. C. Schnell, 1999: Climate assessment for 1998. Bull. Amer. Meteor. Soc., 80, S1–S48, doi:10.1175/ 1520-0477(1999)080,1040:CAF.2.0.CO;2. Bretherton, C. S., C. Smith, and J. M. Wallace, 1992: An intercomparison of methods for finding coupled patterns in climate data. J. Climate, 5, 541–560, doi:10.1175/1520-0442(1992)005,0541: AIOMFF.2.0.CO;2. Bromwich, D. H., A. N. Rogers, P. Kållberg, R. I. Cullather, J. W. C. White, and K. J. Kreutz, 2000: ECMWF analyses and reanalyses depiction of ENSO signal in Antarctic precipitation. J. Climate, 13, 1406–1420, doi:10.1175/ 1520-0442(2000)013,1406:EAARDO.2.0.CO;2. Cai, W., P. G. Baines, and H. B. Gordon, 1999: Southern mid- to high-latitude variability, a zonal wavenumber-3 pattern, and the Antarctic circumpolar wave in the CSIRO coupled model. J. Climate, 12, 3087–3104, doi:10.1175/1520-0442(1999)012,3087: SMTHLV.2.0.CO;2. Carleton, A. M., 1989: Antarctic sea-ice relationships with indices of the atmospheric circulation of the Southern Hemisphere. Climate Dyn., 3, 207–220, doi:10.1007/BF01058236.
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