1. Introduction
Sea ice variability has substantial impacts on the exchange of heat and freshwater between atmosphere and ocean (Raphael 2003; Kurtz et al. 2011; Søren et al. 2011), ocean circulation (Kirkman and Bitz 2011; Ferrari et al. 2014), local weather systems (Vihma 2014; Smith et al. 2017; Ayres and Screen 2019), and ecosystems (Eicken 1992; Arrigo 2014). Therefore, it is imperative to investigate sea ice variability on different time scales and understand the underlying mechanisms from dynamic and thermodynamic perspectives (Turner and Comiso 2017). In contrast to the rapid decline of the Arctic sea ice extent (SIE) under global warming (Stroeve et al. 2007; Notz and Stroeve 2016; Serreze and Meier 2019), Antarctic SIE trend has displayed a complex pattern since the late 1970s (Parkinson 2019), with a record high in 2014 after long-term increases and then dropping to a record low in 2017 and 2022 (Turner and Comiso 2017; J. Wang et al. 2022; Turner et al. 2022). Therefore, the confidence of the long-term trend is low due to large year-to-year fluctuations (Yuan et al. 2017; Maksym 2019). Current climate models have difficulties simulating this variability precisely (Roach et al. 2020; Shu et al. 2015), requiring a better understanding of the driving mechanisms.
The dominant interannual variability structure of Antarctic sea ice is characterized by a dipole-like pattern with out-of-phase sea ice anomalies between the Pacific sector and Atlantic sector, called the Antarctic dipole (ADP; Yuan and Martinson 2001). Previous studies have linked the ADP with individual modes of large-scale climate variability like the Southern Annular Mode (SAM), El Niño–Southern Oscillation (ENSO), wavenumber-3 pattern, and semiannual oscillation, among which SAM and ENSO are the primary drivers (Liu et al. 2004; Simpkins et al. 2012; Maksym 2019). The positive phase of the SAM is characterized by an “annular” structure with a deep low pressure anomaly over Antarctica and a high pressure ring surrounding centered near 45°S (Rogers and van Loon 1982; Thompson and Wallace 2000; Fogt and Marshall 2020). The SAM index normally describes the changing intensity and position of westerly winds (Gong and Wang 1999; Thompson and Wallace 2000). Besides, the SAM pattern also contains a zonally asymmetric component, particularly in the Pacific Ocean, strongly connected to tropical variability and zonal wave-3 pattern (Fogt et al. 2012; Fogt and Marshall 2020; Campitelli et al. 2022).
ENSO events also dominantly impact Antarctic sea ice on interannual time scales (Kwok and Comiso 2002; Yuan 2004; Kwok et al. 2016; Zhang et al. 2021). The perturbation of tropical Pacific sea surface temperatures (SST) can affect atmospheric convection, triggering a Rossby wave train propagating southeastward (Hoskins and Karoly 1981; Karoly 1989; Yu et al. 2011). During El Niño (La Niña) events, this stationary wave results in anticyclonic (cyclonic) anomalies over the Amundsen Sea, leading to a weakening (strengthening) of the climatological Amundsen Sea low (ASL) (Turner 2004; Yuan 2004). Consequently, sea ice will increase (decrease) in the Bellingshausen Sea through cold southerly (warm northerly) winds, and sea ice will decrease (increase) in the Ross Sea through warm northerly (cold southerly) winds (Yuan 2004). This teleconnection has a strong correlation with the large-scale climate variability called the Pacific–South American pattern (Karoly 1989; Mo and Higgins 1998; Yu et al. 2015). The impact of ENSO on Antarctic sea ice is most significant during late austral winter and spring, due to the influence of background atmospheric conditions on the Rossby wave energy propagation (Jin and Kirtman 2009; Song et al. 2011; Simpkins et al. 2012; Yuan et al. 2018). In addition, there are asymmetric impacts between warm (El Niño) and cold (La Niña) ENSO events, stressing the importance of considering the nonlinearity of the sea ice responses (Yuan 2004; Simpkins et al. 2012; Y. Wang et al. 2022). Distinctive impacts also exist between central Pacific (CP) El Niño and eastern Pacific (EP) El Niño on the sea ice in austral spring due to the different locations of their tropical heat sources for atmospheric convection (Zhang et al. 2021). However, there are not enough samples for seasonal analysis during sea ice observations, so the ENSO events are not distinguished into two types in this study.
It is acknowledged that the impact of ENSO on atmospheric circulation at Southern Hemisphere mid–high latitudes depends on the phase of the SAM (L’Heureux and Thompson 2006; Stammerjohn et al. 2008; Fogt et al. 2011). Fogt et al. (2011) revealed that the impact of ENSO is significant only during in-phase conditions, i.e., when La Niña (El Niño) is concurrent with a positive (negative) SAM, or weak SAM conditions. During out-of-phase conditions, i.e., when La Niña (El Niño) is concurrent with a negative (positive) SAM, the impact is largely reduced by the opposing transient eddy momentum fluxes, indicating inverse wave activity fluxes and meridional energy transport in the midlatitudes through eddy–mean flow interactions (Trenberth 1986, 1991). This result is confirmed by Wilson et al. (2016), using the Community Atmospheric Model to assess the El Niño transient eddy dynamics under different SAM regimes. In addition, Gong et al. (2010) suggested that wave breaking characteristics associated with background zonal-mean flow explain the in-phase SAM–ENSO relationship. The correlations between SAM and ENSO are also affected by the type of ENSO (the CP and EP type) and are demonstrated to be more in-phase correlated after the early 1990s (Yu et al. 2015). Therefore, the combination of the SAM and ENSO should have particular influences on Antarctic sea ice through intensified atmospheric and oceanic anomalies. Stammerjohn et al. (2008) investigated the relationship between these combined impacts and the sea ice retreat/advance and showed a similar result to Fogt et al. (2011), with significant sea ice responses particularly in the western Antarctic Peninsula and southern Bellingshausen Sea. Pezza et al. (2012) showed that SAM and ENSO act in synergy on sea ice, with La Niña/positive SAM (LN/pSAM) presenting the most favorable conditions for overall sea ice growth except in the Bellingshausen Sea, using a case of the record-high SIE in summer 2008. However, the record-low Antarctic SIE in summer 2022 is assumed to be connected with the anomalously deep ASL (Turner et al. 2022; J. Wang et al. 2022), which also happened in the context of a combination of La Niña and pSAM.
However, the seasonal behavior of Antarctic sea ice under each combined SAM and ENSO phase, and their dynamic and thermodynamic contributions, has not been systematically investigated. Investigating these contributions helps us attain a better understanding of the complex feedbacks and interactions giving rise to the sea ice variations. Besides, fine representations of the dynamic and thermodynamic processes controlling sea ice changes are critical for the realistic simulations and reliable predictions of sea ice in climate models. Moreover, few studies pay attention to the influence of these large-scale climate modes on the intensification of sea ice concentration (SIC), which is the rate of sea ice change. We address these questions using the sea ice budget method from Holland and Kwok (2012), where the sea ice intensification is decomposed into advection, divergence, and residual thermodynamic-induced changes. This method has been applied in previous studies to validate whether climate models can produce realistic dynamic and thermodynamic contributions for sea ice evolution (Uotila et al. 2014; Lecomte et al. 2016; Holmes et al. 2019). Holland and Kimura (2016) provided the mean seasonal sea ice budget for the entire Antarctic based on satellite observation data, but the detailed mechanisms concerning local variability were not given. Pope et al. (2017) investigated the impacts of El Niño on the observed sea ice budget of West Antarctica, but they neglected the modulation of the SAM on the relationship between El Niño and Antarctic sea ice and focused merely on the large-scale circulation, without considering local forcings. In this study, the seasonal Antarctic sea ice budget caused by combined ENSO and SAM are examined through their dynamic and thermodynamic processes, and each budget component is further examined with reference to local atmospheric forcings.
The paper is organized as follows. In section 2, the data and method used are outlined. Section 3 presents the results of the climatological backgrounds and the dynamic and thermodynamic contributions to sea ice changes for each SAM–ENSO combination. The main conclusions are summarized in section 4 with further discussion.
2. Data and method
a. Data
We analyze monthly mean sea level pressure (SLP), 10-m wind fields (υ10), 850-hPa temperature (T850), mean surface net shortwave (SW; positive downward for all fluxes) and longwave (LW) radiation fluxes, mean surface latent heat (LH) and sensible heat (SH) fluxes, tropical SST (20°N–20°S), 200-hPa geopotential height (Z200), and 200-hPa wind fields from the ERA5 reanalysis (Hersbach et al. 2020). All these variables are retrieved with 0.25° × 0.25° resolution from February 1979 to January 2020. ERA5 is the latest climate reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and has been widely used in previous studies on the Antarctic (Tetzner et al. 2019; Dong et al. 2020; Zhu et al. 2021). However, ERA5 slightly underestimates the daily average cloud fraction and shows positive (negative) biases in the shortwave (longwave) radiation effect (Wang et al. 2020; Cerovečki et al. 2022; Hagman 2022), whereas King et al. (2022) pointed out that ERA5 radiation biases are within the measurement uncertainties. Monthly NOAA Extended Reconstructed Sea Surface Temperature version 5 (ERSST v5) data over the same period are used in this study to depict the SST in the Southern Ocean (Huang et al. 2017).
Daily 25-km gridded SIC (Cavalieri et al. 1996) and sea ice drift (SID) (Tschudi et al. 2019) from the National Snow and Ice Data Center (NSIDC) are used for budget analyses. The SIC data are generated from microwave brightness temperature retrievals from the Nimbus-7 SMMR and DMSP SSM/I–SSM/IS, using the NASA team algorithm. The SID estimates are derived from a merged dataset of different input data sources, including AVHRR, passive microwave data, and NCEP–NCAR reanalysis forecasts. To reduce the noise in ice drift fields and divergence distributions, we smooth the daily SID fields with a 7 × 7 cell square-window filter following Holland and Kimura (2016).
NOAA Climate Prediction Center (CPC) monthly Antarctic Oscillation (AAO) index and oceanic Niño index (ONI) are used to identify the SAM and ENSO phases, respectively. The AAO index is constructed by projecting the daily 700-hPa height anomalies poleward of 20°S onto the AAO pattern. The ONI is determined by the 3-month running mean of SST anomalies in the Niño-3.4 region (5°N–5°S, 120°–170°W). The ONI is first multiplied by −1 so that a positive ONI denotes a La Niña event and vice versa. These indices are detrended and standardized, and thresholds of ±0.5 are set to distinguish the four SAM–ENSO combinations, as shown in Fig. 1, following Fogt et al. (2011). Here, we assume that the removed linear trend of the AAO index is the anthropogenic component forced by ozone and greenhouse gases (Thompson and Solomon 2002).
Combinations of regimes based on AAO index and ONI. ONI is inverted and both indices are detrended and standardized. Each purple circle represents a month. Numbers of each circumstance are given in the parentheses. The combination of La Niña (El Niño) and positive (negative) SAM is abbreviated to LN/pSAM (EN/nSAM) and the rest are similar.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
b. Budget analysis
c. Surface heat flux
d. Methodology
We apply composite analysis to examine the anomaly fields, and each SAM–ENSO combination is defined in Fig. 1, using the Wilcoxon rank sum statistical significance test (Pettitt 2014) to detect whether the composite fields are significantly different from climatology. Each purple circle represents a month. The monthly anomalies are calculated relative to monthly mean climatology based on 1979–2020 in order to remove the seasonal cycle, and then composited for each season and each combination. Composite analysis can provide better insight into the ENSO teleconnection variations compared with correlation analysis (Fogt et al. 2011). We define each austral season on the principle that winter is centered on the month of maximum SIC according to Holland and Kimura (2016), i.e., winter covers August, September, and October (ASO). This kind of seasonal division can provide sufficient samples in each SAM–ENSO combination for composite analysis (Table 1). However, it is noted that the “summer” season defined here includes a month of sea ice growth (April) due to the asymmetry of the Antarctic sea ice seasonal cycle.
The total numbers of samples under each SAM–ENSO combination in each season; the numbers of different years those samples come from are given in the parentheses. The combination of La Niña (El Niño) and positive (negative) SAM is abbreviated to LN/pSAM (EN/nSAM) and the rest are similar.
3. Result
a. Rossby wave train induced by SAM–ENSO interactions
Previous studies have demonstrated the mechanisms of the variations in the ENSO teleconnection intensity under different SAM–ENSO combinations (Gong et al. 2010; Fogt et al. 2011; Wilson et al. 2016). Fogt et al. (2011) revealed that the effects of the teleconnections are connected to interactions between ENSO-induced and SAM-induced transient eddy momentum flux. During in-phase conditions, the transient eddy momentum fluxes act in synergy with each other over the South Pacific, and consequently anomalous transient momentum flux convergence acts to amplify the zonal wind anomalies and maintain the ENSO teleconnections (see Fig. 12 in Fogt et al. 2011). During out-of-phase conditions, they oppose each other, impeding the ENSO signals reaching the South Pacific. These interactions finally affect the wave propagation and breaking and zonal wind anomalies.
Here, we examine the teleconnection associated with tropical SST anomalies and Takaya–Nakamura (T–N) wave activity fluxes (TN01 for short) for each season under different SAM–ENSO combinations. TN01 is a diagnostic tool to demonstrate the propagating stationary Rossby wave train (Takaya and Nakamura 2001) and is calculated using geopotential fields and wind fields in 200 hPa. Figure 2 shows the composites of 200-hPa geopotential height anomalies, tropical SST anomalies, and TN01. It is obvious that during in-phase conditions [LN/pSAM and El Niño/negative SAM (EN/nSAM)] the teleconnections from the tropics are robust and strong pressure anomalies are established in the South Pacific sector, consistent with the conclusions of previous studies (Gong et al. 2010; Fogt et al. 2011; Wilson et al. 2016). The established geopotential height anomalies peak in winter during in-phase events, agreeing with previous studies that ENSO influence peaks during late austral winter and spring (e.g., Jin and Kirtman 2009; Ding et al. 2012; Yiu and Maycock 2019), since we define winter as ASO here. The asymmetries between LN/pSAM and EN/nSAM originate from wave propagation and the tropical heating regions as noted by a few studies before (e.g., Welhouse et al. 2016; Wang et al. 2021), with TN01 showing strong southward propagation in summer in the South Pacific during EN/nSAM, compared with weaker TN01 there during LN/pSAM. During out-of-phase conditions, wave propagations are weakened and distinct pressure anomalies are located over the eastern Antarctic continent and also extend to the western Pacific Ocean and Indian Ocean.
Composites of 200-hPa geopotential height anomalies (contours), tropical SST anomalies (shading), and T-N wave activity fluxes (vectors) of each season and each SAM–ENSO condition: (a)–(d) LN/pSAM, (e)–(h) EN/nSAM, (i)–(l) LN/nSAM, and (m)–(p) EN/pSAM. The black dots represent geopotential height values passing the 95% significance test and white dots for SST values.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
b. Climatological background
Based on the results of wave activity anomalies, we examine the composite distributions of atmospheric and oceanic anomalies including SLP, υ10, T850, SST, SIC, and SID under each SAM–ENSO combination.
In the LN/pSAM condition, there are significant seasonal variations for all the anomalies (Fig. 3). The SLP presents low pressure anomalies over the high-latitude Southern Hemisphere and a marked low pressure center in the eastern Pacific sector, which deepens the ASL (Figs. 3a–d), in line with what Fogt et al. (2011) discovered. These local anomalies grow stronger from summer to winter, and then become weaker in spring, consistent with the teleconnection variations. The position of the low pressure center moves onshore after summer. Induced by the SLP anomalies, υ10 presents intensified westerly winds and cyclonic winds around the low pressure (Figs. 3e–h). However, significant meridional wind anomalies (black vectors) only exist in the northerly winds toward the Antarctic Peninsula and southerly winds over the Ross Sea. Due to the meridional wind anomalies, midlatitude heat and moisture are transported poleward in the Atlantic sector and equatorward in the Pacific sector, resulting in a distinct temperature dipole particularly in autumn and winter. Aligned with the northerly (southerly) flow and warm (cold) overlying atmosphere, SST anomalies present a Bellingshausen–Ross Sea dipole. However, winter SST anomalies do not perfectly match the pattern of the υ10, T850, and Fnet fields (Figs. 3c,g and 7o), in particular showing little response to the strongest wintertime atmospheric anomalies (Fig. 3k). Note that the standard deviations of SST anomalies in the winter Ross Sea are large (not shown). Other SST products (ECMWF Ocean Reanalysis System 5, NOAA Optimal Interpolation SST Analysis) are tested, and the same results are achieved, indicating that other oceanic processes like Ekman heat fluxes and storm perturbations also play a critical role in the ocean’s response (Sen Gupta and England 2006; Ciasto and England 2011; Wilson et al. 2019). Easterly (westerly) wind anomalies cause poleward (equatorward) transports of warm (cold) water and thus positive (negative) Ekman heat flux anomalies (Yeo and Kim 2015). These oceanic contributions warrant further investigations.
Composites of SLP, υ10, T850, SST, SIC, and SID anomalies for each season in the LN/pSAM condition. The gray contour indicates the 15% SIC climatology for each season during 1979–2019. The dots indicate the shaded values passing the 95% significance test. The black vectors indicate the meridional wind and SID values passing the 95% significance test while the gray vectors do not.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
Consequently, SIC anomalies also present a dipole under the influences of both atmospheric and oceanic processes, with negative anomalies in the Bellingshausen/Weddell Sea and positive anomalies in the Amundsen/western Ross Sea. Weaker but significant positive SIC anomalies exist in the Indian Ocean and western Pacific Ocean as well, in concert with the findings of Yadav et al. (2022). The effects are confined at the ice edge in winter but extend to the inner ice pack in other seasons. As shown in Figs. 3m–p, SID anomalies basically coincide with wind anomalies and point at a small angle to the left of the wind vectors caused by Ekman drift. SID anomalies present significant eastward drifts in the eastern Weddell Sea and Indian Ocean, and northward drifts in the northern Ross Sea. The largest SID anomalies exist in winter, corresponding to SLP and wind anomalies. Moreover, the high correlations between the υ10 and SID in most regions are verified through a vector correlation method (not shown; Crosby et al. 1993). Compared with the SAM-only and ENSO-only impacts (Figs. S1, S2 in the online supplemental material) generated by partial correlation analysis (Stuart et al. 2009), we find that when the two modes take effect together, SLP anomalies are more reflective of the SAM, and SST anomalies are more reflective of ENSO. The reason for this phenomenon still needs further investigations from the perspective of oceanic processes.
In the EN/nSAM condition, which is also an in-phase combination, the anomalies are generally opposite to those in Fig. 3, as shown in Fig. 4. A notable difference is that the SLP and T850 anomalies are stronger in summer compared with LN/pSAM conditions, and they peak in autumn rather than winter (Fig. 4b), revealing a seasonal asymmetry between two in-phase relations. Strong positive SST anomalies in the eastern Pacific occur in all seasons (Figs. 4i–l), and the winter anomalies are in contrast to LN/pSAM conditions. Meanwhile, SIC anomalies in EN/nSAM conditions are much stronger in autumn and winter. Significant SID anomalies present opposite distributions to LN/pSAM conditions, which are westward drifts in the Weddell Sea and Indian Ocean and southward drifts in the Ross Sea, and the strength of the SID anomalies is weaker in EN/nSAM winter.
As in Fig. 3, but for the EN/nSAM condition.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
The anomalies are apparently different in the La Niña/negative SAM (LN/nSAM) condition, one of the out-of-phase relations (Fig. 5). The consistent positive SLP anomalies cover the whole Antarctic continent and extend to the western Pacific Ocean and Indian Ocean. Seasonal differences still exist, with SLP anomalies more annular in winter and spring compared with summer and autumn. Therefore, the primary wind anomalies are the easterly winds around the anomalous high pressure, with significant northward winds in the Indian Ocean and northern Ross Sea. The air temperature anomaly dipoles disappear, and significant anomalies only exist over the continent and the western Pacific Ocean. Although not significant, negative T850 anomalies are located in the western Weddell Sea, and positive anomalies along the coast of West Antarctica. Positive SST anomalies are located to the east relative to the T850 anomalies and are stronger in autumn and spring, probably due to oceanic heat advection toward the east. The SIC anomalies do not present the dipole patterns as in-phase conditions, but are basically consistent with SST anomalies, showing significant SIC anomalies in the Indian Ocean and western Pacific Ocean. Compared with EN/nSAM (Fig. 4), significant SID anomalies display similar westward drifts in the Weddell Sea and Indian Ocean but northward drifts in the Ross Sea, implying that ENSO controls sea ice changes in the Ross Sea while the eastern Weddell Sea is affected by SAM.
As in Fig. 3, but for the LN/nSAM condition.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
In the El Niño/positive SAM (EN/pSAM) condition, the anomalies are similar to Fig. 5 but with opposite signs (Fig. 6). Negative SLP anomalies exist in the Indian Ocean and western Pacific Ocean, accompanied by westerly wind anomalies. Few T850 anomalies over the Southern Ocean are significant, with insignificant positive anomalies located in the western Weddell Sea and negative anomalies in the western Pacific sector. Positive SST anomalies move westward to the northern Amundsen Sea compared with LN/nSAM conditions. Significant and stronger SIC and SID anomalies occur, opposite to the distribution in LN/nSAM conditions. The generally eastward drifts also have a northward component in the Weddell Sea and a southward component in the Ross Sea in winter.
As in Fig. 3, but for the EN/pSAM condition.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
These analyses reveal that SIC has different seasonal responses under different SAM–ENSO combinations, affected by both atmosphere and ocean, which cannot be regarded as linear sums between ENSO and SAM events. In-phase conditions mainly have influences on the Atlantic–eastern Pacific sectors and out-of-phase conditions on the western Pacific–Indian sectors. Compared with SAM- and ENSO-only events, SAM exerts more important impacts on SLP and T850 in the SAM–ENSO combination, while ENSO is more important to SST. Moreover, the effects of ENSO are more distinct in the South Pacific, while SAM influences are widespread in the Southern Ocean. To further understand the driving mechanisms of the SIC anomalies, the dynamic and thermodynamic contributions to the SIC budget are now examined.
c. Budget analysis
We employ the SIC budget equation [Eq. (1)] to analyze the dynamic and thermodynamic contributions to the daily SIC intensification anomalies. Composite analyses are conducted for the budget terms in each combination of SAM and ENSO. The residual terms include both thermodynamic contributions and mechanical redistribution. However, they are dominated by thermodynamic processes according to previous studies (Holland and Kwok 2012; Holland and Kimura 2016; Pope et al. 2017), and the regions where ridging is likely to happen are shown in Figs. 7–10. It is noted that the dynamic patterns cannot cover all of the sea ice intensification, especially in summer, because of the missing ice drift data mostly at the ice edge in the Pathfinder product (Holland and Kimura 2016). Meanwhile, only the grid cells of intensification and thermodynamic fields where dynamic fields are valid are shown to maintain consistency among the different terms.
(a)–(l) SIC budget anomaly composites (shading) in the LN/pSAM condition for each season. The dots represent values passing the 95% significance test. The gray contour indicates the 15% SIC climatology for each season during 1979–2019. The black contours in (a)–(d) represent the corresponding SLP anomalies. The black vectors in (e)–(h) indicate the meridional SID values passing the 95% significance test while the gray vectors not. The green contours in (i)–(l) represent the regions where ridging is likely to dominate the residual based on the criterion of negative residual, convergent SID, and SIC > 90%. (m)–(p) Fnet anomalies composites (shading) in the LN/pSAM condition for each season. The black dots represent values passing the 95% significance test. The black vectors indicate the meridional wind values passing the 95% significance test while the gray vectors do not.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
For LN/pSAM conditions, the dominant contributions vary from season to season. The sea ice intensification anomaly in summer is represented mainly by the residual component (Figs. 7a,i), showing a significant positive intensification in the central Weddell Sea, i.e., more sea ice is produced here. Except for a small region near the coast of the Antarctic Peninsula, the residual term is primarily dominated by thermodynamic processes. In autumn and winter, intensifications grow larger and display a dipole between the Atlantic Ocean and eastern Pacific Ocean, as shown in Figs. 3n and 3o. Dynamic processes including divergence and advection cause a similar magnitude of contributions as thermodynamic processes. Specifically, divergence anomalies mainly cause intensification in the inner pack, while advection anomalies cause decreased SIC in the Weddell/Bellingshausen Seas and increased SIC in the Amundsen/Ross Seas and eastern Antarctic at the ice edge (not shown). The effects of divergence/advection on inner/ice edge regions have also been proved in Holland and Kimura (2016). Due to the strong negative SLP anomalies in the Amundsen Sea (Figs. 3b,c), warm air blows southward in the Weddell Sea and Bellingshausen Sea, while cold air blows northward in the Amundsen Sea and Ross Sea. Significant northward SID anomalies in the Ross Sea induce negative anomalies onshore and positive ones offshore (Figs. 7f,g), accompanied by positive thermodynamic freezing (Figs. 7j,k). At the ice edge the freezing processes come from strong cold anomalies, while in the inner pack they are induced by the exposure to cold atmosphere due to the divergent ice drift. The situation is a little different in the Weddell Sea, where southward warm air compacts and melts sea ice simultaneously, including in the area where mechanical redistribution is possible.
In spring, sea ice starts to melt, so we assume that intensification anomalies result mainly from thermodynamic processes. Inner pack sea ice flows outwards in the Weddell Sea and Ross Sea and melts at the ice edge. Comparing the Fnet anomalies (Figs. 7m–p) with the residual anomalies (Figs. 7i–l), positive Fnet anomalies generally correspond to negative sea ice intensification and vice versa. Tracing the sources of the Fnet, we find that SWnet are the prominent source of thermodynamic processes in spring (Fig. S3). During this melting season, increased SW anomalies due to reduced low cloud cover and more medium-height cloud cover (not shown) lead to sea ice loss and decreased albedo (retrieved from ERA5) and consequently result in more SW absorption and more sea ice melting. The Hs and Hl anomalies contributing to sea ice variations are of a magnitude similar to SW and LW anomalies through all seasons. Oceanic heat exchanges are also important to SIC variations, but they are not investigated in this study and still need further examination in the future.
Sea ice intensification in EN/nSAM conditions shows significant negative anomalies in the Amundsen/Ross Seas and positive anomalies in the Weddell Sea in all seasons except spring (Figs. 8a–d), consistent with the seasonal variability of SIC anomalies (Figs. 4m–p). The position of the intensification dipole between the Atlantic and Pacific sectors moves westward in winter compared with autumn (Figs. 8b,c). Northward (southward) drift anomalies in the Weddell Sea (Ross Sea) and accompanying freezing (melting) anomalies in autumn contribute to the dipole anomalies together, while thermodynamic processes give rise to anomalies in the eastern Antarctic. Similar situations happen in winter, along with the frequent possibility of mechanical redistributions around Antarctica (Fig. 8k). The spring intensifications are composed of more significant positive anomalies compared with LN/pSAM, which may come from the thermodynamic feedback suggested by Pope et al. (2017) that sea ice melts earlier here in winter, since T850 and SST anomalies do not obviously explain the thermodynamic forcings. In EN/nSAM conditions, Fnet anomalies are nearly opposite to LN/pSAM and coincide with T850 anomalies (Figs. 4e–h). The magnitude of SW and LW anomalies is significantly larger than LN/pSAM in summer (Fig. S4), and both radiation anomalies oppose each other in the Amundsen/Ross Seas. Consequently, the summer heat fluxes are contributed by Hs and Hl.
As in Fig. 7, but for the EN/nSAM condition.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
Compared with in-phase conditions, dynamic effects play a more important role in summer during LN/nSAM, since the SID anomalies are stronger in the Weddell Sea and Ross Sea (Fig. 9). The clear dipole anomalies in West Antarctica in autumn and winter disappear. Nevertheless, dynamic terms still matter in a different way. The autumn opposing anomalies in the Weddell Sea and Indian Ocean are caused by convergence and advection induced by westward drift, respectively, and are also associated with the thermodynamic effects shown in Fnet. The anomalies in Hs and Hl are notably weaker than in-phase relations (Fig. S5). Similar mechanisms underlie the opposing anomalies between the Amundsen Sea and Ross Sea, which indicates annular SAM-like dynamics, i.e., zonal advection of sea ice, are important to sea ice changes. Spring intensification anomalies indicate more ice decrease in the Weddell Sea and Amundsen Sea as well as less decrease in the Bellingshausen Sea and are controlled generally by thermodynamic forcings.
As in Fig. 7, but for the LN/nSAM condition.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
In EN/pSAM, thermodynamic processes cause a general sea ice increase in summer, except in some regions near the Antarctic Peninsula and western Pacific Ocean, while dynamic processes decrease sea ice in the western Weddell Sea and southern Ross Sea and increase sea ice in the northern Ross Sea (Figs. 10a,e,i). The intensifications in autumn present a circle of negative anomalies at the ice edge and opposite inside, contributed mainly by thermodynamics, with melting anomalies at the ice edge from the surface heat flux and cold freezing anomalies inshore. Mechanisms are similar in winter but result in different spatial distributions due to different Fnet forcings. Spring intensification anomalies are larger than in-phase conditions and resemble residual and heat flux patterns, mainly caused by thermodynamic contributions and especially SWnet (Fig. S6).
As in Fig. 7, but for the EN/pSAM condition.
Citation: Journal of Climate 36, 11; 10.1175/JCLI-D-22-0679.1
4. Summary and discussion
This study targets the impacts of combined SAM and ENSO on Antarctic seasonal sea ice changes. Two kinds of events are defined based on the monthly SAM–ENSO standardized indices, including in-phase events (LN/pSAM and EN/nSAM) and out-of-phase events (LN/nSAM and EN/pSAM).
During in-phase conditions, the teleconnections between the tropics and high latitude in the Southern Hemisphere are robust, and pressure anomalies occur in the South Pacific sector characterized by distinct SLP anomalies in the Amundsen Sea. The 10-m wind speed presents corresponding anomalies, and consequently SIC anomalies also present a dipole between the Weddell/Bellingshausen Seas and the Amundsen/Ross Seas in all seasons except summer. SIC anomalies are primarily affected by SAM-induced atmospheric anomalies and ENSO-induced oceanic anomalies. Analyzing the sea ice budget, we find that thermodynamic and dynamic processes contribute together to intensification anomalies in autumn, winter, and spring, while thermodynamic processes dominate in summer. A common mechanism is that northward drift induces negative anomalies onshore and positive anomalies offshore, accompanied by positive thermodynamic freezing anomalies onshore. In contrast, southward drift is associated with sea ice compacting and melting simultaneously, even leading to possible mechanical redistribution. SWnet dominates Fnet in spring, but Hs and Hl are the primary drivers in other seasons.
During out-of-phase conditions, Rossby wave propagation is weakened, and the anomalies are focused on the western Pacific Ocean and the Indian Ocean, leading to zonal wind anomalies around the anomalous pressure centers. The SIC anomalies present no dipole patterns but are basically consistent with temperature anomalies. In addition to the dynamic and thermodynamic mechanisms mentioned above, dynamic processes caused by zonal sea ice drift and associated thermodynamic responses also play an important role during out-of-phase conditions.
Since a composite analysis is used to achieve a general relation between ENSO–SAM combinations and sea ice change, interannual variability is masked. Therefore, we examine the time series of the MSLP, SIC, and sea ice intensification anomalies for the eastern Pacific Ocean (Indian Ocean) during in-phase (out-of-phase) events, as shown in Fig. S7 (Fig. S8). We find that one specific event generally lasts for a short time, ranging from 1 to 5 months. Sometimes a longer period is dominated by a specific event, though not continuously. During in-phase conditions, most LN/pSAM events correspond to negative MSLP anomalies and positive SIC anomalies, which is the opposite for EN/nSAM. The weaker responses in sea ice intensification anomalies can be attributed to the strong seasonality and meridional compensation of the signals (Figs. 7 and 8). Meanwhile, the Indian Ocean responses to out-of-phase events are more notable for MSLP anomalies, owing to the strong asymmetries between LN/nSAM and EN/pSAM effects on sea ice.
One assumption used in the event classification is that SAM and ENSO are considered two independent Antarctic climate modes, supported by Fig. 1. Nevertheless, the ONI that represents average SSTs across the tropical Pacific might mask the internal connection between the two modes (Seager et al. 2003; Zubiaurre and Calvo 2012; Ding et al. 2012). Seager et al. (2003) revealed a mechanism connecting ENSO and extratropical climate through changes in the subtropical jets, the transient eddies, and the eddy-driven mean meridional circulation. Zubiaurre and Calvo (2012) found that El Niño Modoki events can induce a winter Antarctic stratosphere warming, and a downward-propagating signal contributing to SAM in spring. Ding et al. (2012) suggested that the SAM is correlated with tropical eastern and central Pacific SST in summer, while only with the latter in winter. In fact, the combined effect of SAM and ENSO in this study contains the SAM–ENSO interrelationship. That is, a month when both the standardized AAO index and ONI are larger than 0.5 indicates that strong positive SAM and strong La Niña happen together, while the strong positive SAM could be influenced by the strong La Niña and vice versa. Therefore, our paradigm is not contradictory to the paradigm of previous studies but agrees with some of their findings. As Ding et al. (2012) demonstrated, the Pacific sector variability is related to tropical forcing and the Indian sector variability is related to midlatitude dynamic processes. This conclusion supports our results that in-phase events lead to significant anomalies in the Pacific Ocean while out-of-phase events cause the Indian Ocean anomalies.
Apart from the SAM-accompanied pressure and wind anomalies, the “two-time-scale” response of sea ice to SAM may also matter (Ferreira et al. 2015; Hobbs et al. 2016; Doddridge and Marshall 2017). After a short-term ocean cooling response and sea ice expansion, sustained SAM-induced westerly winds could result in upwelling of warmer water due to Ekman drift and sea ice loss. However, Polvani et al. (2021) demonstrated that SAM only explains 14% of the sea ice trend during the ozone depletion period and is not the primary driver of sea ice trends. Therefore, we assume that the effects of slow SAM–ocean processes on sea ice are small compared with the synchronous impacts. Further studies should investigate this issue through lagged composites or regression in the future.
Our study separates the dynamic and thermodynamic contributions from SIC intensification anomalies and indicates that different combinations of ENSO and SAM can have different impacts in driving regional Antarctic sea ice changes through atmospheric and oceanic processes. However, the accurate contributions of mechanical redistributions still cannot be estimated from observations, which might be underestimated in this study. Moreover, the surface, bottom, and lateral freezing and melting processes are still mixed together. A detailed decomposition method using observations and models should be researched in the future in order to investigate the accurate proportions of each contributing term.
Acknowledgments.
The authors wish to thank three anonymous reviewers for their very helpful comments and suggestions. This is a contribution to the Year of Polar Prediction (YOPP), a flagship activity of the Polar Prediction Project (PPP), initiated by the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO). We acknowledge the WMO WWRP for its role in coordinating this International Research activity. This study is supported by the National Natural Science Foundation of China (41941009, 42006191, 41922044), the National Key Research and Development Program of China (2022YFE0106300), the Guangdong Basic and Applied Basic Research Foundation (2020B1515020025), and the Norges Forskningsråd (Grant 328886).
Data availability statement.
NSIDC sea ice concentration data are available at https://nsidc.org/data/nsidc-0051/versions/2 and sea ice motion data are available at https://nsidc.org/data/nsidc-0116/versions/4. ERA5 reanalysis data are available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The AAO index and ONI are accessible at https://www.cpc.ncep.noaa.gov.
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