1. Introduction
The Southern Ocean plays an integral part in the climate system, and the sea ice coverage in the Southern Ocean affects local and remote climate through its interplay with the ice–albedo feedback, ocean–atmospheric circulation, freshwater, and carbon (Hobbs et al. 2016; Serreze and Meier 2019). Therefore, understanding the processes that drive sea ice variability is of crucial importance for understanding the polar climate variability and climate change. In the context of global warming, Arctic sea ice extent (SIE) has experienced a significant decline since the start of the satellite record (November 1978), while the Antarctic SIE has presented something of a paradox. The total Antarctic SIE underwent a slight but statistically significant increasing trend up to 2015 and then experienced a dramatic decrease in 2016 that slowed the long-term upward trend but has not reversed it (Meehl et al. 2016; Schlosser et al. 2018; G. Wang et al. 2019; Parkinson 2019; Parkinson and Digirolamo 2021). Such Antarctic sea ice trend has drawn much attention since it is opposite to what is expected under global warming. However, most current coupled sea ice–climate models are unable to capture the correct sign of the Antarctic sea ice trend (e.g., Polvani and Smith 2013; Turner et al. 2013; Hobbs et al. 2015; Jones et al. 2016; Purich et al. 2016; Roach et al. 2020; Shu et al. 2020), indicating the limitations in our knowledge of the mechanisms governing Antarctic sea ice change.
To date, there is no consensus on the cause of this overall increasing trend in Antarctic sea ice because the observed trend shows large regional differences and seasonal dependence (Hobbs et al. 2016; Holland 2014). Hobbs et al. (2016) reviewed a diversity of mechanism processes that may drive the Antarctic sea ice trend. Atmospheric processes, particularly the winds, play a crucially important role in producing the regional sea ice trends in most sectors of the Southern Ocean (Holland and Kwok 2012; Matear et al. 2015; Holland et al. 2017a,b). The large-scale interaction between Antarctic sea ice and the climate modes is complicated, and no one single mode can fully explain the sea ice variability (Lefebvre and Goosse 2008; Raphael and Hobbs 2014). The distinct climate modes in extratropical Southern Hemisphere (SH), including the Southern Annular Mode (SAM) (Yuan and Li 2008), the Pacific–South American (PSA) pattern (Irving and Simmonds 2016; Yu et al. 2015), the Amundsen Sea low (ASL) (Hosking et al. 2013; Holland et al. 2018), and the zonal wave 3 (ZW3) of planetary waves (Raphael 2007), are believed to affect the Antarctic sea ice significantly through thermodynamic and dynamic processes. In the context of sea ice trends, the SAM and ASL, which are commonly regarded as an expression of the intensity of the circumpolar atmosphere’s zonal winds and meridional winds respectively, are the most important factors (Hobbs et al. 2016). Positive SAM, characterized by a poleward shift of the zonally symmetric midlatitude westerly jet in response to ozone depletion and greenhouse gas, causes sea ice expansion by inducing equatorward Ekman ice transport (Turner et al. 2009). But such a process may lead to sea ice retreat at longer time scales because of the upwelling of the warm subsurface water via Ekman suction (Sigmond and Fyfe 2010; Bitz and Polvani 2012; Ferreira et al. 2015). The ASL, with the deepening of its climatological low pressure center in the area of the Amundsen/Ross Seas, contributes to the observed dipole sea ice trends between the Ross Sea and Bellingshausen Sea (Ding et al. 2011; Raphael et al. 2016, 2019; Turner et al. 2015, 2017a). Recently, Yu et al. (2021) indicated that the South Pacific Oscillation, a mode defined as the leading feature of monthly mean sea level pressure over the South Pacific, explains 43% of the sea ice concentration (SIC) trend averaged over the Pacific sector in austral autumn.
The decadal variability of the tropical ocean, such as the Atlantic multidecadal oscillation (AMO) and the interdecadal Pacific oscillation (IPO), can modulate the decadal change in the SAM and ASL, which in turn contribute to the observed long-term Antarctic sea ice variations (Li et al. 2014, 2021; Simpkins et al. 2014; Meehl et al. 2016; Yu et al. 2017). However, Hobbs et al. (2016) indicated that the influence of the AMO and the IPO on sea ice trend may be modest. X. Zhang et al. (2021) and Blanchard-Wrigglesworth et al. (2021) recently used nudged Southern Ocean SST/winds anomalies on top of the Community Earth System Model (CESM) large ensemble to highlight the role played by Southern Ocean SST in producing Antarctic-wide trend in SIC.
Except for the long-term increasing trends, Antarctic sea ice also has highly regional and seasonal variations and large interannual variability (Parkinson and Cavalieri 2012; Holland 2014; Hobbs et al. 2016; Eayrs et al. 2019). On seasonal to interannual time scales, there is a wide body of literature available on the linkage between the SH atmospheric circulation and tropical oceanic variability, often referred to as the “tropical–polar teleconnections” (Li et al. 2021). The impact of El Niño–Southern Oscillation (ENSO), a prominent climate phenomenon in the tropical Pacific Ocean, on Antarctica has been well investigated. Convective heating over the tropical Pacific during El Niño (or La Niña) events induces a Rossby wave train that extends poleward and then eastward with alternating high and low pressure centers in the high latitudes of SH, which modulate the ASL (Gloersen 1995; Simmonds and Jacka 1995; Alexander et al. 2002; Ding and Steig 2013; Ciasto et al. 2015; Dou and Zhang 2023). Combined with the zonally asymmetric response in polar jets to ENSO (Liu et al. 2002; Yuan 2004; Yuan et al. 2018), the abnormal circulation pattern associated with Rossby wave train induces a dipole pattern of sea ice anomalies over the Ross and Weddell Seas. The response of Antarctic sea ice to the ENSO is modulated by the background state of the SAM (Stammerjohn et al. 2008; Fogt et al. 2011) and peaks in the austral cold season (Simpkins et al. 2012). The influence of the tropical Indian Ocean on Antarctica has also been noticed. Yuan and Martinson (2000) investigated the correlations between the Antarctic sea ice and global sea surface temperature (SST) based on 20-yr satellite data and found that the SST variations in the equatorial Indian Ocean have a stronger correlation with SIE in a wide band of the Amundsen Sea, Bellingshausen Sea, and Weddell Gyre. Nuncio and Yuan (2015) and Feng et al. (2019) explored the effect of Indian Ocean dipole (IOD) on Antarctic sea ice and found that the IOD in its mature phase (austral spring) significantly contributes to the dipole sea ice anomalies in the Ross and Weddell Seas, which is linearly independent of the ENSO effect. Besides the IOD, the Indian Ocean basin mode was found to have a significant influence on the Antarctic sea ice anomalies in austral autumn and spring (Yu et al. 2022).
Previous studies mainly focused on the polar teleconnections originating from the tropical oceans. Recently, the role played by the extratropical SST over the SH oceans in Antarctic surface climate garnered increasingly more attention. L. Zhang et al. (2021) indicated that the midlatitude South Atlantic SST variability modulates austral summer storm track activity, which in turn alters summer SIC over the Ross and Weddell Seas through thermal and wind-driven forcing. Sato et al. (2021) found that the SST anomalies over the Tasman Sea located in the midlatitude southwestern Pacific trigger planetary waves that regulate the ASL and affect the changes in surface temperature and sea ice around the Antarctic Peninsula. Dou and Zhang (2023) pointed out that the Tasman Sea SST anomalies may act as a bridge in ENSO’s impact on the Antarctic sea ice.
Up until now, there has been little work concerning the impact of the extratropical SST over the Indian Ocean on the sea ice in Antarctic. Rai et al. (2008) pointed out that the Antarctic sea ice is significantly correlated to the southeast Indian Ocean SST. However, the underlying mechanism was not investigated in their study. In the current study, we will scrutinize the linkage between the Antarctic SIC variability and the southern Indian Ocean (SIO) SST and reveal the physical process in this linkage. The rest of the paper is organized as follows. Section 2 outlines the datasets and methods used in this study. In section 3, we present the corresponding results. Finally, section 4 provides the main conclusions and discussion.
2. Data, methods, and model
a. Data
Monthly SIC data are obtained from the U.S. National Snow and Ice Data Center (https://nsidc.org/data) in the polar stereographic projection at a grid cell size of 25 km × 25 km for the period of 1979–2020. This dataset was produced by the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I), and the DMSP Special Sensor Microwave Imager and Sounder (SSMIS) sensors using the revised National Aeronautics and Space Administration (NASA) Team algorithm (DiGirolamo et al. 2022). As the satellite data during the late half of December 1987 and the first half of January 1988 were unavailable, the austral summer mean in 1987/88 is not included in this study.
Monthly atmospheric data are acquired from the new fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), which has better performance in evaluating the Antarctica climate than other reanalysis products (Ramon et al. 2019; Vignon et al. 2019). Monthly SST data are extracted from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST version 5 (ERSST v5; Huang et al. 2017). The Indian Ocean dipole (IOD) mode index is calculated as the difference in the SST anomalies between the western (50°–70°E, 10°S–10°N) and the eastern (90°–110°E, 10°S–0°N) equatorial Indian Ocean region (Saji et al. 1999). The ENSO signal is measured by the SST anomalies in the Niño-3.4 region (5°S–5°N, 170°–120°W) (Trenberth 1997). The SAM index is calculated by extracting the leading empirical orthogonal function (EOF) of the monthly SLP anomalies south of 20°S (Thompson et al. 2005). The PSA index is defined as (H1 + H2 − H3)/3, where H1, H2, and H3 represent 500-hPa heights at 50°S, 45°W; 45°S, 170°W; and 67.5°S, 120°W; respectively (Yuan and Li 2008). The ASL index is simply defined as the area-average pressure at the ASL location (80°–60°S, 90°–62°W) (Hosking et al. 2016). The ZW3 index is the normalized deviation by removing the zonal mean of 500-hPa heights at the center (49°S, 50°E; 49°S, 166°E; and 49°S, 76°W) (Raphael 2007).
b. Methods
The seasonal average is calculated for austral summer [December–February (DJF)], autumn [March–May (MAM)], spring [September–November (SON)], and winter [June–August (JJA)]. The linear trend is excluded from the time series when investigating the interannual relationship between SIC and the SIO SST in section 3b. Statistical significance for correlations and regressions is determined using a two-tailed Student’s t test.
c. Model
To conduct sensitivity experiments, we use the European Center-Hamburg (ECHAM) fifth-generation (version 5.4) atmospheric general circulation model (ECHAM5) (Roeckner et al. 2003). The resolution is triangular 63 (T63) and 19 vertical levels. The initial SST forcing fields are provided by Atmospheric Model Intercomparison Project (AMIP) II SST conditions. The control (CTRL) experiment is forced by observational historical SST. For the sensitivity (SENS) experiment, the observed dipole SST anomalies over the SIO region (Fig. 8b) are superposed on the model’s historical monthly SST in austral spring (September–November) with the initial conditions obtained from CTRL. In this study, all experiments are integrated for 20 years starting from 1 January 1990 and ending on 31 December 2009. Outputs from the last 10 years are applied to construct an ensemble mean of 10 members to reduce the model spinup uncertainties.
3. Results
a. Basic features of Antarctic SIC
Figure 1 exhibits the spatial distributions of the climatological SIC (left panel) and temporal evolutions of the total SIE (right panel) for each season. Antarctic SIC displays observably seasonal variations. The sea ice shrinks to the continent during the austral autumn (MAM) (Fig. 1b) but extends northward (at approximately 55°S) during austral spring (SON) (Fig. 1d) at its largest extent, which is restricted mainly by the Antarctic Circumpolar Current. The high values of standard deviation in SIC anomalies (shading) are mainly located in the peripheral regions, indicating that the SIC anomalies over there are more sensitive to the interannual variability in the atmosphere and oceans. The right panel in Fig. 1 shows the seasonal time series of the standardized total SIE in Antarctica defined by the total area of sea ice coverage with SIC greater than 15%. An overall modest expansion of Antarctic SIE was detected in all seasons from 1979 to 2020, even though there was a dramatic decline in 2016. Significant increasing trends were observed from 1979 to 2014 with the least squares trends reaching 20 × 103 km2 yr−1 at least.
(left) Climatological mean (contours) and standard deviation (shadings) of Antarctic SIC anomalies and (right) the time evolution (black line) of the standardized total Antarctic SIE for (a) austral summer (DJF), (b) autumn (MAM), (c) winter (JJA), and (d) spring (SON) during 1979–2020. Contours of the mean SIC in the left panels are drawn at 10% intervals with the 0% contour being excluded. The dashed lines and texts in the right panels denote the linear least squares fit trends and p values during 1979–2014 (blue) and 1979–2020 (red).
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
Except for the long-term trends, seasonal Antarctic SIE also experiences large interannual variability, particularly in recent years with the highest records during 2012–14 and the lowest values during 2016/17. Note that the SIE did not continue to decline after 2017 as expected but returned toward the normal state; particularly in austral spring (SON), the SIE anomalies became positive again in 2020. In the following subsections, we will investigate the relationship between sea ice and SIO SST on an interannual time scale and then analyze the contribution of SIO SST to the long-term trend in the Antarctic SIC.
b. Interannual relationship between the Antarctic SIC and the SIO SST
Figure 2 displays the seasonal spatial distributions of the correlation coefficients (CCs) between the total Antarctic SIE and global SST. The pan-scale sea ice variability is highly correlated with a belt of cold SST anomalies around Antarctica in each season. The remarkable signals in the Indian Ocean are detected during SON, featuring a dipole pattern with a warm area surrounding western Australia and a cold area centered around 60°E in the high latitudes of the Indian Ocean. Although there are areas with statistical significance in other seasons, they are quite small compared with that in SON, indicating that significant correlations mainly occurred in austral spring. Note that although a significant signal can be found near the Ross Sea, it mainly affects the local sea ice variability. Therefore, we will focus on the linkage between the SIE and SST in SIO during the austral spring. Considering that climate factors such as ENSO, IOD, and SAM may have a potential influence on Antarctic SIE (Hobbs et al. 2016), we also calculate the partial correlations by removing the linear effects of each of these climate factors. The results show that the dipole pattern in the Indian Ocean is still significant (figure not shown), indicating the robustness that the SIO SST is significantly connected with the Antarctic sea ice.
Seasonal spatial distributions of correlation coefficients (CCs) between Antarctic SIE and SST during (a) DJF, (b) MAM, (c) JJA, and (d) SON. The shadings denote the CCs exceeding the 95% confidence level. The area with significant positive correlations in (d) is divided into two subareas indicated by the two red rectangle boxes in the northeast (25°–18°S, 95°–115°E) and southeast (55°–35°S, 95°–135°E), respectively, and the area with significant negative correlations is denoted by the purple rectangle box in the area 65°–45°S, 45°–75°E.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
To quantitatively describe such SIE-related SIO SST variability and its influence on SIC during austral spring, a simple SIO dipole index (SIOD) is obtained by projecting the SIE-regressed SST field over the SIO (65°–18°S, 45°–135°E) onto the normalized SST pattern in the same region. The correlation coefficient (CC) between the SIE and SIOD index is 0.65 (0.65 after detrending), exceeding the 99.9% confidence level based on the Student’s t test. As shown in Fig. 2d, the SIOD is composed of three parts: the north branch (SIOD_NE) and the south branch (SIOD_SE) of the warm anomalies around the western Australia and the cold anomalies in the southwestern Indian Ocean (SIOD_WS). To further investigate the relative contribution of SST in the three areas to the SIE variability, the temporal variations of the detrended SON SIE and SIO SST indexes in the abovementioned three areas are analyzed. As indicated in Fig. 3, all the three parts of SIOD are significantly correlated to the Antarctic SIE, with their CCs above 0.4 and exceeding the 95% confidence level. The SIOD index shows the most prominent in-phase relationship to the SIE with the highest CC reaching 0.65.
Detrended time series of the normalized SIE for the Antarctic (red curve) and the SST indexes (blue curves) for the SIOD, SIOD_NE, SIOD_SE, and SIOD_SW (shown from top to bottom) in austral spring during the period 1979–2020. The CCs are shown in parentheses. The SIOD SST index was calculated by projecting the SIE-regressed SST field over the SIO (65°–15°S, 60°–130°E) onto the normalized SST pattern in the same region. The SIOD_NE, SIOD_SE, and SIOD_SW indices are the averaged SST in the northeastern red box (25°–18°S, 95°–115°E), the southeastern red box (55°–35°S, 95°–135°E), and the southwestern purple box (65°–45°S, 45°–75°E) shown in Fig. 2, respectively.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
The spatial distributions of CCs between the SIOD index and Antarctic SIC are shown in Fig. 4a. A wide range of significant positive SIC anomalies can be observed, particularly in the Ross Sea (150°E–150°W), Indian Ocean (60°–90°E), and Weddell Sea (0°–60°W). The total Antarctic SIE is closely linked to the sum SIE of the above three regions with their CC being 0.84, especially tightly related to the SIE in the Ross Sea with their CC reaching up to 0.9. The partial correlations are applied to remove the ENSO, IOD, SAM, ASL, PSA, and ZW3 signals, respectively. As shown in Figs. 4b–g, the above significant correlations still exist, although a slight reduction in the Weddell Sea after the SAM signal is removed. This implies that the intimate connection of SIE and SIOD may be linearly independent of the effects of ENSO, IOD, and SAM. The influence of the SIOD on the local SIC variability in the Indian Ocean can be readily understood due to the local cold (warm) SST anomaly in favor of sea ice formation (melting) near the sea ice edge (Fig. 2d). However, how does the SIOD SST modulate the remote SIC change in the Ross Sea and Weddell Sea? The possible mechanism will be explored in the following.
(a) Spatial distributions of CCs between the Antarctic SIC and SIOD index during SON. (b)–(g) As in (a), but for the partial CCs with the linear effects of SON Niño-3.4, IOD, SAM, ASL, PSA, and ZW3 being removed, respectively. The dotted areas denote the CCs exceeding the 90% confidence level.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
Local SST anomalies can force the Rossby wave train via convection activity and synoptic system activity (McIntosh and Hendon 2018). To explore the Rossby wave train forced by the SIOD, we calculate the RWS, which generates the Rossby wave train by the anomalous vorticity source associated with the upper-level divergence (Takaya and Nakamura 1997, 2001). As shown in Fig. 5a, the warm SST anomalies around the western Australia force remarkable upward motion with anomalous divergence that generates the source of the Rossby wave train centered at about 40°S, 100°E, while the downward motions are associated with the sink of Rossby wave train to the west of southwestern cold SST anomalies. We also checked the contributions of the SIOD_NE, SIOD_SE, and SIOD_SW SST anomalies to the wave source, respectively. As shown in Figs. 5b–d, the SST anomalies in each area contribute to the RWS anomalies centered at about 40°S, 100°E, while the warm SST anomalies around the western Australia have a greater contribution. The wave source centered at about 40°S, 100°E can excite a poleward propagating Rossby wave train along the waveguide of the subtropical jet (McIntosh and Hendon 2018).
Regressed divergent winds (vectors; 3 m s−1) and RWS (shading; 10−11 s−1) at 200 hPa, and anomalous pressure vertical velocity at 500 hPa (dotted stippling for upward motion < −0.003 Pa s−1 and hatched stippling for downward motion > 0.003 Pa s−1) against standardized (a) SIOD, (b) SIOD_NE, (c) SIOD_SE, and (d) SIOD_SW indices during SON.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
Figure 6 displays the simultaneous wave activity flux and streamfunction regressed against the standardized SON SIOD index. A distinct wave train pattern originates from the SIO with alternating positive and negative streamfunction anomalies, transports wave energy westward to the Ross Sea, and then propagates northeastward to South America generally following a great circle route. The wave train from southeast Pacific to the Weddell Sea is relatively weak. However, it becomes strong after the ENSO signal is removed (Fig. 6b). In general, the wave train associated with SIOD SST is radically different from that triggered by ENSO (Yuan 2004) and the Tasman Sea SST (Sato et al. 2021; Dou and Zhang 2023), which induces a dipole SIC anomaly over the eastern Ross Sea and Weddell Sea. However, it somewhat resembles the wavelike structure excited by the IOD alone after removing ENSO’s signal as shown in Fig. 3d in Nuncio and Yuan (2015). To check if it is associated with IOD, we apply the partial regressions with the linear effect of SON IOD being excluded. As seen in Fig. 6c, the wave train pattern and atmospheric circulation related to SIOD change insignificantly, indicating the independence of the connection between this wave train and SIOD SST anomalies. Subsequently, this wave train results in equivalent barotropic cyclonic circulations (negative contours in Fig. 7) over the Indian Ocean and the Ross and Weddell Seas where the increased SIC anomalies occur (orange shadings in Fig. 7)
Regressions of SON (a) streamfunction (shading; m2 s−1) and wave activity flux (vectors; m2 s−2) at 300 hPa (WAF300) onto the standardized SON SIOD index. (b),(c) As in (a), but for the partial regressions with the linear effect of SON Niño-3.4 and IOD being removed, respectively. Dotted areas indicate anomalies exceeding the 90% confidence level.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
Regressions of SON horizontal wind (vectors; m s−1) and geopotential height (contours; m) anomalies at (a) 850, (b) 500, and (c) 200 hPa onto the standardized SON SIOD index. The wind vectors exceeding the 90% confidence level are drawn. Shading indicates the SIC anomalies (%) regressed onto the SIOD index.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
To further address the atmospheric response to the SIOD SST anomalies during spring, we also conduct numerical experiments using the ECHAM5 model. As shown in Fig. 8, an apparent abnormal wave activity flux propagates from the SIO region to the Weddell Sea with prominent equivalent barotropic cyclonic circulations over the Ross and Weddell Seas. The simulated cyclones over SIO in the upper-middle troposphere (Figs. 8c,d) are significant, while they are not as apparent as in the reanalysis in the lower troposphere (Fig. 8b) and the anomalous cyclones to the south of Africa are remarkable. These biases may be caused by the model uncertainty or the difference in the recording periods with simulations from 1990 to 2009 but the reanalysis from 1979 to 2020. In general, the simulated atmospheric circulation anomalies to a large extent capture the observed spatial distribution features. The numerical experiments further demonstrate that the variability of SIOD SST can generate an atmosphere teleconnection pattern to modulate the circulation anomalies over the Indian Ocean and the Ross and Weddell Seas.
(a) 300-hPa streamfunction (shading; m2 s−1) and wave activity flux (vectors; m2 s−2) anomalies in response to the SIOD SST simulated by the ECHAM5 model (SENS minus CTRL). (b)–(d) As in (a), but for the horizontal wind (vectors; m s−1) and geopotential height (contours; m) at 850, 500, and 200 hPa, respectively. The shading in (b) denotes the observed SST composite differences between the high and low standardized SIOD index (measured by one standard deviation), which are superposed on the historical SST as surface boundary conditions in SENS run. Dotted areas indicate anomalies exceeding the 90% confidence level.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
Large-scale atmospheric circulation plays a dominant role in modulating surface climate change (including the surface temperature and sea ice) over the polar region through changing water vapor transport and surface energy budget (Sato and Simmonds 2021; Gao et al. 2022). To illustrate how the atmospheric circulation forced by SIOD SST regulates the sea ice change, Figs. 9a–d show the water vapor variability, downward longwave radiation (DLR), and surface temperature correlated to the SIOD during SON. During the positive phase of SIOD, the equivalent barotropic cyclonic circulations (Fig. 7) over the Indian Ocean and the Ross and Weddell Seas cause diverging water vapor flux by modulating moisture transportation, which decreases atmospheric water vapor and results in less DLR. Such a configuration cools the surface air temperature that is conducive to the sea ice formation over the peripheral regions of the Indian Ocean, Ross Sea, and Weddell Sea, where the SIC anomalies exhibit high interannual variability (Fig. 1d). We also display the surface energy budget anomalies associated with SIOD (Figs. 9e–h). The net surface heat flux feedback is dominated by the turbulent flux component with a similar dipole pattern over the SIO, and the surface net solar radiation anomalies contrast the surface net thermal radiation in the region north to the sea ice edge. Net solar radiation also contributes to the heat loss along the sea ice edge, especially the Ross and Weddell Seas. The net air–sea heat flux distribution implies the loss of heat in the Ross Sea and Weddell Sea, which favors the sea ice formation. The situation is reversed under the circumstance in the negative phase of SIOD.
CCs of the SON SIOD with SON (a) vertically integrated moisture flux (vectors) and vertically integrated water vapor flux convergence (shadings), (b) total column water vapor, (c) downward longwave radiation, (d) skin temperature, (e) net surface heat flux, (f) surface net solar radiation, (g) surface net thermal radiation, and (h) turbulent heat flux. Dotted areas indicate anomalies exceeding the 90% confidence level. The blue lines mark the observed climatological sea ice edge (the 15% ice concentration limit) in SON.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
The above analysis indicates that the SIOD SST during SON can significantly influence the contemporaneous Antarctic SIE via inducing a downstream teleconnection pattern that regulates the water vapor convergence and surface energy budget. However, can the dipole SST anomalies in SON be detected in advance? To confirm this issue, Fig. 10 displays the correlations of preceding SST and low-level circulation over SIO with the SON SIE. A dipole pattern of CCs can be observed in the preceding MAM with significant positive CCs off southwestern Australia and weak negative CCs in the high latitudes of the Indian Ocean. Such a dipole pattern gradually strengthens in the following JJA with a concomitant cyclonic circulation being established over SIO, which in turn promotes the SIOD SST development via air–sea interaction. Subsequently, the SIOD develops to its peak phase in SON and then induces SIC anomalies over the Indian Ocean and the Ross and Weddell Seas. The dipole SST anomalies that appear in the preceding MAM may provide a potential predictable source of the SON SIC variability.
CCs of the SON SIE with SST (shading), geopotential heights (contours), and horizontal winds (vectors) at 850 hPa in preceding (a) DJF, (b) MAM, and (c) JJA and in (d) simultaneous SON. The shading indicates anomalies exceeding the 90% confidence level. Vectors are drawn only for those above the 90% confidence level.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
c. The contribution of SIO SST to the record low SIC in 2016
Antarctic SIC declined dramatically in a wide range within the Indian Ocean and the Amundsen/Bellingshausen, Weddell, and Ross Seas (Fig. 11a), leading to a record low in late austral spring 2016 (Fig. 1d). Many studies have been done to explain the physical mechanisms of this event. This unprecedented reduction has been primarily attributed to the changes in near-surface winds, which are largely forced by natural atmospheric and oceanic systems (Z. Wang et al. 2019). The strong negative IOD event, featured by a convective heating anomaly in the eastern Indian Ocean, triggered a zonal wavenumber-3 pattern in spring 2016 (G. Wang et al. 2019; Meehl et al. 2019) and led to strong meridional flow and southward heat advection anomalies in the regions of strongest sea ice decline (Schlosser et al. 2018). The preceding extreme El Niño event in austral summer 2015/16 induced warm SST anomalies and contributed to the sea ice retreat over the Ross, Amundsen, and Bellingshausen Seas that persisted to the following austral spring 2016 (Stuecker et al. 2017). The weakening of the southern stratospheric polar vortex accompanied by a negative SAM event resulted in a substantial significant decrease of SIC over the Ross Sea through modulating the ASL (Turner et al. 2017b; Wang et al. 2021). In addition, the decadal-scale warming trend in the upper Southern Ocean, triggered by a negative decadal trend of wind stress curl with positive SAM trend and negative IPO, also contributed to the dramatic decline (Meehl et al. 2019). These studies indicate the complexity of the potential mechanisms driving the dramatic Antarctic SIC decline in 2016.
The anomalies of (a) SIC (%), and (b) SST (shading; °C), horizontal winds (vectors; m s−1), and geopotential heights (contours; m) at 850 hPa in SON 2016. The anomalies are calculated based on the climatology during 1979–2020.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
Here, we found that the SIO SST anomalies also contributed to the large decline in Antarctic SIE in spring 2016. As shown in Figs. 1d and 11b, the SIOD was in its extremely negative phase in SON 2016, with cold SST anomalies surrounding western Australia and warm anomalies over the high latitudes of the Indian Ocean, accompanied by a wave train starting from the SIO and arcing across the mid- to high latitudes. This wave train resembled the reversed one related to the SIOD shown in Fig. 7, especially the anticyclonic circulations over the Indian Ocean and Ross and Weddell Seas. Such a circulation pattern was devoted to the SIC melting over the Indian Ocean and Ross and Weddell Seas through the wind-driven horizontal moisture transport and the feedback of sea ice and water vapor as mentioned above. It is worth noting that the SIOD SST anomalies might be unable to explain the sea ice loss over the Amundsen/Bellingshausen Sea in 2016, which previously has been attributed to some other factors such as ENSO and SAM as reviewed above.
d. Relationship between trends in SIC and SIO SST
The above analysis indicates that the SIOD SST is significantly related to the large-scale Antarctic SIC over the Indian Ocean and Ross and Weddell Seas on the interannual time scale. It is interesting to examine if the trend in SIC can also be affected by the trend in the SIOD. As shown in Fig. 1d, the dramatic decline in SON 2016 slowed down the overall trend of the observed total SIE; the increasing trend from 1979 to 2014 was significant with the least squares trends above 20 × 103 km2 yr−1. Figure 12a displays the spatial distribution of the observed trend in SIC during SON from 1979 to 2014. Consistent with previous studies (Simpkins et al. 2013), the SIC featured significantly increasing trends in the Ross Sea and the Indian Ocean and weak decreasing trends over the Weddell and Bellingshausen Seas. Figure 12b shows the proportion of the SIC trend congruent with SON SIOD by calculating the multiplication of the linear trend of the SIOD and the regressed SIC anomalies against the detrended SIOD index. The pattern of interannual variability in SIC associated with the SIOD has projected onto the pattern of the SIC trend congruent with SIOD. The residual (i.e., the portion of the trend that could not be linearly explained by the SIOD) is regarded as the linear portion subtracted from the original trend (Fig. 12c). It can be discerned that SIOD is unable to explain the dipole trends in the Weddell Sea and Indian Ocean. However, a large fraction of the trend in the Ross Sea is congruent with the SIOD, with the SIOD-related congruency peaking above 60%, implying SIOD could account for a significant component of the SIC trend in the Ross Sea.
(a) Spatial distribution of the trends in the Antarctic SIC in SON (shading; % yr−1) from 1979 to 2014. (b) The proportion of trends congruent with SON SIOD and (c) the residual trends after removing the effects of the SON SIOD. Crosses mark the trends above the 95% confidence level.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0655.1
4. Summary and discussion
In summary, we have shown that the pan-scale Antarctic SIC variability during austral spring is significantly related to a dipole pattern of SST anomalies in SIO (SIOD), characterized by warm anomalies off the western Australia and cold anomalies centered around 60°E in high latitudes. The relationship between the Antarctic SIC and SIOD in austral spring is linearly independent of the effects of the ENSO, IOD, and SAM signals. Evidence from data diagnoses and numerical experiments indicates that the positive phase of the SIOD SST anomalies induces a downstream wave train with alternative negative and positive geopotential height anomalies, which establishes from the SIO to the Weddell Sea sector. Consequently, large-scale cyclonic systems prevail over the SIO and the Ross and Weddell Seas, leading to water vapor divergence over these regions by modulating the moisture advection. Subsequently, the reduced water vapor causes negative downward surface net flux that cools the surface temperature and then contributes to the sea ice increasing in the SIO, Weddell Sea, and especially the Ross Sea, where the atmospheric and SIC response to the SIOD SST anomalies are the most significant. This scenario reverses in the case of the negative SIOD phase. The SIOD in its extremely negative phase during 2016 spring strengthened the SH high-latitude wave train with anticyclonic circulations over the SIO and the Ross and Weddell Seas, which may contribute to the dramatic SIC melting in 2016 spring. Although previous studies have demonstrated that the record low SIC in 2016 was related to the IOD event (G. Wang et al. 2019; Meehl et al. 2019), ENSO (Stuecker et al. 2017), SAM, and IPO (Turner et al. 2017b; Meehl et al. 2019; Wang et al. 2021), our results indicate that the SIOD SST may also contribute to the extreme event. Moreover, congruency analysis indicates that in a linear sense, the trend in the SIOD SST is able to explain a significant component of the observed SIC trend in the Ross Sea during SON.
Apart from the SIOD, previous studies have revealed that the Mascarene high (MH) and Indian Ocean subtropical dipole (IOSD) are also active over the SIO and exert immense impacts on the weather climate change over Australia, Africa, and East Asia (Xue et al. 2004; Feng et al. 2014; Zhao et al. 2022; Miyamoto et al. 2022). The MH is also termed as the Indian Ocean subtropical high, which is a high pressure belt near the Mascarene Islands in the SIO (20°–40°S, 45°–100°E) (Vidya et al. 2020). The IOSD is computed from the SST anomaly difference between the western (37°–27°S, 55°–65°E) and eastern (28°–18°S, 90°–100°E) Indian Ocean, which was first identified in the studies of the relationship between the SST anomalies and the south-central African rainfall anomalies (Behera and Yamagata 2001). The IOSD is confined to the midlatitudes of the IO, while the SIOD defined in our study contains more signal in the IO mid- to high latitudes, especially the SST anomalies near the southeast Australia and southeastern IO. We also checked the relationships of MH and IOSD with the Antarctic SIC and found that the effects of MH and IOSD are confined to the Weddell Sea. By contrast, the SIOD defined in this study has the most significant correlation with the pan-scale Antarctic SIC during austral spring.
This study discovered that a dipole pattern in SIO SST during austral spring can influence the contemporaneous Antarctic SIC on an interannual time scale as well as its long-term trend. However, how the SST dipole pattern is formed is not dealt with in this study. Previous studies have indicated that the development of the IOSD is directly related to the MH variability (Fauchereau et al. 2003; Suzuki et al. 2004; Hermes and Reason 2005; Morioka et al. 2010, 2012). Moreover, by using a coupled general circulation model, Morioka et al. (2013) demonstrated that the climate variability in both low and high latitudes, such as ENSO and the Antarctic circumpolar wave, may also generate the IOSD through modulating the variations in the MH. The SIOD is significantly correlated with IOSD in austral spring during the period 1979–2020 with the CC reaching 0.47, which exceeds the 95% confidence level. Therefore, it is worth investigating in the future the roles played by the climate modes of the MH, ENSO, and Antarctic circumpolar wave in the SIOD formation to identify the drivers of the SIOD SST variability revealed in our study.
Acknowledgments.
We thank the three anonymous reviewers for their constructive comments, which are very helpful in improving this paper. This research was jointly supported by the National Natural Science Foundation of China (42288101) and China’s National Key Research and Development Program (2019YFC1509105).
Data availability statement.
The data of this study are public and freely available. Monthly sea ice concentration data are available at https://nsidc.org/data/NSIDC-0051/versions/1. The atmospheric circulation data are available at https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset&text=ERA5. Monthly sea surface temperature is available at https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html.
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