1. Introduction
During the modern satellite era, sea ice in the Southern Ocean has been experiencing substantial multiscale variability. A lot of atmospheric and oceanic variability modes may have played roles in the multiscale variations, including the Madden–Julian oscillation (MJO) (Lee and Seo 2019; Hsu et al. 2021), the Amundsen Sea low (ASL) (Turner et al. 2013; Raphael et al. 2016; Clem et al. 2017), the Southern Annular Mode (SAM) (Pezza et al. 2012; Clem et al. 2016; Deb et al. 2017), a quasi-stationary wavenumber-3 pattern (ZW3) (Raphael 2007; Kusahara et al. 2018), the Indian Ocean Basin mode (IOBM) and Indian Ocean dipole (IOD) (Nuncio and Yuan 2015; Feng et al. 2019; Yu et al. 2022), El Niño–Southern Oscillation (ENSO) (e.g., Yuan and Martinson 2000; Turner 2004; Ding et al. 2011), the interdecadal Pacific oscillation/Pacific decadal oscillation (IPO/PDO) (Meehl et al. 2016; Purich et al. 2016), and the Atlantic multidecadal oscillation (AMO) (Li et al. 2014, 2015; Yu et al. 2017).
On an interannual time scale, sea ice variability is especially evident. It hit a record high in 2014, but soon after a record low in 2017 (Parkinson 2019; Eayrs et al. 2021). Following a modest recovery, it hit a record low twice consecutively in the austral summers [December–February (DJF)] of 2021/22 and 2022/23 (Raphael and Handcock 2022; Wang et al. 2022; Liu et al. 2023; Zhang and Li 2023).
ENSO, as the strongest air–sea coupled mode occurring in the tropical Pacific, is a key factor affecting the Antarctic sea ice (Turner 2004; Yuan 2004). ENSO triggers stationary Rossby wave responses, affecting sea ice dynamically and thermodynamically (e.g., Karoly 1989; Ding et al. 2012; Yu et al. 2015). During El Niño, the anomalous convection near the date line extending into the eastern tropical Pacific just north of the equator, along with the descending across the southeastern tropical Indian Ocean/Maritime Continent and the South Pacific convergence zone (SPCZ), can act as wave sources to force upper-tropospheric Rossby wave trains (e.g., Li et al. 2003; Cai et al. 2011; Clem et al. 2019; Hu et al. 2019; Sun et al. 2022) and vice versa for La Niña condition. These Rossby wave trains propagate southeastward and generate an anomalous anticyclone (cyclone) centered in the Amundsen Sea, weakening (strengthening) the ASL and thus modulating the sea ice distribution both dynamically and thermodynamically (e.g., Hosking et al. 2013; Wang et al. 2019; Zhang et al. 2021).
However, ENSO exhibits a considerable diversity in both spatial pattern and temporal evolution (e.g., see reviews by Timmermann et al. 2018; Okumura 2019). As for its warm phase, El Niño can be classified into two types, the central Pacific (CP) and eastern Pacific (EP) El Niño, according to the location of maximum SST warmth in the tropical Pacific (Fu and Joseph 1985; Ashok et al. 2007; Yu and Kao 2007). Our recent study suggests that these two types of El Niño have a distinct effect on the Antarctic sea ice (Zhang et al. 2021). In austral spring [September–November (SON)], EP El Niño is associated with a strong positive phase of IOD in the equatorial Indian Ocean. The tropical heat sources associated with EP El Niño and IOD force two branches of southeastward-propagating Rossby wave trains, which induce an anomalous anticyclone over the eastern Ross–Amundsen–Bellingshausen Seas and result in sea ice loss (growth) in the eastern Ross–Amundsen Seas (the Bellingshausen–Weddell Seas). In comparison, CP El Niño, without IOD in accompany, excites just a relatively weak and westward-shifted Rossby wave train that generates an anomalous anticyclone in the eastern Ross–Amundsen Seas with 20°W of that caused by EP El Niño, therefore only causing increased sea ice concentration (SIC) in the Bellingshausen Sea. In the following austral summer (DJF) and autumn [March–May (MAM)], other studies suggest that CP El Niño has a similar but stronger impact compared with EP (Song et al. 2011; Wilson et al. 2014; Zhang et al. 2021). Both EP and CP El Niño lead to a dipolar structure of SIC anomalies in the West Antarctic, which is similar to but much weaker than that caused by EP event during SON.
As for its cold phase, La Niña events exhibit a notable diversity in durations, rather than in spatial patterns (e.g., Okumura and Deser 2010; Choi et al. 2013). Typically, similar to its counterpart El Niño, La Niña peaks in boreal winter (DJF), decays rapidly in spring (MAM), and disappears or transits into an opposite phase in the following summer [June–August (JJA)], but there is another kind of La Niña, which occurs just following the disappearance of the preceding one in boreal summer (JJA) or autumn (SON). During recent decades, this flavor of La Niña seems more active and has occurred seven times since 1980 (Fig. 1a). This two-winter lasting La Niña was named as the double-peaked, double-dip, follow-up, multiyear, or 2-yr La Niña (Okumura and Deser 2010; Hu et al. 2014; DiNezio et al. 2017; Luo et al. 2017; Zhang et al. 2019b; Park et al. 2021). In the future, such double-peaked La Niña has been projected to increase with a high frequency ranging from 19% ± 11% in a low-emission scenario to 33% ± 13% in a high-emission scenario (Geng et al. 2023).
Temporal evolution of 3-month running mean Niño-3.4g index (°C) for (a) the seven double-peaked La Niña events (i.e., 1983–85, 1995–97, 1998–2000, 2007–09, 2010–12, 2016–18, and 2020–22) and (b) the two single-peaked La Niña events (i.e., 1988/89 and 2005/06). The Niño-3.4g index is defined as the difference in the averaged SST anomalies in the Niño-3.4 region (5°N–5°S, 170°–120°W) minus that in the western equatorial Pacific (5°N–5°S, 120°–150°E). Gray shadings denote austral summer (DJF).
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
Several studies have revealed that the first and second La Niña episodes (the first La Niña and the second La Niña hereafter) in such a consecutive 2-yr La Niña event are distinct from each other, not only in their onset and developing mechanisms but also in their climate impacts (e.g., Okumura et al. 2011; Raj Deepak et al. 2019). The occurrence of the first La Niña can be attributed to the eastward-propagating cold Kelvin waves, which result from the reflection of the preceding El Niño–induced Rossby waves at the western boundary of the tropical Pacific (Suarez and Schopf 1988; Battisti and Hirst 1989), or the discharge processes of the equatorial Pacific upper ocean heat content, which lead to a net heat transport from the equatorial to off-equatorial regions during El Niño (Jin 1997a,b). In comparison, the development of the second La Niña is caused by several quite different processes, including the nonlinear atmospheric responses to the climatological SST and its seasonal cycle in the tropical Pacific (e.g., Ohba and Ueda 2009; Dommenget et al. 2013), the asymmetric ocean response to wind forcing in the delayed-oscillator conceptual model (e.g., Choi et al. 2013; DiNezio and Deser 2014), or the forcing of remote oceans by modulating the pantropical Walker circulations or via atmospheric Rossby wave processes (e.g., Luo et al. 2017; Zhang et al. 2019b).
As for their climate effects, previous studies have shown that the first La Niña and the second La Niña on the Northern Hemispheric climate are quite distinct from each other (Okumura et al. 2017; Raj Deepak et al. 2019; Iwakiri and Watanabe 2020). For example, Okumura et al. (2017) displayed that the second La Niña favors severe droughts in the United States during boreal cold seasons compared with the first La Niña (Hoerling et al. 2013; Okumura et al. 2017). Both Raj Deepak et al. (2019) and Iwakiri and Watanabe (2020) suggested that they have different impacts on the precipitation and temperature patterns over East Asia during boreal warm seasons. Recently, whether the first La Niña and the second La Niña have a different impact on the Antarctic sea ice attracted attention. Zhu and Yu (2022) found that during JJA of the developing year of La Niña, both the first and second events induce a tripolar SIC anomaly, but the second La Niña–induced tripolar pattern has an eastward shift compared with the first La Niña (see their Figs. 2a,b). Such a zonal shift in SIC distribution can be attributed to the different IOD conditions between the two austral winters (JJA); that is, the weak positive phase and the strong negative phase of IOD during JJA of the first La Niña and the second La Niña, respectively, produce different Pacific–South American (PSA) modes and SAMs, giving rise to a zonally shifted tripolar SIC pattern during the life cycle of the double-peaked La Niña (Zhu and Yu 2022). The study has advanced our understanding of the diversity of the ENSO–Antarctic connections.
La Niña usually develops during SON and peaks during DJF. In comparison, IOD peaks during SON. This implies that the impact of La Niña when accompanied by IOD may be stronger during SON or DJF than JJA. This is seen by stronger atmospheric or sea ice anomalies linked to ENSO during SON (e.g., Jin and Kirtman 2009; Song et al. 2011; Zhang et al. 2021; Chen et al. 2023; Kim et al. 2023). Additionally, previous studies have shown that La Niña plays an important role in causing the record low Antarctic sea ice in November–December 2016 and DJF 2021/22 through the Rossby wave train (Stuecker et al. 2017; Zhang and Li 2023). Whether the first La Niña and the second La Niña induce distinct atmospheric responses and further exert different impacts on the Antarctic sea ice is not well documented. This forms the motivation of the present study.
In the study, the composite analysis is used to examine the possible impacts of double-peaked La Niña on the Antarctic sea ice first. The results suggest that the first La Niña and the second La Niña have distinctive impacts during SON. Then, the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 5 (CAM5) (Neale et al. 2010), is used to conduct experiments to verify such influences specially from the perspective of Southern Hemisphere atmospheric circulation.
2. Datasets and methods
a. Datasets
Monthly SIC from January 1870 to present with a horizontal resolution of 1.0° × 1.0° is provided by the Met Office Hadley Centre (Rayner et al. 2003). Monthly zonal and meridional sea ice velocities with a horizontal resolution of 1.0° × 1.0° for the period from January 1958 to present are derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ocean Reanalysis System, version 5 (ORAS5) (Zuo et al. 2019). To verify the composite result of SIC anomalies, the ORAS5 SIC is also used. Monthly optimum interpolation SST version 2 on a 1.0° × 1.0° grid from December 1981 to present comes from the National Oceanic and Atmospheric Administration (NOAA) (Reynolds et al. 2002).
Monthly atmospheric circulation variables are obtained from the fifth major global reanalysis produced by ECMWF (ERA5) (Hersbach et al. 2020), including sea level pressure (SLP), air temperature at 2 m (T2m), horizontal wind fields at 10 m, 300-hPa geopotential height and zonal wind, and low cloud cover. Besides, monthly outgoing longwave radiation (OLR), surface sensible heat (SH) and latent heat (LH) fluxes, and surface net longwave (LW) and net shortwave (SW) radiation fluxes from the ERA5 are also used. The ECMWF convention for vertical fluxes is that a positive sign indicates a downward flux. All the ERA5 variables have horizontal resolutions of 0.25° × 0.25° and cover the period from January 1940 to present.
b. Methods
To verify the responses of Southern Hemispheric atmospheric circulation to anomalous tropical SST forcing associated with La Niña, several numerical experiments are conducted by using CAM5 (Neale et al. 2010). The CAM5 forms the main atmosphere component of the Community Earth System Model, version 1 (CESM1). The fact that CAM5 is used rather than its updated version, CAM6, is due to the following: 1) many studies suggest its good performance in simulating the tropical–Antarctic teleconnections (e.g., Zheng et al. 2018; Huang et al. 2022; Sun et al. 2023) and 2) it consumes fewer computing resources. In the study, a total of three experiments are conducted. In the first experiment, the CAM5 is driven by the monthly climatological SST, namely the control run. In the second, the CAM5 is forced with the composite SST anomalies within 30°N–35°S (i.e., the red box in Fig. 4a) during the first La Niña superimposed on monthly SST climatology, referred to as Exp_1stLN. The SST anomalies adjacent to the red box in Fig. 4a increase or decrease linearly to zero within 30°–35°N and 35°–40°S. No additional heating source (diabatic heating = 0) is prescribed elsewhere. The third is similar to Exp_1stLN, but forced with the second La Niña-related tropical SST anomaly, referred to as Exp_2ndLN. Therefore, the differences in the Exp_1stLN or Exp_2ndLN minus the control run can be used to estimate the atmospheric responses to the first La Niña and the second La Niña, respectively. The first 5 years are discarded as model spinup, and the model results averaged over the last 45 years of a 50-yr integration are used to approximate the responses.
In the study, the analysis period is 43 years from January 1980 to December 2022. Monthly anomalies are calculated relative to the monthly climatology of 1981–2010. All the anomalies are quadratically detrended before analysis. We use a gradient index (Niño-3.4g index hereafter), which is defined as the difference in the averaged SST anomalies in the Niño-3.4 region (5°N–5°S, 170°–120°W) minus that in the western equatorial Pacific (5°N–5°S, 120°–150°E), to identify ENSO events (DiNezio et al. 2017). This index captures the weaker or stronger zonal SST gradient associated with both El Niño and La Niña and is highly correlated with the conventional Niño-3.4 index (r = 0.97 during December 1981–2022). Based on this Niño-3.4g index with a threshold below −0.5°C for a minimum of five consecutive months, seven double-peaked La Niña events in 1983–85, 1995–97, 1998–2000, 2007–09, 2010–12, 2016–18, and 2020–22 along with two single-peaked La Niña in 1988/89 and 2005/06 are identified during 1981–2022 (Fig. 1 and Table 1), verifying that the double-peaked La Niña occurs more frequently under global warming (Zhang et al. 2019a,b; Geng et al. 2023). Besides, the Southern Ocean adjacent to the Antarctic is divided into six sectors, including the Indian Ocean (20°–90°E), the western Pacific (90°–160°E), the Ross Sea (160°E–130°W), the Amundsen Sea (130°–100°W), the Bellingshausen Sea (100°–60°W), and the Weddell Sea (60°W–20°E), as shown in Fig. 2 (Parkinson and Cavalieri 2012; Zhang et al. 2021).
Selected seven cases of the first and second La Niña events during 1982–2022.
Identification of six sectors around the Antarctic in the present study, including the Indian Ocean (20°–90°E), the western Pacific (90°–160°E), the Ross Sea (160°E–130°W), the Amundsen Sea (130°–100°W), the Bellingshausen Sea (100°–60°W), and the Weddell Sea (60°W–20°E), which is identical to the sectors used in Zhang et al. (2021).
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
3. Observed relationships between the tropical SST and Antarctic sea ice
Figure 3 compares the composite of seasonal mean SIC anomalies for the seven selected first and second La Niña events (Table 1). In SON of the developing year corresponding to the first La Niña, the distribution of SIC anomalies exhibits a tripolar pattern, with positive anomalies in the Ross Sea and the northeastern Weddell Sea, respectively, sandwiching negative anomalies in the Bellingshausen Sea (Fig. 3a). In comparison, most parts of the Southern Ocean are dominated by negative SIC anomalies during the second La Niña, except for the eastern Ross–western Amundsen Seas where strong positive anomalies are observed (Fig. 3e). In particular, the negative SIC anomalies in the western Ross Sea and the northeastern Weddell Sea are significantly different from those during the first La Niña (the two red boxes in Fig. 3i).
Composite seasonal mean SIC anomalies (%) in austral (a),(e) spring (SON), (b),(f) summer (DJF), (c),(g) autumn (MAM), and (d),(h) winter (JJA) for (left) the first La Niña and (center) the second La Niña. September–December and January–August are the months in the developing and decaying years of La Niña, respectively. (i)–(l) The differences in seasonal mean SIC anomalies between the first La Niña and the second La Niña. Dark green and black hatchings indicate significance at the 90% and 95% levels based on the Student’s t test, respectively. Red (60°–70°S, 150°E–165°W, and 52°–62°S, 25°W–25°E) and blue (55°–75°S, 165°–25°W) boxes in (a), (e), and (i) indicate the focused areas hereafter.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
During DJF, SIC anomalies in two La Niña episodes are alike (Figs. 3b,f,j). There are two meridional dipole-like structures in the central–eastern Ross Sea and the western Weddell Sea, respectively, which are distinct from those in SON. In the central–eastern Ross Sea (the western Weddell Sea), there are negative (positive) SIC anomalies confined offshore the coastal Antarctic along with positive (negative) anomalies in the north. During MAM, SIC anomalies during both the first La Niña and the second La Niña are similar to those during the preceding summer, but with a much weaker amplitude (Figs. 3c,g). During the following JJA, SIC anomalies during the second La Niña bear a resemblance to the first La Niña (Fig. 3l), with stronger negative (positive) SIC anomalies in the northern Weddell Sea (the eastern Ross–Amundsen Seas) (Figs. 3d,h).
The above results indicate that SIC anomalies corresponding to the first La Niña and the second La Niña vary from season to season. Only austral spring (SON) SIC anomalies show statistically significant differences compared with the other three seasons. In particular, sea ice anomalies in the western Ross Sea and the northeastern Weddell Sea (the two red boxes in Fig. 3e) during the second La Niña are quite distinct from those during the first La Niña. To verify the composite results, the ORAS5 SIC with a time span from January 1958 to the present is also used. Two more double-peaked La Niña (i.e., 1970–72 and 1973–75) events are added to the composite results. The distributions of the composite SIC anomalies using nine double-peaked La Niña events bear a resemblance to those using seven events (figure not shown), indicating that the composite results are robust.
As mentioned above, the impacts of ENSO on the Southern Hemispheric atmospheric circulations and the Antarctic sea ice are strongest during SON. In particular, the standard deviation of the composite SON SIC anomalies is less than 0.1 in most of the statistically significant areas (figure not shown), indicating that the distributions of SIC anomalies between different double-peaked La Niña events exhibit a strong similarity and less spread. Additionally, the Antarctic sea ice during SON reaches the maximum and also features a much larger variability compared with the other seasons (Zhang et al. 2021; Zhang and Li 2023). Thus, the present study will focus on SON hereafter. For the convenience of understanding and description, the key region is divided into three, i.e., the red and blue boxes in Figs. 3a, 3e, and 3i; considering sea ice in the blue box is mainly governed by the anomalous cyclone centered in the Amundsen Sea, while that in two red boxes (i.e., the western Ross Sea and the northeastern Weddell Sea) is dominated by the anomalous anticyclone remotely east to New Zealand and the anomalous anticyclone and cyclone in the Weddell Sea, respectively (Fig. 6).
First, the tropical Pacific SST anomalies associated with the first La Niña appear as a zonal dipole between the central–eastern equatorial Pacific and the regions across the western and northwestern tropical Pacific and the SPCZ (Fig. 4a). Peak SST cooling is mainly confined within 10°N–10°S in the central–eastern equatorial Pacific. In the tropical Indian Ocean, a statistically significant negative phase of IOD can be observed. In addition, weak but statistically significant positive and negative SST anomalies occur in the middle and southern tropical Atlantic, respectively. In the tropics, the atmospheric circulation is sensitive to the subtle changes in SST. In response to these tropical SST anomalies, descent and ascent anomalies develop over the central–western tropical Pacific and the regions across the eastern tropical Indian Ocean/Maritime Continent, warming pool, and the SPCZ, respectively (Fig. 4d). This is consistent with the previous studies that the La Niña-related tropical diabatic forcing strengthens the convection over the eastern tropical Indian Ocean/Maritime Continent and the SPCZ regions via the enhanced Walker cell (e.g., Wang et al. 2003; Supari et al. 2018).
Composite SON mean (a),(b) SST anomalies (°C) and (d),(e) OLR anomalies (W m−2) for (a),(d) the first La Niña and (b),(e) the second La Niña. The differences in (c) SST anomalies and (f) OLR anomalies, respectively, between the first La Niña and the second La Niña. Dark green and black hatchings indicate significance at the 90% and 95% levels, respectively. SST anomalies in red boxes (30°N–35°S, 35°–20°E) in (a) and (b) are used to force the CAM5.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
In the second La Niña, the negative SST anomalies in the central–eastern tropical Pacific are largely weakened but characterized by a broader meridional extent compared with the first La Niña (Figs. 4b,c). The averaged SST anomaly in the Niño-3.4 region is −0.67°C, which weakens by 39.1% compared with the first La Niña (i.e., −1.10°C). In the tropical Indian Ocean, SST anomalies bear a resemblance to those during the first La Niña, but with a weaker amplitude (Fig. 4c). Both anomalous subsidence and convection are largely weakened (Figs. 4e,f). Unlike the first La Niña, SST anomalies in the tropical Atlantic during the second La Niña exhibit a triple structure, with positive anomalies in the equatorial tropical Atlantic sandwiched with two negative anomalies in the northern and southern tropical Atlantic.
In the tropics, both anomalous ascent and descent can act as Rossby wave sources to affect the extratropics and the high latitudes (e.g., Wang et al. 2003; Cai et al. 2011; Clem et al. 2019). As shown in Fig. 5a, the 300-hPa geopotential height anomalies associated with the first La Niña are characterized by a PSA pattern emanating from the central equatorial Pacific and propagating southeastward into West Antarctic, with a negative (central equatorial Pacific)–positive (far east to New Zealand)–negative (Amundsen Sea)–positive (west of Argentina) center structure. This PSA pattern has an equivalent barotropic structure over the extratropics (Cai et al. 2011). Similarly, the second La Niña forces a Rossby wave train with its first positive center shifted westward by ∼20° in longitude compared with the first La Niña (Figs. 5c,e). It is worthwhile noting that, in addition to the PSA pattern, another Rossby wave train, which emanates from the southeastern tropical Indian Ocean and propagates southeastward, can also be seen. This Indian Ocean–forced Rossby wave train finally merges into the La Niña–forced one in the West Antarctic region. However, no such wave train appears during the first La Niña. Unlike the first La Niña–induced Rossby wave train, which dissipates in the region west of Argentina, the second La Niña–induced height anomalies keep propagating eastward after it arrives in the West Antarctic and finally reaches the Indian Ocean sector of Antarctic. The 300-hPa geopotential height anomalies remotely over the Indian Ocean sector should not be explained as the direct Rossby wave response to tropical forcing, but as an equilibrium response between the Rossby wave and transient eddies (Peng et al. 2003; Li et al. 2007). In the mid–high latitudes, transient eddy feedback plays an important role in forming and maintaining the anomalous atmospheric circulation. The southwestern Atlantic is situated in the entrance region of the upper atmospheric westerly jet along 50°S (i.e., the green lines in Figs. 5a,c) and is also closely connected to the climatological storm track in the Southern Hemisphere (Fig. 1d in Sun et al. 2022). From an atmospheric perspective, the second La Niña–induced Rossby wave train may cause perturbations of the southern Atlantic storm track after it propagates into the Weddell Sea, subsequently feedbacking onto geopotential height anomalies through transient eddy–mean flow interaction.
Composite SON mean 300-hPa geopotential height (HGT300) anomalies (m) for (a) the first La Niña and (c) the second La Niña (contours; interval: 8 m). (e) The differences in HGT300 anomalies between the first La Niña and the second La Niña. The green lines in (a) and (c) indicate the 30 m s−1 contours of climatological zonal wind. Light and dark gray shadings indicate significance at the 90% and 95% levels, respectively. (b),(d) The corresponding T–N wave activity flux (vectors; m2 s−2) together with the 300-hPa streamfunction (contours; interval: 1 × 106 m2 s−1; 106 m2 s−1). Red solid, black solid, and blue dash lines denote positive, zero, and negative anomalies, respectively. Purple vectors indicate the Rossby wave path.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
To examine the Rossby wave pathway, we calculated the horizontal T–N wave activity fluxes (Takaya and Nakamura 2001), as shown in Figs. 5b and 5d. During the first La Niña, there is only one Rossby wave train that emanates from the central equatorial Pacific and propagates southeastward (Fig. 5b), resulting in negative geopotential height anomalies centering in the Amundsen Sea (Fig. 5a). In comparison, the tropical heat sources associated with the second La Niña excite two branches of southeastward-propagating wave trains (Fig. 5d). The two wave trains merge into one in the West Antarctic and then keep propagating eastward into the Indian Ocean sector. Finally, they result in a negative (the Amundsen Sea)–positive (the western Weddell Sea)–negative (the eastern Weddell Sea)–positive (the Indian Ocean sector) center structure in geopotential height anomalies over the Southern Ocean (Fig. 5c). This is consistent with previous finding that the Rossby wave train forced by both the tropical Indian and Pacific Oceans can propagate further east (Nuncio and Yuan 2015; Zhang et al. 2021). Thus, the SST anomalies in both the tropical Pacific and Indian Oceans during the second La Niña can coordinate to affect the sea ice in the West Antarctic via Rossby wave trains, but this is not the case for the first La Niña.
4. Possible mechanisms
Previous studies have demonstrated that the lower-tropospheric winds play important roles in modulating the Antarctic sea ice distributions via dynamic processes (i.e., wind-driven sea ice drift effect), or thermodynamical processes (i.e., horizontal heat advection), or both (e.g., Wassermann et al. 2006; Holland and Kwok 2012; Wang et al. 2019). As seen above, the first La Niña induces only one wave train deepening ASL but weakening the Weddell Sea low (Fig. 6a), while the second La Niña forces two branches of wave trains (Fig. 6d). These different atmospheric responses will shape the distinct sea ice distributions (Fig. 6g).
Composite SON mean (a),(d) SLP anomalies (contours; hPa), (b),(e) anomalous horizontal winds (vectors) together with their meridional components (shadings) at 10 m (m s−1), and (c),(f) anomalous sea ice velocities (vectors) together with their meridional components (shadings; cm s−1) for (left) the first La Niña and (center) the second La Niña. The differences in (g) SLP anomalies, (h) anomalous wind fields, and (i) anomalous sea ice velocities between the first La Niña and the second La Niña. Red solid, black solid, and blue dash lines in (a) and (d) denote positive, zero, and negative anomalies, respectively. “H” (or “A”) and “L” (or “C”) indicate anomalous anticyclone and cyclone, respectively. Light (or dark green) and dark (or black) gray shadings (hatching) indicate significance at the 90% and 95% levels, respectively. Red and blue boxes are the same as those in Fig. 3.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
a. Dynamic processes
In the first La Niña, the intensified ASL favors intensified southerly and northerly winds in the eastern Ross–western Amundsen Seas and the Bellingshausen Sea, respectively (Fig. 6b). The former provides a favorable condition for the offshore movements and northward extension of sea ice in the eastern Ross Sea (Figs. 6b,c), while the latter leads to a southward transport and shrink of sea ice in the Bellingshausen Sea. In the second La Niña, the induced anomalous cyclone and the associated winds are even stronger (Figs. 6d,e,g,h), resulting in stronger sea ice growth in the eastern Ross–western Amundsen Seas (Fig. 6f). In particular, the anomalous northerly winds shift eastward to reach the central Weddell Sea, causing southward shrink of sea ice in the northwestern Weddell Sea (cp. Figs. 6e,h and 3a,e).
In comparison, the most statistically significant difference between the first La Niña and the second La Niña is in the western Ross Sea (the upper red box in Fig. 3i). It is partly related to the anomalous anticyclone in the southern Pacific far east to New Zealand induced by the second La Niña (Fig. 6d), which leads to a southeastward transport of sea ice (Fig. 6f). Neither statistically significant anomalous northerly nor southward transport of sea ice is induced by the first La Niña (Figs. 6b,c). The increased sea ice there may be caused by the thermodynamical process rather than the dynamic process, which will be analyzed later.
The northeastern Weddell Sea is another region where SIC anomalies are significantly distinct (the bottom red box in Fig. 3i). Dynamic effect can account for the difference. The first La Niña causes statistically significant southwesterly wind anomalies in the eastern Weddell Sea (Fig. 6b), which lead to intensified offshore movement and sea ice growth in the northeastern Weddell Sea (Figs. 3a and 6c). Unlike the first La Niña, no statistically significant wind anomaly can be seen during the second La Niña (Fig. 6e), indicating that the dynamic effect is not the key to the sea ice loss there (Fig. 6f).
b. Thermodynamic processes
In the first La Niña, colder surface air temperature appears in the west Antarctic region (Fig. 7a), which is advected into the eastern Ross Sea (170°–130°W) by the intensified southerly winds related to the deepened ASL, leading to surface cooling and sea ice increases there (Figs. 7a,b). These induced temperature and sea ice anomalies may change heat fluxes at the air–sea interface and cloudiness and amplify sea ice anomalies (e.g., Hu et al. 2019; Wang et al. 2019; Zhang et al. 2021; Zhang and Li 2023). The cold SST anomalies tend to lead to an increase in lower cloud covers (e.g., Wu and Kinter 2010; Vihma 2014; Nicolas et al. 2017; Sledd and L’Ecuyer 2021). Thus, we examine the contributions of surface heat fluxes, including SH, LH, net LW, and net SW radiation fluxes to the sea ice anomalies below.
Composite SON mean (a),(c) surface air temperature anomalies at 2 m (T2m) and (b),(d) SST anomalies for (left) the first La Niña and (center) the second La Niña. The differences in (e) T2m and (f) SST anomalies, respectively, between the first La Niña and the second La Niña. The units are in degrees Celsius. Vectors indicate 10-m wind anomalies, which are the same as those in Figs. 6b, 6e, and 6h, but only wind speeds above 0.3 m s−1 are shown. Dark green and black hatchings indicate significance at the 90% and 95% levels, respectively. Red and blue boxes are the same as those in Fig. 3.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
From Fig. 8, in the eastern Ross Sea the induced SST coolness (170°–130°W) leads to an increase in lower cloud covers (Fig. 9b), which causes a reduction in net SW radiation fluxes (Fig. 8d), thus cooling the surface (Fig. 7b) (Soden et al. 2004; Ahlgrimm and Forbes 2012; Zhang and Li 2023). This indicates that the positive feedback among colder SST, reduced net SW radiation flux, and increased cloudiness may have contributed to the sea ice growth in the eastern Ross Sea. In comparison, the contributions of surface SH and LH fluxes and net LW radiation flux are negligible (Figs. 8a–c and 9a).
Composite SON mean surface (a),(e) LH and (b),(f) SH flux anomalies, (c),(g) net LW, and (d),(h) net SW radiation flux anomalies for (left) the first La Niña and (center) the second La Niña. The differences in anomalous (i) LH, (j) SH, (k) net LW, and (l) net SW between the first La Niña and the second La Niña. The units are in watts per square meter. Dark green and black hatchings indicate significance at the 90% and 95% levels, respectively. Red and blue boxes are the same as those in Fig. 3.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
Composite SON mean (a),(c) total surface heat flux anomalies (W m−2) and (b),(d) low cloud cover anomalies (%) for (left) the first La Niña and (center) the second La Niña. The differences in anomalous (e) total surface heat fluxes and (f) low cloud covers between the first La Niña and the second La Niña. Total surface heat flux anomalies are estimated by the sum of LH, SH, net LW, and net SW anomalies. Dark green and black hatchings indicate significance at the 90% and 95% levels, respectively. Red and blue boxes are the same as those in Fig. 3.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
In the Bellingshausen Sea, there is reduced SIC corresponding to the northerly wind anomalies. Both warmer air mass and seawater in the midlatitudinal southeastern Pacific are advected into the Bellingshausen Sea by the northerly wind anomalies (Figs. 6b and 7a,b). Additionally, the induced SST warmth also has played a role in reducing the lower cloud cover (Fig. 9b), thereby leading to an increase in the net SW radiation flux (Fig. 8d). The net SW radiation flux dominates the total surface heat flux (Figs. 8a–d and 9a). This enhanced net SW radiation flux in turn strengthens the SST warmth and thus amplifies the sea ice loss.
Similar to the first La Niña, the second La Niña induces anomalous southerly (northerly) winds along the west (east) rim of the deepened ASL to enhance the northward (southward) advection of colder air (warmer air and seawater) into the eastern Ross–western Amundsen Seas (the northwestern Weddell Sea) (Figs. 6e and 7c,d) and increase (decrease) sea ice there (Fig. 3e). In addition, increased (reduced) lower cloud cover appears in the eastern Ross–western Amundsen Seas (the northwestern Weddell Sea) due to the SST cooling (warmth) (Figs. 7d and 9d). These increased (decreased) lower cloud covers cool (warm) the ocean surface by reducing (increasing) SW radiation (Fig. 8h) and ultimately increasing (reduce) SIC there. In comparison, the contributions of surface SH and LH fluxes and net LW radiation flux are minor (Figs. 8e–h and 9c).
In the western Ross Sea (the upper red box in Figs. 3a,e), the thermodynamic processes play an important role in shaping the SIC. During the first La Niña, there are statistically significant colder SST anomalies (Fig. 7a), co-occurring with the increased SIC (Fig. 3a). In addition, these cold anomalies favor an increase in lower cloud covers (Fig. 9b), which in turn strengthens the surface cooling via reducing the net SW radiation (Figs. 7b and 8d). Thus, the thermodynamic processes are more important than the dynamic processes. In contrast, the second La Niña causes warmer T2m and SST in the southwestern Pacific (between 150°E and 170°W) (Figs. 7c,d). The associated warm air or seawater can be advected into the western Ross Sea, thus contributing to the sea ice loss there. Besides, the induced surface warmth also strengthens the net SW radiations via reducing the lower cloud covers (Figs. 8h and 9d) and amplifies the sea ice loss via the positive feedback among warmer SST, decreased lower cloud covers, and increased net SW radiation fluxes. Compared with net SW radiation, the roles of surface SH and LH flux and net LW radiation flux are negligible (Figs. 8e–h and 9c).
In the northeastern Weddell Sea, colder SST anomalies during the first La Niña favor an SIC increase and subsequently a positive feedback among SST coolness, increased lower cloud covers, and decreased net SW radiation fluxes (see the bottom red boxes in Figs. 7b, 8d, and 9b). This indicates the importance of both dynamic and thermodynamic processes. During the second La Niña, the modest warmer T2m and SST anomalies may have contributed to the sea ice loss (Figs. 7c,d), but no feedback between SST anomalies, lower cloud covers, and net SW radiation fluxes is observed (Figs. 8h and 9d). Also, the impacts of surface SH and LH fluxes and net LW radiation flux are negligible (Figs. 8e–h and 9c).
5. Responses of atmospheric circulation to tropical heating
The results of CAM5 sensitive experiments are used to verify the observed tropical–Antarctic teleconnection (Fig. 5). From Fig. 10, in both Exp_1stLN and Exp_2ndLN, the atmospheric responses resemble the observational composite (cp. Figs. 10a,d with Figs. 5a,c). This indicates that tropical heating is the primary driver for the anomalous atmospheric circulations. In the Exp_1stLN, the tropical forcing excites only one Rossby wave that emanates from the southwest tropical Pacific and propagates southeastward (Fig. 10a), which leads to a strong anomalous anticyclone centered in the Amundsen Sea (Fig. 10b). Unlike the composite results, this Rossby wave train keeps propagating eastward when it arrives in the Weddell Sea, rather than dissipating in the west of Argentina (Figs. 5a and 10a).
Responses of (a),(d) 300-hPa geopotential heights (contours; m; interval: 5 m), (b),(e) 925-hPa wind fields (vectors; m s−1), and (c),(f) 925-hPa temperatures (°C) to the tropical SST anomalies (30°N–35°S) during (left) the first La Niña and (center) the second La Niña. The differences in (g) geopotential heights, (h) wind fields, and (i) temperatures between the first La Niña and the second La Niña. Purple vectors in (a) and (d) indicate the Rossby wave path. Dark green and black hatchings indicate significance at the 90% and 95% levels, respectively. Red and blue boxes are the same as those in Fig. 3.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
In the Exp_2ndLN, the SST anomalies in the tropical Indian and Pacific Oceans force two branches of southeastward-propagating wave trains, which cause a strong anomalous cyclone at the lower troposphere over the Ross–Amundsen–Bellingshausen Seas (Figs. 10d,e). The cold and warm advection to the west and east of the anomalous cyclone generates colder and warmer temperature anomalies in the eastern Ross–Amundsen Seas and the Bellingshausen–Weddell Seas, respectively (Figs. 10e,f), in agreement with the observed results (Fig. 3e). Thus, the SST anomalies in both the tropical Indian and Pacific Oceans collaboratively shape the anomalous SIC pattern in the Antarctic, especially in the West Antarctic, during the second La Niña.
6. Conclusions
During recent decades, La Niña events often persist for 2 years and peak twice in two consecutive boreal winters with a weakened amplitude in between spring and autumn. Such double-peaked La Niña was projected to occur more frequently in a high-emission scenario in the future (Geng et al. 2023). Whether the first and second episodes in such an event have a different impact on simultaneous austral spring (SON) Antarctic sea ice is investigated based on observational analyses and model experiments. The observational composite reveals that the first La Niña leads to a tripolar distribution of SIC anomalies, with more SIC in the Ross Sea and in the northeastern Weddell Sea sandwiching reduced SIC in the Bellingshausen Sea (Fig. 3a). In comparison, the second La Niña causes a decrease in most parts of the Southern Ocean, except for the eastern Ross–western Amundsen Seas where more SIC is seen (Fig. 3e). The SIC difference during the first La Niña and the second La Niña is most evident in the western Ross Sea and the northeastern Weddell Sea (Fig. 3i). The detailed mechanisms are summarized in Fig. 11.
Schematic of the physical mechanisms for the different impacts of (a),(b) the first La Niña and (c),(d) the second La Niña on the Antarctic sea ice during SON. The shadings in the tropic and Southern Ocean indicate SST and SIC anomalies, respectively. The first La Niña causes a strong cyclone anomaly over the eastern Ross–Amundsen–Bellingshausen Seas along with a weak anticyclone over the Weddell Sea via exciting one single southeastward-propagated Rossby wave train, while the second La Niña induces three anomalous anticyclones and two anomalous cyclones over the Southern Ocean by forcing two branches of Rossby wave trains that emanate from the southeastern tropical Indian Ocean and the central equatorial Pacific, respectively. These different atmospheric circulation anomalies shape a difference in sea ice distributions between the two La Niña episodes through both dynamic and thermodynamic processes. Consequently, the first La Niña induces a tripolar distribution of SIC with a negative anomaly in the Bellingshausen Sea sandwiched by two positive anomalies in the Ross Sea and the northeastern Weddell Sea, while the second causes a SIC reduction in most parts of the Southern Ocean except for the eastern Ross–western Amundsen Seas where an increase is observed. Additionally, positive feedback among SST, low cloud cover, and net SW contributes to amplifying sea ice anomalies.
Citation: Journal of Climate 37, 12; 10.1175/JCLI-D-23-0392.1
The first La Niña, with peaked SST cooling mainly confined into the narrow central–eastern equatorial Pacific within 10°N–10°S, excites one single Rossby wave train propagating southeastward to West Antarctic. Consequently, a strong anomalous cyclone occurs in the eastern Ross–Amundsen–Bellingshausen Seas along with a weak anomalous anticyclone in the Weddell Sea. During the second La Niña, the cool SST anomalies in the tropical Pacific are largely weakened but characterized by a broader meridional extension. It triggers two branches of southeastward-propagating wave trains emanating from the southeastern tropical Indian Ocean and from the central equatorial Pacific, respectively. These two wave trains combine into one in the South Pacific, keep propagating eastward to the Indian Ocean sector, and finally induce three anomalous anticyclones and two anomalous cyclones in the Southern Ocean. In particular, both the anomalous cyclone and anticyclone centered in the Amundsen Sea and the western Weddell Sea are stronger than those during the first La Niña. These different atmospheric anomalies shape a different distribution of sea ice between the two La Niña episodes through both dynamic and thermodynamic processes.
During the first La Niña, the deepened ASL increases (reduces) SIC in the eastern Ross Sea (the Bellingshausen Sea) by causing offshore northward (onshore southward) movements of sea ice as well as the northward cold (southward warm) advection. The induced SST coolness (warmth) further causes an increase (a reduction) in lower cloud covers, which in turn strengthens the SST coolness (warmth) via reducing (increasing) the net SW radiation fluxes, thus amplifying the sea ice growth (loss). In comparison, during the second La Niña, increased (decreased) sea ice occurred in the eastern Ross–western Amundsen Seas (the northwestern Weddell Sea) along with a westward shift.
The first La Niña causes sea ice to increase in both the western Ross Sea and the northeastern Weddell Sea, but it is opposite for the second La Niña (the red boxes in Fig. 3). For the first (second) La Niña, sea ice increase (loss) in the western Ross Sea can be attributed to the colder SST (warmer SST and T2m anomalies). The positive feedback between SST, lower cloud cover, and net SW radiation flux plays a role in amplifying the sea ice anomaly. As for the southeastern Pacific, the second La Niña causes an anomalous anticyclone. The associated northerly and northwesterly anomalies in the west and southwest rims of anticyclone also contribute to the sea ice loss directly. In the northeastern Weddell Sea, the first La Niña causes southeasterly anomalies together with SST coolness contributing to the sea ice growth through both dynamic and thermodynamic processes. In contrast, there is only weak SIC loss during the second La Niña, which might be caused by the modest warmer T2m and SST anomalies thermodynamically.
The CAM5 results confirm the difference between the wave trains triggered by the first La Niña and the second La Niña. This indicates that the SST anomalies in the tropical Pacific and Indian Oceans during the second La Niña can coordinate to affect the Antarctic sea ice, but this is not the case during the first La Niña.
One may note that the numerical experiments in the study do not reproduce perfectly the observational composite. For example, the Rossby wave train forced by the first La Niña dissipates in the west of Argentina in the composite, whereas keeps propagating eastward to reach the Weddell Sea in the simulated result (Figs. 5a,e and 10a,g). The stimulated lower-level temperature patterns in the Exp_1stLN and Exp_2ndLN are similar to each other (Figs. 10c,f,i), both exhibiting differences from the composite, especially in the East Antarctic. Why the simulations have bigger biases at high latitudes is not resolved and remains open. The candidate explanation is due to the fact that the AMIP experiment has averaged out internal variability in the extratropics, or that the composites are from a limited number of events that may still include internal variability. Nonetheless, the anomalous meridional winds together with the temperature anomalies at the lower level over the West Antarctic are well captured by the CAM5. Thus, the conclusion that the first La Niña and the second La Niña have a different impact on Antarctic sea ice may be robust.
Acknowledgments.
This work is jointly supported by the National Key Research and Development Program of China (2023YFF0805101), the NSFC projects (Grants 42205066 and 42376250), and the High-Level Talent Scientific Research Start-Up Fund Project of Beibu Gulf University (23KYQD38).
Data availability statement.
Datasets used in the study are freely accessed via the following websites: SIC at https://www.metoffice.gov.uk/hadobs/index.html; NOAA SST at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html; ORAS5 reanalysis at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-oras5?tab=form; and ERA5 reanalysis at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview and https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=overview. The CAM5 output in this study is available on application to the corresponding author.
REFERENCES
Ahlgrimm, M., and R. Forbes, 2012: The impact of low clouds on surface shortwave radiation in the ECMWF model. Mon. Wea. Rev., 140, 3783–3794, https://doi.org/10.1175/MWR-D-11-00316.1.
Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.
Battisti, D. S., and A. C. Hirst, 1989: Interannual variability in a tropical atmosphere–ocean model: Influence of the basic state, ocean geometry and nonlinearity. J. Atmos. Sci., 46, 1687–1712, https://doi.org/10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2.
Cai, W., P. van Rensch, T. Cowan, and H. H. Hendon, 2011: Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. J. Climate, 24, 3910–3923, https://doi.org/10.1175/2011JCLI4129.1.
Chen, X., S. Li, and C. Zhang, 2023: Distinct impacts of two kinds of El Niño on precipitation over the Antarctic Peninsula and West Antarctica in austral spring. Atmos. Oceanic Sci. Lett., 16, 100387, https://doi.org/10.1016/j.aosl.2023.100387.
Choi, K.-Y., G. A. Vecchi, and A. T. Wittenberg, 2013: ENSO transition, duration, and amplitude asymmetries: Role of the nonlinear wind stress coupling in a conceptual model. J. Climate, 26, 9462–9476, https://doi.org/10.1175/JCLI-D-13-00045.1.
Clem, K. R., J. A. Renwick, J. McGregor, and R. L. Fogt, 2016: The relative influence of ENSO and SAM on Antarctic Peninsula climate. J. Geophys. Res. Atmos., 121, 9324–9341, https://doi.org/10.1002/2016JD025305.
Clem, K. R., J. A. Renwick, and J. McGregor, 2017: Large-scale forcing of the Amundsen Sea low and its influence on sea ice and West Antarctic temperature. J. Climate, 30, 8405–8424, https://doi.org/10.1175/JCLI-D-16-0891.1.
Clem, K. R., B. R. Lintner, A. J. Broccoli, and J. R. Miller, 2019: Role of the South Pacific convergence zone in West Antarctic decadal climate variability. Geophys. Res. Lett., 46, 6900–6909, https://doi.org/10.1029/2019GL082108.
Deb, P., M. K. Dash, S. P. Dey, and P. C. Pandey, 2017: Non-annular response of sea ice cover in the Indian sector of the Antarctic during extreme SAM events. Int. J. Climatol., 37, 648–656, https://doi.org/10.1002/joc.4730.
DiNezio, P. N., and C. Deser, 2014: Nonlinear controls on the persistence of La Niña. J. Climate, 27, 7335–7355, https://doi.org/10.1175/JCLI-D-14-00033.1.
DiNezio, P. N., and Coauthors, 2017: A 2 year forecast for a 60–80% chance of La Niña in 2017–2018. Geophys. Res. Lett., 44, 11 624–11 635, https://doi.org/10.1002/2017GL074904.
Ding, Q., E. J. Steig, D. S. Battisti, and M. Küttel, 2011: Winter warming in West Antarctica caused by central tropical Pacific warming. Nat. Geosci., 4, 398–403, https://doi.org/10.1038/ngeo1129.
Ding, Q., E. J. Steig, D. S. Battisti, and J. M. Wallace, 2012: Influence of the tropics on the Southern Annular Mode. J. Climate, 25, 6330–6348, https://doi.org/10.1175/JCLI-D-11-00523.1.
Dommenget, D., T. Bayr, and C. Frauen, 2013: Analysis of the non-linearity in the pattern and time evolution of El Niño Southern Oscillation. Climate Dyn., 40, 2825–2847, https://doi.org/10.1007/s00382-012-1475-0.
Eayrs, C., X. Li, M. N. Raphael, and D. M. Holland, 2021: Rapid decline in Antarctic sea ice in recent years hints at future change. Nat. Geosci., 14, 460–464, https://doi.org/10.1038/s41561-021-00768-3.
Feng, J., Y. Zhang, Q. Cheng, X. S. Liang, and T. Jiang, 2019: Analysis of summer Antarctic sea ice anomalies associated with the spring Indian Ocean dipole. Global Planet. Change, 181, 102982, https://doi.org/10.1016/j.gloplacha.2019.102982.
Fu, C., and J. O. Fletcher, 1985: Two patterns of equatorial warming associated with El Niño. Sci. Bull., 30, 1360–1364.
Geng, T., F. Jia, W. Cai, L. Wu, B. Gan, Z. Jing, S. Li, and M. J. McPhaden, 2023: Increased occurrences of consecutive La Niña events under global warming. Nature, 619, 774–781, https://doi.org/10.1038/s41586-023-06236-9.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Hoerling, M., and Coauthors, 2013: Anatomy of an extreme event. J. Climate, 26, 2811–2832, https://doi.org/10.1175/JCLI-D-12-00270.1.
Holland, P. R., and R. Kwok, 2012: Wind-driven trends in Antarctic sea-ice drift. Nat. Geosci., 5, 872–875, https://doi.org/10.1038/ngeo1627.
Hosking, J. S., A. Orr, G. J. Marshall, J. Turner, and T. Phillips, 2013: The influence of the Amundsen–Bellingshausen Seas low on the climate of West Antarctica and its representation in coupled climate model simulations. J. Climate, 26, 6633–6648, https://doi.org/10.1175/JCLI-D-12-00813.1.
Hsu, P.-C., Z. Fu, H. Murakami, J.-Y. Lee, C. Yoo, N. C. Johnson, C.-H. Chang, and Y. Liu, 2021: East Antarctic cooling induced by decadal changes in Madden-Julian oscillation during austral summer. Sci. Adv., 7, eabf9903, https://doi.org/10.1126/sciadv.abf9903.
Hu, X., S. A. Sejas, M. Cai, Z. Li, and S. Yang, 2019: Atmospheric dynamics footprint on the January 2016 ice sheet melting in West Antarctica. Geophys. Res. Lett., 46, 2829–2835, https://doi.org/10.1029/2018GL081374.
Hu, Z.-Z., A. Kumar, Y. Xue, and B. Jha, 2014: Why were some La Niñas followed by another La Niña? Climate Dyn., 42, 1029–1042, https://doi.org/10.1007/s00382-013-1917-3.
Huang, S., J. Qin, S. Li, Z. Yuan, and Y. Mbululo, 2022: Spatiotemporal variability of the southern second mode and its influence on June precipitation in Southern China. J. Geophys. Res. Atmos., 127, e2022JD036762, https://doi.org/10.1029/2022JD036762.
Iwakiri, T., and M. Watanabe, 2020: Multiyear La Niña impact on summer temperature over Japan. J. Meteor. Soc. Japan, 98, 1245–1260, https://doi.org/10.2151/jmsj.2020-064.
Jin, D., and B. P. Kirtman, 2009: Why the Southern Hemisphere ENSO responses lead ENSO. J. Geophys. Res., 114, D23101, https://doi.org/10.1029/2009JD012657.
Jin, F.-F., 1997a: An equatorial ocean recharge paradigm for ENSO. Part I. Conceptual model. J. Atmos. Sci., 54, 811–829, https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2.
Jin, F.-F., 1997b: An equatorial ocean recharge paradigm for ENSO. Part II. A stripped-down coupled model. J. Atmos. Sci., 54, 830–847, https://doi.org/10.1175/1520-0469(1997)054<0830:AEORPF>2.0.CO;2.
Karoly, D. J., 1989: Southern Hemisphere circulation features associated with El Niño-Southern Oscillation events. J. Climate, 2, 1239–1252, https://doi.org/10.1175/1520-0442(1989)002<1239:SHCFAW>2.0.CO;2.
Kim, J., D. Kang, M.-I. Lee, E. K. Jin, J.-S. Kug, and W. S. Lee, 2023: Remote influences of ENSO and IOD on the interannual variability of the West Antarctic Sea Ice. J. Geophys. Res. Atmos., 128, e2022JD038313, https://doi.org/10.1029/2022JD038313.
Kusahara, K., P. Reid, G. D. Williams, R. Massom, and H. Hasumi, 2018: An ocean-sea ice model study of the unprecedented Antarctic sea ice minimum in 2016. Environ. Res. Lett., 13, 084020, https://doi.org/10.1088/1748-9326/aad624.
Lee, H.-J., and K.-H. Seo, 2019: Impact of the Madden-Julian oscillation on Antarctic sea ice and its dynamical mechanism. Sci. Rep., 9, 10761, https://doi.org/10.1038/s41598-019-47150-3.
Li, S., W. A. Robinson, M. P. Hoerling, and K. M. Weickmann, 2007: Dynamics of the extratropical response to a tropical Atlantic SST anomaly. J. Climate, 20, 560–574, https://doi.org/10.1175/JCLI4014.1.
Li, T., B. Wang, C.-P. Chang, and Y. Zhang, 2003: A theory for the Indian Ocean dipole-zonal mode. J. Atmos. Sci., 60, 2119–2135, https://doi.org/10.1175/1520-0469(2003)060<2119:ATFTIO>2.0.CO;2.
Li, X., D. M. Holland, E. P. Gerber, and C. Yoo, 2015: Rossby waves mediate impacts of tropical oceans on West Antarctic atmospheric circulation in austral winter. J. Climate, 28, 8151–8164, https://doi.org/10.1175/JCLI-D-15-0113.1.
Li, X., D. M. Holland, E. P. Gerber, and C. Yoo, 2014: Impacts of the north and tropical Atlantic Ocean on the Antarctic Peninsula and sea ice. Nature, 505, 538–542, https://doi.org/10.1038/nature12945.
Liu, J., Z. Zhu, and D. Chen, 2023: Lowest Antarctic sea ice record broken for the second year in a row. Ocean-Land-Atmos. Res., 2, 0007, https://doi.org/10.34133/olar.0007.
Luo, J.-J., G. Liu, H. Hendon, O. Alves, and T. Yamagata, 2017: Inter-basin sources for two-year predictability of the multi-year La Niña event in 2010–2012. Sci. Rep., 7, 2276, https://doi.org/10.1038/s41598-017-01479-9.
Meehl, G. A., J. M. Arblaster, C. M. Bitz, C. T. Y. Chung, and H. Teng, 2016: Antarctic sea-ice expansion between 2000 and 2014 driven by tropical Pacific decadal climate variability. Nat. Geosci., 9, 590–595, https://doi.org/10.1038/ngeo2751.
Neale, R. B., and Coauthors, 2010: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 274 pp.
Nicolas, J. P., and Coauthors, 2017: January 2016 extensive summer melt in West Antarctica favoured by strong El Niño. Nat. Commun., 8, 15799, https://doi.org/10.1038/ncomms15799.
Nuncio, M., and X. Yuan, 2015: The influence of the Indian Ocean dipole on Antarctic sea ice. J. Climate, 28, 2682–2690, https://doi.org/10.1175/JCLI-D-14-00390.1.
Ohba, M., and H. Ueda, 2009: Role of nonlinear atmospheric response to SST on the asymmetric transition process of ENSO. J. Climate, 22, 177–192, https://doi.org/10.1175/2008JCLI2334.1.
Okumura, Y. M., 2019: ENSO diversity from an atmospheric perspective. Curr. Climate Change Rep., 5, 245–257, https://doi.org/10.1007/s40641-019-00138-7.
Okumura, Y. M., and C. Deser, 2010: Asymmetry in the duration of El Niño and La Niña. J. Climate, 23, 5826–5843, https://doi.org/10.1175/2010JCLI3592.1.
Okumura, Y. M., M. Ohba, C. Deser, and H. Ueda, 2011: A proposed mechanism for the asymmetric duration of El Niño and La Niña. J. Climate, 24, 3822–3829, https://doi.org/10.1175/2011JCLI3999.1.
Okumura, Y. M., P. DiNezio, and C. Deser, 2017: Evolving impacts of multiyear La Niña events on atmospheric circulation and U.S. drought. Geophys. Res. Lett., 44, 11 614–11 623, https://doi.org/10.1002/2017GL075034.
Park, J.-H., S.-I. An, J.-S. Kug, Y.-M. Yang, T. Li, and H.-S. Jo, 2021: Mid-latitude leading double-dip La Niña. Int. J. Climatol., 41, E1353–E1370, https://doi.org/10.1002/joc.6772.
Parkinson, C. L., 2019: A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic. Proc. Natl. Acad. Sci. USA, 116, 14 414–14 423, https://doi.org/10.1073/pnas.1906556116.
Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic sea ice variability and trends, 1979–2010. Cryosphere, 6, 871–880, https://doi.org/10.5194/tc-6-871-2012.
Peng, S., W. A. Robinson, and S. Li, 2003: Mechanisms for the NAO responses to the North Atlantic SST tripole. J. Climate, 16, 1987–2004, https://doi.org/10.1175/1520-0442(2003)016<1987:MFTNRT>2.0.CO;2.
Pezza, A. B., H. A. Rashid, and I. Simmonds, 2012: Climate links and recent extremes in Antarctic sea ice, high-latitude cyclones, Southern Annular Mode and ENSO. Climate Dyn., 38, 57–73, https://doi.org/10.1007/s00382-011-1044-y.
Purich, A., and Coauthors, 2016: Tropical Pacific SST drivers of recent Antarctic sea ice trends. J. Climate, 29, 8931–8948, https://doi.org/10.1175/JCLI-D-16-0440.1.
Raj Deepak, S. N., J. S. Chowdary, A. R. Dandi, G. Srinivas, A. Parekh, C. Gnanaseelan, and R. K. Yadav, 2019: Impact of multiyear La Niña events on the South and East Asian summer monsoon rainfall in observations and CMIP5 models. Climate Dyn., 52, 6989–7011, https://doi.org/10.1007/s00382-018-4561-0.
Raphael, M. N., 2007: The influence of atmospheric zonal wave three on Antarctic sea ice variability. J. Geophys. Res., 112, D12112, https://doi.org/10.1029/2006JD007852.
Raphael, M. N., and M. S. Handcock, 2022: A new record minimum for Antarctic sea ice. Nat. Rev. Earth Environ., 3, 215–216, https://doi.org/10.1038/s43017-022-00281-0.
Raphael, M. N., and Coauthors, 2016: The Amundsen Sea low: Variability, change, and impact on Antarctic climate. Bull. Amer. Meteor. Soc., 97, 111–121, https://doi.org/10.1175/BAMS-D-14-00018.1.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Q. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.
Sledd, A., and T. S. L’Ecuyer, 2021: Emerging trends in Arctic solar absorption. Geophys. Res. Lett., 48, e2021GL095813, https://doi.org/10.1029/2021GL095813.
Soden, B. J., A. J. Broccoli, and R. S. Hemler, 2004: On the use of cloud forcing to estimate cloud feedback. J. Climate, 17, 3661–3665, https://doi.org/10.1175/1520-0442(2004)017<3661:OTUOCF>2.0.CO;2.
Song, H.-J., E. Choi, G.-H. Lim, Y. H. Kim, J.-S. Kug, and S.-W. Yeh, 2011: The central Pacific as the export region of the El Niño-Southern Oscillation sea surface temperature anomaly to Antarctic sea ice. J. Geophys. Res., 116, D21113, https://doi.org/10.1029/2011JD015645.
Stuecker, M. F., C. M. Bitz, and K. C. Armour, 2017: Conditions leading to the unprecedented low Antarctic sea ice extent during the 2016 austral spring season. Geophys. Res. Lett., 44, 9008–9019, https://doi.org/10.1002/2017GL074691.
Suarez, M. J., and P. S. Schopf, 1988: A delayed action oscillator for ENSO. J. Atmos. Sci., 45, 3283–3287, https://doi.org/10.1175/1520-0469(1988)045<3283:ADAOFE>2.0.CO;2.
Sun, X., S. Li, and S. Liess, 2022: The asymmetric connection of SST in the Tasman Sea with respect to the opposite phases of ENSO in austral summer. Adv. Atmos. Sci., 39, 1897–1913, https://doi.org/10.1007/s00376-022-1421-y.
Sun, X., S. Li, and D. Yang, 2023: Air–sea coupling over the Tasman Sea intensifies the ENSO-related South Pacific atmospheric teleconnection. Adv. Clim. Change Res., 14, 363–371, https://doi.org/10.1016/j.accre.2023.06.001.
Supari, F. Tangang, E. Salimun, E. Aldrian, A. Sopaheluwakan, and L. Juneng, 2018: ENSO modulation of seasonal rainfall and extremes in Indonesia. Climate Dyn., 51, 2559–2580, https://doi.org/10.1007/s00382-017-4028-8.
Takaya, K., and H. Nakamura, 2001: A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. J. Atmos. Sci., 58, 608–627, https://doi.org/10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2.
Timmermann, A., and Coauthors, 2018: El Niño–Southern Oscillation complexity. Nature, 559, 535–545, https://doi.org/10.1038/s41586-018-0252-6.
Turner, J., 2004: The El Niño-southern oscillation and Antarctica. Int. J. Climatol., 24 (1), 1–31, https://doi.org/10.1002/joc.965.
Turner, J., T. Phillips, J. S. Hosking, G. J. Marshall, and A. Orr, 2013: The Amundsen Sea low. Int. J. Climatol., 33, 1818–1829, https://doi.org/10.1002/joc.3558.
Vihma, T., 2014: Effects of Arctic sea ice decline on weather and climate: A review. Surv. Geophys., 35, 1175–1214, https://doi.org/10.1007/s10712-014-9284-0.
Wang, B., R. Wu, and T. Li, 2003: Atmosphere–warm ocean interaction and its impacts on Asian–Australian monsoon variation. J. Climate, 16, 1195–1211, https://doi.org/10.1175/1520-0442(2003)16<1195:AOIAII>2.0.CO;2.
Wang, J., H. Luo, Q. Yang, J. Liu, L. Yu, Q. Shi, and B. Han, 2022: An unprecedented record low Antarctic sea-ice extent during austral summer 2022. Adv. Atmos. Sci., 39, 1591–1597, https://doi.org/10.1007/s00376-022-2087-1.
Wang, Z., J. Turner, Y. Wu, and C. Liu, 2019: Rapid decline of total Antarctic sea ice extent during 2014–16 controlled by wind-driven sea ice drift. J. Climate, 32, 5381–5395, https://doi.org/10.1175/JCLI-D-18-0635.1.
Wassermann, S., C. Schmitt, C. Kottmeier, and I. Simmonds, 2006: Coincident vortices in Antarctic wind fields and sea ice motion. Geophys. Res. Lett., 33, L15810, https://doi.org/10.1029/2006GL026005.
Wilson, A. B., D. H. Bromwich, K. M. Hines, and S.-H. Wang, 2014: El Niño flavors and their simulated impacts on atmospheric circulation in the high southern latitudes. J. Climate, 27, 8934–8955, https://doi.org/10.1175/JCLI-D-14-00296.1.
Wu, R., and J. L. Kinter III, 2010: Atmosphere-ocean relationship in the midlatitude North Pacific: Seasonal dependence and east-west contrast. J. Geophys. Res., 115, D06101, https://doi.org/10.1029/2009JD012579.
Yu, J.-Y., and H.-Y. Kao, 2007: Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958–2001. J. Geophys. Res., 112, D13106, https://doi.org/10.1029/2006JD007654.
Yu, J.-Y., H. Paek, E. S. Saltzman, and T. Lee, 2015: The early 1990s change in ENSO–PSA–SAM relationships and its impact on Southern Hemisphere climate. J. Climate, 28, 9393–9408, https://doi.org/10.1175/JCLI-D-15-0335.1.
Yu, L., S. Zhong, J. A. Winkler, M. Zhou, D. H. Lenschow, B. Li, X. Wang, and Q. Yang, 2017: Possible connections of the opposite trends in Arctic and Antarctic sea-ice cover. Sci. Rep., 7, 45804, https://doi.org/10.1038/srep45804.
Yu, L., S. Zhong, T. Vihma, C. Sui, and B. Sun, 2022: The impact of the Indian Ocean Basin Mode on Antarctic sea ice concentration in interannual time scales. Geophys. Res. Lett., 49, e2022GL097745, https://doi.org/10.1029/2022GL097745.
Yuan, X., 2004: ENSO-related impacts on Antarctic sea ice: A synthesis of phenomenon and mechanisms. Antarct. Sci., 16, 415–425, https://doi.org/10.1017/S0954102004002238.
Yuan, X., and D. G. Martinson, 2000: Antarctic sea ice extent variability and its global connectivity. J. Climate, 13, 1697–1717, https://doi.org/10.1175/1520-0442(2000)013<1697:ASIEVA>2.0.CO;2.
Zhang, C., and S. Li, 2023: Causes of the record-low Antarctic sea-ice in austral summer 2022. Atmos. Oceanic Sci. Lett., 16, 100353, https://doi.org/10.1016/j.aosl.2023.100353.
Zhang, C., S. Li, F. Luo, and Z. Huang, 2019a: The global warming hiatus has faded away: An analysis of 2014–2016 global surface air temperatures. Int. J. Climatol., 39, 4853–4868, https://doi.org/10.1002/joc.6114.
Zhang, C., J.-J. Luo, and S. Li, 2019b: Impacts of tropical Indian and Atlantic Ocean warming on the occurrence of the 2017/2018 La Niña. Geophys. Res. Lett., 46, 3435–3445, https://doi.org/10.1029/2019GL082280.
Zhang, C., T. Li, and S. Li, 2021: Impacts of CP and EP El Niño events on the Antarctic sea ice in austral spring. J. Climate, 34, 9327–9348, https://doi.org/10.1175/JCLI-D-21-0002.1.
Zheng, F., J. Li, F. Kucharski, R. Ding, and T. Liu, 2018: Dominant SST mode in the Southern Hemisphere extratropics and Its influence on atmospheric circulation. Adv. Atmos. Sci., 35, 881–895, https://doi.org/10.1007/s00376-017-7162-7.
Zhu, T., and J.-Y. Yu, 2022: A shifting tripolar pattern of Antarctic sea ice concentration anomalies during multi-year La Niña events. Geophys. Res. Lett., 49, e2022GL101217, https://doi.org/10.1029/2022GL101217.
Zuo, H., M. A. Balmaseda, S. Tietsche, K. Mogensen, and M. Mayer, 2019: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: A description of the system and assessment. Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019.
