Analysis of summer Antarctic sea ice anomalies associated with the spring Indian Ocean dipole

Feng, Jiajun; Zhang, Yuanzhi; Cheng, Qiuming; Liang, X. San; Jiang, Tingchen

doi:10.1016/j.gloplacha.2019.102982

Cited by 14 publications

(13 citation statements)

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“…Variation in surface air temperature (SAT) over the Antarctic is an indicator of global SAT change [1]. Variations in the Antarctic SAT partly contribute to climate anomalies in East Asia [2], Tropics [3], and Arctic [4] through the atmosphere-ocean bridges. Establishing how the Antarctic SAT modulates the sea ice extent and thickness over the Southern Hemisphere will improve the predictability of global climate change.…”

Section: Introductionmentioning

confidence: 99%

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

Fang

2021

Advances in Meteorology

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The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p = 0.04 ) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p = 0.05 ). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p = 0.01 ∼ 0.27 ) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p = 0.04 ∼ 0.42 ) with a peak over the higher latitudes.

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Section: Introductionmentioning

confidence: 99%

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

Fang

2021

Advances in Meteorology

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Section: Indian Ocean Dipolementioning

confidence: 99%

“…Two studies (Feng et al, 2019;Nuncio and Yuan, 2015) have found evidence for a relationship between the IOD and sea ice around Antarctica. Associations were found predominantly with sea ice around East Antarctica, though Feng et al (2019) also described relationships with sea ice in the adjacent Weddell and Ross Seas. The authors suggest that IOD-driven anomalies in regional circulation and meridional heat/moisture transport may be responsible for the links with sea ice, driving changes in the process of sea ice growth and decline.…”

Section: Indian Ocean Dipolementioning

confidence: 99%

“…As described in Chapter 1, our understanding of the influence that the IOD has on Antarctic sea ice is limited. Some studies (Feng et al, 2019;Nuncio and Yuan, 2015;Rai et al, 2008) have found evidence for a link between the IOD and sea ice in the Southern Hemisphere. These studies theorise that this is due to the excitation of an atmospheric Rossby wave train by tropical Indian ocean SSTs, which then propagates southward to Antarctica, altering regional circulation and thus driving changes in the sea ice.…”

Section: Introductionmentioning

confidence: 99%

“…Ferreira et al, 2015;Hall and Visbeck, 2002;Lefebvre and Goosse, 2008;Liu et al, 2004;Purich et al, 2016), and a few have addressed the influence of the IOD (e.g. Feng et al, 2019;Nuncio and Yuan, 2015;Rai et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Sea ice and large-scale atmospheric variability around East Antarctica

Isaacs¹

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Antarctica’s sea ice cover is an important component in the global climate system. The variability and recent trends of sea ice concentration are, however, not accurately reproduced by models. Evaluating model performance is hampered because the processes that determine sea ice distribution are not yet well understood, particularly in the East Antarctic region. Here I explore the relationships between recent climate variability and sea ice around East Antarctica, the spatial variability in these relationships, and the impacts that these may have on other aspects of the climate and cryosphere. To achieve this, I analysed satellite-derived HadlSST sea ice concentration (SIC) alongside ERA5 atmospheric reanalysis data for the period between 1979-2018. I found that variability in sea ice coverage around East Antarctica was affected by El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM), and Zonal Wave 3 (ZW3). Additionally, I found that the influence of each of these modes varied spatially and temporally, and that sea ice variability affected how regional scale climate responded to changes in large-scale circulation. Summer and autumn SIC around Dronning Maud Land between 10°E and 70°E exhibited a statistically significant negative correlation with the Niño 3.4 index. Analysis of ERA5 data suggests that a southward‐propagating atmospheric wave train triggered by SST anomalies in the tropical Pacific extends into Dronning Maud Land and alters sea ice concentration by encouraging meridional airflow. Shifts in meridional flow in Dronning Maud Land affected sea ice thermodynamically, by altering local heat transport and in turn altering sea ice formation and melt. Sea ice around the Western Pacific sector (WPS) of East Antarctica showed a significant association with variability in the IOD and the SAM. The IOD was correlated with SIC in all seasons but summer. The IOD-SIC relationship is likely driven by an IOD-associated atmospheric wave-train which propagates polewards from the tropical Indian Ocean to Wilkes Land, altering regional circulation and in turn affecting SIC through changes to local climate and sea ice transport. The correlation between WPS SIC and the SAM shifts from positive in summer and autumn to negative in winter and spring, and is likely due to the influence of the SAM on katabatic winds and coastal polynyas, which in turn affect SIC. A significant correlation was observed between SIC variability around East Antarctica and precipitation variability across the continent and the near-coastal Southern Ocean. Further analysis showed that SIC affected how continental precipitation responded to large-scale atmospheric circulation, including modes such as ZW3 and the SAM. Specifically, increased southward moisture flux was only associated with increased precipitation in the inland coastal regions of the continent when SIC was anomalously low. These findings suggest that any future decrease in sea ice may result in greater coupling of climate variability with continental precipitation.

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Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach

Chen

Sun

Wang

et al. 2022

Intl Journal of Climatology

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Despite significant advances in seasonal climate forecasts, the reliability of both dynamical and empirical models for the Indian Ocean Dipole (IOD) prediction is still limited to a lead time of one season or less. In this study, the skill of the NCEP Climate Forecast System version 2 (CFSv2) for the IOD prediction during the period 1982-2014 is evaluated. The results indicate that the model performance for the IOD prediction is the worst in spring among the four seasons, which is manifested in the fact that a skilful prediction of spring IOD event is limited to a lead time of only about 1-2 months. To improve the forecast of spring IOD events, a physical-empirical (PE) model and a convolutional neural network (CNN) model are established in the present study. The IOD in April-May-June (AMJ) is taken as the predictand, and the CFSv2-predicted sea surface height (SSH) in AMJ and the observed Laptev sea ice in the preceding December are used as the two predictors. The original CFSv2-predicted IOD time series has an insignificant correlation with the observed IOD time series with a temporal correlation coefficient (TCC) of 0.03; the PE model (CNN model) can largely improve the IOD prediction with a TCC of 0.74 (0.77) between the PE-model-predicted (CNN-model-predicted) IOD and the observed IOD during AMJ. Thus, the PE model and the CNN model developed in the present study can be applied to improve the IOD predictions from numerical models in the future.

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Analysis of summer Antarctic sea ice anomalies associated with the spring Indian Ocean dipole

Cited by 14 publications

References 28 publications

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

Sea ice and large-scale atmospheric variability around East Antarctica

Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach

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