Spatiotemporal analysis of sea ice in the Weddell Sea of Antarctic based on GTWR

doi:10.21203/rs.3.rs-4944871/v1

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Spatiotemporal analysis of sea ice in the Weddell Sea of Antarctic based on GTWR

https://doi.org/10.21203/rs.3.rs-4944871/v1

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The Geographical and Temporal Weighted Regression (GTWR) method is employed to assess the impact of various environmental factors on sea ice concentration (SIC) in the Weddell Sea. Initially, MODIS-derived SIC was used to evaluate the accuracy of six SIC products derived from different satellite sensors and algorithms. The MWRI/NT2 product demonstrated the highest correlation with the MODIS data, validating its reliability for further analysis. Using the MWRI/NT2 product, along with ERA5 and NCEP/NCAR reanalysis datasets, we investigated the interannual and seasonal trends in SIC and sea ice extent (SIE) from 2011 to 2023. The results indicate a declining trend in SIE at a rate of -6.2 ± 1.9×10³ km²/yr, with the most significant ice loss occurring in autumn. The GTWR analysis highlights significant spatial and temporal variability in the factors influencing SIC in the Weddell Sea. The Latent heat flux (LH) emerged as the most influential factor, with a median standardized regression coefficient of 1.44. The LH primarily promotes sea ice growth by cooling the surface through the condensation of atmospheric water vapor. Zonal winds also played a critical role, particularly by promoting sea ice formation through Ekman transport of cold surface water. However, wind speed had a minimal impact on SIC, likely due to the lack of directional data in the ERA5 dataset. In contrast, the impact of net radiation (NR) varied significantly across the region, complicating its overall influence on sea ice dynamics. Sensible heat flux (SH) generally supported ice growth, except in the central Weddell Sea, where local conditions caused SH to inhibit ice formation. These findings underscore the complex interplay of environmental factors in shaping SIC.

Earth and environmental sciences/Climate sciences/Cryospheric science

Earth and environmental sciences/Climate sciences

Earth and environmental sciences/Environmental sciences

Antarctica is the largest cold source for the operation of global ocean and atmospheric circulation heat engines. Antarctica influences climate variations and is a vulnerable region for global climatic and environmental changes. Consequently, Antarctica plays a crucial role in global atmospheric and oceanic circulation.1. Sea ice is the most significant form of coverage on the Antarctic surface and has a substantial impact on the atmosphere and climate of the Southern Ocean and the Antarctic continent2,3. The reflectivity of the sea ice surface is much higher than that of seawater, affecting the absorption of solar energy by the sea surface and thus altering its radiative and energy balance4. Therefore, studying the Weddell Sea sea ice is of great significance to global climate research, as changes in sea ice patterns can influence global weather systems, sea level rise, and subsequently impact coastal communities and human living environments worldwide.

The passive microwave radiometers, which include data collection from the Special Sensor Microwave Imager Sounder (SSMIS), Microwave Radiation Imager (MWRI), and Advanced Microwave Scanning Radiometer 2 (AMSR2), are the primary means for monitoring SIC5,6. The algorithms primarily used to estimate SIC based on passive microwave data include Bootstrap (BT), NASA Team (NT), Fully Constrained Least Squares (FCLS), Enhanced NASA Team (NT2), ASI (Arctic Radiation and Turbulence Interaction Study Sea Ice), and Fully Constrained Least Squares with Positivity (FCLS-P)7. The SIC products are derived by different

institutions using various algorithms from remote sensing data obtained through different satellite sensors. These products include the University of Bremen's UB-AMSR2/ASI, the National Snow and Ice Data Center's NSIDC-SSMIS/NT, NSIDC-SSMIS/CDR, NSIDC-AMSR2/NT2, the Ocean and Sea Ice Satellite Application Facility products OSI-SAF/BR-BST, the National Satellite Ocean Application Service's NSOAS-SMR/NT, and the National Satellite Meteorological Center's NSMC-MWRI/NT28,9. These products vary in spatial and temporal resolution, and their accuracy differs across regions. Therefore, an accuracy assessment must be conducted first when selecting the SIC products for scientific research.

The changes and trends in Weddell Sea's sea ice are closely tied to oceanic and atmospheric dynamics, as evidenced by several studies10-13. The uneven sea ice extent (SIE) trend in the Weddell Sea is related to the Southern Oscillation (SAM), El Niño Southern Oscillation (ENSO) and surface heat flux14-20. The SAM is positively linked to air temperature in the Weddell Sea, as shown by current studies, but it is negatively correlated in most areas of the east Antarctica and parts of west Antarctica21,22. The impact of SAM and ENSO on SIE is very apparent, especially during the summer season23. Consequently, during the Antarctic summer, the SIE demonstrates a positive correlation with the intensity of the SAM phase, while during other seasons, this association becomes less pronounced and shifts to negative. During the Antarctic summer and autumn, stratospheric ozone depletion and increased carbon dioxide levels lead to an increase in the SAM, causing the sea surface temperature to rise and the seawater to warm24. However, during winter, as sea ice migrates northward, a positive SAM phase facilitates the expansion of sea ice in peripheral regions while simultaneously causing reductions along the coastlines.

In this study, The MODIS Sea Ice Concentration (SIC) data has been chosen as the benchmark to evaluate other SIC products derived from various satellite sensors and algorithms. The MODIS was selected due to its high spatial resolution and extensive validation in previous studies, which confirm its reliability in accurately depicting sea ice characteristics25. Unlike sensors limited to specific parts of the electromagnetic spectrum, MODIS integrates data from visible, infrared, and microwave spectral bands26,27. This multispectral capability enhances its ability to detect sea ice under diverse atmospheric conditions, such as cloud cover and varying levels of solar illumination, which are common challenges in polar regions.

Recent studies on the SIC have primarily employed traditional modeling techniques, such as linear regression and time-series analysis, to explore the impact of environmental factors like sea surface temperature (SST), atmospheric pressure, and wind patterns28,29. These models, while valuable, often assume static relationships across space and time, potentially overlooking the dynamic and heterogeneous nature of polar environments. Given the spatial and temporal variability of SIC, there is a growing need for more sophisticated modeling approaches. Geographically and Temporally Weighted Regression (GTWR) offers a novel solution by allowing for the estimation of parameters that vary across both space and time, thus capturing the spatiotemporal non-stationarity inherent in SIC data30,31,32. The use of GTWR in this context is innovative, as it provides a more nuanced understanding of how different environmental factors influence SIC in various regions and time periods, offering deeper insights than traditional methods.

In existing studies, data released by the National Snow and Ice Data Center (NSIDC) and other organizations are commonly used to analyze the SIC and SIE changes. Although several institutions worldwide have released sea ice concentration datasets, there is limited research on selecting the optimal products for specific regions, such as the Weddell Sea. The effects of long-term climatic factors on sea ice changes are typically explored by most studies, but these effects change over time. Therefore, six SIC products released by the NSIDC, the University of Bremen (UB), and the National Satellite Meteorological Center (NSMC) were compared with the SIC derived from MODIS. The differences between these products were analyzed, and their accuracy was evaluated. By using the optimal product, the characteristics and patterns of sea ice changes in the Weddell Sea over 13 years were identified, and the correlation between sea ice interannual and seasonal variability and ocean-atmosphere forcing factors were explored, such as the SST, the wind parameters, the net heat flux (NHF), the SAM, the ENSO, the Atlantic Meridional Mode (AMM), and the Indian Ocean Dipole Mode (IOD). Reanalysis data were utilized for statistical analysis, including significant trends and composite difference analysis. This study demonstrates the long-term effects of sea ice variability across all four seasons and reveals the mechanisms of their interaction with corresponding atmospheric variations in the Weddell Sea region. we apply the GTWR to capture the localized effects of environmental variables such as wind speed, atmospheric pressure, and heat fluxes on SIC in the Weddell Sea, providing a more nuanced analysis of sea ice dynamics.

Assessment of Antarctic SIC products based on MODIS data

Through the preprocessing of 36 MODIS images and the inversion of SIC, the data albedo, ice-water binarization map, and MODIS 25km$\:\times\:$25km spatial resolution SIC were obtained. By comparing the bias, mean absolute deviation (MAD), root mean square deviation (RMSD), and correlation coefficient (R) (see Table 1) between the SIC inverted from MODIS and the SIC products released by various organizations, the consistency between different SIC products and high-resolution optical remote sensing data-derived SIC was verified and analyzed. AMSR2/NT2, SSMIS/CDR, and MWRI/NT2 show high correlation coefficients with the verification data SIC, at 0.92, 0.91, and 0.94, respectively.

The bias analysis indicates that the bias of MWRI/NT2 is the smallest, at just 0.23%, while the bias of SSMIS/NT is the largest, at -9.63%. Because the NT algorithm has a low recognition capacity for new ice and melt ponds, there is a significant bias compared to the SIC inverted from MODIS.

The analysis of MAD and RMSD shows that the MAD and RMSD of SSMIS/NT are the largest, at 11.20% and 15.35%, respectively, while those of MWRI/NT2 are the smallest, at 3.68% and 9.52%.

In summary, MWRI/NT2 has the highest correlation coefficient, the lowest MAD, and the lowest RMSD among the six SIC products, achieving the highest consistency with the SIC inverted from MODIS. Therefore, the MWRI/NT2 SIC product was used for subsequent research analysis. The NT2 algorithm reduces the effect of sea ice surface changes using the polarization ratio, making it more effective than the NT algorithm. The MWRI/NT2 product can detect finer-scale SIC changes than the 25km$\:\times\:$25km spatial resolution SIC products. The influencing factors include variations in sensors used for obtaining brightness temperature data, differences in satellite observation times, algorithmic differences, variations in spatial resolution, differences in post-processing methods, and sensitivity to climate and environmental factors.

Table 1

Bias, MAD, RMSD and correlation coefficients between six sic products and the SIC of MODIS.
Products	Bias	MAD	RMSD	R
SSMIS/NT	-9.63	11.20	15.35	0.66
SSMIS/BT	-2.85	6.36	14.68	0.86
SSMIS/CDR	-1.64	5.10	11.53	0.91
AMSR2/NT2	1.84	3.69	10.75	0.92
AMSR2/ASI	-7.02	8.68	15.41	0.88
MWRI/NT2	0.23	3.68	9.52	0.94

Seasonal sea ice changes

Summer changes

Over the past 13 years, the summer SIC in the Weddell Sea has shown an overall decreasing trend, with a decline rate of -8.9 ± 2.1×10³km²yr⁻¹ (Table A8). A strong inverse relationship exists between SST and SIC (r=-0.62, p < 0.01) (Table A9), which leads to a reduce of SIC with high SST (Fig. 1(a) and (b)). Due to the substantial increase in SST north of 70°S and the decrease south of 70°S (eastern Antarctic Peninsula), both thermal and pressure differences between low and high latitudes have resulted in the strengthening of the westerly belt33,34. As shown in Fig. 1(c), the wind speed and zonal wind between 60°S and 70°S significantly increase, indicating the strengthening of the westerlies. The intensification of the westerlies promotes the northward transport of colder surface waters through Ekman transport. This phenomenon leads to Ekman suction, enhancing the mid-layer water transport from mid-latitudes to high latitudes. Furthermore, a weakening of the westerlies leads to a reduction in the Weddell Gyre circulation, which in turn causes an upwelling of the thermocline, resulting in an increase in surface water temperatures and a subsequent reduction in sea ice33. Additionally, correlation analysis results demonstrate that both wind speed and zonal wind have an inverse relationship with SIC (zonal wind: r=-0.42, p < 0.05; wind speed: r=-0.62, p < 0.01) (Table A9). Changes in wind speed can affect the intensity of sea ice advection, which subsequently influences both increase and decrease of sea ice. As shown in Fig. 1(d), wind speed increases between 60°S and 70°S, promotes sea ice advection towards the northeastern Weddell Sea and reduces the SIC. Therefore, both SST and wind speed have a significant inverse relationship with SIC, which together affect the changes of sea ice during summer24.

Autumn changes

In autumn, the SIC in the northern and northeastern regions of the Weddell Sea decreases significantly, with a decline rate of -10.7 ± 2.3×103km2 year-1 (Table A8), marking it as the season with the most severe sea ice loss in the Weddell Sea. There is an inverse relationship between the trend in SST and SIC, indicated by a correlation of -0.66 (p < 0.01) (Table A9, Fig. 2(a) and (b)). The westerly winds strengthen in the northwestern and northeastern Weddell Sea (Fig. 2(c)). Correlation analysis shows that in autumn, there is a strong negative relationship between both wind speed and zonal wind and the SIC (zonal wind: r=-0.60, p < 0.01; wind speed: r=-0.77, p < 0.01) (Table A9). (1) Northwestern region: This intensified circulation causes the thermocline to rise, subsequently leading to the northward migration of warm water into the Weddell Sea. Additionally, the enhanced Ekman transport increases the movement of cold surface water from high to low latitudes, triggering the Ekman suction phenomenon, which facilitates the influx of warm low-latitude water into the Weddell Sea. The rise in wind speed enhances the movement of sea ice, leading to its northwestward drift. These three factors collectively promote the melting of sea ice. (2) Northeastern region: In the northeastern Weddell Sea, changes in the wind speed and zonal wind are minimal24. A decrease in both the wind speed and zonal wind is observed in some areas, where sea ice experience loss (Fig. 2(c) and (d)). Therefore, we divided the Weddell Sea at the − 20°W meridian into eastern and western sections and separately analyzed the correlations of the SIC with the SST, wind speed and zonal wind. The results show that in the eastern Weddell Sea, the SST, wind speed and zonal wind all exhibit an inverse relationship with the SIC (Eastern SST: r = -0.62, p < 0.01; Eastern zonal wind: r = -0.70, p < 0.01; Eastern wind speed: r = -0.77, p < 0.01). In the western Weddell Sea, the correlation coefficients of the SIC with the SST, wind speed and zonal wind are − 0.68, -0.39 and − 0.40, respectively (Table A9). Therefore, in the northeastern Weddell Sea, the primary cause of changes in SIC is variations in SST, while the impacts of the wind speed and zonal wind on SIC are minor.

In summary, in the northwestern Weddell Sea during the autumn, the reduction in the SIC is primarily due to rising SST and the intensification of zonal and meridional winds. In the northeastern Weddell Sea, the dominant factor influencing the decrease in the SIC is the SST.

Winter changes

In winter, the trend in SIC changes in the Weddell Sea is minimal, with a notable reduction primarily near 60°S, especially at the northern tip of the Antarctic Peninsula (Fig. 3(a)). The rate of sea ice loss is -1.3 ± 0.5×10³km²yr⁻¹ (Table A8). Similar to autumn, the correlation between the SIC and SST, wind speed and zonal wind is different in the eastern and western Weddell Sea. (1) In the northwestern Weddell Sea, at the northern tip of the Antarctic Peninsula, the correlation coefficients between the SIC and the SST, zonal wind, and wind speed are − 0.57, -0.35, and − 0.53, respectively (Table A9). The correlation analysis results indicate that as the trend in changes in the SIC weakens, the correlation also diminishes. The findings from the correlation analysis suggest indicate that changes in the SIC are primarily influenced by variations in sea surface temperature and wind speed. The strengthening of westerly and northwesterly winds in this region (Fig. 3(c) and (d)) enhances both sea ice advection and Ekman suction. This, in turn, facilitates the influx of warm water from lower latitudes. As a result of this warm water intrusion, sea surface temperatures at the northern tip of the Antarctic Peninsula exhibit an upward trend (Fig. 3(b)). Consequently, the SIC at the northern tip of the Antarctic Peninsula significantly decreases. (2) In the northeastern Weddell Sea, SST has a strong negative correlation with SIC, as shown by a correlation coefficient of -0.71 (p < 0.01) (Table A9), and shows no significant correlation with the wind speed and zonal wind. Therefore, the decrease in the SIC in this region is primarily due to the increase in SST.

Spring changes

During spring, SIC in most areas of the Weddell Sea shows a declining trend, with the northwest experiencing the most pronounced decrease. The rate of sea ice decline is -6.2 ± 1.9×10³ km²yr⁻¹ (Table A8) (Fig. 4(a)). The SIC displays strong negative correlations with the SST, wind speed, and zonal wind, exhibiting correlation coefficients of -0.65, -0.71, and − 0.74, respectively (Table A9). The SST shows an upward trend in the northern Weddell Sea (Fig. 4(b)), resulting in higher seawater temperatures and hastening the dissolution of sea ice. During spring, the strengthening of westerly winds (Fig. 4(c)) enhances Ekman transport, which drives the movement of colder polar surface water toward lower latitudes. This intensified Ekman transport also increases Ekman suction, leading to the influx of warm water into the Weddell Sea. Additionally, the strengthening of westerly winds contribute to the intensification of the Weddell Gyre, which in turn promotes increased vertical mixing and the upward movement of warmer water from the thermocline35. The declining trend in SIC in the eastern Weddell Sea is driven by the substantial strengthening of westerly and northwesterly winds, which promotes sea ice advection and causes the ice to drift eastward (Fig. 4 (c) and (d)). The pronounced declining trend in SIC at the northern tip of the Antarctic Peninsula is caused by intensified sea ice advection, a result of the increased wind speeds due to westerly winds from the Bellingshausen Sea being blocked by the Antarctic Peninsula. This phenomenon is also observed in the summer, autumn and winter. The SST along the eastern flank of the Antarctic Peninsula is showing a declining trend, while the western side exhibits an increasing trend. The Antarctic Peninsula acts as a barrier, preventing the transfer of temperature and wind between its eastern and western sides1,36. As can be seen from Fig. 4, as the SST in the west of the Antarctic Peninsula increases, the SIC decreases significantly. Due to the Antarctic Peninsula's resistance to heat transfer, the SST in the east of Antarctic Peninsula changes very little, resulting in almost no change in SIC24.

Long-term sea ice changes

From 2011 through 2023, the SIE in the Weddell Sea exhibited significant seasonal variations and interannual fluctuations. In 2014, the SIE reached its maximum extent of 4.08×10⁶km², declining to its minimum extent of 3.17×10⁶km² by 2023 (Fig. 5). Overall, the SIE shows a decreasing pattern, characterized by an average yearly reduction rate of -6.6 ± 1.3×10³ km²yr^− 1 (Table A8).

Seasonal analysis indicates that the most significant declining trend occurs in autumn, with a rate of -10.7 ± 2.3×10³ km² yr⁻¹, while the smallest decline is observed in winter, at -1.3 ± 0.5×10³ km² yr⁻¹ (Table A8). According to statistical analysis, there is a significant negative correlation between SIE and SST across seasons (r=-0.81, p < 0.01) (Table A2), with the negative correlation occurring in summer (r=-0.89, p < 0.01) (Table A3). Changes in SST are the primary factors affecting the expansion and retreat of sea ice37. The largest SIE in 2014 corresponded to the lowest SST of -0.21°C, while the smallest SIE in 2023 corresponded to the highest SST of 0.25°C (Fig. 6). This finding is consistent with existing research, which indicates that increases in SIE correspond with decreases in SST38.

The NHF represents the net exchange of heat between a system, such as the ocean, and its surrounding environment. A positive NHF indeed means that the ocean is losing heat to the atmosphere. Conversely, a negative NHF indicates that the ocean is gaining heat from the atmosphere. The NHF is crucial for driving atmospheric circulation, affecting ocean temperatures and influencing the formation and melting of sea ice. Statistical analysis reveals that the relationship between the NHF and SIE varies by season. In spring and summer, a substantial positive relationship is observed between NHF and SIE, with correlations of r = 0.72 (p < 0.01) for spring and r = 0.3 (p < 0.1) for summer. Conversely, in autumn and winter, the relationship becomes negative, with correlations of r=-0.65 (p < 0.01) for autumn and r=-0.52 (p < 0.01) for winter (Tables A3-A6, Fig. 6).

Over the past 13 years, the sea ice cover in the Weddell Sea has undergone significant changes, which are closely related to the continuing ocean-atmosphere interactions. To deepen the understanding of these processes, a correlation analysis was conducted on Weddell Sea sea ice with various climate variability patterns (Fig. 5, Table A2). The SAM refers to the climate variations caused by the northward or southward shifts of the westerly wind belt or low-pressure systems surrounding Antarctica39,40, directly reflecting variations in the atmospheric pressure patterns around Antarctica. It significantly influences the SST and the strength of the westerly winds in the Weddell Sea. Although annually there is no significant correlation between the SIE and the SAM, seasonally, the SIE correlates positively with the SAM in summer (r = 0.29, p < 0.1), and negatively in winter (r=-0.28, p < 0.1) (Tables A2-A6, Fig. 6). The expansion and contraction of sea ice are driven by the positive phase of the SAM, which leads to changes in the westerlies and alters the pressure differences between the Antarctic low pressure belt and the subtropical high pressure belt34,41,42.

The IOD is associated with variations in the differences between the SST and sub-surface ocean temperatures in the eastern and western regions of the Indian Ocean. The IOD demonstrates significant negative correlations with the SIE during summer (r=-0.62, p < 0.01) and autumn (r=-0.33, p < 0.1), and a significant positive correlation in spring (r = 0.31, p < 0.1) (Tables A2-A6, Fig. 6). This may be attributed to the proximity of the Indian Ocean to the eastern Weddell Sea, where changes in the SST of the western Indian Ocean and variations in the IOD facilitate changes in the Southern Hemisphere westerlies, easily influencing sea ice changes in the eastern Weddell Sea. The results of this study indicate that there is no significant correlation between the ENSO and AMM with SIE throughout the year (Tables A2-A6, Fig. 6).

High sea ice minus low sea ice

In order to investigate how ocean-atmosphere heat exchange influences sea ice changes, we conducted a principal component analysis on the SIE. Based on the results, years with a standard deviation greater than 1 were classified as high sea ice years, while those with a standard deviation less than − 1 were defined as low sea ice years (Table A1). We determined the mean values for the SIC, NHF, and SST during periods of high and low sea ice, and derived a composite difference by subtracting the average values of low ice years from those of high ice years. Using this composite difference, we investigated the composite variation patterns of the SIC, NHF and SST (Fig. 7).

In summer, the positive anomalies in the SIC were observed in the Weddell Sea, particularly along the western and eastern coastal areas. At the same time, most regions experienced the positive anomalies in the NHF and negative anomalies in the SST (Fig. 7(a), (b) and (c)). Correlation analysis shows that in summer, the SIC is positively correlated with the NHF, with a coefficient of 0.30 (p < 0.1), and negatively correlated with the SST, with a coefficient of -0.32 (p < 0.1) (Table A3). Consequently, the positive NHF anomalies and negative SST anomalies that occur in summer collectively promote the formation of sea ice.

In autumn, the positive SIC anomalies in the mid-sections of the Weddell Sea are accompanied predominantly by the negative NHF anomalies (Fig. 7(d) and (e)). This could result from the heat gathered over the summer dissipating into the atmosphere during the autumn43. The SST predominantly exhibits negative anomalies (Fig. 7(f)). Consequently, the increase in the SIC during autumn is associated with the combined effects of NHF (correlation coefficient of -0.65, p < 0.01) and SST (correlation coefficient of -0.71, p < 0.01). (Fig. 7(d)–(f) and Table A4).

In winter, the significant positive anomalies in the SIC occur near 60°S in the northern sector of the Weddell Sea, while both the NHF and SST exhibit negative anomalies in the same area (Fig. 7(h) and (i)). In winter, positive anomalies in SIC are linked to negative anomalies in NHF, with a correlation of -0.51 (p < 0.01), and SST, with a correlation of -0.71 (p < 0.01) (Table A5).

In spring, the positive SIC anomalies persist across most areas of the Weddell Sea (Fig. 7(h)). The positive NHF anomalies dominate in most regions south of 60°S, while the SST primarily exhibits negative anomalies (Fig. 7(k) and (l)). This indicates that the rise in sea ice correlates with positive anomalies in NHF and negative anomalies in SST, respectively. In spring, the SIC anomalies shows a strong positive correlation with the NHF anomalies (r = 0.71, p < 0.01) and a strong negative correlation with the SST anomalies (r=-0.70, p < 0.01) (Table A6).

Correlation analysis between the NHF and SST shows that their relationship varies with the seasons. During summer and spring, the NHF and SST exhibit significant negative correlations, with coefficients of -0.32 (p < 0.1) and − 0.90 (p < 0.01), respectively. This may be due to the higher solar radiation in these seasons, which enhances the release of the LH and sensible heat flux (SH), thereby increasing NHF from the ocean to the atmosphere during years with high sea ice. During autumn and winter, NHF and SST exhibit significant positive correlations, with coefficients of 0.62 (p < 0.01) and 0.29 (p < 0.1), respectively (Tables A3-A6). In summer and autumn, the SST decreases significantly, while the decrease is less pronounced in winter and spring. This may be attributed to the reduced solar radiation received by the Weddell Sea in winter and spring, leading to increased upward radiation from open seawaters in subsequent seasons44. Consequently, the relationships and variations between the NHF and SST are not constant but may change over time and with the seasons.

Spatial and Temporal Analysis of Environmental Factors Influencing SIC

The GTWR accounts for spatial and temporal heterogeneity in modeling, allowing various environmental factors to have unique impacts on SIC at different geographic locations and time points31,32. Initially, correlation analysis and multicollinearity tests were conducted to screen the environmental factors, including 10m zonal wind, 10m meridional wind, 10m wind speed, mean sea-level pressure (MSL), SST, LH, SH, and NR flux (NR), derived from the ERA5 reanalysis dataset. The results of the correlation analysis indicate a high degree of correlation between SST and other environmental factors, particularly with MSL and LH (Table A7). The results of the multicollinearity test show that the variance inflation factor (VIF) for SST is 27.4 (Table 3). The VIF is a statistical measure used to assess the degree of multicollinearity, indicating how the interrelationships among independent variables affect the precision and stability of a linear regression model45,46,47. Typically, the VIF of less than 10 is considered acceptable for multicollinearity. Since the VIF for SST exceeds 10, it was excluded from the analysis. We then employed 10m zonal wind, 10m meridional wind, 10m wind speed, MSL, LH, SH, and NR as independent variables, with SIC as the dependent variable, to construct the GTWR model, quantifying the effects of these factors on SIC.

The statistical results of the regression coefficients for each environmental variable in the GTWR model are presented in the Table 2. The regression coefficients shown are standardized coefficients, where a larger absolute value indicates a greater impact on SIC48. The LH exhibits the most significant impact on SIC, with a median value of 1.44. This is followed by 10m zonal wind, 10m meridional wind, MSL, SH, and NR. The influence of 10m wind speed is the least, with a median value of 0.09.

Table 2

Statistics of regression coefficients of environmental factors in GTWR model
	Mean	First quartile	Second quartile*	Third quartile
10m u-component of wind	0.49	0.07	0.29	0.65
10m v-component of wind	0.41	0.08	0.28	0.60
Mean sea level pressure	0.48	0.08	0.29	0.69
10m wind speed	0.13	0.02	0.09	0.18
Latent heat flux	2.04	0.36	1.44	3.01
Net thermal radiation	0.37	0.07	0.24	0.53
Sensible heat flux	0.76	0.15	0.52	1.11

Table 3

Statistics of regression coefficients of environmental factors in GTWR model
Environmental factor	Collinearity statistics
Environmental factor	Allowance	VIF
10m u-component of wind	0.159	6.302
10m v-component of wind	0.417	2.397
Mean sea level pressure	0.194	5.145
Sea surface temperature	0.063	25.323
10m wind speed	0.138	7.225
Latent heat flux	0.133	7.791
Net thermal radiation	0.376	2.662
Sensible heat flux	0.311	3.218

Figure 8 shows the change in the regression coefficients of zonal wind on SIC. In most regions, the zonal wind promotes sea ice growth. This is because the strengthening of the zonal wind leads to an accelerated Ekman transport, which moves colder surface water northward49. This cold water transport lowers the sea surface temperature in the northern areas, facilitating sea ice formation. Particularly under the influence of the Weddell Gyre, the enhanced zonal wind may push cold water northward while reducing heat exchange between the atmosphere and the ocean, thereby stabilizing sea ice50,51. In contrast, at the northern tip of the Antarctic Peninsula, zonal wind suppresses sea ice growth, and in 2013, the extent of this suppression expanded. This is due to the significant impact of the Antarctic Peninsula’s topography on local wind fields. The peninsula’s terrain may amplify wind speeds in certain areas, increasing ocean surface turbulence and enhancing vertical mixing of the water column52. This mixing can bring warmer deep water to the surface, inhibiting sea ice formation.

Figure A1 illustrates the variation in the regression coefficients of meridional wind on SIC. It can be observed that, in most regions, meridional wind promotes sea ice growth. This is because the strengthening of meridional wind transports cold air from the Antarctic continent to the Weddell Sea, lowering sea surface temperatures and reducing sea ice melt, which in turn facilitates the formation and expansion of sea ice53. Under the influence of the Ekman effect, meridional wind induces a shift in water flow at the ocean surface. Southerly winds typically push sea ice northward, while northerly winds tend to push warmer surface waters southward54. This transport mechanism helps establish cold water bands in the Weddell Sea, creating favorable conditions for sea ice expansion. However, in the central Weddell Sea, meridional wind suppresses SIC growth in certain areas. This suppression is likely due to strong southerly winds altering sea ice distribution by pushing it northward, leading to reduced sea ice coverage in the central Weddell Sea. Additionally, changes in northerly winds may enhance Ekman suction of deeper warm waters, bringing them to the surface, further inhibiting sea ice growth.

Figure A2 shows the variation in regression coefficients of MSL on SIC from 2011 to 2023. It is evident that MSL inhibits sea ice growth in the northern Weddell Sea while promoting it along the southern coastal regions of the Weddell Sea. In the northern Weddell Sea, higher MSL is typically associated with anticyclonic weather systems. These high-pressure systems often bring clear skies, increasing solar radiation, which raises surface temperatures and thus inhibits sea ice formation4. Additionally, high-pressure systems generally result in weaker winds, potentially reducing the transport of cold air from the Antarctic continent, further suppressing sea ice growth. In contrast, along the southern coastal regions of the Weddell Sea, lower MSL is typically linked to cyclonic systems, which often bring stronger winds, particularly facilitating the transport of cold air from the Antarctic continent to the ocean. This situation is conducive to sea ice growth. Furthermore, lower pressure may enhance Ekman transport effects along the coast, pushing colder surface water upward, which helps maintain or increase sea ice coverage. However, the impact of these factors has shown a declining trend over the years. This may be due to changes in climate patterns in the Antarctic region as global warming progresses. Changes in heat exchange between the atmosphere and ocean, accelerated ice sheet melting, and alterations in atmospheric circulation may all contribute to weakening the direct influence of MSL on sea ice growth55. Over time, these changes have likely reduced the impact of pressure systems on sea ice dynamics.

Figure A3 shows the variation in regression coefficients of wind speed on SIC. It can be observed that the impact of wind speed on SIC does not display a clear spatial pattern. This lack of pattern may be due to the inherently low influence of wind speed on SIC. Additionally, in the ERA5 dataset, wind speed is not a vector variable, meaning it lacks directional information. However, in the Weddell Sea, wind direction is particularly important for influencing sea ice dynamics.

Figure A4 shows the variation in regression coefficients of LH on SIC from 2011 to 2023. It is evident that LH promotes sea ice growth in most regions. This is because LH involves the condensation of water vapor in the air, a process that releases heat56. Typically, in the Weddell Sea, when atmospheric water vapor encounters the colder sea surface, condensation occurs. The LH released during this condensation is absorbed by the surrounding air, raising the air temperature, which in turn triggers further condensation of water vapor57. This process leads to surface cooling, thereby promoting sea ice formation, as the formation of sea ice requires sea water temperatures to be close to the freezing point.

Figure A5 shows the variation in regression coefficients of NR on SIC. It can be observed that the impact of NR on SIC does not display a clear pattern. This lack of consistency is due to the fact that the balance of NR is influenced by factors such as cloud cover, ice surface reflectivity, atmospheric composition, and surface characteristics. These factors make the effect of NR on sea ice complex and difficult to predict.

In the western and eastern Weddell Sea, SH promotes sea ice growth by cooling the ice surface (Figure A6). However, in the central Weddell Sea, the phenomenon of SH inhibiting sea ice growth may be closely related to the region's unique atmospheric and oceanic conditions. In the central area, northerly winds and ocean currents may transport relatively warm air and water to this region. SH transfers heat from the atmosphere to the ice surface, leading to ice melt or inhibiting new ice formation58. This localized transport of warm air and water causes the effect of SH in the central region to differ significantly from that in the western and eastern regions, thereby suppressing sea ice growth.

A comprehensive analysis was conducted on the seasonal variations and trends in sea ice within the Weddell Sea region from 2011 to 2023, utilizing the MWRI/NT2 product, which shows the highest correlation with the SIC inverted from MODIS. This approach ensures the accuracy and reliability of the SIC data. Additionally, and the relationship between sea ice changes and climatic factors was explored and analyzed.

By comparing various the SIC products with the SIC inverted from MODIS, it was found that the MWRI/NT2 SIC product performs best in terms of correlation and error metrics, demonstrating its effectiveness in observing sea ice changes.

The SIE in the Weddell Sea has shown a significant declining trend over the past 13 years, particularly in the spring, summer and autumn seasons, with smaller changes in winter. The average annual decline rate is -6.6 ± 1.3×10³km²yr⁻¹. In the spring and summer of the Weddell Sea, the SIC exhibits a significant negative correlation with the SST, wind speed and zonal wind. In autumn and winter, different correlation results were found between the eastern and western parts of the Weddell Sea. In the east, the SIC is primarily influenced by the SST, with which it exhibits a significant negative correlation. In the west, the SIC shows a significant negative correlation with the SST, wind speed and zonal wind. We speculate that the occurrence of these conditions is due to the Antarctic Peninsula blocking the transfer of heat and wind between its eastern and western sides. The specific mechanisms responsible for this phenomenon will be the focus of our future research.

Research indicates that changes in SIC in the Weddell Sea during summer and spring are primarily influenced by the SST, while in autumn and winter, the SIC changes are jointly influenced by the NHF and SST. In summer and spring, the sea ice changes are primarily attributed to ocean warming, while in autumn and winter, they are driven by ocean-atmosphere interactions.

The GTWR was employed in this study to assess the spatial and time heterogeneity in the impact of various environmental factors on the SIC across the Weddell Sea. The complex and region-specific nature of these influences was highlighted by the analysis. For instance, LH was found to be a significant promoter of sea ice growth in most regions. The impact of wind speed exhibited no clear space-time pattern, likely due to its low overall influence and the lack of directional data in ERA5. The Zonal and meridional winds generally supported sea ice formation, though topographical effects near the Antarctic Peninsula sometimes led to suppression. SH promoted sea ice growth in the western and eastern regions but had a suppressive effect in the central Weddell Sea, likely due to the influx of warmer air and water. The varied responses of SIC to mean sea-level pressure further underscored the dynamic interactions between atmospheric and oceanic processes, particularly in the context of ongoing climate change. These findings demonstrate the critical importance of considering spatial heterogeneity in climate models and suggest that regional responses to environmental factors can significantly differ, influencing sea ice dynamics in complex ways.

Passive microwave sea ice concentration product

The institutions that release sea ice concentration products include the NSIDC in the United States, the UB in Germany, the European Organization for the Exploitation of Meteorological Satellites, and the NSMC of the China Meteorological Administration. The SIC products are generated based on algorithms such as BT, NT, NT2, and ASI, based on passive microwave data obtained by instruments onboard various satellites. The six commonly used Antarctic sea ice concentration products utilized in this study are presented in Table 4.

Table 4

6 passive microwave remote sensing sea ice concentration products.
Serial Number	Product Name	Publishing Agency	Sensor	Algorithm	Time Resolution	Spatial Resolution
1	SSMIS/BT59	National Snow and Ice Center	SSM/I-SSMIS	BT	day	25km
2	SSMIS/NT60	National Snow and Ice Center	SSM/I-SSMIS	NT	day	25km
3	SSMIS/CDR61	National Snow and Ice Center	SSM/I-SSMIS	CDR	day	25km
4	AMSR2/NT262	National Snow and Ice Center	AMSR-E/2	NT2	day	12.5km
5	AMSR2/ASI63	University of Bremen, Germany	AMSR-E/2	ASI	day	6.25km
6	MWRI/NT2	National Satellite Meteorological Center	MWRI	NT2	day	12.5km

MODIS Data

The SIC is inverted by using 500m×500m spatial resolution MODIS L1B images to compare and verify the differences between various passive microwave SIC products (https://ladsweb.modaps.eosdis.nasa.gov/). First, the MODIS L1B product is preprocessed, including radiometric calibration, reprojection, and bow-tie removal. The MODIS L1B product is zenith-corrected using the MOD03 product, and the MOD35 product is used for cloud mask processing to obtain cloudless clear sky pixels64,65. Before zenith angle correction, attention should be paid to whether the various observation angles in SolarZenith are within a reasonable dynamic range. In order to reduce the impact of excessive solar zenith angles, solar zenith angle data less than 85° should be selected.

ECMWF Reanalysis v5 reanalysis data set

The ERA5 reanalysis dataset (0.25°×0.25° grid)66, obtained from the European Centre for Medium-Range Weather Forecasts(https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form), was used to analyze the impact of various climate parameters on sea ice, including 10m wind speed, zonal wind, meridional wind, and 2m SST.

NOAA Data

The heat flux and monthly climate indicators used in this paper (such as LH, SH, LW, SW, SAM, ENSO, AMM and IOD) are obtained from the National Oceanic and Atmospheric Administration (https://psl.noaa.gov/data)67. This study utilizes the ENSO data derived from the Nino 3.4 region. (5°N-5°S, 120°-170°W).

Retrieving SIC from MODIS data

This paper uses the reflectivity data of bands 1 (0.620–0.670µm), 3 (0.459–0.479µm), and 4 (0.545–0.565µm) of the MODIS L1B product to invert SIC, and the calculation formula is:

$$\:\text{A}=0.3265\times\:{\text{A}}_{1}+0.4364\times\:{\text{A}}_{3}+0.2366\times\:{\text{A}}_{4}$$

where A is the broad-band albedo of the top atmosphere, and A₁, A₃, and A₄ are the corresponding reflectivity values of the 1st, 3rd, and 4th bands of MODIS, respectively.

The Otsu algorithm is used to classify ice and water in images68,69. Finally, the mean method is used to calculate the SIC of 25km×25km.

Statistical and comparative analysis methods

Four statistical parameters are utilized in this article for accuracy assessment: bias, MAD, RMSD, and correlation coefficient (r).

$\:\text{b}\text{i}\text{a}\text{s}=\frac{\sum\:_{\text{i}=1}^{\text{n}}\left({\text{X}}_{\text{i}}-{\text{Y}}_{\text{i}}\right)}{\text{n}}$	(2)
$\:\text{M}\text{A}\text{D}=\frac{\sum\:_{\text{i}=1}^{\text{n}}\left\|{\text{X}}_{\text{i}}-{\text{Y}}_{\text{i}}\right\|}{\text{n}}$	(3)
$\:\text{R}\text{M}\text{S}\text{D}=\sqrt{\frac{\sum\:_{\text{i}=1}^{\text{n}}{\left({\text{X}}_{\text{i}}-{\text{Y}}_{\text{i}}\right)}^{2}}{\text{n}}}$	(4)
$\:\text{r}=\frac{\sum\:_{\text{i}=1}^{\text{n}}\left({\text{X}}_{\text{i}}-\stackrel{-}{\text{X}}\right)\left({\text{Y}}_{\text{i}}-\stackrel{-}{\text{Y}}\right)}{\sqrt{\sum\:_{\text{i}=1}^{\text{n}}{\left({\text{X}}_{\text{i}}-\stackrel{-}{\text{X}}\right)}^{2}}\sqrt{\sum\:_{\text{i}=1}^{\text{n}}{\left({\text{Y}}_{\text{i}}-\stackrel{-}{\text{Y}}\right)}^{2}}}$	(5)

where $\:{X}_{i}$ is the SIC of a product; $\:{Y}_{i}$ is the SIC estimated by MODIS; $\:\stackrel{-}{\text{X}}$ and $\:\stackrel{-}{\text{Y}}$ are the means of $\:{X}_{i}$ and $\:{Y}_{i}$ respectively; n is the total number of samples.

SIE estimation method

At present, many scholars at home and abroad usually use 15% as the SIC threshold to calculate the sea ice range and SIE when dividing sea ice areas70. The SIE is obtained by summing the SIC of the unit pixel in the study area and multiplying it by the corresponding actual area. The calculation formula is as follows:

$$\:\text{S}=\sum\:_{\text{i}=1}^{\text{n}}\text{C}\text{i}\times\:{\text{R}}^{2}$$

where S is the SIE, $\:{\text{C}}_{\text{i}}$ is the SIC of the pixel (SIC > 15%), R is the spatial resolution of the image, and n is the number of pixels in the image.

NHF calculation method

The NHF is determined by the LH, the SH, the LW, and the SW. The NHF over the ocean surface ($\:{Q}_{net}$) is expressed as follows71:

$$\:{Q}_{net}={Q}_{LH}{+{Q}_{SH}+Q}_{LW}+{Q}_{SH}$$

where $\:{\text{Q}}_{\text{n}\text{e}\text{t}}$ is the NHF, $\:{\text{Q}}_{\text{L}\text{H}}$ is the LH flux, $\:{\text{Q}}_{\text{S}\text{H}}$ is the SH flux, $\:{\text{Q}}_{\text{L}\text{W}}$ is the LW, and $\:{\text{Q}}_{\text{S}\text{W}}$ is the SW. $\:{\text{Q}}_{\text{n}\text{e}\text{t}}$ is the NHF.

GTWR

The GTWR model is a localized linear regression model that reflects the spatiotemporal non-stationarity characteristics of research data by estimating parameters that vary across space and time. This model calculates the spatiotemporal weighting matrix by incorporating spatial and temporal coordinates into the model. Spatiotemporal non-stationarity implies that the parameters describing the spatiotemporal relationships change over time and space. The GTWR model used in this study to assess the impact of various environmental factors on sea ice concentration in the Weddell Sea is based on the following Eq. 30:

$${Y_i}={\beta _0}\left( {{u_i},{v_i},{t_i}} \right)+\sum\limits_{k} {{\beta _k}\left( {{u_i},{v_i},{t_i}} \right)} {X_{ik}}+{\varepsilon _i}$$

where ${Y_i}$ is the dependent variable of the ${i^{th}}$ sample point; ${X_{ik}}$ is the ${k^{th}}$ independent variable of the ${i^{th}}$ sample point; ${\varepsilon _i}$ is the random error; ${u_i}$ is the longitude coordinate of the ${i^{th}}$ sample point; ${v_i}$ is the latitude coordinate of the ${i^{th}}$ sample point; ${t_i}$ is the time coordinate of the ${i^{th}}$ sample point; $\left( {{u_i},{v_i},{t_i}} \right)$ is the spatio-temporal dimension coordinates of the ${i^{th}}$ sample point; ${\beta _0}\left( {{u_i},{v_i},{t_i}} \right)$ is the constant term of the ${i^{th}}$ sample point; and ${\beta _k}\left( {{u_i},{v_i},{t_i}} \right)$ is the regression coefficient of the ${k^{th}}$ independent variable at the ${i^{th}}$ sample point, with the least squares estimation as follows72:

$${\widehat {\beta }_k}\left( {{u_i},{v_i},{t_i}} \right){\left[ {{X^T}W\left( {{u_i},{v_i},{t_i}} \right)X} \right]^{ - 1}}{X^T}W\left( {{u_i},{v_i},{t_i}} \right)Y$$

where $W\left( {{u_i},{v_i},{t_i}} \right)$ is the spatiotemporal weight matrix, which is the attenuation function of the spatiotemporal distance and is calculated by the following formula73:

$${W_{ij}}=\exp \left[ { - \frac{{{{\left( {d_{{ij}}^{{ST}}} \right)}^2}}}{{{h^2}}}} \right]$$

where h is a non-negative parameter, called the spatiotemporal bandwidth, which represents the spatiotemporal range of the sampling point. We select the spatiotemporal bandwidth corresponding to the minimum second-order corrected Akaike information criterion (AICc) value as the optimal spatiotemporal bandwidth. The spatiotemporal distance ${d^{ST}}$ is calculated as follows:

$${d^{ST}}=\sqrt {\lambda \left[ {{{\left( {{u_i} - {u_j}} \right)}^2}+{{\left( {{v_i} - {v_j}} \right)}^2}} \right]+\mu {{\left( {{t_i} - {t_j}} \right)}^2}}$$

where ${\left( {{u_i} - {u_j}} \right)^2}+{\left( {{v_i} - {v_j}} \right)^2}$ is the spatial distance between sample points i and j ; ${\left( {{t_i} - {t_j}} \right)^2}$ is the temporal distance between sample points i and j ; $\lambda$ and $\mu$ are the scaling factors of spatial distance and temporal distance, respectively.

Competing interests

The author declares no competing interests.

Author Contribution

Conceptualization, D.YR. and L.X.; methodology, D.YR. and G.JY.; software, D.XF. and Y.Y.; validation, Y.GY., S.HP. and L.X.; formal analysis, Y.Y.; investigation, Y.GY. and S.HP.; data curation, D.YR.; writing—original draft preparation, D.YR.; writing—review and editing, D.YR., D.XF., L.X. and G.JY.; visualization, D.YR. and L.X.; supervision, Y.GY. and S.HP.; project administration, L.X.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

The study is supported by the National Natural Science Foundation of China (grant Nos. 42192535 & 42274006 & 42104084). The authors gratefully acknowledge the support of the above programs and funds. The authors are grateful to the National Snow and Ice Data Center, the University of Bremen, the National Satellite Meteorological Center, the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction/National Center for Atmospheric Research, and the Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center for providing the experimental data used in this study.

Data Availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Comiso, J.C. & Nishio, F. Trends in the sea ice cover using enhanced and compatible AMSR-E,SSM/I, and SMMR data. J. Geophys. Res. Oceans113, 1-22. DOI: https://doi.org/10.1029/2007JC004257 (2008).
Clem, K.R., Lintner, B.R., Broccoli, A.J. & Miller, J.R. 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 (2019).
Tan, B. et al. A novel strategy to analyse the form drag on pressure ridges and the air-ice drag coefficient in the north-western Weddell Sea. Appl. Math. Model.58, 158-165. https://doi.org/10.1016/j.apm.2017.09.046 (2018).
Duspayev, A., Flanner, M. G. & Riihelä, A. Earth's Sea Ice Radiative Effect From 1980 to 2023. Geophys. Res. Lett.51(14), e2024GL109608. https://doi.org/10.1029/2024GL109608.
Liang, S. Research on Remote Sensing Inversion Methods of Polar SIC and Thickness. (University of Chinese Academy of Sciences, 2022).
Liu, T.T., Yang, Z.J., Wang, Z.M. & Gao, K.F. Accuracy Evaluation of Arctic SIC Estimation Using FY-3D Microwave Radiometer Data. Geomatics and Information Science of Wuhan University46(12), 1843-1851 (2021).
Shi, K.Q. et al. Remote Sensing Inversion of SIC from Medium Resolution Imaging Spectroradiometer. National Remote Sensing Bulletin25(3), 753-764 (2021).
Yang, Z.J., Wang, Z.M. & Liu, T.T. Accuracy Evaluation of Arctic SIE and Edge Estimation Using Fengyun-3D Satellite Microwave Data. Chinese Journal of Polar Research35, 46-58 (2023).
Guo, H. et al. Comparison and Evaluation of Seven Common Antarctic Passive Microwave SIC Products from Both Domestic and International Sources. Acta Oceanolog. Sin.45, 141-159 (2023).
Zwally, H.J. et al. Variability of Antarctic sea ice 1979–1998. J. Geophys. Res. Oceans107(C5), 9-1-9-19. https://doi.org/10.1029/2000JC000733 (2002).
Zhang, J.L. Increasing Antarctic Sea Ice under Warming Atmospheric and Oceanic Conditions. J. Clim.20, 2515-2529. https://doi.org/10.1175/JCLI4136.1 (2007).
Parkinson, C.L. & Cavalieri, D.J. Antarctic sea ice variability and trends, 1979-2010. Cryosphere6(4), 871-880. https://doi.org/10.5194/tc-6-871-2012(2012).
Turner, J. et al. Recent Decrease of Summer Sea Ice in the Weddell Sea, Antarctica. Geophys. Res. Lett.47(11), e2020GL087127. https://doi.org/10.1029/2020GL087127 (2020).
Yuan, X.J. ENSO-related impacts on Antarctic sea ice: a synthesis of phenomenon and mechanisms. Antarct. Sci.16(4), 415-425. https://doi.org/10.1017/S0954102004002238 (2004).
Stammerjohn, S.E. et al. Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and Southern Annular Mode variability. J. Geophys. Res. Oceans113(C3). https://doi.org/10.1029/2007JC004269 (2008).
McKee, D.C. et al. Climate impact on interannual variability of Weddell Sea Bottom Water. J. Geophys. Res. Oceans116(C5). https://doi.org/10.1029/2010JC006484 (2011).
Murphy, E.J., Clarke, A., Abram, N.J. & Turner, J. Variability of sea-ice in the northern Weddell Sea during the 20th century. J. Geophys. Res. Oceans119(7), 4549-4572. https://doi.org/10.1002/2013JC009511 (2014).
Vernet, M. et al. The Weddell Gyre, Southern Ocean: Present Knowledge and Future Challenges. Rev. Geophys.57(3), 623-708. https://doi.org/10.1029/2018RG000604 (2019).
Jun, S.Y. et al. The internal origin of the west-east asymmetry of Antarctic climate change. Sci. Adv.6(24). https://doi.org/10.1126/sciadv.aaz1490 (2020).
Holland, P.R. & Kwok, R. Wind-driven trends in Antarctic sea-ice drift. Nat. Geosci.5, 872-875. https://doi.org/10.1038/ngeo1627 (2012).
Lefebvre, W., Goosse, H., Timmermann, R. & Fichefet, T. Influence of the Southern Annular Mode on the sea ice–ocean system. J. Geophys. Res. Oceans109(C9). https://doi.org/10.1029/2004JC002403 (2004).
Marshall, G.J., Di Battista, S., Naik, S.S. & Thamban, M. Analysis of a regional change in the sign of the SAM-temperature relationship in Antarctica. Clim. Dyn.36, 277-287. https://doi.org/10.1007/s00382-009-0682-9 (2011).
Kwok, R. et al. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008. J. Geophys. Res. Oceans114. https://doi.org/10.1029/2009JC005312 (2009).
Kumar, A., Yadav, J. & Mohan, R. Seasonal sea-ice variability and its trend in the Weddell Sea sector of West Antarctica. Environ. Res. Lett. 16(2), 024046. https://doi.org/10.1088/1748-9326/abdc88 (2021).
Salomonson, A.A., Barnes, W.L., Maymon, P.W., Montgomery, H.E. & Ostrow, H. MODIS: advanced facility instrument for studies of the Earth as a system. IEEE Trans. Geosci. Remote Sens. 27(2), 145-153. https://doi.org/10.1109/36.20292 (1989).
Lu, C., Zhang, Y., Zheng, Y., Wu, Z., & Wang, Q. Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network. GPS Solut.27(1), 15. https://doi.org/10.1007/s10291-022-01357-6 (2023)
Masuoka, E., Roy, D., Wolfe, R., Moriseet, J.,Sinno, S., Teague, M., Saleous, N., Devadiga, S., Justice, C.O. & Nickeson, J. MODIS Land Data Products: Generation, Quality Assurance and Validation. Land Remote Sens. Glob. Environ. 509–531. https://doi.org/10.1007/978-1-4419-6749-7_22 (2010).
Screen, J.A., Deser, C., Smith, D.M., Zhang, X., Blackport, R., Kushner, P.J., Oudar, T., McCusker, K.E. & Sun, L. Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. Nat. Geosci.11, 155-163. https://doi.org/10.1038/s41561-018-0059-y (2018).
Zakhvatkina, N.Y., Demchev, D., Sandven, S., Volkov, V.A. & Komarov, A.S. SAR Sea Ice Type Classification and Drift Retrieval in the Arctic. In Sea Ice in the Arctic: Past, Present and Future (Johannessen, O.M., Bobylev, L.P., Shalina, E.V., Sandven, S.) 247-299 (Springer International Publishing: Cham, 2020).
Que, X., Ma, X., Ma, C., Liu, F. & Chen, Q. Spatiotemporal Weighted Regression. In Encyclopedia of Mathematical Geosciences (ed. Daya Sagar, B.S., Cheng, Q., McKinley, J., Agterberg, F.) 1-7 (Springer International Publishing: Cham, 2020).
Hu, N., Zhang, Z., Duffield, N., Li, X., Dadashova, B., Wu, D., Yu, S., Ye, X., Han, D. & Zhang, Z. Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data. Comput. Urban Sci. 2024, 4(6), https://doi.org/10.1007/s43762-024-00117-1 (2024).
Wu, S., Wang, Z., Du, Z., Huang, B., Zhang, F. & Liu, R. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. Int. J. Geogr. Inf. Sci.35(3), 582-608, https://doi.org/10.1080/13658816.2020.1775836 (2020).
De Santis, A., Maier, E., Gomez, R. & Gonzalez, I. Antarctica, 1979-2016 sea ice extent: total versus regional trends, anomalies, and correlation with climatological variables. Int. J. Remote Sens.38, 7566-7584. https://doi.org/10.1080/01431161.2017.1363440 (2017).
Lee, S.K. et al. Wind-driven ocean dynamics impact on the contrasting sea-ice trends around West Antarctica. J. Geophys. Res. Oceans122(5), 4413-4430. https://doi.org/10.1002/2016JC012416 (2017).
Jullion, L. et al. The contribution of the Weddell Gyre to the lower limb of the Global Overturning Circulation. J. Geophys. Res. Oceans119(6), 3357-3377. https://doi.org/10.1002/2013JC009725 (2014).
Turner, J., Marshall, G.J. & Lachlan-Cope, T.A. Analysis of synoptic-scale low pressure systems within the Antarctic Peninsula sector of the circumpolar trough. Int. J. Climatol.18(3), 253-280. https://doi.org/10.1002/(SICI)1097-0088(19980315)18:3<253::AID-JOC248>3.0.CO;2-3 (1998).
Maheshwari, M., Singh, R.K., Oza, S.R. & Kumar, R. An Investigation of the Southern Ocean Surface Temperature Variability Using Long-Term Optimum Interpolation SST Data. ISRN Oceanography2013, 392632. https://doi.org/10.5402/2013/392632 (2013).
Parkinson, C.L. 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.116(29), 14414-14423. https://doi.org/10.1073/pnas.1906556116 (2019).
Thompson, D.W.J. & Solomon, S. Interpretation of recent Southern Hemisphere climate change. Science296, 895-899. https://doi.org/10.1126/science.1069270 (2002).
Simmonds, I. Comparing and contrasting the behaviour of Arctic and Antarctic sea ice over the 35 year period 1979-2013. Ann. Glaciol56(69),18-28. https://doi.org/10.3189/2015AoG69A909 (2017).
Comiso, J.C. et al. Positive Trend in the Antarctic Sea Ice Cover and Associated Changes in Surface Temperature. J. Clim.30(6), 2251-2267. https://doi.org/10.1175/JCLI-D-16-0408.1 (2017).
Screen, J.A., Bracegirdle, T.J. & Simmonds, I. Polar Climate Change as Manifest in Atmospheric Circulation. Current Climate Change Reports4, 383-395 (2018).
England, M., Polvani, L. & Sun, L. Contrasting the Antarctic and Arctic Atmospheric Responses to Projected Sea Ice Loss in the Late Twenty-First Century. J. Clim.31(16), 6353-6370. https://doi.org/10.1175/JCLI-D-17-0666.1 (2018).
Yuan, X. & Martinson, D. Antarctic Sea Ice Extent Variability and Its Global Connectivity. J. Clim.13(10), 1697-1717. https://doi.org/10.1175/1520-0442(2000)013<1697:ASIEVA>2.0.CO;2 (2000).
Liu, X. et al. Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study. Infect. Dis. Poverty.12, 108. https://doi.org/10.1186/s40249-023-01163-3 (2023).
Giacalone, M., Panarello, D. & Mattera, R. Multicollinearity in regression: an efficiency comparison between Lp-norm and least squares estimators. Qual. Quant.52, 1831-1859. https://doi.org/10.1007/s11135-017-0571-y (2018).
O’brien, R.M. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Qual. Quant.41, 673-690. https://doi.org/10.1007/s11135-006-9018-6 (2007).
Regression analysis with standardized variables. In Understanding Regression Analysis (ed. Allen, M.P.) 46-50 (Springer US: Boston, MA, 1997).
Lenn, Y.D. & Chereskin, T.K. Observations of Ekman Currents in the Southern Ocean. J. Phys. Oceanogr.39(3), 768-779. https://doi.org/10.1175/2008JPO3943.1 (2009).
Fahrbach, E., Rohardt, G., Schröder, M. & Strass, V. Transport and structure of the Weddell Gyre. Ann. Geophys.12, 840-855. https://doi.org/10.1007/s00585-994-0840-7 (1994).
Purich, A. & Doddridge, E.W. Record low Antarctic sea ice coverage indicates a new sea ice state. Commun. Earth Environ.4, 314. https://doi.org/10.1038/s43247-023-00961-9 (2023).
Spence, P., Griffies, S.M., England, M.H., Hogg, A.M., Saenko, O.A. & Jourdain, N.C. Rapid subsurface warming and circulation changes of Antarctic coastal waters by poleward shifting winds. Geophys. Res. Lett.41(13), 4601-4610, https://doi.org/10.1002/2014GL060613 (2014).
Zhifang, F., Wallace, J.M. & Thompson, D.W.J. The Relationship between the Meridional Profile of Zonal-mean Geostrophic Wind and Station Wave at 500 hPa. Adv. Atmos. Sci.18, 692-700. https://doi.org/10.1007/BF03403494 (2001).
Achatz, U. The Meridional Circulation. In Atmospheric Dynamics (ed. Achatz, U.) 343-405 (Springer Berlin Heidelberg: Berlin, Heidelberg, 2022).
Cordero, R.R., Feron, S., Damiani, A., Llanillo, P.J., Carrasco, J., Khan, A.L., Bintanja, R., Ouyang, Z. & Casassa, G. Signature of the stratosphere–troposphere coupling on recent record-breaking Antarctic sea-ice anomalies. The Cryosphere.17(11), 4995-5006, https://doi.org/10.5194/tc-17-4995-2023 (2023).
Roberts, A., Allison, I. & Lytle, V.I. Sensible- and latent-heat-flux estimates over the Mertz Glacier polynya, East Antarctica, from in-flight measurements. Ann. Glaciol.33, 377-384. https://doi.org/10.3189/172756401781818112 (2017).
Wettlaufer, J.S., Untersteiner, N. & Colony, R. Estimating Oceanic Heat Flux from Sea-Ice Thickness and Temperature Data. Ann. Glaciol.14, 315-318. https://doi.org/10.3189/S026030550000882X (2017).
Park, H.-S., Lee, S., Son, S.-W., Feldstein, S.B. & Kosaka, Y. The Impact of Poleward Moisture and Sensible Heat Flux on Arctic Winter Sea Ice Variability. J. Clim.28(13), 5030-5040, https://doi.org/10.1175/JCLI-D-15-0074.1 (2015).
Markus, T., Comiso, J. C. , & Meier, W. N. AMSR-E/AMSR2 Unified L3 Daily 25 km Brightness Temperatures & Sea Ice Concentration Polar Grids, Version 1 [Data Set]. NASA National Snow and Ice Data Center Distributed Active Archive Centerhttps://doi.org/10.5067/TRUIAL3WPAUP (2018).
Brodzik, M. J. & Stewart, J. S. Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 5 [Data Set]. NASA National Snow and Ice Data Center Distributed Active Archive Centerhttps://doi.org/10.5067/3KB2JPLFPK3R (2016).
Meier, W. N., Fetterer, F. , Windnagel, A. K. & Stewar, J. S. t. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4 [Data Set]. National Snow and Ice Data Centerhttps://doi.org/10.7265/efmz-2t65 (2021).
Meier, W. N., Markus, T. & Comiso, J. C. AMSR-E/AMSR2 Unified L3 Daily 12.5 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth Polar Grids, Version 1 [Data Set]. NASA National Snow and Ice Data Center Distributed Active Archive Centerhttps://doi.org/10.5067/RA1MIJOYPK3P (2018).
Spreen, G., Kaleschke, L. & Heygster, G. Sea ice remote sensing using AMSR-E 89 GHz channels. J. Geophys. Res.113(C02S03). https://doi.org/10.1029/2005JC003384 (2008).
Cavalieri, D.J., Markus, T., Hall, D.K., Ivanoff, A. & Glick, E. Assessment of AMSR-E Antarctic Winter Sea-Ice Concentrations Using Aqua MODIS. IEEE Trans. Geosci.48(9), 3331-3339. https://doi.org/10.1109/TGRS.2010.2046495 (2010).
Chen, Y., Zhao, X., Pang, X. & Ji, Q. Daily sea ice concentration product based on brightness temperature data of FY-3D MWRI in the Arctic. Big Earth Data6(2), 164–178. https://doi.org/10.1080/20964471.2020.1865623 (2022).
Tetzner, D., Thomas, E. & Allen, C. A Validation of ERA5 Reanalysis Data in the Southern Antarctic Peninsula—Ellsworth Land Region, and Its Implications for Ice Core Studies. Geosciences9(7), 289. https://doi.org/10.3390/geosciences9070289 (2019).
Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc.77, 437-472. https://doi.org/10.1175/1520-0477(1996)077 (1996).
Hao, G. & Su, J. A study on the dynamic tie points ASI algorithm in the Arctic Ocean. Acta Oceanolog. Sin.34, 126-135. https://doi.org/10.1007/s13131-015-0659-y (2015).
Shi, Q. et al. Step-by-Step Validation of Antarctic ASI AMSR-E Sea-Ice Concentrations by MODIS and an Aerial Image. IEEE Trans. Geosci.59(1), 392-403. https://doi.org/10.1109/TGRS.2020.2989037 (2021).
Comiso, J.C., Cavalieri, D.J., Parkinson, C.L.. & Gloersen, P. Passive microwave algorithms for sea ice concentration: A comparison of two techniques. Remote Sens. Environ.60(3), 357-384. https://doi.org/10.1016/S0034-4257(96)00220-9 (1997).
Budillon, G., Fusco, G. & Spezie, G. A study of surface heat fluxes in the Ross Sea (Antarctica). Ant. Sci.12(2), 243-254. https://doi.org/10.1017/S0954102000000298 (2000).
Xu, X., Luo, X., Ma, C. & Xiao, D. Spatial-temporal analysis of pedestrian injury severity with geographically and temporally weighted regression model in Hong Kong. Transp. Res. Part F Traffic Psychol. Behav.69, 286-300. https://doi.org/10.1016/j.trf.2020.02.003 (2020).
Ma, X., Zhang, J., Ding, C. & Wang, Y. A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Comput. Environ. Urban Syst.70, 113-124, https://doi.org/10.1016/j.compenvurbsys.2018.03.001 (2018).

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Spatiotemporal analysis of sea ice in the Weddell Sea of Antarctic based on GTWR

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\(\:\text{b}\text{i}\text{a}\text{s}=\frac{\sum\:_{\text{i}=1}^{\text{n}}\left({\text{X}}_{\text{i}}-{\text{Y}}_{\text{i}}\right)}{\text{n}}\)	(2)
\(\:\text{M}\text{A}\text{D}=\frac{\sum\:_{\text{i}=1}^{\text{n}}\left\|{\text{X}}_{\text{i}}-{\text{Y}}_{\text{i}}\right\|}{\text{n}}\)	(3)
\(\:\text{R}\text{M}\text{S}\text{D}=\sqrt{\frac{\sum\:_{\text{i}=1}^{\text{n}}{\left({\text{X}}_{\text{i}}-{\text{Y}}_{\text{i}}\right)}^{2}}{\text{n}}}\)	(4)
\(\:\text{r}=\frac{\sum\:_{\text{i}=1}^{\text{n}}\left({\text{X}}_{\text{i}}-\stackrel{-}{\text{X}}\right)\left({\text{Y}}_{\text{i}}-\stackrel{-}{\text{Y}}\right)}{\sqrt{\sum\:_{\text{i}=1}^{\text{n}}{\left({\text{X}}_{\text{i}}-\stackrel{-}{\text{X}}\right)}^{2}}\sqrt{\sum\:_{\text{i}=1}^{\text{n}}{\left({\text{Y}}_{\text{i}}-\stackrel{-}{\text{Y}}\right)}^{2}}}\)	(5)