Rapid decline in Arctic sea ice has created more open water for ocean wave development and highlighted the importance of wave–ice interactions in the Arctic. Some studies have made contributions to our understanding of the potential role of the prognostic floe size distribution (FSD) in sea ice changes. However, these efforts do not represent the full interactions across atmosphere, ocean, wave, and sea ice. In this study, we implement a modified joint floe size and thickness distribution (FSTD) in a newly developed regional atmosphere–ocean–wave–sea ice coupled model and conduct a series of pan-Arctic simulations with different physical configurations related to FSD changes, including FSD-fixed, FSD-varied, lateral melting rate, wave-fracturing formulation, and wave attenuation rate. Firstly, our atmosphere–ocean–wave–sea ice coupled simulations show that the prognostic FSD leads to reduced ice area due to enhanced ice–ocean heat fluxes, but the feedbacks from the atmosphere and the ocean partially offset the reduced ice area induced by the prognostic FSD. Secondly, lateral melting rate formulations do not change the simulated FSD significantly, but they influence the flux exchanges across atmosphere, ocean, and sea ice and thus sea ice responses. Thirdly, the changes in FSD are sensitive to the simulated wave height, wavelength, and wave period associated with different wave-fracturing formulations and wave attenuation rates, and the limited oceanic energy imposes a strong constraint on the response of sea ice to FSD changes. Finally, our results also demonstrate that wave-related physical processes can have impacts on sea ice changes with the constant FSD, suggesting the indirect influences of ocean waves on sea ice through the atmosphere and the ocean.

From 2021 to 2023, satellite records reveal that February Antarctic sea ice extent reached record lows in 2022 and 2023. Simultaneously, the Antarctic ice sheet experienced a transient mass gain and rebounded temporarily from a decadal decline since 2002. The reasons behind these dramatic changes are unknown. Here, we show that the triple-dip La Niña event during 2021–2023 (referred to as TD_LN2023) played a major role in these changes. Compared to a previous triple-dip La Niña event (1999–2001), the tropical-Antarctic teleconnections during TD_LN2023 were stronger. A more pronounced southward shift of the Ferrel Cell was identified as a key driver for the enhanced tropical-Antarctic teleconnections during TD_LN2023 against the background of intensified westerly winds and tropical expansion. The poleward increase, which facilitated poleward atmospheric heat and moisture transport, contributed to the sea ice extent decline and the ice sheet mass growth. Additionally, this southward shift strengthened the Rossby wave train, which, sustained by enhanced stratosphere-troposphere coupling, amplified the Pacific-South American pattern, and further promoted regional sea ice decline. Finally, this southward shift, associated with the southward shift of the westerly jet, enhanced Ekman suction, bringing subsurface warm water to the surface and contributing to pan-Antarctic low sea ice. The physical processes outlined in the case study are further validated through empirical orthogonal function and regression analysis. Under global warming, multi-year La Niña events are projected to occur more frequently. The evolving tropical-Antarctic teleconnections in the context warrant close attention.

Sea ice models are essential tools for simulating the thermodynamic and dynamic processes of sea ice and the coupling with the polar atmosphere and ocean. Popular models such as the Community Ice CodE (CICE) are usually based on non-moving, locally orthogonal Eulerian grids. However, the various in situ observations, such as those from ice-tethered buoys and drift stations, are subjected to sea ice drift and are, hence, by nature Lagrangian. Furthermore, the statistical analysis of sea ice kinematics requires the Lagrangian perspective. As a result, the offline sea ice tracking with model output is usually carried out for many scientific and validational practices. Certain limitations exist, such as the need for high-frequency model outputs, as well as unaccountable tracking errors. In order to facilitate Lagrangian diagnostics in current sea ice models, we design and implement an online Lagrangian tracking module in CICE under the coupled model system of CESM (Community Earth System Model). In this work, we introduce its design and implementation in detail, as well as the numerical experiments for the validation and the analysis of sea ice deformation. In particular, the sea ice model is forced with historical atmospheric reanalysis data, and the Lagrangian tracking results are compared with the observed buoys' tracks for the years from 1979 to 2001. Moreover, high-resolution simulations are carried out with the Lagrangian tracking to study the multi-scale sea ice deformation modeled by CICE. Through scaling analysis, we show that CICE simulates multi-fractal sea ice deformation fields in both the spatial and the temporal domain, as well as the spatial–temporal coupling characteristics. The analysis with model output on the Eulerian grid shows systematic difference with the Lagrangian-tracking-based results, highlighting the importance of the Lagrangian perspective for scaling analysis. Related topics, including the sub-daily sea ice kinematics and the potential application of the Lagrangian tracking module, are also discussed.

Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter.

Current climate models suggested that Antarctic sea ice cover would decrease substantially under cumulative CO₂ emission, but little is known whether large decrease in Antarctic sea ice can influence the occurrence of strong El Niño. Using time slice coupled and uncoupled model experiments, we show that in response to half reduction of Antarctic sea ice projected near the end of the 21st century, the frequency of strong El Niño would be increased by ~40%. It is contributed by enhanced thermocline, Ekman, and zonal advective positive feedbacks that are partly offset by enhanced thermodynamic damping. The strong warming and weakened westerly winds in the southeastern Pacific generate an anomalous Rossby wave propagating into the eastern subtropical and tropical Pacific, favoring stronger El Nino, and air-sea coupling and ocean feedbacks play a critical role in the teleconnection. Unexpectedly, compare to halved Antarctic sea ice, the ice-free Antarctic leads to a decrease in the frequency of strong El Niño, which is largely due to a substantial increase in thermodynamic damping. We also show that a large portion of the increase of strong El Niño events under greenhouse warming might be connected with Antarctic sea-ice loss, though increased greenhouse gas plays an important role.

Two coupled climate models that participated in the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0), and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3) were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air–ice heat flux, radiation penetration and absorption, melt ponds, and ice–ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis products from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of the Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes in spatial patterns of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have the potential for broad application in climate models for simulation and future climate scenario projection, especially for those with underestimated SIT.