Abstract Compound heatwave and drought events (CHDEs) in South China (SC) have intensified in early autumn, yet their driving factor remains unclear. Based on reanalysis data and numerical experiments, this study investigates the potential influence of the summer northeastern Arctic Sea ice concentration (NEASIC) on the interannual variation of September CHDEs in the SC. Results demonstrate that positive NEASIC anomalies during summer trigger a quasi‐barotropic Rossby wave train, originating over the Greenland Sea, arching across the North Atlantic and the Mediterranean–Caspian region, and extending into East Asia. This wave dynamically drives a northward‐shifted and intensified East Asian subtropical jet and anomalous anticyclonic circulation over SC. The resulting subsidence induces moisture flux divergence, suppresses cloud cover, and enhances surface radiative forcing, explaining about 28.4% of the CHDEs variability per interquartile NEASIC increase. This mechanism enhances predictive frameworks for subtropical compound extremes, emphasizing the role of NEASIC in regional climate resilience strategies.

Explainable spatiotemporal deep learning for subseasonal super-resolution forecasting of Arctic sea ice concentration during the melting season

DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration

Environmental variability shapes species' population dynamics. Yet, the mechanisms linking environmental changes to individual-level metrics (e.g. foraging behaviour, body condition) and reproductive outcomes in the wild remain poorly understood. Energetics play a central role in mediating trade-offs between self-maintenance and reproduction under fluctuating environmental conditions. As such, it provides a powerful framework for identifying how individual responses to environmental variation scale up to influence population dynamics. Using a unique long-term monitoring and bio-logging dataset spanning over 25 years providing continuous measures of diving behaviour, feeding activity and daily energy expenditure, this study investigates how individual responses to environmental variation affect population dynamics. Focusing on Adélie penguins (Pygoscelis adeliae) during the energetically demanding chick-rearing phase, we integrated individual-level foraging and energetics data with colony-wide reproductive metrics to elucidate how environmental cues lead to life-history trade-offs. Winter sea-ice conditions exhibited a quadratic relationship with key individual behavioural and energetic parameters. Specifically, increased sea-ice concentration and delayed ice retreat led to longer foraging trips, reduced time spent diving and poorer body condition. At the population level, while energy expenditure was not associated with changes in reproductive outcome, increased foraging effort (time spent feeding per day) led to enhanced fledging success. Adverse on-land conditions, such as higher snowfall, had negative impacts on reproductive outcomes. These findings support the central role of energy as a common currency of maintenance and reproduction. By linking individual energetics to demographic performance, our work advances our understanding of how energy allocation strategies in response to environmental stressors shape population dynamics. These insights are crucial for improving predictive models of population trajectories and offer valuable guidance for conservation strategies aimed at mitigating the impacts of global change on ecosystems.

Abstract. I evaluated a novel NIMBUS-5 Electrically Scanning Microwave Radiometer (ESMR) sea-ice concentration (SIC) data product. 50 Landsat-1 Multispectral Scanner (MSS) images obtained in the Northern Hemisphere during 1974 were manually classified into open water and ice, mapped onto the ESMR product's grid (25 km resolution) and used to compute Landsat-1 SIC. The resulting ∼3300 grid cells, covering mostly compact sea ice, have a mean difference (median), standard deviation, and linear correlation coefficient of −1.4 % (0.0 %), 6.0 %, and ∼0.9, respectively. This suggests using this novel ESMR SIC data product as an extension of existing SIC climate data records back in time.

Abstract. To address research gaps in understanding Arctic Amplification, we use data from ERA5, an observational surface temperature dataset, and sea ice concentration to examine the seasonal, spatial and decadal evolution of Arctic 2 m and lower tropospheric temperatures and lower tropospheric (surface to 850 hPa) static stability over the past 45 years. A Local Amplification Anomaly (LAA) metric is used to examine how spatial patterns of Arctic 2 m temperature anomalies compare to anomalies for the globe as a whole. Pointing to impacts of seasonally-delayed albedo feedback, growing areas of end-of-summer (September) open water largely co-locate with the strongest positive anomalies of 2 m temperatures through autumn and winter and their growth through time; small summer trends reflect the effects of a melting sea ice cover. Because of seasonal ice growth, the association between rising 2 m temperatures and sea ice weakens from autumn into winter, except in the Barents Sea where there have been prominent downward trends in winter ice extent. Imprints of variable atmospheric circulation are prominent in the Arctic temperature evolution. Low-level (surface to 850 hPa) stability over the Arctic increases from autumn through winter, consistent with the greater depth of surface-based atmospheric heating seen in autumn. However, trends towards weaker static stability dominate the Arctic Ocean in autumn and winter, especially over areas of September and wintertime ice loss. Sea ice thinning, leading to increased conductive heat fluxes though the ice, likely also contributes to reduced stability.

Interannual climate changes and increasing atmospheric CO2 (AtmCO2) have significantly altered sea surface partial pressure of CO2 (pCO2) in the Beaufort Sea (BS). Yet, their decadal variability and underlying mechanisms remain inadequately understood. Using observational data and the Regional Arctic System Model (RASM), a decreasing trend transition of the BS summer surface pCO2 was identified at around 2010–2012. Sensitivity cases reveal that the decadal trend transition in surface pCO2 (early: 4.12 ± 0.80 μatm/yr, p < 0.05 and late: 1.23 ± 2.22 μatm/yr, p > 0.05) is driven by interannual climate changes. While the long-term increase in AtmCO2 does not directly drive surface pCO2 trend transition, it reduces its magnitude. The sensitivity experiment with no interannual AtmCO2 changes from 1990 reveals that the statistically significant contributor of the decadal trend transition in surface pCO2 is the concurrent transition in sea ice concentration (SIC, early: −0.0120 ± 0.0037/yr, p < 0.05 and late: 0.0101 ± 0.0063/yr, p > 0.05). The decadal trend transitions in the subsurface and deep layer pCO2 are negligible compared to that in the sea surface pCO2 due to the insignificant influence of interannual climate changes on subsurface and deep layer pCO2. The surface pCO2 decadal trend transition is significantly correlated with a trend transition of CO2 sink. On seasonal timescales, the effects of SIC on the decadal trend transition of pCO2 occur primarily within the duration of open-water (DOW), and align with the decadal trend transitions in the open-water start day, end day, and DOW. The magnitude of sea surface pCO2 trend transition increases as the magnitude of the DOW trend transition increases.

Integrating wavelet transform with deep learning for Arctic sea ice concentration prediction

An effective deep-learning prediction of Arctic sea-ice concentration based on the U-Net model

Abstract The sea ice growth (SIG) in the Kara Sea plays a crucial role in the Arctic climate system. Many studies have examined its long‐term trend, but whether its variability has changed is less clear. Using observations and reanalysis data, we observe an intensified interannual variability of the Kara Sea SIG during boreal late winter (December–March) since 2004/2005. This arises from the retreat of active ice production zones in response to the strengthened modulation of the westward‐shifted North Atlantic Oscillation (NAO). Using a sea ice concentration budget, we quantitatively partition the NAO's contribution to total SIG variability post‐2004 (∼63.3%), revealing comparably dominant roles of thermodynamic and dynamic processes. During this period, the negative NAO‐associated surface winds concurrently cool sea surface and export sea ice from the Kara Sea, thereby injecting freshwater into the lower latitudes. Our study advances the understanding of the regional air‐ice‐ocean climate feedbacks in recent Arctic.

Convection associated with the leading mode of subseasonal variability of the tropical atmosphere, the Madden–Julian Oscillation (MJO), can excite Rossby wave trains that extend well into the extratropics and allow the MJO to modulate many components of the Earth system. To improve our understanding of teleconnections between the MJO and Antarctic sea ice, composite anomalies of daily change in sea ice concentration (ΔSIC) from 1989 to 2019 were binned by phase 0–20 days after an active MJO and compared to anomalies of surface air temperature, the meridional component of surface wind, and sea-level pressure. In May, ΔSIC anomalies were strongest in the Indian Ocean (IO) sector, 16 days after phase 8. There, a wavenumber-three pattern in sea-level pressure anomalies associated with the MJO resulted in anomalously poleward winds and warmer temperatures over the central and eastern IO that were collocated with anomalously negative ΔSIC. Furthermore, anomalously equatorward winds and colder temperatures in the western IO were collocated with anomalously positive ΔSIC. In July, ΔSIC anomalies were strongest in the Weddell Sea (WS) sector nine days after an active MJO in phase 2. There, a wavenumber-three pattern in sea-level pressure anomalies resulted in anomalously poleward winds and warmer temperatures over the western and central WS that were collocated with negative ΔSIC anomalies; anomalously equatorward winds and colder temperatures over the eastern WS were collocated with positive ΔSIC anomalies. In September, the largest ΔSIC anomalies were observed in the IO and WS sectors six days after an active MJO in phase 8. No meaningful modulation of sea ice anomalies was found after an active MJO in November or January. These results extend our understanding of teleconnections between the MJO and Antarctic sea ice on the subseasonal time scale.

Sea-ice algae account for a substantial part of annual primary production in ice-covered waters and are an important component of the Arctic marine food web. With climate-induced changes to snow and sea-ice cover and their impact on the surface ocean, such as earlier melt, thinner ice, and increased upper-ocean stratification, a shift toward earlier and more extensive nutrient limitation on ice algal growth can be expected. Therefore, increasing our understanding of the processes governing nutrient supply and uptake by sea-ice algae is essential. Here, we compiled a pan-Arctic dataset of concentrations of sea-ice and sub-ice nutrients and sea-ice chlorophyll a (chl a) to assess their regional and seasonal variability, as well as the relationship of sea-ice algae and nutrient dynamics in the Arctic Ocean. This dataset indicates that bottom sea-ice nutrient and chl a concentrations were highest in the central Canadian Arctic Archipelago (Resolute Passage) due to tidal-driven mixing at the ocean-ice interface, and lowest in the Arctic Ocean basins. At the regional scale, Pacific and Atlantic Water influence variability in sea-ice and sub-ice nutrient concentrations. Significant positive relationships of bottom sea-ice nutrient versus chl a concentrations were ubiquitous across the Arctic during the ice algal bloom, suggesting intracellular nutrient storage as an important mechanism to support ice algal growth. This relationship in turn alters nutrient ratios within the sea ice relative to sub-ice waters, decreasing NOx:PO4 ratios, while increasing NOx:Si(OH)4 ratios. In contrast, bottom sea-ice nutrient-chl a relationships were less common and sometimes negative when nutrient concentrations were low, likely reflecting nutrient limitation. In conclusion, we have demonstrated a pan-Arctic, yet regionally specific, influence of the ice algal community on bottom sea-ice nutrient concentrations.

Abstract This study investigates the atmospheric mechanisms driving high-frequency sea ice extremes in West Antarctic, focusing on the Ross and Bellingshausen-Amundsen (hereafter Bell-Amundsen) seas. Extreme sea ice extent (SIE) episodes from 1989 to 2020 were analyzed, revealing their associations with atmospheric circulation patterns, meridional temperature advection and turbulent heat fluxes (THF). The results show that extreme SIE events (both maximum, SIE max , and minimum, SIE min ) are influenced by Rossby wave modes with strong barotropic components at high latitudes, which enhances meridional heat transport by eddy vortices. In the Ross Sea, SIE max (SIE min ) events are characterized by a strong anomalous cyclone (anticyclone) around the Bellingshausen Sea that intensifies the transport of colder (warmer) air equatorward (poleward). In the Bell-Amundsen Sea, the wave modes exhibit dipole-type patterns between this region and the Weddell Sea, which facilitate similar cold and warm air transport during these events. Atmospheric blocking events also play a critical role by influencing heat transport at the sea-air interface. We also discovered that weaker horizontal winds predominate during SIE max , while stronger winds are observed in SIE min episodes, particularly around large negative and positive sea ice concentration (SIC) anomalies in both regions. Southerly winds intensify north of the SIC edge during SIE max , highlighting the key role of equatorward cold air advection. Moreover, notable variations in THF are associated with intensified transient meridional winds. Overall, this research sheds light on how atmospheric patterns drive high-frequency sea ice variability, providing insights for understanding Antarctic climate dynamics.

Abstract Sea ice in the Ross Sea plays a critical role in the formation of dense water masses, ice sheet stability, and air‐sea gas exchange, and also supports unique ecosystems. However, its seasonal and spatial variability makes it challenging to include in model simulations. To address this, new sea ice records that extend beyond the satellite era and include periods of climate change are essential. This new sediment record from Moubray Bay, northwest Ross Sea, reconstructs environmental conditions between ∼11,300 and ∼10,900 cal yr BP—a time of rapid retreat of marine‐based ice sheets and coastal glaciers in the region. The diatom assemblage is dominated by three taxa: Fragilariopsis curta , Corethron pennatum , and Chaetoceros resting spores. Variations in their relative abundances reveal changes in wind strength, water column structure, and sea ice concentration and duration. Between ∼11,300 and ∼11,200 cal yr BP, environmental conditions are characterized by a stabilized water column due to fresh meltwater influx, and weaker winds, which resulted in shorter sea ice duration and reduced winter sea ice concentration. This continued after ∼11,200 cal yr BP but stronger winds linked to deepening of Amundsen Sea Low‐like circulation triggered short‐term water column stratification. Sea ice concentration and duration increased after ∼11,100 cal yr BP driven by cooling of the sea surface by stronger southerly winds. Concurrent changes in early Holocene marine and terrestrial climate records from the Ross Sea indicate a shift in atmospheric circulation during the early Holocene.

Abstract This study investigates the influence of tropical Atlantic sea surface temperature (SST) variability on boreal autumn (August–September–October, ASO) sea ice concentration (SIC) in the Beaufort Sea. Although Arctic warming and sea ice retreat have been well‐documented, the influence of tropical Atlantic SST on Arctic sea ice remains understudied. Using reanalysis data sets and model simulations, we demonstrate that tropical Atlantic warming significantly reduces Beaufort Sea SIC. This warming triggers a Rossby wave train that propagates northeastward, altering the high‐latitude atmospheric circulation and inducing southerly flow anomalies. These anomalies enhance moisture transport, increase cloud cover, and amplify downward longwave radiation accelerating sea ice melt. Our findings, supported by Community Atmosphere Model version 5 and Community Earth System Model simulations, highlight the crucial role of tropical‐Arctic teleconnections in Arctic sea ice variability.

Abstract A satellite‐based temperature‐dependent parameterization of the Wegener‐Bergeron‐Findeisen (WBF) process that takes into account the subgrid‐scale variability of cloud thermodynamic phase within mixed‐phase clouds is developed and implemented in version 5.3 of the Community Atmosphere Model (CAM5.3). Its impact on cloud microphysical and macrophysical properties in experiments with prescribed sea surface temperature and sea ice concentrations as well as the cloud feedback response to a global warming perturbation is investigated. The parameterization significantly improves overestimates in the mass of ice within mixed‐phase clouds and ice effective radius relative to satellite observations, the former being superior to tuning the WBF process with a multiplicative constant. The parameterization also reduces overall biases in cloud fraction with respect to satellite observations, however, is due to compensating biases in existing simulated low biases in low‐level cloud cover and new increased biases in non‐low‐level cloud cover. The increased bias in non‐low‐level cloud cover is due to decreases in the rate of autoconversion of cloud ice that is a side effect of the WBF parameterization. While the WBF parameterization can significantly impact the magnitude of model biases in cloud properties and the cloud feedback, it does not significantly change their spatial distribution. Before observational constraints on WBF process rates become available, it is recommended that temperature‐dependent scalings of the WBF process are used to account for the subgrid‐scale variability of cloud phase rather than a constant scaling parameter as the former type of parameterization can more realistically simulate cloud properties relative to satellite observations.

Abstract Increased CO 2 and anthropogenic aerosols (AAs) coexist in the atmosphere with opposite radiative effects. Their resultant climate responses may not fully offset each other, leading to some nonlinear changes, particularly in summer precipitation over East Asia (EA). However, how the coexistence of CO 2 and AAs causes the nonlinear effect on EA precipitation remain unclear. Through analyses of fully coupled model simulations, this study shows that when both forcings are included, the precipitation over North China in July–August decreases less than the linear combination of the changes induced by individual forcings, indicating a nonlinear increase of precipitation. These nonlinear changes over EA likely originate from the Arctic and North Atlantic region. Specifically, the Arctic sea-ice sensitivity to the CO 2 -induced warming is higher than that to the AA-induced cooling, leading to a large increase of sea ice concentration over the East Siberian–Beaufort Seas in the nonlinear effect. This can excite an anomalous atmospheric Rossby wave propagating southeastward into the North Atlantic, which increases low cloudiness to cool the surface over the midlatitude North Atlantic. This cooling subsequently generates an anomalous Rossby wave train pattern across Eurasia with two branches propagating toward EA. The concurring circulation changes over EA ultimately result in nonlinear precipitation changes there. This study suggests that the nonlinear climate response in the high latitudes can remotely induce nonlinear changes in the lower latitudes and highlights the need to consider nonlinear effects in analyzing externally forced climate changes over EA.

Fine-scale detailed estimation of sea ice concentration (SIC) is pivotal for maritime safety, scientific exploration, and environmental surveillance. However, current datasets frequently present challenges due to their limited resolution, thereby hindering fine-scale analysis of sea ice conditions. This paper introduces a novel Deep Self-Attention Residual U-Net (Deep SARU-Net) architecture to address the limitations inherent in existing super-resolution estimation techniques. By harnessing distinctive multi-stage self-attention mechanisms, orthogonal rectangular convolutional kernels, and residual modules, this architecture significantly augments both the precision and generalizability of SIC super-resolution estimation tasks. Experimental results demonstrate that in the vicinity of the Chukchi Sea, the Deep SARU-Net method exhibits superior performance in terms of both RMSE and SSIM values compared to other models, showcasing its efficacy. Furthermore, generalization analyses across diverse sea regions confirm the model’s universality.

Abstract. A substantial body of work has explored the use of sea ice concentration (SIC) and sea ice thickness (SIT) observations to initialize modeled estimates of the unobserved Arctic sea ice state via data assimilation (DA). While many recent studies have highlighted the particular value of incorporating SIT observations to this end, the influence of local sea ice conditions on the efficacy of assimilating various observation types has not been sufficiently evaluated. This work utilizes a single-column sea ice model to represent three common Arctic sea ice regimes: pack ice, seasonal ice, and first-year ice. An ensemble data assimilation framework is then used to assimilate synthetic observations of SIC, SIT, and two types of sea ice freeboard in each regime. Results demonstrate substantial variation in observation efficacy across observation types and sea ice conditions. In particular, SIT and laser altimeter freeboard observations are found to have a broadly positive impact in thick ice regimes, while SIC effectively constrains thinner, more marginal sea ice regimes. A need for regime-tailored DA strategies and further experimentation with underutilized sea ice observation types is strongly implied.

Abstract. The Siberian Arctic Ocean (SAO) is the largest integrator and redistributor of Siberian freshwater resources and acts to significantly influence the Arctic climate system. Moreover, the SAO is experiencing some of the most notable climate changes in the Arctic, and advection of anomalous Atlantic- (atlantification) and Pacific-origin (pacification) inflow waters and biota continue to play a major role in reshaping the SAO in recent decades. In this study, we use a large collection of mooring data to create a coherent picture of the spatiotemporal patterns and variability of currents and shear in the upper SAO during the past decade. Although there was no noticeable trend in the upper SAO's current speed and shear from 2013 to 2023, their seasonal cycle has significantly strengthened. The cycle reveals a strong relationship between upper ocean currents and their shear with sea ice conditions – particularly during transitional seasons – evidenced by a strong negative correlation (−0.94) between seasonal sea ice concentration and current shear. In the shallow (< 20–30 m) summer surface mixed layer, currents have increased because strong stratification prevents wind energy from propagating into the deeper layers. In this case, strong near-inertial currents account for more than half of the summertime current speed and shear. In the winter, a thicker surface layer is created by deep upper SAO ventilation due to atlantification, which distributes wind energy to far deeper (> 100 m) layers. These findings are critical to understanding the ramifications for mixing and halocline weakening, as well as the rate of atlantification in the region.