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Arctic sea‐ice loss is influenced by multiple positive feedbacks, sparking concerns of accelerated loss in the coming years or even a tipping point, where a sea‐ice equilibrium disappears at a given CO2 value and sea ice rapidly evolves to a new steady state. Such a tipping point would imply a bi‐stability of the Arctic climate—where multiple steady‐state Arctic climates are possible at the same CO2 value. Previous works have sought to establish the existence of bi‐stability using a range of models, from zero‐dimensional sea ice thermodynamic models to fully coupled global climate models, with conflicting results. Here, we present a new model of the Arctic that includes both sea‐ice thermodynamics and key atmospheric feedbacks in a simple framework. We exploit the model's simplicity to identify physical mechanisms that control the timing and extent of sea‐ice bi‐stability, and the abruptness of ice loss. We show that longwave radiation feedbacks can have a strong influence on Arctic surface climate from atmospheric temperature increases alone, even without major contributions from clear‐sky moisture or convective clouds suggested previously. While winter sea‐ice bi‐stability is robust to changes in uncertain model parameters in this study, summer sea ice is more sensitive. Finally, our model indicates that positive feedbacks may modulate the CO2 threshold of sea‐ice loss and the width of bi‐stability much more strongly than the abruptness of loss. These results lead to a comprehensive understanding of the conditions that favor Arctic sea‐ice bi‐stability, particularly the role of atmospheric feedbacks, in both future and past climates. Plain Language Summary: Arctic sea ice is declining rapidly under global warming, threatening high‐latitude communities and ecologies. Some mechanisms may cause this sea ice loss to accelerate, leading to concerns about the possibility of a sea ice "tipping point," in which ice is lost very abruptly and irreversibly at a threshold value of CO2. Previous works have used a range of tools, from simple sea ice thermodynamic models to state‐of‐the‐art global climate models, to identify whether such a tipping point exists and have found conflicting results. In this work, we present a novel model of the Arctic climate that combines a thermodynamic model of sea ice with atmospheric feedbacks in a simple framework. We run this model across large ranges of climatic conditions to broadly identify the conditions that favor a sea ice tipping point. In addition, we isolate the mechanisms that are key to setting the timing and abruptness of complete sea ice loss, separating the contributions of different atmospheric feedbacks that have previously been unexplored. We find that both summer and winter sea ice tipping points are possible and that the responsible atmospheric mechanisms are different than those that have been suggested previously. Key Points: Winter sea ice bi‐stability is very robust across model parameters, while summer sea ice bi‐stability exists in narrower parameter regimesAtmospheric feedbacks determine the width of the model sea‐ice bi‐stability and the CO2 value at which Arctic sea ice loss occursLongwave feedbacks–a key contributor to the model bi‐stability–are driven more by tropospheric temperature than by clouds or moisture [ABSTRACT FROM AUTHOR]

Studies have indicated that the decrease in the extent of Arctic sea ice in recent years has had a significant impact on the Arctic ecosystem and global climate. In order to understand the evolution of sea ice, it is becoming increasingly imperative to have continuous observations of Arctic-wide sea ice with high spatial resolution. Passive microwave sensors have the benefit of being less susceptible to weather, wider coverage, and higher temporal resolution. However, it is challenging to retrieve accurate parameters of sea ice due to the low spatial resolution of passive microwave images. Therefore, improving the spatial resolution of passive microwave images is beneficial for reducing the uncertainty of sea ice parameters. In this paper, four competitive multi-image super-resolution (MISR) networks are selected to explore the applicability of the networks on multi-frequency Advanced Microwave Scanning Radiometer 2 (AMSR2) images of Arctic sea ice. The upsampling factor is set to 4 in the experiment. Firstly, the optimal input lengths of the image sequence for the four MISR networks are found, and then the best network on different frequency band images is further identified. Furthermore, some factors, including seasons, sea ice motion, and polarization mode of images, that may affect the super-resolution (SR) results are analyzed. The experimental results indicate that utilizing images from winter yields superior SR results. Conversely, SR results are the worst during summer across all four MISR networks, exhibiting the largest difference in PSNR of 4.48 dB. Additionally, the SR performance is observed to be better for images with smaller magnitudes of sea ice motion compared to those with larger motions, with the maximum PSNR difference of 2.04 dB. Finally, the SR results for vertically polarized images surpass those for horizontally polarized images, showcasing an average advantage of 4.02 dB in PSNR and 0.0061 in SSIM. In summary, valuable suggestions for selecting MISR models for passive microwave images of Arctic sea ice at different frequency bands are offered in this paper. Additionally, the quantification of the various impact factors on SR performance is also discussed in this paper, which provides insights into optimizing MISR algorithms for passive microwave sea ice imagery. [ABSTRACT FROM AUTHOR]

The westerly wind on the poleward side of the summer polar jet stream (PJS) over the Western Hemisphere has significantly weakened since the 1980s. A weak summer PJS causes warming surface temperatures and deficient precipitation over Alaska and western North America, favoring extreme wildfire events. This study investigates influences of Arctic sea ice loss on the summer PJS variability over the Western Hemisphere. Regression analysis first provides observational evidence that Arctic sea ice reduction is related to a weakening summer Western Hemisphere PJS at interannual time scales. Atmospheric model ensemble simulations are then used to demonstrate that Arctic sea ice loss significantly contributes to observed Western Hemisphere Arctic warming and reduced meridional temperature gradient between midlatitudes and the pole in the lower and middle troposphere, acting to weaken the troposphere zonal wind and vertical wind shear from 55° to 75°N, and about 20–30% of observed weakened summer PJS trend during 1979–2014. Observational analysis and the model-based results also indicate that a significant portion of the observed trends of the PJS and vertical wind shear during 1979–2014 might be attributed to the decadal variability of the summer North Atlantic Oscillation (NAO). In the future climate, as more and more ice melts in the summer, the weakening effect of sea ice on the PJS will continue and will be superimposed onto the natural decadal variability of the PJS. [ABSTRACT FROM AUTHOR]

Increasing global warming is creating lot of dynamics in polar sea ice extent and concentration. Many works have explored the impact of Arctic sea ice extent over temperature, rainfall in global context. In India specific context, analyzing the impact of Arctic sea ice over Indian Ocean sea surface temperature is critical as the study is important for understanding the monsoon behaviour, aquatic life conditions and effect of storms over Indian coast. This work proposes a novel optimized multivariate long short term memory model for predicting the Indian Ocean sea surface temperature at a fine grained level using the Arctic sea ice extent data. The hyperparameters for optimizing the deep learning model were identified and optimized using Grass hopper swarm optimization algorithm. The model training was itself optimized using hinge loss error minimization. The prediction error is measured in term of root mean square and it was atleast 32% lower in proposed solution compared to artificial neural network and 18% lower compared to convolutional neural network techniques. [ABSTRACT FROM AUTHOR]

This study examines the impact of the winter Arctic sea ice concentration (ASIC) anomaly on the succedent summer tropical cyclone genesis frequency (TCGF) over the western North Pacific (WNP) and provides a new insight into the underlying physical mechanisms. There is a significant time-lagged relation between winter ASIC anomalies over Greenland-Barents-Kara (GBK) seas and the following summer TCGF over the southeastern part of the WNP. This delayed association is attributable to large-scale circulation anomalies and the air-sea interaction processes over the North Pacific induced by the winter ASIC anomalies. Specifically, a higher winter ASIC over the GBK seas induces an atmospheric wave train that propagates southeastward to the North Pacific. The associated cyclonic anomaly over the mid-latitude North Pacific is accompanied by southwesterly wind anomalies over the subtropics and results in sea surface temperature (SST) warming by reducing upward surface heat fluxes. This SST warming is maintained and further extends southward to the tropical Pacific in the following summer via a wind-evaporation-SST feedback, which in turn forces overlying atmospheric circulation via a Gill-type atmospheric response, including a pair of cyclonic and anticyclonic anomalies in the low- and upper-level troposphere, respectively, over the WNP. These atmospheric anomalies favor TC genesis over the southeastern part of the WNP by decreasing the vertical wind shear and increasing the convection, low-level vorticity and humidity. The above processes apply to the years when lower ASIC winters are followed by decreased TC genesis over the southeastern part of the WNP except for opposite signs of SST and atmospheric circulation anomalies. This study suggests that the winter ASIC anomaly over the GBK seas is a potential predictor for the prediction of the WNP TCGF in the following summer. [ABSTRACT FROM AUTHOR]

For spatial linear models, the classical maximum-likelihood estimators of both regression coefficients and variance components can be biased when the covariates are measured with errors. This work introduces a theoretically backed-up estimation framework for the spatial linear errors-in-variables model in a functional approach. Compared with the structural models, the functional approach treats the unobserved true covariates as fixed unknown parameters without imposing additional structures, thus leading to more robust parameter inference. Our model parameters are estimated simultaneously based on a set of unbiased estimating equations. Under some regularity conditions, we prove the consistency of the proposed estimating-equation estimators and derive their asymptotic distribution. In addition, a consistent variance estimator is developed for the estimating-equation estimators. To handle large spatial datasets, we provide two approaches to obtain scalable estimations based on our proposed estimating equations, where the required computational time and storage are reduced to be linear with sample size for each estimating-function evaluation. Simulation studies under different settings show that our estimators are consistent and the scalable algorithms work well. Finally, the proposed method is applied to studying the relationship between Arctic sea ice and related geophysical variables. for this article are available online. [ABSTRACT FROM AUTHOR]

The ice‐free Arctic in summer not only symbolizes human‐induced climate change but also highlights the need to critically consider climate change adaptation policies. To constrain projections when the Arctic Ocean will first become ice‐free, studies have typically combined the historical observations of the Arctic sea ice area (SIA) with future emission scenario simulations. However, these studies primarily relied on the historical climatology and trend of the Arctic SIA, without considering regional variations. In this study, we analyze September SIA projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations, while considering Arctic sub‐regions. Additionally, we assess the impact of incorporating sub‐region September SIA when constraining the first year of ice‐free Arctic in September. CMIP6 models generally overestimate the historical September SIA decreasing trend in the central Arctic, whereas they underestimate this trend in the surrounding shelf seas. The central Arctic, where the region expected to retain sea ice for the longest period in the future, holds particular significance for projecting when the Arctic will first become ice‐free. Consequently, when we employ the historical trend of September SIA in the central Arctic as a constraint, observationally‐constrained projections suggest a delay of 12 years (2056) for the first ice‐free September compared to raw/unconstrained projections (2044 in model average) under a high‐emission scenario. These findings underscore the importance of considering model biases in central Arctic SIA when constraining projections of the first year of ice‐free Arctic, which may occur later than previously projected in many studies. Plain Language Summary: The Arctic sea ice area (SIA) has steadily decreased over the historical period, and future projections indicate a continuous decrease throughout the twenty‐first century, which can potentially lead to an ice‐free Arctic in September. However, there are large uncertainties in climate model projections regarding when the first year of ice‐free Arctic in September will emerge, even under specific emissions scenarios. This study employs a statistical analysis that incorporates historically observed information to generate observationally‐constrained future projections. Particularly, we focus on the decline in September central Arctic SIA, a critical region expected to be the last to become ice‐free Arctic in September in the future. The latest climate model simulations mostly overestimate the observed September SIA decrease in the central Arctic during the historical period. Moreover, the climate model simulations exhibit robust statistical relationship between the historical trends in September central Arctic SIA and the projected year of the first ice‐free Arctic in September. Utilizing this relationship combined with the observed information for the historical trend of September central Arctic SIA, our constrained projections suggest as 12 years delay in the first year of ice‐free Arctic in September compared to the original model projections under a high‐emission scenario. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) models generally overestimate the observed September central Arctic sea ice area (SIA) decreaseHistorical central Arctic SIA trend positively correlates with the first year of ice‐free Arctic in September, serving as a constraintConstrained projections suggest 12 years delayed September ice‐free Arctic emergence than raw projections under a high‐emission scenario [ABSTRACT FROM AUTHOR]

Extratropical cyclones are critical weather systems that affect large-scale weather and climate changes at mid-high latitudes. However, prior research shows that there are still great difficulties in predicting extratropical cyclones for occurrence, frequency, and position. In this study, mean sea level pressure (MSLP) data from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) are used to calculate the variance statistics of the MSLP to reveal extratropical cyclone activity (ECA). Based on the analysis of the change characteristics of ECA in the Northern Hemisphere, the intrinsic link between ECA in the Northern Hemisphere and Arctic sea ice is explored. The results show that the maximum ECA mainly occurs in winter over the mid-high latitudes in the Northern Hemisphere. The maximum ECA changes in the North Pacific and the North Atlantic, which are the largest variations in the Northern Hemisphere, are independent of each other, and their mechanisms may be different. Furthermore, MSLP is a significant physical variable that affects ECA. The North Atlantic Oscillation (NAO) and North Pacific Index (NPI) are significant indices that impact ECA in the North Atlantic and North Pacific, respectively. The innovation of this paper is to explore the relationship between the activity of extratropical cyclones in the Northern Hemisphere and the abnormal changes in Arctic sea ice for the first time. The mechanism is that the abnormal changes in summer-autumn and winter Arctic sea ice lead to the phase transition of the NPI and NAO, respectively, and then cause the occurrence of ECA in the North Pacific and North Atlantic, respectively. Arctic sea ice plays a crucial role in the ECA in the Northern Hemisphere by influencing the polar vortex and westerly jets. This is the first exploration of ECAs in the Northern Hemisphere using Arctic sea ice, which can provide some references for the in-depth study and prediction of ECAs in the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

Turbulent motions in the Arctic stable boundary layer are characterized by intermittency, but they are rarely investigated due to limited observations, in particular over the sea‐ice surface. In the present study, we explore the characteristics of turbulent intermittency over the Arctic sea‐ice surface using data collected during the Multidisciplinary drifting Observation for the Study of Arctic Climate expedition from October 2019 to September 2020. We first develop a new algorithm, which performs well in identifying the spectral gap over the Arctic sea‐ice surface. Then the characteristics of intermittency are investigated. It is found that the strength of intermittency increases under the conditions of light surface wind speed, small surface wind speed gradient, and strong surface air temperature gradient. The momentum flux, sensible heat flux, and latent heat flux calculated by raw eddy‐covariance fluctuations are overestimated by 3%, 10%, and 24%, respectively, because submesoscale motions are included. Furthermore, the characteristics of the atmospheric boundary layer structure under various intermittency conditions reveal that strong low‐level jets are favorable to surface turbulent motions that result in weak intermittency, while strong temperature inversions above the surface layer suppress surface turbulent motions and lead to strong intermittency. Plain Language Summary: In polar regions, turbulent mixing in the near‐surface layer is weak and is often modulated by strong stable stratification, thus the intermittent state of turbulence is universal. The intermittency might lead to inaccuracy of the Monin‐Obukhov similarity theory, which assumes that turbulence is fully developed. In this study, by using a year of eddy‐covariance observations collected over the Arctic sea‐ice surface, we first develop an automatic algorithm to identify the spectral gap where the Hilbert spectrum presents an abrupt drop in low frequency. Using only high‐frequency turbulent motions, the turbulent data is reconstructed. This algorithm has the potential to quantify the characteristics of turbulence intermittency in polar regions. Based on our analysis, the intermittency results in the greatest relative influence on the latent heat flux compared with the momentum and sensible heat fluxes. Furthermore, we found the following meteorological conditions often lead to the enhancement of intermittency: low surface wind speed and wind speed gradient; strong surface air temperature gradient; weak low‐level jets; and strong temperature inversions above the surface layer. These findings help to improve our understanding of turbulent intermittency over the Arctic sea‐ice surface. Key Points: A new algorithm, which performs well in identifying the spectral gap over the Arctic sea‐ice surface, is proposedThe characteristics of intermittency under different meteorological conditions are revealedThe effects of intermittency on eddy‐covariance fluxes are assessed [ABSTRACT FROM AUTHOR]

Abstract The ice‐free Arctic in summer not only symbolizes human‐induced climate change but also highlights the need to critically consider climate change adaptation policies. To constrain projections when the Arctic Ocean will first become ice‐free, studies have typically combined the historical observations of the Arctic sea ice area (SIA) with future emission scenario simulations. However, these studies primarily relied on the historical climatology and trend of the Arctic SIA, without considering regional variations. In this study, we analyze September SIA projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations, while considering Arctic sub‐regions. Additionally, we assess the impact of incorporating sub‐region September SIA when constraining the first year of ice‐free Arctic in September. CMIP6 models generally overestimate the historical September SIA decreasing trend in the central Arctic, whereas they underestimate this trend in the surrounding shelf seas. The central Arctic, where the region expected to retain sea ice for the longest period in the future, holds particular significance for projecting when the Arctic will first become ice‐free. Consequently, when we employ the historical trend of September SIA in the central Arctic as a constraint, observationally‐constrained projections suggest a delay of 12 years (2056) for the first ice‐free September compared to raw/unconstrained projections (2044 in model average) under a high‐emission scenario. These findings underscore the importance of considering model biases in central Arctic SIA when constraining projections of the first year of ice‐free Arctic, which may occur later than previously projected in many studies.

With global warming, Arctic sea ice, as one of the important factors regulating climate, has put forward new requirements for research. At present, the ground penetrating radar (GPR) is a powerful tool to obtain the structure of Arctic sea ice. Traditional offset imaging techniques no longer meet research requirements, and the two-parameter full waveform inversion (FWI) method has received widespread attention. To solve the high nonlinearity and ill-posed problem of FWI, the L-BFGS optimization algorithm and Wolfe criterion of inexact line search were used to update the model. The parameter scale factor, multiscale inversion strategy, and total variation (TV) regularization were introduced to optimize the inversion results. Finally, the inversion of anomalous bodies with different scales and different physical parameters is carried out, which verifies the reliability of the proposed method for dual-parameter imaging of Arctic sea ice and provides a powerful tool for the study of Arctic sea ice. [ABSTRACT FROM AUTHOR]

Arctic river discharge is one of the important factors affecting sea-ice melting of Arctic shelf seas. However, such effects have not been given much attention. In this study, the changes in discharge of the Ob, Yenisei, and Lena Rivers and the sea ice of the Kara and Laptev Seas during 1979–2019 were analyzed. Substantial increases in discharge and heat from the discharge and decreases in sea ice concentration (SIC) were detected. The effects of changes in discharge and riverine heat on sea ice changes were investigated. The results showed that the influence of the discharge, accumulated discharge, heat, and accumulated heat on SIC mainly occurred at the beginning and final stages of sea-ice melting. Discharge accelerated the melting of sea ice by increasing the absorption of solar radiation as the impurities contained in the discharge washed to the sea ice surface during the initial and late stages of sea-ice melting. Changes in cumulative riverine heat from May to September greatly contributed to the SIC changes in the Kara and Laptev Seas at the seasonal scale. The SIC reduced by 1% when the cumulative riverine heat increased by 213.2 × 106 MJ, 181.5 × 106 MJ, and 154.6 × 106 MJ in the Lena, Yenisei, and Ob Rivers, respectively, from May to September. However, even in the plume coverage areas in the Kara and Laptev Seas, discharge changes from the three rivers had a limited contribution to the reduction in SIC at annual scales. This work is helpful for understanding the changes in Arctic sea ice. [ABSTRACT FROM AUTHOR]

Three-dimensional numerical modeling is used to assess the sensitivity of the sea ice state in the Arctic deep-water basin to changing the riverine heat influx. The change in the sea ice volume due to the heat coming with river waters relative to its annual average value is analyzed. Numerical experiment results demonstrate an increase in the riverine heat contribution to the Arctic sea ice decline at the end of the second decade of the present century. This process is most manifested in the deep-water part of the Eastern Hemisphere, which is determined by immediate proximity to the largest Siberia's rivers and the system of the circulation of shelf seas and deep-water basins under conditions of increasing air temperature and sea ice reduction in the Arctic. [ABSTRACT FROM AUTHOR]

The amount of snow on Arctic sea ice impacts the ice mass budget. Wind redistribution of snow into open water in leads is hypothesized to cause significant wintertime snow loss. However, there are no direct measurements of snow loss into Arctic leads. We measured the snow lost in four leads in the Central Arctic in winter 2020. We find, contrary to expectations, that under typical winter conditions, minimal snow was lost into leads. However, during a cyclone that delivered warm air temperatures, high winds, and snowfall, 35.0 ± 1.1 cm snow water equivalent (SWE) was lost into a lead (per unit lead area). This corresponded to a removal of 0.7–1.1 cm SWE from the entire surface—∼6%–10% of this site's annual snow precipitation. Warm air temperatures, which increase the length of time that wintertime leads remain unfrozen, may be an underappreciated factor in snow loss into leads. Plain Language Summary: The amount of snow on Arctic sea ice impacts how quickly the ice grows in the winter and melts in the summer. Cracks in the ice, known as leads, expose ocean water that snow can be blown into, reducing the amount of snow on the ice and thus impacting ice growth and melt. We found that in typical wintertime conditions, very little snow is blown into leads. However, if there is fresh snowfall, it is uncommonly warm and it is very windy at the same time when leads are forming, a large amount of snow can be blown into the ocean. Accounting for the impacts of air temperature on this process will enable scientists to better understand how much snow is on Arctic sea ice, and hence how quickly the ice grows in the winter and melts in the summer, and how this might change in a future, warmer, Arctic. Key Points: Minimal snow was lost into leads in observations of three cases in typical wintertime, cold, moderately windy conditions on Arctic sea iceIn an atmospheric advection event with air temperature above −10°C, high wind, and fresh snowfall, most recent snowfall was lost into leadsWarm air temperatures increase the duration of unfrozen water in leads, which may be an underappreciated factor in snow loss into leads [ABSTRACT FROM AUTHOR]

From 17 November to 27 December 2022, extremely cold snowstorms frequently swept across North America and Eurasia. Diagnostic analysis reveals that these extreme cold events were closely related to the establishment of blocking circulations. Alaska Blocking (AB) and subsequent Ural Blocking (UB) episodes are linked to the phase transition of the North Atlantic Oscillation (NAO) and represent the main atmospheric regimes in the Northern Hemisphere. The downstream dispersion and propagation of Rossby wave packets from Alaska to East Asia provide a large-scale connection between AB and UB episodes. Based on the nonlinear multi-scale interaction (NMI) model, we found that the meridional potential vorticity gradient (PVy) in November and December of 2022 was anomalously weak in the mid-high latitudes from North America to Eurasia and provided a favorable background for the prolonged maintenance of UB and AB events and the generation of associated severe extreme snowstorms. However, the difference in the UB in terms of its persistence, location, and strength between November and December is related to the positive (negative) NAO in November (December). During the La Niña winter of 2022, the UB and AB events are related to the downward propagation of stratospheric anomalies, in addition to contributions by La Niña and low Arctic sea ice concentrations as they pertain to reducing PVy in mid-latitudes. [ABSTRACT FROM AUTHOR]

Our previous study revealed the link between Barents–Kara sea ice and rainfall in eastern China. This study continues evaluating the performance of multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating this linkage. Most CMIP6 models can simulate Arctic sea ice coverage in the present climate system, although the sea ice extent in the edge areas show some biases. Only a few models can roughly reproduce the observed rainfall dipole pattern associated with Arctic sea ice variability. The linkage between Arctic sea ice variability in winter and eastern China rainfall in early summer is performed through a long memory of the sea ice, the stratospheric variability as the mediator, and downward propagation of stratospheric signals. Very few CMIP6 models can exhibit a realistic interannual relationship between the Arctic sea ice and China rainfall. The selected high-skill models with a more realistic linkage between sea ice and China rainfall present a clear downward impact of the stratospheric circulation anomalies associated with sea ice variability. The reversal of the Northern Hemisphere Annular Mode (NAM) from the negative phase in early winter to the positive phase in spring in the high-skill models and observations denotes the important role of the stratosphere as a mediator to bridge the Arctic sea ice and China rainfall. The long memory of the Arctic sea ice with the stratosphere as the mediator has a deep implication on the seasonal forecasts of East Asian countries. [ABSTRACT FROM AUTHOR]

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.

A conceptual model connecting seasonal loss of Arctic sea ice to midlatitude extreme weather events is applied to the 21st-century intensification of Central Pacific trade winds, emergence of Central Pacific El Nino events, and weakening of the North Pacific Aleutian Low Circulation. According to the model, Arctic Ocean warming following the summer sea-ice melt drives vertical convection that perturbs the upper troposphere. Static stability calculations show that upward convection occurs in annual 40- to 45-d episodes over the seasonally ice-free areas of the Beaufort-to-Kara Sea arc. The episodes generate planetary waves and higher-frequency wave trains that transport momentum and heat southward in the upper troposphere. Regression of upper tropospheric circulation data on September sea-ice area indicates that convection episodes produce wave-mediated teleconnections between the maximum ice-loss region north of the Siberian Arctic coast and the Intertropical Convergence Zone (ITCZ). These teleconnections generate oppositely directed trade-wind anomalies in the Central and Eastern Pacific during boreal winter. The interaction of upper troposphere waves with the ITCZ air-sea column may also trigger Central Pacific El Nino events. Finally, waves reflected northward from the ITCZ air column and/or generated by triggered El Nino events may be responsible for the late winter weakening of the Aleutian Low Circulation in recent years.

Abstract Complex Earth systems under stress from global heating can resist change for only so long before tipping into transitional chaos. Convergent trajectories of change in Arctic, Amazon and other systems suggest a biosphere tipping point (BTP) in this mid-century. The BTP must be prevented and therefore offers a hard deadline against which to plan, implement, monitor, adjust and accelerate climate change mitigation efforts. These should be judged by their performance against this deadline, requiring mitigation investments to be compared and selected according to the unit cost of their dated mitigation value (tCO2edmv) outcomes. This unit of strategic effectiveness is created by exponentially discounting annual GHG savings in tCO2e against a dated BTP. Three proof of concept cases are described using a BTP in 2050 and a 10% discount rate, highlighting three key ways to prevent the BTP. The most reliably cost-effective for mitigation, and richest in environmental co-benefits, involves protecting high carbon-density natural ecosystems. Restored and regenerating natural ecosystems also yield abundant environmental co-benefits but slower mitigation gains. Improving choice awareness and building capacity to promote decarbonisation in all economic sectors is cost-effective and essential to meeting national net zero emission goals. Public mitigation portfolios should emphasise these three strategic elements, while private ones continue to focus on renewable energy and linked opportunities. Further research should prioritise: (1) consequences of an Arctic Ocean imminently free of summer sea ice; (2) testing the tCO2edmv metric with various assumptions in multiple contexts; and (3) integrating diverse co-benefit values into mitigation investment decisions.

Observations show Arctic sea ice has declined and midlatitude storminess has weakened during Northern Hemisphere (NH) summertime. It is currently unclear whether Arctic sea ice loss impacts summertime storminess because most previous work focuses on other seasons. Here we quantify the impact of Arctic sea ice loss on NH summertime storminess using equilibrium and transient climate model simulations. The equilibrium simulations show mid‐to‐late 21st century Arctic sea ice loss weakens summertime storminess, but only in the presence of ocean coupling. With ocean coupling, the equator‐to‐pole temperature and atmospheric energy gradients significantly weaken due to increased surface turbulent flux in the polar region following Arctic sea ice loss. The transient simulations show Arctic sea ice loss does not significantly weaken summertime storminess until the late 21st century. Furthermore, Arctic Amplification, which is dominated by Arctic sea ice loss in the present day, does not significantly impact the present‐day weakening of summertime storminess. Plain Language Summary: Present‐day summertime climate change in the NH is characterized by rapid Arctic sea ice loss and weakening of weather systems. While these trends are projected to continue throughout the 21st century, their connection is not well understood. Using climate model simulations, we show mid‐to‐late 21st century Arctic sea ice loss weakens the summertime weather system by decreasing the equator‐to‐pole energy (and temperature) difference. In addition, we demonstrate transient Arctic sea ice loss does not significantly contribute to weakening summertime weather system until the late 21st century. The results suggest that present‐day Arctic sea ice loss and Arctic Amplification have not contributed significantly to the observed weakening of weather systems. Key Points: Equilibrium and transient climate model simulations are used to quantify the summertime storminess response to Arctic sea ice lossThe equilibrium response to mid‐to‐late 21st century Arctic sea ice loss involves weaker summertime storminess due to ocean couplingThe transient weakening of present‐day summertime storminess is not significantly affected by Arctic sea ice loss and Arctic Amplification [ABSTRACT FROM AUTHOR]

Since the 1980s, rapid Arctic sea ice loss has been observed, and its potential influences on the midlatitude weather and climate have been extensively examined, but strongly debated. This study instead investigates influences of Arctic sea ice loss on the Eastern Hemisphere westerly wind on the poleward side of the polar front jet (PFJ), which has significantly weakened in winter but strengthened in summer since 1980s. Observational analyses indicate that the Eastern Hemisphere PFJ anomalies in winter and summer are strongly correlated with Northern Hemisphere (NH) atmospheric circulation, surface air temperature and precipitation changes, and that the winter PFJ variability is significantly correlated to Barents-Kara sea ice anomalies from autumn and winter at interannual time scales. About 58% of the observed winter PFJ weakened trend during 1979–2014 is congruent with the autumn sea ice index over the Barents-Kara Seas. Atmospheric model ensemble simulations from four models show that Arctic sea ice loss contributes to Arctic warming in winter and reduces the northern Eurasia-pole temperature contrast, leading to an significant weakening winter PFJ. Ensemble mean effects of four models and each model due to Arctic sea ice loss explain about 10–20% of the observed winter PFJ weakening trend, because the model simulations underestimate observed impacts of sea ice reduction on Arctic troposphere warming. In summer, the strengthening PFJ is mostly controlled by increasing greenhouse gases and sea surface temperature changes outside the Arctic, but is little affected by Arctic sea ice loss. Our observational and numerical results consistently suggest that a dynamical pathway linking observed Arctic sea ice loss to northern Eurasian winter is through its weakening effect on the westerly wind on the poleward side of the PFJ. [ABSTRACT FROM AUTHOR]

Simple Summary: The rapid decline of Arctic sea ice (ASI) has significantly impacted the global climate, polar ecosystems, and shipping courses. Precise long-term and high-resolution changes in ASI estimates are crucial for adapting to climate change and developing Arctic marine resources. Satellite altimeters have been applied to detect ASI for several decades. However, the mission periods of various altimetry satellites are limited, making it challenging to estimate the long-term change process of ASI thickness from the observations of a single altimetry satellite or simply combining multi-source satellite altimetry data. The purpose of this study is to comprehensively obtain continuous long-term ASI freeboard, thickness, and volume characteristics using the gridded nadirization method from Envisat, CryoSat-2, and ICESat-2 altimeter data. The relationship between surface temperature and surface wind field is also investigated. The freeboard, thickness, extent, and area of ASI consistently showed loss trends during 2002–2021, and sea ice volume decreased by 5437 km3/month. Sea surface temperature and sea surface wind field are two of the essential influencing factors on ASI variations. This study will assist in clarifying the relationship between climate variations and the ASI decline. Satellite altimeters have been used to monitor Arctic sea ice (ASI) thickness for several decades, but whether the different altimeter missions (such as radar and laser altimeters) are in agreement with each other and suitable for long-term research needs to be investigated. To analyze the spatiotemporal characteristics of ASI, continuous long-term first-year ice, and multi-year ice of ASI freeboard, thickness, and volume from 2002 to 2021 using the gridded nadirization method from Envisat, CryoSat-2, and ICESat-2, altimeter data are comprehensively constructed and assessed. The influences of sea surface temperature (SST) and sea surface wind field (SSW) on ASI are also discussed. The freeboard/thickness and extent/area of ASI all varied seasonally and reached their maximum and minimum in April and October, March and September, respectively. From 2002 to 2021, the freeboard, thickness, extent, and area of ASI all consistently showed downward trends, and sea ice volume decreased by 5437 km3/month. SST in the Arctic rose by 0.003 degrees C/month, and the sea ice changes lagged behind this temperature variation by one month between 2002 and 2021. The meridional winds blowing from the central Arctic region along the eastern coast of Greenland to the North Atlantic each month are consistent with changes in the freeboard and thickness of ASI. SST and SSW are two of the most critical factors driving sea ice changes. This study provides new data and technical support for monitoring ASI and exploring its response mechanisms to climate change. [ABSTRACT FROM AUTHOR]

The causality of the link between Autumn Barents‐Kara (BK) sea ice and the winter North Atlantic Oscillation (NAO) is uncertain, given teleconnections stemming from the tropics may influence both the extra‐tropics and the Arctic. We explore the relationship between tropical rainfall and BK sea ice in autumn, by nudging the tropics to follow observed variability in otherwise free running ensemble simulations. Tropical forcing alone can skillfully reproduce a significant fraction of observed interannual NAO variability in late autumn. We also show that interannual variability in the NAO is strongly related to simulated BK sea ice. As a result, we are able to reproduce some of the observed link between tropical rainfall and autumn BK sea ice. However, only during the strong 1997 El Niño are clear tropical influences at high latitudes found. Large ensembles and strong tropical forcing are required to detect tropical forced variability in models at high latitudes. Plain Language Summary: Regional variations in Arctic sea ice during autumn have been linked to large scale weather patterns over the Atlantic, which impact on European weather. Commonly, this can be attributed to the North Atlantic Oscillation (NAO). Whether the relationship with sea ice and the NAO is physical, or coincidental, is unclear, given that remote tropical weather can impact on both Arctic sea‐ice and the NAO. In this study, we explore how the tropics could impact sea ice in the Barents‐Kara (BK) seas during autumn, by constructing controlled experiments where we specify the state of the tropical atmosphere. We repeat this many times, to determine how much variability there is in this relationship. We find that there is a link between tropical weather and large‐scale weather patterns in the Atlantic, with a weak link found between tropical weather and BK sea‐ice. However, during years when tropical convection is particularly active in the east Pacific, we can see a much stronger impact on the Arctic during autumn. Our results indicate that the link between the tropics and Arctic may be too weak in models and is only detectable during years where the tropical variability is particularly strong. Key Points: Tropical nudging experiments reproduce some of the observed interannual variability in autumn Barents‐Kara sea iceTropical nudging reproduces a significant fraction of observed autumn North Atlantic Oscillation (NAO) variability, and the anticorrelation of NAO with sea iceOnly during the strong El Niño of 1997 is the model able to reproduce the strong observed teleconnection from the tropics to sea ice [ABSTRACT FROM AUTHOR]

The Unified Forecast System (UFS) is the next generation modeling infrastructure under development for NOAA's operational numerical weather/climate predictions. This study is the first attempt with UFS for seasonal predictions application. In particular, 9‐month hindcasts are performed starting from every month during 2007–2020. The UFS performance in predicting Arctic sea ice was compared to hindcasts by (a) the current operational Climate Forecast System version 2 (CFSv2) and (b) an experimental sea ice prediction system (CFSm5) at NOAA. Evaluations indicate that UFS demonstrates consistently improved skills than both CFSv2 and CFSm5 in seasonal sea ice predictions, together with more realistic climatological sea ice distributions. Diagnostics suggest that the better climatological sea ice distributions in UFS is related to its atmospheric states simulated with FV3, the atmospheric component of UFS, and reinforced by associated ocean circulations. In addition, applications of the multi‐model ensemble strategy do not present skill improvements over the UFS forecasts alone. Plain Language Summary: Arctic sea‐ice cover illustrates significant year‐to‐year fluctuations superimposed on the long‐term decline trend. Seasonal forecasts of the ice interannual fluctuations could play an important role in providing planning information for shipping industries, improving management of ocean and coastal resources in the Arctic, and better serving Northern communities. In this study, we evaluated the capability of the Unified Forecast System (UFS) in predicting the Arctic sea ice coverage. The UFS is the next generation modeling infrastructure under development for NOAA's operational numerical weather/climate predictions, which has not been tried with seasonal predictions application before. Evaluations suggested that UFS demonstrates consistently improved skills in seasonal sea ice predictions than two systems currently in operations at NOAA, together with more realistic climatological sea ice distributions. Our further diagnostics suggest that the improvement is mainly related to the atmospheric states simulated with UFS, but is also reinforced by associated ocean circulations. Key Points: This work is the first attempt with the Unified Forecast System (UFS) for seasonal predictionsUFS presents better performance in seasonal prediction of Arctic sea ice than current operational systems at NOAABetter sea ice predictions with UFS are mainly related to its better representation of atmospheric states [ABSTRACT FROM AUTHOR]

PM2.5 pollution frequently occurs over the North China Plain (NCP) during early winter, which is usually modulated by the northeast Asian anomalous anticyclone (NAAA). Research into the NAAA and the related PM2.5 pollution has mainly focused on the individual impact of El Niño or Arctic sea ice, with less analysis on their synergistic effect. This study finds that the October Barents–Kara Seas sea ice and the early winter El Niño synergistically exert distinct remote influence on the NAAA via propagation of Rossby waves and finally deteriorate air quality over the NCP. More specifically, the more Barents‐Kara Seas sea ice in October tends to cause a cooling there in November, which stimulates a Rossby wave train and thus strengthens the early winter NAAA. As a result, the weakened ventilation conditions and stagnant air support PM2.5 pollution over the NCP. Further results reveal that if there are El Niño and the more October Barents‐Kara Seas sea ice at the same time, the probability of a strong NAAA is 57% and the associated probability of PM2.5 pollution over the NCP is 43%, which are more than that if only the more Barents‐Kara Seas sea ice (0%) or El Niño (14%). Considering the key role of the NAAA to PM2.5 pollution over the NCP, we therefore construct a simple linear model that can skillfully predict early winter NAAA at a lead time of 1 month, which shows good performance with correlation coefficient of up to 0.61 in raw NAAA. Plain Language Summary: In this study, we find that the more Barents‐Kara Seas sea ice in October and the mature El Niño have a synergistic effect on the northeast Asian anomalous anticyclone and the related PM2.5 pollution over the North China Plain (NCP) from November to the following January. If there are El Niño and the more October Barents‐Kara Seas sea ice at the same time, the probability of a strong northeast Asian anomalous anticyclone (NAAA) is 57% and the associated probability of PM2.5 pollution over the NCP is 43%, which are more than that if only the more Barents‐Kara Seas sea ice or El Niño. A simple linear model based on the Niño3 index and the Barents‐Kara Seas sea ice index in October can skillfully predict early winter NAAA, which shows good performance. Key Points: The more October Barents‐Kara Sea (BKS) sea ice and El Niño synergistically exert distinct influence on the northeast Asian anomalous anticyclone (NAAA) and the related PM2.5 pollution over the North China Plain (NCP)Probability of the strong NAAA and the NCP PM2.5 pollution are 57% and 43%, respectively, during El Niño and the more October BKS sea iceA Linear model based on El Niño and October Barents‐Kara Seas sea ice can skillfully predict early winter NAAA at a lead time of 1 month [ABSTRACT FROM AUTHOR]

Sea ice cover plays an important role in modulating local temperature through heat and moisture fluxes. The influence of thin ice, lead and polynya has been well investigated, however, the effect of perennial ice (also called multiyear ice, MYI) has not. This study is motivated by it and investigated a hypothesis that changes in MYI concentration in winter triggers changes in short-term local 2-m air temperature. The hypothesis was tested using time series analysis of the two parameters and correlation between them. Data from the winters of 2004–2009 were used for the examination at three spatial scales. The hypothesis is found to be potentially accepted when MYI exists in a consolidated ice regime with negligible thin ice or open water in the surroundings, and the air temperature is low enough. Conditions for the acceptance of the hypothesis were quantitatively identified. The qualifications entail that the ice cell must experience daily change of MYI concentration meanwhile satisfy the criteria of total ice concentration (TIC), young ice concentration (YIC) and air temperature (Tair) in the surrounding area, which are TIC >88.3%, YIC

In recent decades, Arctic-amplified warming and sea-ice loss coincided with a prolonged wintertime Eurasian cooling trend. This observed Warm Arctic–Cold Eurasia pattern has occasionally been attributed to sea-ice forced changes in the midlatitude atmospheric circulation, implying an anthropogenic cause. However, comprehensive climate change simulations do not produce Eurasian cooling, instead suggesting a role for unforced atmospheric variability. This study seeks to clarify the source of this model-observation discrepancy by developing a statistical approach that enables direct comparison of Arctic-midlatitude interactions. In both historical simulations and observations, we first identify Ural blocking as the primary causal driver of sea ice, temperature, and circulation anomalies consistent with the Warm Arctic–Cold Eurasia pattern. Next, we quantify distinct transient responses to this Ural blocking, which explain the model-observation discrepancy in historical Eurasian temperature. Observed 1988–2012 Eurasian cooling occurs in response to a pronounced positive trend in Ural sea-level pressure, temporarily masking long-term midlatitude warming. This observed sea-level pressure trend lies at the outer edge of simulated variability in a fully coupled large ensemble, where smaller sea-level pressure trends have little impact on the ensemble mean temperature trend over Eurasia. Accounting for these differences bring observed and simulated trends into remarkable agreement. Finally, we quantify the influence of sea-ice loss on the magnitude of the observed Ural sea-level pressure trend, an effect that is absent in historical simulations. These results illustrate that sea-ice loss and tropospheric variability can both play a role in producing Eurasian cooling. Furthermore, by conducting a direct model-observation comparison, we reveal a key difference in the causal structures characterizing the Warm Arctic–Cold Eurasia Pattern, which will guide ongoing efforts to explain the lack of Eurasian cooling in climate change simulations.

The observed sea ice concentration (SIC) over the Arctic has receded substantially in recent decades, and future model projections predict a seasonally ice-free Arctic in the second half of this century. Nevertheless, the impact of the Pacific on Arctic sea ice projections has yet to receive much attention. Observations show that summertime Arctic SIC growth events are related to the weakening of the Aleutian low and cooling events over the equatorial Pacific, and vice versa. We demonstrate that under various Coupled Model Intercomparison Project Phase 6 projections, the models in which the impact of El Niño-driven SIC loss is significantly higher than the La Niña-related SIC growth tend to turn seasonally ice-free by about 10–20 years ahead of the ensemble mean under high-emission future scenarios. We show how the non-linear impact of the El Niño Southern Oscillation (ENSO) on Arctic SIC resulted in a faster decline of summertime sea ice. The ENSO-related SIC changes in the multi-model ensemble mean of Arctic SIC are considerably lower than the internal variability and anthropogenic-driven changes. However, the asymmetric interannual ENSO effects over several decades and the resultant changes in surface heat fluxes over the Arctic lead to significant differences in the timing of sea ice extinction. Our results suggest that climate models must capture the realistic tropical Pacific–Arctic teleconnection to better predict the long-term evolution of the Arctic climate.

The Arctic climate is changing rapidly, along with intensified melting of sea ice, which has significant impacts on surface air temperature (SAT) in Eurasia. This study reveals that the subseasonal response of SAT to the autumn Kara–Laptev Sea ice (KLSIC) differs significantly between early and late winter. The response of SAT to KLSIC forms a warm Arctic–cold Eurasia pattern in early winter. Conversely, the negative anomaly response of SAT to KLSIC in late winter is only distributed in the band range of Eurasia, without significant positive SAT anomaly over the Arctic Ocean. After further examination of the separate physical mechanisms involved in early and late winter, it is found that a decrease in KLSIC in autumn can lead to a strengthened Ural high and Siberian high in the Arctic–Eurasia region, which is conducive to cold events in the mid-latitudes of Eurasia in early winter. For late winter, a westward shift in the response of atmospheric circulation to KLSIC leads to a negative anomaly feedback of North Sea surface temperature, which triggers the propagation of Rossby waves to the Sea of Japan through the wave activity flux. Meanwhile, the deep trough of East Asia is strengthened and extends to the southeast, guiding northern cold air to the western Pacific. Our results highlight that different subseasonal effects of sea ice should be considered in Eurasian climate prediction, rather than only consider the effects in winter mean.

The Arctic sea ice (ASI) is expected to decrease with further global warming. However, considerable uncertainty remains regarding the temperature range that would lead to a completely ice-free Arctic. Here, we combine satellite data and a large suite of models from the latest phase of the Coupled Model Intercomparison Project (CMIP6) to develop an empirical, observation-based projection of the September ASI area for increasing global mean surface temperature (GMST) values. This projection harnesses two simple linear relationships that are statistically supported by both observations and model data. First, we show that the September ASI area is linearly proportional to the area inside a specific northern hemisphere January–September mean temperature contour T _c . Second, we use observational data to show how zonally averaged temperatures have followed a positive linear trend relative to the GMST, consistent with Arctic amplification. To ensure the reliability of these observations throughout the rest of the century, we validate this trend by employing the CMIP6 ensemble. Combining these two linear relationships, we show that the September ASI area decrease will accelerate with respect to the GMST increase. Our analysis of observations and CMIP6 model data suggests a complete loss of the September ASI (area below 10 $\mathbf{^6}$ km $\mathbf{^2}$ ) for global warming between $1.5\,^\circ$ C and $2.2\,^\circ$ C above pre-industrial GMST levels.

Winter cold extremes (WCEs) frequently plague densely populated areas of Asia, leading to substantial economic losses and even fatalities. It has been found that the late autumn sea ice concentration (SIC) anomalies in the northern (SICN) and southern Arctic (SICS) are significantly positively and negatively correlated with the occurrence frequency of WCE in Asia, respectively (Wang and Su 2024). Our study demonstrates that the impacts of SICN and SICS have strengthened after 1999/2000. Specifically, before 1999/2000, the influences of SICN and SICS on the Asian WCE (AWCE) were relatively weak, possibly related to the weak intensity of SICS and the limited correlation between SICN and SICS. After 1999/2000, the interannual variability of SICS became larger and anti-correlated with that of SICN, resulting in a stronger teleconnection between the Arctic SIC and AWCE. It is revealed that after 1999/2000, the greater loss of SICS modified atmospheric stability through changes in surface heat fluxes and surface upward longwave radiation fluxes. This alteration weakened the magnitudes of westerly winds and increased the frequency of blocking events over the northern Eurasian continent, leading directly to a higher occurrence of cold extremes in Asia. These interdecadal differences in the influence of Arctic SIC on AWCE may be associated with long-term climate change.

Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.

Sea ice concentration and thickness are key parameters for Arctic shipping routes and navigable potential. This study focuses on the changes in shipping routes and the estimation of navigable potential in the Arctic Northern Sea Route and Transpolar Sea Route during 2021–2050 based on the sea ice data predicted by eight CMIP6 models. The Arctic sea ice concentration and thickness vary among the eight models, but all indicate a declining trend. This study indicates that, under the two scenarios, the least-cost route will migrate more rapidly from the low-latitude route to the high-latitude route in the next 30 years, showing that the Transpolar Sea Route will be navigable for Open Water (OW) and Polar Class 6 (PC6) before 2025, which is advanced by nearly 10 years compared to previous studies. The sailing time will decrease to 16 and 13 days for OW and PC6 by 2050, which saves 3 days compared to previous studies. For OW, the navigable season is mainly from August to October, and the Northern Sea Route is still the main route, while for PC6, the navigable season is mainly from July to January of the following year, and the Transpolar Sea Route will become one of the important choices.

A large ensemble of simulations from a high-top atmospheric general circulation model are conducted to compare the atmospheric responses from loss of Arctic sea ice thickness and sea ice concentration. The response to projected sea ice thickness loss indicates substantial surface warming over the Arctic Ocean and up to 1°C of cooling in Eurasia. While the dynamic circulation response from sea ice thickness loss is smaller in magnitude, it has a similar spatial anomaly pattern as that due to sea ice concentration loss. This pattern resembles the negative phase of the Northern Annular Mode. The simulations reveal that sea ice thickness loss enhances the thermodynamic and large-scale circulation response from sea ice anomalies. These results stress the importance of considering a realistic sea ice thickness distribution in future atmospheric general circulation model sea ice perturbation experiments.

Climate change in the Arctic has substantial impacts on human life and ecosystems both within and beyond the Arctic. Our analysis of CMIP6 simulations shows that some climate models project much larger Arctic climate change than other models, including changes in sea ice, ocean mixed layer, air‐sea heat flux, and surface air temperature in wintertime. In particular, dramatic enhancement of Arctic Ocean convection down to a few hundred meters is projected in these models but not in others. Interestingly, these models employ the same ocean model family (NEMO) while the choice of models for the atmosphere and sea ice varies. The magnitude of Arctic climate change is proportional to the strength of the increase in poleward ocean heat transport, which is considerably higher in this group of models. Establishing the plausibility of this group of models with high Arctic climate sensitivity to anthropogenic forcing is imperative given the implied ramifications. Plain Language Summary: Arctic climate is projected to change dramatically in the future based on the latest climate models. However, there is a large discrepancy between these climate models. We find that simulated Arctic climate change tends to be much larger in models using the community ocean model "NEMO" as their ocean component. Future Arctic climate change in winter strongly depends on the strength of the increase in poleward ocean heat transport, which is considerably higher in the NEMO‐family climate models. There are some indications that this group of models can better reproduce the observed winter sea ice decline and mixed layer depth in the Barents Sea in their historical simulations. We suggest that investigating the model physics in the NEMO‐family climate models in comparison with other models may ultimately help to reduce the overall uncertainty in climate change projections. Key Points: Future Arctic climate change in winter strongly depends on the strength of the increase in poleward ocean heat transport (OHT)Arctic climate change in the NEMO‐family climate models is much more pronouncedNEMO‐family climate models have larger increases in poleward OHT [ABSTRACT FROM AUTHOR]

Changes in Arctic sea ice since the middle of the last century are explored in this study. Both observations and climate model simulations show an overall sea ice expansion during 1953–1970 but a general sea ice decline afterward. Anthropogenic aerosols, nature forcing and atmospheric ozone changes are found to contribute to the sea ice expansion in the early period. Their effects are strong generally in late boreal summer. On the other hand, greenhouse gas warming has a dominant effect on diminishing Arctic sea ice cover during 1971–2005, especially in September. Internal climate variability also plays a role in the Arctic sea ice change during 1953–1970. However, it cannot solely explain the Arctic sea ice decline since the 1970s. [ABSTRACT FROM AUTHOR]

In winter 2013, a sea ice breakout in the Beaufort Sea produced extensive fracturing and contributed to record ice export. Rheinlænder et al. (2022, https://doi.org/10.1029/2022GL099024) simulated this event using the neXtSIM sea ice model, reproducing a realistic progression of lead opening and ice drift following the track of an anticyclone. Their simulations indicate strong winds and thin ice are key factors in breakouts. We discuss observational records giving additional insight into the mechanisms controlling breakout events, including the role of wind direction. Breakouts are common and have occurred under weaker winds than in 2013 and in thicker ice of previous decades. During 2013 and other events, patterns of lead opening during breakout followed changes in wind direction relative to the coast with anticyclone position. For skillful predictions of future breakouts, models must reproduce this behavior, and their performance should be assessed across a range of wind and ice conditions. Plain Language Summary: In winter, the Beaufort Sea is covered by a layer of sea ice that is usually frozen against the southern and eastern coastlines that bound the sea. When winds continuously blow from east to west away from these boundaries, the ice cover can tear apart and rapidly drift away from the coasts in what is called a breakout event. The prediction of such dynamic events is important for those who navigate the region. Rheinlænder et al. (2022, https://doi.org/10.1029/2022GL099024) recently used the neXtSIM sea ice model to simulate realistic ice cracking and drift during an exceptionally strong breakout that occurred in 2013. The authors highlighted strong winds and thin sea ice as key factors for breakout. We use observational records to provide additional insight into processes during Beaufort breakouts. Records of many similar events demonstrate that breakouts are common under weather patterns that produce east‐to‐west winds, even for weaker winds and thicker ice than in 2013. Across observed events, wind direction influences sea ice fracturing patterns and breakout timing. To ensure that model predictions of future sea ice breakouts are accurate, simulations should be compared against observations of multiple recorded events that span a range of wind and ice conditions. Key Points: Observational data provide context for a 2013 Beaufort Sea (BS) breakout simulated with the neXtSIM model by Rheinlænder et al. (2022, https://doi.org/10.1029/2022GL099024)While the 2013 event was exceptional, winter sea ice breakout is common under anticyclonic winds including in years with thicker ice packsWind direction relative to the coast influences the timing and location of lead opening during breakout in the BS [ABSTRACT FROM AUTHOR]

The Northeast China cold vortex (NCCV) during late summer (from July to August) is identified and classified into three types in terms of its movement path using machine learning. The relationships of the three types of NCCV intensity with atmospheric circulations in late summer, the sea surface temperature (SST), and Arctic sea ice concentration (SIC) in the preceding months, are analyzed. The sensitivity tests by the Community Atmosphere Model version 5.3 (CAM5.3) are used to verify the statistical results. The results show that the coordination pattern of East Asia-Pacific (EAP) and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode (NIOD) during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV. While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer—which is forced by SST anomalies in the South Indian Ocean dipole mode (SIOD) in the preceding June and SIC anomalies in the Barents Sea in the preceding April—causes the intensity anomaly of the second type. The third type is atypical and is not analyzed in detail. Sensitivity tests, jointly forced by the SST and SIC in the preceding period, can well reproduce the observations. In contrast, the results forced separately by the SST and SIC are poor, indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months. [ABSTRACT FROM AUTHOR]

The variability of the atmospheric circulation described by the AO and NAO indices has a significant impact on the fluctuations of climate parameters in the Baltic Sea region. Many research show, that a decline in Arctic Sea ice extent increases the likelihood of negative phases of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) in winter, and in the future could lead to more frequent relatively cold weather events during winter months in Baltic Sea region. This research is important for understanding possible teleconnections between Arctic Sea ice and atmospheric circulation (AO and NAO). In addition, this research helps to better understand the dynamics of Arctic Sea ice extent and Baltic Sea region air temperature, as well as AO and NAO circulation impact on the air temperature in the Baltic Sea region. A statistically significant decrease in the Arctic Sea ice extent and a statistically significant increase in the air temperature in the Baltic Sea region have been determined. The obtained results show that the expansion of the Arctic Sea ice in January-February with a delay of one month determines the AO and NAO values (correlation coefficients vary from 0.32 to 0.55). This means that if Arctic Sea ice decreases during winter months, AO and NAO indices are more likely to become negative as well. However, as the obtained correlations are not very strong, the results are insufficient to confirm the hypothesis that the Arctic Sea ice extent impacts negative AO and NAO phases likelihood and requires further research. A strong correlation between the Baltic Sea region air temperature and the values of AO/NAO indices has been established. The obtained results show that the Baltic Sea region air temperature variability in December-March is greatly influenced by the AO and NAO phases. [ABSTRACT FROM AUTHOR]

We are facing both a short-term emergency cooling crisis and a long-term greenhouse gas (GHG) draw down planetary ecological crisis. We must address both. The first requires emergency direct cooling, or temporary "triage" or a "tourniquet, for our bleeding planet." The second requires rapid GHG emissions reductions and draw down and natural planetary regeneration that realistically will take at least a few decades and may take a century or more. Conflating the challenge and opportunity of the second crisis with a response to the first crisis will not produce a rapid and credible global response to the second crisis because of structural economic inequity and fossil fuel dependency that is deeply embedded in the current global economy. Realistically, we need emergency direct climate cooling to address the first crisis and a long-term binding global cap and trade emissions trading system to address the second. The Florin proposal that conditions Stratospheric Aerosol Injection (SAI) direct climate cooling on credible GHG emissions and draw down is a step in the right direction, but omits other direct climate cooling methods and effectively makes the deployment of SAI contingent on a global emissions trading system (ETS) that may not be possible before the deployment of SAI becomes necessary. Rather than conflating our two climate crises, or conditioning the solution of the first on a solution to the second, we need to address both on an emergency basis by putting all options on the table as called for in the Healthy Planet Action Coalition (HPAC) proposal. JEL Classification : Q54, Q55, Q56, Q57, Q58 [ABSTRACT FROM AUTHOR]

A record-breaking Meiyu-Baiu rainfall hit East Asia in June–July 2020. The warm Indian Ocean (IO) has been identified as a primary cause, but it cannot explain the heavy rainfall in July, a striking characteristic of the 2020 Meiyu-Baiu rainfall. A remarkable retreat of Arctic sea ice in the late spring and early summer of 2020 also promoted Meiyu-Baiu rainfall by favoring North Asian blockings and cold air outbreaks. However, its importance compared with IO warming is unclear. Our result shows that the abundant moisture supply to the 2020 Meiyu-Baiu rainfall mainly stems from anomalous meridional wind convergence, while the excessive ascending motions are due to warm advection tied to enhanced mid-troposphere westerlies. AGCM experiments are used to examine the relative importance of IO warming and Arctic sea ice anomalies. In June, IO warming is responsible for the atmospheric circulation anomalies around the Meiyu-Baiu region and accounts for ~ 75% of the Meiyu-Baiu rainfall anomalies, despite the Arctic sea-ice loss explaining most circulation anomalies over Eurasian high latitudes. In July, both IO warming and Arctic sea-ice loss are necessary for meridional convergence, enhanced westerlies, and thus the heavy rainfall over the Meiyu-Baiu region. Their effects are interdependent rather than additive. Strong IO warming is rarely observed alongside severe Arctic sea-ice loss before 2020 because of their discordant interannual variations. In the future, the combined effects of IO warming and Arctic sea-ice loss on the Meiyu-Baiu rainfall may become more pronounced as their long-term trends continue. [ABSTRACT FROM AUTHOR]

Trait-based approaches connect the traits of species to ecosystem functions to estimate the functional diversity of communities and how they may respond to environmental change. For the first time, we compiled a traits matrix across 11 traits for 28 species of Arctic ice meiofauna, including Copepoda (Subclass), Nematoda (Phylum), Acoela (Order), Rotifera (Phylum), and Cnidaria (Phylum). Over 50 years of pan-Arctic literature were manually reviewed, and trait categories were assigned to enable future trait–function connections within the threatened ice-associated ecosystem. Approximately two-thirds of the traits data were found at the genus or species level, ranging from 44% for Nematoda to 100% for Cnidaria. Ice meiofauna were shown to possess advantageous adaptations to the brine channel network within sea ice, including a majority with small body widths < 200 μm, high body flexibility, and high temperature and salinity tolerance. Diets were found to be diverse outside of the algal bloom season, with most organisms transitioning to ciliate-, omnivore-, or detritus-based diets. Eight species of the studied taxa have only been recorded within sea ice, while the rest are found in a mixture of sympagic–pelagic–benthic habitats. Twelve of the ice meiofauna species have been found with all life stages present in sea ice. Body width, temperature tolerance, and salinity tolerance were identified as traits with the largest research gaps and suffered from low-resolution taxonomic data. Overall, the compiled data show the degree to which ice meiofauna are adapted to spending all or portions of their lives within the ice. [ABSTRACT FROM AUTHOR]

In the Arctic, short‐lived summer snowstorms can provide snow cover that can increase surface reflectivity and heat capacity. Despite their potential importance, little research has been done to understand the impact of summer snowstorms on basin‐scale Arctic sea ice cover. Our observational analysis shows that a summer snowstorm event is accompanied by cyclonic ice drift, increases in surface albedo and surface air cooling that can persist for up to ∼2 weeks, dampening sea ice loss. Specifically, multiple snowstorm events in a summer, on average, results in net increase in sea ice extent of ∼0.2 × 106 km2 by early September. Experiments with a sophisticated ice‐ocean model framework indicate that the initial expansion of sea ice extent is driven by cyclonic wind‐driven ice drifts driving sea ice southwards and increasing albedo around the summer ice edge, however the thermal effects from the associated snowfall and atmospheric conditions result in a stronger overall impact on basin‐averaged sea ice extent at seasonal scales. Additional model experiments were carried out to isolate the physical processes contributing to the thermal response of Arctic sea ice to summer snowstorms. Our results show the impact of surface air cooling on sea ice extent is about 3.5 times larger than the snowfall/albedo response. However, our simulated albedo response is weaker than the observed response, likely due to the negligible difference in surface albedo between old snow and freshly fallen snow—a limiting factor in our analysis and a topic worthy of future focus. Plain Language Summary: In this study we seek to better understand the impact on summer Arctic sea ice cover from short‐lived summer snowstorms. We use satellite‐derived observational data to show that summer snowstorms in the Arctic are accompanied by surface air cooling, increases in sea ice cover, and increases in averaged Arctic Ocean surface reflectivity (sea ice is more reflective than the ocean). Using a sophisticated sea ice‐ocean model and targeted sensitivity studies, we quantify the impact of summer Arctic snowstorms on sea ice, showing that multiple summer snowstorms can substantially increase the end of summer Arctic sea ice extent. Additional model experiments suggest that surface air cooling associated with a given summer snowstorm plays a bigger role in reducing sea ice loss compared to the direct impact of snowfall and increases in reflectance. Snowstorms are also associated with anti‐clockwise winds and ice drifts that push Arctic sea ice southwards, increasing sea ice cover and surface reflectance around the ice edge but decreasing sea ice cover and reflectance within the Central Arctic. Our findings suggest that the continued warming of the Arctic and decreases in summer snowfall could play an important role in accelerating Arctic sea ice decline in future decades. Key Points: Summer Arctic snowstorms result in positive sea ice extent and albedo anomalies due to associated changes in ice drift and air coolingSea ice‐ocean model experiments suggest that snowstorm–induced surface air cooling is the dominant driver of reduced seasonal sea ice lossReductions in the prevalence and severity of summer Arctic snowstorms could accelerate future Arctic sea ice loss [ABSTRACT FROM AUTHOR]

Melting and evolution of Arctic Ocean sea ice in summer are one of the main factors affecting sea ice albedo and climate. At present, remote sensing images of sea ice are mainly acquired with the help of payload platforms, such as aircraft or satellites. The above methods have a large measurement scale and a short period. Sea ice research also requires suitable equipment for fine-scale, visualized long-term tracking observations of target sea ice regions. This article investigates the feasibility of measuring interannual variability in sea ice morphology in the Arctic sea ice environment using a low-power gimbal camera automated by satellite communications. Year-round observational data from the arctic field were used. The sea ice images were defogged and edge-enhanced by a newly developed algorithm with the help of human visual enhancement principles. During the 11th Chinese National Arctic Science Expedition, the system was placed on perennial ice in the northern Beaufort Basin to monitor sea ice appearance changes and the external state of the ice mass balance buoy. The system fuses visually enhanced sea ice images with ice balance buoy data from the same ice floe in a multi-scale environmental sequence to obtain sea ice observations with image sequences. The data complement the need for long-duration, fixed-ice, and short-visual-range observations in sea-ice studies. The equipment design methods and image processing algorithms involved in the experimental process have accumulated engineering experience for this application area. The research methodology provides a new framework for long-term, and visualized sea ice observation methods.

The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14–18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°–46°) were superior to low incidence angles (21°–27°) due to the overall higher accuracies.

High air pollutions of PM2.5 concentrations have become a serious environmental problem in China during recent decades, causing significant influences on urban air quality and human health. In the study, we investigate the variations of the December PM2.5 in Eastern China and the possible causes during 2000–2020. The empirical orthogonal function (EOF) analysis is employed to reveal the dominant patterns of PM2.5 variability in Eastern China. The EOF1 shows a consistent variability in the whole of the Eastern China, which reflects a consistent emission pattern in Eastern China in past two decades. The EOF2 exhibits a North-South dipole pattern, which is closely tied to the changes of atmospheric circulations. The increase of PM2.5 in the North Eastern China is mainly related to the decrease of wind speed, the decrease of boundary layer height and the increase of inversion temperature, while the decrease of PM2.5 in the South Eastern China is affected by the increase of local precipitation. Two atmospheric wave trains are identified that affect the dipole distribution of PM2.5 in Eastern China. The southern one is affected by ENSO, and the northern one is jointly affected by ENSO, sea surface temperature of Labrador Sea and sea ice concentration near Kara Sea. Finally, we reconstructed a comprehensive atmospheric external forcing index based on these factors. We find that the comprehensive index can well reproduce the North-South dipole distribution of PM2.5 in Eastern China, indicating the plausible effects of the atmospheric external forcings and the prediction potential for the variations of PM2.5 in Eastern China.

Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model's quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions. [ABSTRACT FROM AUTHOR]

In September 2007, Arctic sea ice plummeted to a shocking record minimum at the time. The amount of ice lost that summer was equal to that lost over the previous 25 years. As Arctic sea ice escapes scientists' predictions, scholars in the social sciences and humanities have critically interrogated "nature"/"culture" divides that treat the time of nature as unchanging and distinct from human beings. This essay examines what concept of time emerges through Arctic sea ice as an analytic lens. By this, I mean scientific knowledge of sea ice and the conceptual possibilities for thinking ice temporalities and environmental time-reckoning that it opens up. Attending to these possibilities suggests different kinds of "clocks" to help reckon the time of environmental changes in the form of (1) climate anomalies (e.g., deviations in ice thickness), which offer a different way of telling environmental time that attends to the physical specificity of substances; and (2) the Arctic Oscillation, a semi-periodic world weather pattern that emerges from the thick of relationships among ice, atmosphere, ocean, and now humans, generating a collective planetary time. Finally, I argue that the relational human–nonhuman production of planetary time shifts the focus in social studies of time from collective time-reckoning, which assumes entities have a socioculturally determined concept of time, toward temporal coordination as a less anthropocentric mode of ordering shared realities. Coordination decenters "the Human" as an epistemic ordering principle and enlarges ordering to include a diversity of nonhuman ways of being. Through temporal coordination, environmental prediction would be the ordering of a collective reality that a multiplicity of human and nonhuman ways of being make together rather than the search for a more precise clock, or development of better technoscientific means to capture nonhuman temporalities external to human beings. [ABSTRACT FROM AUTHOR]

In winter 2020/2021, several episodes of cold air outbreaks have wreaked havoc across much of China, resulting in exceptionally low temperatures over northern‐to‐southeastern China. Our analysis shows that the anomalous circulation leading to the cold wave in China was characterized as: (a) a strengthening of the Siberian high, (b) a deepening of the East Asian trough, and (c) a weakening of the stratospheric polar vortex. Prior to the winter, in summer and autumn, the largest sea ice loss along the Siberian coast was observed. We therefore address the question whether the substantial sea ice loss has contributed to the cold wave. First, applying singular value decomposition (SVD) analysis, we find strong coherence between the autumn Arctic sea ice coverage and temperatures over the central‐to‐east Asian region with the sea ice component (SVD1_SIC) reaching a record high in 2020 and displaying a decadal shift around 2005. Associated with both the interannual and decadal variations of the SVD1_SIC, there are similar circulation anomalies as described in (a), (b) and (c) above. Second, applying self‐organizing maps, wintertime daily air temperatures are clustered into nine nodes with Node‐1 mostly resembling "warm Arctic, cold Eurasia" pattern. The frequency of Node‐1 shows a similar decadal shift as observed in SVD_SIC1 above, with a correlation of about 0.56. Associated with Node‐1, the circulation anomalies are consistent with features highlighted in (a), (b) and (c) above. Thus, taking all the evidence into account, we conclude that the substantial sea ice loss along the Siberian coast contributed significantly to the 2020/2021 winter cold wave in China. The mechanism behind the contribution in this study highlights that the Arctic sea ice loss is favourable for certain circulation patterns on climate timescale, reinforcing disruptive chain of reactions on weather timescale, and leading to severe cold events. [ABSTRACT FROM AUTHOR]