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This study explores the combined effects of autumn snow cover anomalies on the Tibetan Plateau (TP) and Northeast Asia (NEA) on winter Eurasian temperature using observational data for the period 1972–2021 and a linear baroclinic model (LBM). Distinctive wintertime temperature patterns are found across the Eurasian continent corresponding to increased autumn snow cover in NEA when TP experiences normal, above‐normal, or below‐normal snow cover condition. In the scenario with an anomalous increase in autumn snow cover in NEA in combination with normal snow cover condition in TP, the overall winter temperature anomalies tend to be generally weak across the Eurasian continent. In years when autumn TP snow cover is above normal, the spatial distribution of winter temperature anomalies over the Eurasian continent associated with more NEA snow cover exhibits a 'cold Arctic‐warm Eurasia' (CAWE) pattern. The emergence of this CAWE pattern can be attributed to the low‐pressure system induced by the intensified NEA snow cover, which is further reinforced by the atmospheric wave train generated by negative North Atlantic sea surface temperature anomalies (SSTAs) in winter. This low‐pressure system amplifies the polar vortex and causes cooling in polar regions. Simultaneously, southeasterly winds along the southwestern flank of the North Pacific high‐pressure system contribute to warming in the mid‐latitude regions of Eurasia. While in years when autumn snow cover in TP is less than normal, more snow cover over NEA is accompanied by a 'warm Arctic‐cold Eurasia' (WACE) temperature anomaly pattern prevalent during the winter season. The decrease in autumn Barents‐Kara Sea ice is accompanied by a circulation pattern akin to the negative phase of the Arctic Oscillation during winter, favouring the southward intrusion of cold air, thus contributing to this WACE temperature anomaly pattern. Further analysis reveals that the impact of snow cover on the WACE temperature pattern is, for the most part, independent of the Arctic sea ice. [ABSTRACT FROM AUTHOR]

ABSTRACT Summer snow plays an essential role in Arctic hydrology and in maintaining mass and energy balance of sea ice. However, there are great challenges in retrieving long‐term summer snow depths over Arctic sea ice. Here, we proposed a combined novel five‐variable long short‐term memory (hereafter CN5VLSTM) model based on brightness temperature data to yield warm‐season snow depth estimates. Then, year‐round snow depth estimates were obtained for the first time. The CN5VLSTM model and five additional snow depth methods were assessed during the warm season based on the ice mass balance buoy (IMB), Alfred Wegener Institute (AWI) snow buoy (AWI‐SB) and Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy (MOSAiC‐SB). According to the three buoy products, the accuracy of the CN5VLSTM‐derived snow depth was highest among the five snow depth estimates with RMSEs of 10.2, 16.4, and 10.1 cm, respectively. Except for in May, the Arctic snow depth showed mainly a downward trend in warm months, and a significant downward trend was found in the Central Arctic. Excluding the Barents Sea, Kara Sea and Canadian Archipelago, the average year‐round snow depth decreased in the other subregions, and a significant negative trend was observed in the East Siberian and Chukchi Seas. Snowfall was an important factor that was related to the changes in snow depth in the East Siberian and Chukchi Seas. This study can provide new insights into the evolution characteristics of summer snow depth. [ABSTRACT FROM AUTHOR]

Arctic sea-ice surface temperature (IST) is an important environmental and climatic parameter. Currently, wide-swath sea-ice surface temperature products have a spatial resolution of approximately 1000 m. The Medium Resolution Spectral Imager (MERSI-I) offers a thermal infrared channel with a wide-swath width of 2900 km and a high spatial resolution of 250 m. In this study, we developed an applicable single-channel algorithm to retrieve ISTs from MERSI-I data. The algorithm accounts for the following challenges: (1) the wide range of incidence angle; (2) the unstable snow-covered ice surface; (3) the variation in atmospheric water vapor content; and (4) the unique spectral response function of MERSI-I. We reduced the impact of using a constant emissivity on the IST retrieval accuracy by simulating the directional emissivity. Different ice surface types were used in the simulation, and we recommend the sun crust type as the most suitable for IST retrieval. We estimated the real-time water vapor content using a band ratio method from the MERSI-I near-infrared data. The results show that the retrieved IST was lower than the buoy measurements, with a mean bias and root-mean-square error (RMSE) of −1.928 K and 2.616 K. The retrieved IST is higher than the IceBridge measurements, with a mean bias and RMSE of 1.056 K and 1.760 K. Compared with the original algorithm, the developed algorithm has higher accuracy and reliability. The sensitivity analysis shows that the atmospheric water vapor content with an error of 20% may lead to an IST retrieval error of less than 1.01 K. [ABSTRACT FROM AUTHOR]

Abstract Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in the late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply the Liang-Kleeman information flow method to five different large ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the 1970-2060 period to investigate the extent to which fluctuations in winter sea-ice volume, air temperature and ocean heat transport drive changes in subsequent summer Arctic sea-ice extent. This allows us to go beyond classical correlation analyses. Results show that air temperature is the most important controlling factor of summer sea-ice extent at interannual time scale, and that winter sea-ice volume and Atlantic Ocean heat transport play a secondary role. If we replace air temperature by net shortwave and downward longwave radiations, we find that the sum of influences from both radiations is almost similar to the air temperature influence, with the longwave radiation being dominant in driving changes in summer sea-ice extent. Finally, we find that the influence of air temperature is more prominent during periods of large sea-ice reduction and that this temperature influence has overall increased since 1970.

Due to their significant influence on large‐scale atmospheric circulation and climate anomalies, the variability of Arctic sea ice and Eurasian snow cover during late autumn and their combined effects have garnered increasing attention. This study aims to investigate the physical mechanism underlying the covariation among the Barents‐Kara Seas (BKS) sea ice concentration (SIC), Eurasian snow cover extent (SCE) and the ensuing winter Eurasian surface air temperature (SAT). The statistics results of singular value decomposition suggest a significant linkage between the decreased BKS SIC, zonal "negative–positive" dipole SCE anomalies over Eurasia in November and cold Eurasian SAT in January–February (JF). Observational diagnosis analyses about the meridional moisture, heat transport and surface heat flux demonstrate that subpolar Eurasian anticyclonic circulation plays a crucial role in connecting the predominant modes of SIC and SCE. Furthermore, the BKS SIC and Eurasian SCE anomalies can jointly excite upward‐propagating planetary waves into the stratosphere, while simultaneously reducing the subpolar meridional temperature gradient. This results in westerly wind deceleration and favours the continuous planetary wave propagation. Consequently, the stratospheric polar vortex is significantly weakened, along with negative Northern Annular Mode anomalies propagating downward from the stratosphere to troposphere. Negative‐phase Arctic Oscillation anomalies correspondingly develop during JF, resulting in widespread cold anomalies over the Eurasian continent. These results are further confirmed by numerical sensitivity experiments from the Community Atmosphere Model forced by the above mentioned SIC and SCE anomalies. The empirical hindcast model analyses further suggest that the prediction skill of JF Eurasian SAT is enhanced when both the November BKS SIC and Eurasian SCE signals are considered. [ABSTRACT FROM AUTHOR]

Changes in Arctic sea ice have exerted remarkably effects on the Eurasian climates, but it is unclear whether Arctic sea ice also contributes to Northwest China's ongoing summer drought. This study investigates the influence of the interannual variability of Arctic sea ice on the June drought in Northwest China from 1979 to 2021. It reveals that the early‐autumn sea ice in the East Siberian Sea is correlated with drought conditions in June in Northwest China, with a more pronounced connection during the period of 2000/2001–2020/2021 (P2) compared to 1979/1980–1999/2000 (P1). Mitigated drought in Northwest China is associated with anomalously high sea ice concentration (SIC) in the East Siberian Sea. Further analysis suggests that the strengthened link may be due to greater SIC variability in the East Siberian Sea during P2 than P1. In P2, positive early‐autumn SIC anomaly is linked to anomalous northeasterly winds, promoting drier soil and widespread cooling in the East European Plain. This dry soil signal may persist into the ensuing spring and early summer, inducing an anticyclonic circulation anomaly over Siberia, which could facilitate the water vapor convergence in Northwest China, thereby enhancing humidity conditions in the region. The insights from this study could offer valuable information for improved prediction of droughts in Northwest China. Plain Language Summary: This study examines the impact of Arctic sea ice changes on summer droughts in Northwest China. It was found that early‐autumn sea ice in the East Siberian Sea correlates with drought conditions in Northwest China. Increased sea ice concentration appears to help mitigate drought in Northwest China. This connection was stronger between 2000/2001 and 2020/2021 compared to 1979/1980 and 1999/2000. During 2000/2001–2020/2021, more sea ice leads to drier soil and cooling in the East European Plain, which may affect the following spring and early summer, thereby increasing humidity in Northwest China. These findings could improve predictions of drought conditions in Northwest China. Key Points: Study links East Siberian Sea ice to Northwest (NW) China's summer drought, especially after 2000Increased sea ice is linked to soil dryness and cooling in East European Plain, possibly subsequently enhance humidity in NW ChinaInsights on Arctic ice variability help predict NW China's droughts, aiding response strategies [ABSTRACT FROM AUTHOR]

Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in the late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply the Liang-Kleeman information flow method to five different large ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the 1970-2060 period to investigate the extent to which fluctuations in winter sea-ice volume, air temperature and ocean heat transport drive changes in subsequent summer Arctic sea-ice extent. This allows us to go beyond classical correlation analyses. Results show that air temperature is the most important controlling factor of summer sea-ice extent at interannual time scale, and that winter sea-ice volume and Atlantic Ocean heat transport play a secondary role. If we replace air temperature by net shortwave and downward longwave radiations, we find that the sum of influences from both radiations is almost similar to the air temperature influence, with the longwave radiation being dominant in driving changes in summer sea-ice extent. Finally, we find that the influence of air temperature is more prominent during periods of large sea-ice reduction and that this temperature influence has overall increased since 1970. [ABSTRACT FROM AUTHOR]

Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment. One of the key research aspects is the investigation of the temperature, salinity, and density parameters of sea ice to obtain essential insights. During the 11th Chinese National Arctic Research Expedition, acoustic velocity was measured on an ice core at a short-term ice station, however, temperature, salinity, and density were not measured. In the present work, we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature, salinity, and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice. We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers. The results indicate that within the normalized depth range of 0.43–0.94, the ranges for temperature, salinity, and density are −0.48-−0.29 °C, 1.63–3.35, and 793.1–904.1 kgm−3, respectively. [ABSTRACT FROM AUTHOR]

In this study, the performance of 24 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the dynamic processes of Arctic sea ice concentration (SIC)‐ and El Niño‐Southern Oscillation (ENSO)‐ forced teleconnection during winter is subjectively and objectively evaluated. The Arctic SIC‐forced teleconnection is associated with a warm Arctic‐cold Eurasian pattern of surface temperature (T2m), a low Arctic‐high Eurasian pattern of sea level pressure (SLP), and a southeastward propagating wave‐train originating from Arctic in the upper troposphere. The ENSO‐forced teleconnection is associated with a poleward propagating wave‐train originating from tropical Pacific in the upper troposphere, a low North Pacific‐high Arctic pattern of SLP, and a cold North Pacific‐warm Greenland pattern of T2m. The metrics of Taylor skill scores and Distance between indices of simulation and observation (DISO) are used to objectively and quantitatively evaluate the performance of models. The results of subjective and objective evaluation are essentially consistent. The CanESM5, MPI‐ESM1‐2‐HR, EC‐Earth3, and MRI‐ESM2‐0 models have the best performance in simulating the Arctic SIC‐forced teleconnection. The CESM2, ACCESS‐CM2, NESM3, NorESM2‐MM, CAS‐ESM2‐0, MRI‐ESM2‐0 models have the best performance in simulating the ENSO‐forced teleconnection. The two best‐performing multi‐model ensembles well reproduce the dynamic processes of the Arctic SIC‐ and ENSO‐ forced teleconnection. The diversity of model performance is attributed to the different skills of different models in simulating the interannual variability of Arctic SIC, the anomalous deep warm high over the Barents‐Kara Seas, the interannual variability of tropical Pacific SSTs, and the wave number of poleward propagating Rossby waves. Plain Language Summary: The connection between Arctic and tropical climates has an important influence on the climate in Northern Hemisphere. The Arctic sea ice‐driven teleconnection may induce increased cold surges toward the low latitude regions of East Asia, while the El Niño‐Southern Oscillation (ENSO)‐driven teleconnection may induce increased temperatures over northern North America and Greenland. The Coupled Model Intercomparison Project Phase 6 (CMIP6) models consists of state‐of‐the‐art numerical models that are widely used in climate simulation and prediction. Hence, it is important to understand the performance of these models in simulating the dynamic processes of Arctic‐tropical climate connection and the potential reasons. In this study, the dynamic processes in ocean‐atmosphere associated with the Arctic sea ice‐ and ENSO‐ driven teleconnection are first analyzed using observed/re‐analysis data. The performance of 24 CMIP6 models in simulating the dynamic processes of Arctic sea ice‐ and ENSO‐ driven teleconnection is then subjectively and objectively evaluated. The results indicate a diversity of performance in these models. This diversity are mainly caused by the different skills of models in simulating the interannual variability of SIC and ENSO as well as the associated atmospheric circulation anomalies. Key Points: The performance of CMIP6 models has a diversity in simulating the dynamic processes of Arctic‐tropical climate connectionBest‐performing multi‐model ensembles with good skill in simulating dynamic processes of Arctic‐tropical climate connection are selectedThe diversity in performance of models are affected by the skill of models in simulating the interannual variability sea ice and SSTs [ABSTRACT FROM AUTHOR]

Arctic sea ice has undergone a significant decline in the Barents–Kara Sea (BKS) since the late 1990s. Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes (mainly referring to La Niña events). The occurrence of multi-year La Niña (MYLA) events has increased significantly in recent decades, and their impact on Arctic sea ice needs to be further explored. In this study, we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña (OTLA) years. The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years. This is because MYLA events tend to be accompanied by a warm Arctic–cold continent pattern with a barotropic high pressure blocked over the Urals region. Consequently, more frequent northward atmospheric rivers intrude into the BKS, intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice. However, in the early winter of OTLA years, a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere, which obstructs the atmospheric rivers to the south of Iceland. We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes. Considering the rapid climate changes in the past, more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades. [ABSTRACT FROM AUTHOR]

Abstract Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model’s skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change.

Based on the CICE sea ice model and the PDAF parallel data assimilation framework, this paper uses the local error subspace transform Kalman filter method (LESTKF) to assimilate the sea ice concentration, sea ice thickness and sea ice freeboard data into the model, and designs experiments to study the improvement of multi-parameter assimilation on the simulation of Arctic sea ice concentration and range. The results show that data assimilation has a good improvement effect on the simulation of Arctic sea ice concentration and range. The average deviation, root mean square error and mean absolute error of the assimilation experiment are significantly reduced compared with the control experiment. The assimilation experiment improves the simulation of sea ice concentration and range most obviously in summer. Multi-parameter assimilation can improve the prediction accuracy and reliability of Arctic sea ice change.

The shrinking Arctic sea-ice area (SIA) in recent decades is a striking manifestation of the ongoing climate change. Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration (SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period 1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used HadISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in HadISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km2 in March and 1.5 mln km2 in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century. [ABSTRACT FROM AUTHOR]

The state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models. Plain Language Summary: Snowpack, one of the most essential parts of the cryosphere, affects the energy exchange between sea ice and the atmosphere, thus contributing to sea ice change and the global climate system. Atmospheric rivers (ARs) are long and narrow transient corridors of water vapor. Because ARs transmit more than 90% of water vapor to high latitudes, increasing scientific and societal interest has focused on the impact of ARs on sea ice. However, little attention has been given to the relationship between ARs and snow depth. Therefore, reliable ARs and snow depths from 2002 to 2022 were obtained. We found that increased ARs drive autumn‐winter snow depth increases and spring‐summer snow depth decreases based on ARs and snow depth records. ARs cause changes in snow depth by altering physical processes related to ARs (e.g., 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and the cloud radiative effect (CRE)). Key Points: A novel snow depth record over Arctic sea ice was generated using the triple collocation methodMore frequent atmospheric rivers (ARs) lead to autumn‐winter snow depth increases and spring‐summer snow depth reductionsThe 2 m air temperature, rainfall, snowfall, and cloud radiative effect (CRE) are important in the AR‐induced changes in snow depth [ABSTRACT FROM AUTHOR]

Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model's skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the impact of Arctic sea-ice loss on mid-latitude weather and climate in the Northern Hemisphere has attracted widespread attention. Here, using both observations and model simulations, the influence of early-winter Barents-Kara Seas (BKS) sea-ice loss on the winter surface air temperatures in China and the underlying mechanisms are investigated. The results showed that BKS sea-ice loss could induce cooling anomalies over Northeast China, North China, Central China, and Northwest China during winter, with significant increases in both the number of extreme cold days and the intensity of extreme low temperatures over these regions. Furthermore, the respective roles of tropospheric pathway and stratospheric pathway are investigated. For the tropospheric pathway, an eastward propagating wave train stimulated by sea-ice loss induces negative geopotential height anomalies over the western Pacific, favorable for the transport of cold airmass into China. In terms of the stratospheric pathway, sea-ice loss leads to the extension of stratospheric polar vortex edge toward North China by modulating upward propagating planetary waves, further enhancing the tropospheric cooling there. The quantitative analysis indicates that the impact of stratospheric pathway on surface cooling over Northeast China associated with BKS sea-ice loss is more important than that over other regions in China. These results could improve our understanding of the potential linkage between Arctic sea-ice loss and winter weather extremes over China. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between sea ice concentration (SIC) in the Arctic Ocean and the Boreal Summer Intraseasonal Oscillation (BSISO) from 1991 to 2020 and its underlying mechanism. A significantly positive (negative) correlation was found between the frequency of phase 7 (3) of BSISO1 (30–60 d) and the preceding winter SIC, which is located the north of the East Siberian-Beaufort Sea (ESBS). Compared with low-SIC years, the conditions including northeasterly vertical wind shear, an enhanced ascending branch of the anomalous Walker circulation, an eastward water vapour transport channel, and an increased humidity gradient induce active convection over the Philippine Sea in high-SIC years, which benefits (hinders) to phase 7 (3) of BSISO1. The positive SIC anomaly during the transition from winter to spring influences local temperature and pressure through anomalous local sensible heat flux. This anomaly induces wave activity flux from the ESBS, which converges over the Bering Sea, enhancing the Aleutian Low (AL). Subsequently, the AL triggers an anomalous subtropical anticyclone through wave-mean flow interaction in the North Pacific. Due to southerly wind stress and increased sea surface heat flux, positive sea surface temperature anomalies near Japan persist in the summer, heating the lower troposphere and increasing baroclinicity. Significant positive geopotential heights and anticyclone anomalies occur over Japan, accompanied by a negative vorticity anomaly. The enhanced ascending motion over the Philippine Sea, facilitated by Ekman pumping, favours convection and influences the frequency of phases 7 and 3. [ABSTRACT FROM AUTHOR]

Retrieval of Thin-Ice Thickness (TIT) using thermodynamic modeling is sensitive to the parameterization of the independent variables (coded in the model) and the uncertainty of the measured input variables. This article examines the deviation of the classical model's TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness. Moreover, it estimates the uncertainty of the output in response to the uncertainties of the input variables. The parameterized independent variables include atmospheric longwave emissivity, air density, specific heat of air, latent heat of ice, conductivity of ice, snow depth, and snow conductivity. Measured input parameters include air temperature, ice surface temperature, and wind speed. Among the independent variables, the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth, followed ice conductivity. The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity, atmospheric emissivity, and snow conductivity and depth. The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data. From in situ measurements, the uncertainties of the measured air temperature and surface temperature are found to be high. The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error. The results show that the overall uncertainty of TIT to air temperature, surface temperature, and wind speed uncertainty is around 0.09 m, 0.049 m, and −0.005 m, respectively. [ABSTRACT FROM AUTHOR]

Freezing temperature parameterization significantly impacts the heat balance at sea‐ice bottom and, consequently, the simulated sea‐ice thickness. Here, the single‐column model ICEPACK was used to investigate the impact of the freezing temperature parameterization on the simulated sea‐ice thermodynamic growth during the MOSAiC expedition from October 2019 to September 2020. It is shown that large model errors exist with the standard parameterization and that different formulations for calculating the freezing temperature impact the simulated sea‐ice thickness significantly. Considering the winter mixed layer temperature, a modified parameterization of the freezing point temperature based on Mushy scheme was developed. The mean absolute error (ratio) of simulating sea‐ice thickness for all buoys reduces from 7.4 cm (4.9%) with the "Millero" scheme, which performs the best among the existing schemes in the ICEPACK model, to 4.2 cm (2.9%) with the new developed scheme. Plain Language Summary: The heat transferred from the ocean to the sea‐ice influences the growth and melting of the sea‐ice. Freezing temperature is an essential parameter for calculating the heat transfer. Nevertheless, few studies have attempted to evaluate the impact of different freezing temperature parameterizations on the simulated sea‐ice thermodynamic growth. This study uses observed atmosphere and ocean data to force a single‐column model. Using different methods to calculate the freezing temperature significantly impacts the simulated sea‐ice thickness. After a series of testing and comparisons, we have developed a modified parameterization of freezing temperature that significantly reduces the simulation deviation from the observations. Key Points: Different parameterizations of the freezing temperature significantly influence the simulated sea‐ice thicknessA modified‐Mushy parameterization method is developed for the freezing temperature, significantly improving ice thickness simulation [ABSTRACT FROM AUTHOR]

Arctic amplification caused by the rapid loss of Arctic sea ice has emerged as a crucial factor in affecting global weather and climate in recent decades. However, it remains unknown whether this rapid loss has exerted a specific impact on tropical cyclone (TC) activity over the eastern North Pacific (ENP). Here, we examine the influence of springtime (March–May) sea ice concentration (SIC) in the Barents Sea (SIC-BS), a key region for Arctic SIC changes, on TC genesis frequency over the ENP during the TC season (June–October) during 1970–2021. Results show that the reduced SIC-BS was favorable for more TC geneses over the ENP in terms of interannual variability. Further analyses based on dynamical diagnosis demonstrate that the rapid loss of SIC-BS leads to an upward transport of turbulent heat fluxes, facilitating the propagation of the Rossby wave train from the Barents Sea to the ENP via the western United States. This process subsequently leads to increase in upper-level divergence, mid-level upward motion, and lower-level vorticity, thereby accounting for the formation of more TCs over the ENP. This mechanism is further substantiated by the Coupled Model Intercomparison Project phase 6 (CMIP6). These results not only establish a possible connection between Arctic sea ice and tropical climate, but also hold important implications for understanding future changes in TC activity over the ENP. [ABSTRACT FROM AUTHOR]

Using the observed precipitation data of 154 meteorological stations in East of Northwest China， NCEP/NCAR atmosphere reanalysis and Arctic sea ice data from 1961 to 2020， the possible impacts and mechanisms of the key area Arctic sea ice on the main distribution mode of precipitation in July during the main flood season in East of Northwest China was analyzed using SVD and other climatic statistic methods.The results show that there are two mainly sea ice modes influencing the distribution pattern of precipitation anomaly in July in East of Northwest China.One is that when the sea ice density in the Barents Sea and the Kuril Islands is relatively small （large）， and that in Davis Strait is relatively large （small）， wave trains propagating southeastward from the Davis Strait and southwestward from the Sea of Okhotsk are stimulated， resulting in the anomaly field of "high in the west and low in the east" （"low in the west and high in the east"） at the 500 hPa geopotential height anomaly field over the East of Northwest China， which leads to the regional precipitation anomaly exhibiting a "consistent less （more）" pattern.The other is that when the sea ice density in the eastern part of the Beaufort Sea is relatively small， the wave train from the Caspian Sea to the Okhotsk Sea is stimulated and matched with the positive geopotential height anomaly over South China， making the cold air path eastward and southward， as well as the subtropical high stronger， which together lead to the precipation characterized by "less in the north and more in the south"， on the contrary， the pattern of precipitation anomaly is reversed.The prediction model， which is established with sea ice in key areas as the prediction factors derived by “SVD projection method”， has certain prediction ability for grasping the precipitation anomaly trend and the spatial distribution pattern of the main flood season in July in East of Northwest China， especially for the "regional consistent less type" and "north more and south less type".

There are significant differences in snow depth predictions among different earth system models, and many models underestimate snow depth, restricting their application. Here, major factors influencing snow depth changes in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were identified and evaluated. Based on satellite‐derived snow depth and CMIP6 data, an ensemble learning model based on multiple deep learning methods (hereafter referred to as the Multi‐DL model) was developed to predict future snow depth. According to satellite observations and two Operation IceBridge products, the Multi‐DL model yielded root mean square errors of 7.48, 6.20, and 6.17 cm. A continuous decrease in snow depth was observed from 2002 to 2100, and the rate of decrease accelerated with increasing emissions. Under the highest emission scenario, the first snow‐free year occurred in 2047, within the same decade as the first ice‐free year (2056). The predicted warm season snow depth was sensitive to sea ice velocity, sea ice concentration (siconc), precipitation, sea surface temperature (tos) and albedo, while the predicted cold season snow depth was sensitive to tos, air temperature, and siconc. The above parameters introduce some snow depth uncertainty. This method provides new ideas for predicting snow depth, and the generated snow depth records can provide data support for formulating Arctic‐related policies. Plain Language Summary: Snow depth, a vital parameter of snowpack atop sea ice, is essential in the cryosphere. The rapid changes in snow depth on sea ice are attracting increasing amounts of attention to the changes in future snow depth. Multiple models have been used to predict a continuous decrease in snow depth. However, there are significant differences in snow depth predictions among different earth system models, and many models underestimate snow depth. Here, we propose a snow depth prediction model (i.e., the Multi‐DL model) based on multiple deep learning methods. The established model was evaluated using satellite‐derived snow depth data and two Operation IceBridge products. The results indicated that the model performed well in snow depth simulations. By using the Multi‐DL model and simulated data under four Coupled Model Intercomparison Project Phase 6 emission scenarios, snow depth was generated for four emission scenarios from 2015 to 2100. The results indicated that snow depth showed an accelerated decreasing trend under high‐emission scenarios, while the decrease in snow depth remained stable or was alleviated under low‐emission scenarios. Key Points: A novel ensemble learning model is developed for predicting snow depth over sea iceAn accelerated decline in snow depth is observed under the SSP3‐370 and SSP5‐585 scenariosWarm season snow depths are sensitive to sea ice velocity, sea ice concentration, precipitation, sea surface temperature, and albedo [ABSTRACT FROM AUTHOR]

Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite observations between 2015 and 2020, these matters are delved into this study. A discernible overall growth trend in Arctic traffic occupancy occurs from 2015 to 2020 during summer and autumn. Excluding passenger ships, the traffic occupancy trend for each ship type closely parallels that for all ships. Variations in traffic occupancy along the Northeast Passage dominate that in the entire Arctic. As sea ice diminishes, both Arctic traffic occupancy and its variability noticeably increase. Further examination of the relationship between shipping activities and ice conditions reveals that increased traffic occupancy corresponds significantly to diminishing sea ice extent, and the constraint imposed by sea ice on Arctic traffic occupancy weakens, while the 6-year AIS data could lead to uncertainties. In summary, as the Arctic sea ice declines continuously, not only sea ice but also additional social, military, and environmental factors constraining marine activities should be considered in the future operation of Arctic shipping. [ABSTRACT FROM AUTHOR]

The impacts of Arctic sea ice on climate in middle and high latitudes have been extensively studied. However, its effects on climate in low latitudes, particularly on summer monsoon rainfall in the South China Sea (SCS), have received limited attention. Thus, this study investigates the connection between the Arctic sea ice concentration (SIC) anomaly and the early summer monsoon rainfall (ESMR) in the SCS and its underlying physical mechanism. The results reveal a significant positive correlation between the Barents Sea (BS) SIC in May and the ESMR in the SCS. When there is more (less) SIC in the Barents Sea (BS) during May, this results in a positive (negative) anomaly of the local turbulent heat flux, which lasts until June. This, in turn, excites an upward (downward) air motion anomaly in the vicinity of the BS, causing a corresponding downward (upward) motion anomaly over the Black Sea. Consequently, this triggers a wave train similar to the Eurasian (SEU) teleconnection, propagating eastward towards East Asia. The SEU further leads to an (a) upward (downward) motion anomaly and weakens (strengthens) the western Pacific subtropical high (WPSH) over the SCS, which is accompanied by a southwest adequate (scarce) water vapor anomaly transporting from the Indian Ocean, resulting in more (less) precipitation in the SCS. Furthermore, the response of ESMR in the SCS to the SIC in the BS is further verified by using the Community Atmosphere Model version 5.3 (CAM5.3). This study introduces novel precursor factors that influence the South China Sea summer monsoon (SCSSM), presenting a new insight for climate prediction in this region, which holds significant implications. [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. [ABSTRACT FROM AUTHOR]

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The Multivariate compound heatwaves (MCHWs) present a significant risk to human health and ecological diversity, which attract widespread attention from researchers and society. The intensity and variability of summer MCHWs over Western Europe (WE) (MCHWs_WE) have substantially increased over the last two decades. The growing chance of such events is likely related to human activity‐induced climate change. However, the possible contributions from multiple atmospheric boundary forcing remain unclear. This study investigates the combined effects of North Atlantic sea surface temperature (SST), Tibetan Plateau (TP) snow cover (SC), and Arctic sea ice (SIC) on the variability of extreme summer MCHWs_WE. Observational analysis shows that an intensified anticyclonic system prevailing over WE, one of the centers of an atmospheric wave train dominating the North Atlantic‐Europe sector and persists from late spring to summer, is the essential system contributing to the extreme MCHWs_WE. Spring North Atlantic SST anomalies in the form of a dipole pattern persist from spring to summer and contribute to the summer extreme MCHWs_WE by exciting a downward propagating Rossby wave train pattern. Additionally, excessive late spring SC over the western TP and SIC over the Arctic, which are stimulated by the North Atlantic anomalous SST‐related wave train, can intensify the MCHWs_WE‐related anticyclonic system through vertical circulation and wave energy transport. The study further quantifies the relative contributions of the aforementioned factors. The findings of this study could potentially offer valuable insights for improving the prediction skill of summer extreme MCHWs_WE. Plain Language Summary: Western Europe (WE) has faced more and more multivariate compound heatwaves (MCHWs) during summer in recent decades, which pose a greater human health risk than univariate heatwaves. In this study, Our study identifies MCHWs in WE as being characterized by high temperatures and humidity, mainly influenced by a strong anticyclonic high pressure system. Furthermore, the strengthening of the MCHWs_WE‐related anticyclone is a consequence of the combined impacts of preceding North Atlantic sea surface temperature (SST), Tibetan Plateau (TP) snow cover (SC), and Arctic sea ice (SIC). Spring positive dipole pattern of North Atlantic SST anomalies, exhibiting persistence from spring to summer, excite downward propagating Rossby waves. There wave trains from the Atlantic to Eurasia serve as a background field that enhances the linkage between SIC, TPSC, and the MCHWs_WE‐related anticyclonic system. These factors ultimately contribute to the strengthening of anticyclones over West Eurasia (WE) through vertical circulation and wave energy transport, which jointly impact MCHWs_WE. Key Points: Multivariate Compound heatwaves in Western Europe (MCHWs_WE) become more frequent in recent decades, which are linked to intensified anticyclonic systemThe development of the anticyclone over WE is associated with the dipole pattern of North Atlantic sea surface temperature, excess Tibetan Plateau snow cover and Arctic sea iceCombined effects of multiple forcing factors can account for approximately 40% of the variability in summer MCHWs_WE [ABSTRACT FROM AUTHOR]

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change. The rapid changes in Arctic sea ice have been widely concerned. However, the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series. In this study, the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data. The results indicated that Arctic sea ice was declining at a remarkably high rate of −5.4 × 104 km2/a in sea ice area (SIA) and −2.2 cm/a in sea ice thickness (SIT) during 1979 to 2020, and the reduction of SIA and SIT was the largest in summer and the smallest in winter. Spatially, compared with other sub-regions, SIA showed a sharper declining trend in the Barents Sea, Kara Sea, and East Siberian Sea, while SIT presented a larger downward trend in the northern Canadian Archipelago, northern Greenland, and the East Siberian Sea. Regarding to the seasonal trend of sea ice on sub-region scale, the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons, especially in summer and winter, i.e., the sub-regions linked to the open ocean exhibited a higher decline rate in winter; however, the other sub-regions blocked by the coastlines presented a greater decline rate in summer. For SIT, the sub-regions such as the Beaufort Sea, East Siberian Sea, Chukchi Sea, Central Arctic, and Canadian Archipelago always showed a higher downward rate in all seasons. Furthermore, a striking freeze-thaw asymmetry of Arctic sea ice was also detected. Comparing sea ice changes in different dimensions, sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension. The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period, and the rate of sea ice melting was 0.3 × 104 km2/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions, respectively. Moreover, there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004, respectively, in the horizontal/vertical dimension. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop an improved thin sea ice thickness (SIT) retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data. This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort, Chukchi, East Siberian, Laptev and Kara seas and utilized the microwave polarization ratio (PR) at incidence angle of 40°. The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact, reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature. The relationship between the SIT and PR was found to be almost stable across the five selected regions. The SIT retrievals were then compared to other two existing algorithms (i.e., UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen) and validated against independent SIT data obtained from moored upward looking sonars (ULS) and airborne electromagnetic (EM) induction sensors. The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error (RMSE) being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data. The proposed algorithm can be used for thin sea ice thickness (<1.0 m) estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical. [ABSTRACT FROM AUTHOR]

During 2020, the Arctic is marked by extremely low sea ice coverage and hot climate. September Sea Ice Extent (SIE) was about 2.3 million km2 below the 1979–2014 mean and the 2nd lowest on the 1979–2020 record, while regional summer (June–August, JJA) mean 2 m air temperature (TAS) was about 1.3 °C above the 1979–2014 mean and was the hottest on record at the time. Locally, September Sea Ice Concentration (SIC) was approximately 70% lower and JJA TAS can be as much as 6.0 °C higher than the 1979–2014 mean. Although the proximate cause for the extreme event was the continuously favorable atmospheric circulation patterns, wind conditions and ice-albedo feedback, the main objective of this paper is probabilistic extreme event attribution studies to assess the anthropogenic influence. Based on the CMIP6 multi-model ensemble products, modeled long-term trends of Arctic sea ice and TAS are consistent with observed trends when including anthropogenic forcing or greenhouse gas (GHG) forcing, while cannot exhibit observed trends with only aerosol or natural forcing. Further analysis reveals that human influence including GHG forcing has substantially increased the probability of occurrence of the 2020-like extreme events, which are rare in aerosol-only or natural-only forcing. The frequencies of 2020-like low SIE increase by 19 times with all forcing and 16 times with GHG forcing than with natural forcing. Future climate simulations under different Shared Socioeconomic Pathway (SSP) scenarios of SSP126, SSP245 and SSP585 show that the 2020-like extreme event that is currently considered rare is projected to become the norm and almost occur 1-in-1 year beyond 2041–2060. The probabilities will be approximately in the range of 0.84–1.00 for SIE and 0.76–0.99 for TAS from low emission of SSP126 to high emission of SSP585.

The Tibetan Plateau (TP) and the Arctic are the most sensitive regions to global climate change. However, the interdecadal varibility of winter extreme precipitation over the TP and its linkage with Arctic sea ice are still unclear. In this study, the characteristics and mechisnems of the TP extreme precipitation (TPEP) influenced by Arctic sea ice on interdecadal timescale are studied based on the daily precipitation, monthly sea ice concentration and ERA5 reanalysis data from 1980 to 2018. We found that the dominant mode of the TPEP in winter mostly exhibits a uniform spatial variation on the interdecadal timescale, with an opposite weak variation in the southeastern TP, and the Arctic sea ice concentration (SIC) before 2002 are larger than that after 2003. The interdecadal variation of TPEP is affected by two teleconnection wave trains regulated by the Barents and Kara Sea ice. In the light ice years, a remarkable positive geopotential height (HGT) anomaly appears over the Barents‒Kara Sea and a remarkable negative HGT anomaly is located over the Lake Baikal. Two wave trains originating over the Barents‒Kara Sea can be observed. The southern branch forms a wave train through the North Atlantic along the subtropical westerly jet stream, showing a ‘+ − + − +’ pattern of HGT anomalies from Arctic to the TP. Negative HGT anomaly controls the western TP, which creates dynamic and water vapor conditions for the TPEP. The northern branch forms a wave train through the Lake Baikal and the southeast of the TP, showing a ‘+ − +’ HGT anomaly distribution. Positive HGT anomaly controls the southeastern TP, which is not conducive to precipitation in the region. When the SIC in the Barents‒Kara Sea increases, the situation is opposite. The above analysis also reveals the reason for the difference in the east‒west distribution of the TPEP.

Sea ice is a central part of the Arctic climate system, and its changes have a significant impact on the Earth's climate. Yet, its state, especially in summer, is not fully understood and correctly predicted in dynamical forecast systems. In this study, the seasonal prediction skill of Arctic sea ice is investigated in a high‐resolution dynamical forecast system, the China Meteorological Administration Climate Prediction System (CMA‐CPSv3). A 7‐month‐long retrospective forecast is made every other month from 2001 to 2021. Employing the anomaly correlation coefficient as the metric of the prediction skill, we show that CMA‐CPSv3 can predict regional Arctic sea ice extent and sea ice thickness up to 7 lead months. The Bering Sea exhibits the highest prediction skill among the 14 Arctic subregions. CMA‐CPSv3 outperforms the anomaly persistence forecast in the Bering Sea, Sea of Okhotsk, Laptev Sea, and East Siberian Sea. The sources of the sea ice prediction skill partly come from the good performance of upper ocean temperature in CMA‐CPSv3. This holds true not only for winter sea ice in the Arctic marginal seas but also for summer sea ice in several Arctic central seas. Furthermore, CMA‐CPSv3 exhibits a strong relationship between the variability of sea ice and surface heat fluxes. This underscores the importance of enhancing the representation of air‐sea heat exchanges in dynamical forecast systems to improve the prediction skill of sea ice. Plain Language Summary: The reduction of Arctic sea ice has a significant impact on the climate and ecosystems, and accurately predicting Arctic sea ice is of broad interest. In this work, we investigate the seasonal prediction skill of sea ice in a high‐resolution climate model. Using the anomaly correlation coefficient as the skill metric, we find that the prediction skill of sea ice is good up to 7 months and varies by region and target month. Notably, the Bering Sea shows the highest prediction accuracy among the 14 Arctic subregions. Then, we explore the sources of sea ice prediction skill and find that the skill is closely related to the good performance of upper ocean temperature in the model. Furthermore, we show that the regional Arctic sea ice variability is significantly modulated by surface heat fluxes. These results suggest that improving the representation of air‐sea heat exchanges in climate models can enhance the prediction skill of sea ice. Our study contributes to an improved understanding and predicting of the Arctic sea ice variability. Key Points: The China Meteorological Administration Climate Prediction System (CMA‐CPSv3) is used for seasonal predictions of Arctic sea iceCMA‐CPSv3 has skill to predict regional Arctic sea ice up to 7 months and shows the highest skill in the Bering SeaGood performance of ocean subsurface temperature provides crucial sources of regional sea ice prediction skills [ABSTRACT FROM AUTHOR]

During the Arctic night, clouds regulate surface energy budgets through longwave warming alone. During fall, any increase in low‐level clouds will increase surface cloud warming and could potentially delay sea ice formation. While an increase in clouds due to fall sea ice loss has been observed, quantifying the surface warming is observationally challenging. Here, we use a new observational data set of surface cloud warming at instantaneous 330 m × 90 m spatial resolution. By instantaneously co‐locating surface cloud warming and sea ice observations in regions where sea ice varies, we find October large surface cloud warming values (>80 W m−2) are much more frequent (∼+50%) over open water than over sea ice. Notably, in November large surface cloud warming values (>80 W m−2) occur more frequently (∼+200%) over open water than over sea ice. These results suggest more surface warming caused by low‐level opaque clouds in the future as open water persists later into the fall. Plain Language Summary: Over the past 40 years, Arctic sea ice cover has decreased in all months of the year, but especially in late summer and early fall. Through their impact on energy budgets, clouds have the potential to increase or decrease sea ice decline. More low‐level clouds over open water than over sea ice during non‐summer seasons have already been observed. But quantifying their radiative effect remains challenging. Therefore, this study seeks to answer the following question: By how much late fall Arctic clouds can change surface warming in response to sea ice loss? Using cloud surface warming data at high spatio‐temporal resolution, we found that large surface cloud warming values, higher than 80 W m−2, occurs much more frequently over open water than over sea ice during October and November months. This suggests that Arctic clouds favor sea ice loss by delaying sea ice recovery. As the Arctic continues to warm up due to human activities, cloud surface warming will delay sea ice freeze‐up later into the fall and may amplify Arctic sea ice loss. Key Points: During October, large surface cloud warming with values higher than 80 W m−2 occurs ∼+50% more often over open water than over sea iceCompared to October, November large surface cloud warming (>80 W m−2) occurs even more frequently (∼+200%) over open water than over sea iceMore frequent large surface warming caused by low‐level opaque clouds occurs as open water persists later into the fall [ABSTRACT FROM AUTHOR]

The unprecedented sea ice loss in the Arctic is due to its stronger surface warming than other parts across the globe, resulting in far‐flung effects on weather and climate at different spatial and temporal scales. We investigate how Arctic Sea ice and Indian summer monsoon rainfall (ISMR) are associated at seasonal scale and when preconditioned by Arctic Oscillation (AO) phases. This study also examines the potential predictability using correlation and composite analysis. The results indicate a significant decline in sea ice extent (SIE) at a rate of 0.055 Mkm2/year (p < 0.05). The strong association between SIE and precipitation in the Indian region is confirmed by correlation values ranging from −0.6 to 0.6 (p < 0.05). The spatial patterns of seasonal SIE and precipitation association remain consistent for 1979–2021. We found that the prevailing AO phases influence the association of sea ice with ISMR precipitation. The high correlation between SIE and ISMR anomalies suggests that Arctic Sea ice could be a reliable predictor for ISMR. [ABSTRACT FROM AUTHOR]

Passive microwave (PM) observations have been used to monitor ice retreat in the Arctic. However, various PM sea ice concentration (SIC) algorithms are prone to underestimate ice fraction during summer. We evaluated the accuracy of 2002–2019 low SICs in the Central Arctic Ocean of four PM products from the University of Bremen, the National Snow and Ice Data Center (NSIDC), and the Ocean and Sea Ice Satellite Application Facility (OSI SAF), and two reanalysis data sets from the fifth generation of European ReAnalysis (ERA5) and the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2). Three reference fields were used: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) true‐color composites, (b) MODIS sea ice extent, and (c) multi‐product ensemble (MPE‐SIC) comprising the median of collocated SIC estimates. Our results indicate SICs derived from the Advanced Microwave Scanning Radiometer ‐ Earth Observing System (AMSR‐E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) high frequency channels have the best accuracy. Reanalysis SICs indicate almost identical patterns as their remote sensing inputs. The assessment shows that the Bremen (+1.06%) and NSIDC (+0.99%) SICs are higher than the median field, while the OSI‐401 (−6.65%) and OSI‐408 (−4.64%) have negative mean deviations. The mean error of MODIS‐derived SIC (−0.80%) is smaller than PM SICs. These small mean values belie wide distributions of values. The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96. Key Points: We compared Arctic sea ice concentration (SIC) using passive microwave (PM), optical, and reanalysis data sets for the summers of 2002–2019The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96The average differences between multi‐product ensemble (MPE‐SIC) and PM SIC range from −6.65% to +1.09% [ABSTRACT FROM AUTHOR]

Food is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide. [ABSTRACT FROM AUTHOR]

Extratropical cyclones are critical weather systems affecting climate change in mid and high-latitude regions. Researching the characteristics, patterns, and movements of extratropical cyclones is helpful for improved prediction and understanding of global climate change. Currently, there are still great difficulties in predicting extratropical cyclones. Also, few prior studies have investigated the potential impact of Arctic sea ice on extratropical cyclone activity (ECA). This study utilizes updated ERA5 data and an improved ECA identification method to reveal ECA in the Pacific. The results demonstrate that the Pacific ECA primarily occurs during the cold season (November to March), and the North Pacific region has the maximum ECA. More remarkably, a strong linkage exists between the preceding summer-fall anomalous changes in the Arctic sea ice and the cold season Pacific ECA. We discover that Arctic sea ice could modify the local pressure field, changing the southern boundary of the Pacific sector polar vortex in winter, which in turn influences the intensity of the westerly jet stream and eventually impacts the Pacific ECA during the cold season. Our exploration will provide references for further study and prediction of ECA in the Pacific Ocean. [ABSTRACT FROM AUTHOR]

Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea ice has amplified solar radiation and heat absorption from the ocean, exacerbating both polar ice loss and global warming. It has also accelerated changes in sea ice movement, posing safety risks for ship navigation. In recent years, numerical prediction models have dominated the field of sea ice movement prediction. However, these models often rely on extensive data sources, which can be limited in specific time periods or regions, reducing their applicability. This study introduces a novel approach for predicting Arctic sea ice motion within a 10-day window. We employ a Self-Attention ConvLSTM deep learning network based on single-source data, specifically optical flow derived from the Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz data, covering the entire Arctic region. Upon verification, our method shows a reduction of 0.80 to 1.18 km in average mean absolute error over a 10-day period when compared to ConvLSTM, demonstrating its improved ability to capture the spatiotemporal correlation of sea ice motion vector fields and provide accurate predictions. [ABSTRACT FROM AUTHOR]

This study explores the linkage between summertime temperature fluctuations over midlatitude Eurasia and the preceding Arctic sea ice concentration (SIC) by utilizing the squared norm of the temperature anomaly, the essential part of local eddy available potential energy, as a metric to quantify the temperature fluctuations with weather patterns on various timescales. By comparing groups of singular value decomposition (SVD) analysis, we suggest a significant linkage between strong (weak) August 10-to-30-day temperature fluctuations over mid-west Asia and enhanced (decreased) Barents-Kara Sea ice in the previous February. We find that when the February SIC increases in the Barents-Kara Sea, a zonal dipolar pattern of SST anomalies appears in the Atlantic subpolar region and lasts from February into the summer months. Evidence suggests that in such a background state, the atmospheric circulation changes evidently from July to August, so that the August is characterized by an amplified meridional circulation over Eurasia, weakened westerlies, and high-pressure anomalies along the Arctic coast. Moreover, the 10-to-30-day wave becomes more active in the North Atlantic–Barents-Kara Sea–Central Asia regions and manifests a more evident southward propagation from the Barents-Kara Sea into the Ural region, which is responsible for the enhanced 10-to-30-day wave activity and temperature fluctuations in the region. [ABSTRACT FROM AUTHOR]

Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents, Arctic ecosystems, and fisheries, as well as Arctic maritime navigation. Simulations and projections of winter sea ice in the Barents Sea based on the latest 41 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are investigated in this study. Results show that most CMIP6 models overestimate winter sea ice in the Barents Sea and underestimate its decreasing trend. The discrepancy is mainly attributed to the simulation bias towards an overly weak ocean heat transport through the Barents Sea Opening and the underestimation of its increasing trend. The methods of observation-based model selection and emergent constraint were used to project future winter sea ice changes in the Barents Sea. Projections indicate that sea ice in the Barents Sea will continue to decline in a warming climate and that a winter ice-free Barents Sea will occur for the first time during 2042–2089 under the Shared Socioeconomic Pathway 585 (SSP5-8.5). Even in the observation-based selected models, the sensitivity of winter sea ice in the Barents Sea to global warming is weaker than observed, indicating that a winter ice-free Barents Sea might occur earlier than projected by the CMIP6 simulations. [ABSTRACT FROM AUTHOR]

In recent decades, Arctic summer sea ice extent (SIE) has shown a rapid decline overlaid with large interannual variations, both of which are influenced by geopotential height anomalies over Greenland (GL-high) and the central Arctic (CA-high). In this study, SIE along coastal Siberia (Sib-SIE) and Alaska (Ala-SIE) is found to account for about 65% and 21% of the Arctic SIE interannual variability, respectively. Variability in Ala-SIE is related to the GL-high, whereas variability in Sib-SIE is related to the CA-high. A decreased Ala-SIE is associated with decreased cloud cover and increased easterly winds along the Alaskan coast, promoting ice—albedo feedback. A decreased Sib-SIE is associated with a significant increase in water vapor and downward longwave radiation (DLR) along the Siberian coast. The years 2012 and 2020 with minimum recorded ASIE are used as examples. Compared to climatology, summer 2012 is characterized by a significantly enhanced GL-high with major sea ice loss along the Alaskan coast, while summer 2020 is characterized by an enhanced CA-high with sea ice loss focused along the Siberian coast. In 2012, the lack of cloud cover along the Alaskan coast contributed to an increase in incoming solar radiation, amplifying ice-albedo feedback there; while in 2020, the opposite occurs with an increase in cloud cover along the Alaskan coast, resulting in a slight increase in sea ice there. Along the Siberian coast, increased DLR in 2020 plays a dominant role in sea ice loss, and increased cloud cover and water vapor both contribute to the increased DLR. [ABSTRACT FROM AUTHOR]

Sea ice, one of the most dominant barriers to Arctic shipping, has decreased dramatically over the past four decades. Arctic maritime transport is hereupon growing in recent years. To produce a long-term assessment of trans-Arctic accessibility, we systematically revisit the daily Arctic navigability with a view to the combined effects of sea ice thickness and concentration throughout the period 1979–2020. The general trends of Navigable Windows (NW) in the Northeast Passage show that the number of navigable days is steadily growing and reached 89±16 days for Open Water (OW) ships and 163±19 days for Polar Class 6 (PC6) ships in the 2010s, despite high interannual and interdecadal variability in the NWs. More consecutive NWs have emerged annually for both OW ships and PC6 ships since 2005 because of the faster sea ice retreat. Since the 1980s, the number of simulated Arctic routes has continuously increased, and optimal navigability exists in these years of record-low sea ice extent (e.g., 2012 and 2020). Summertime navigability in the East Siberian and Laptev Seas, on the other hand, varies dramatically due to changing sea ice conditions. This systematic assessment of Arctic navigability provides a reference for better projecting the future trans-Arctic shipping routes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The surface energy budget is crucial for Arctic sea ice mass balance calculation and climate systems, among which turbulent heat fluxes significantly affect the air–sea exchanges of heat and moisture in the atmospheric boundary layer. Satellite observations (e.g. CERES and APP-X) and atmospheric reanalyses (e.g., ERA5) are often used to represent components of the energy budget at regional and pan-Arctic scales. However, the uncertainties of the satellite-based turbulent heat fluxes are largely unknown, and cross-comparisons with reanalysis data and in-situ observations are limited. In this study, satellite-based turbulent heat fluxes were assessed against in-situ observations from the N-ICE2015 drifting ice station (north of Svalbard, January–June 2015) and ERA5 reanalysis. The turbulent heat fluxes were calculated by two approaches using the satellite-based ice surface temperature and radiative fluxes, surface atmospheric parameters from ERA5, and snow/sea ice thickness from the pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). We found that the bulk-aerodynamic formula based results could better capture the variations of turbulent heat fluxes, while the maximum entropy production based estimates are comparable with ERA5 in terms of root-mean-square error (RMSE). CERES-based estimates outperform the APP-X-based ones but ERA5 performs the best in all seasons (RMSE of 18 and 7 W m−2 for sensible and latent heat flux, respectively). The air–ice temperature/humidity differences and the surface radiation budget were found the primary driving factors in the bulk-formula method and maximum entropy production (MEP) method, respectively. Furthermore, errors in the surface and near-surface temperature and humidity explain almost 50% of the uncertainties in the estimates based on the bulk-formula, whereas errors in the net radiative fluxes explain more than 50% of the uncertainties in the MEP-based results.

Updating predictions of the response of high-profile, at-risk species to climate change and anthropogenic stressors is vital for informing effective conservation action. Here, we review two prior generations of Bayesian network probability models predicting changes in global polar bear (Ursus maritimus) population status, and provide a contemporary update based on recent research findings and sea-ice projections by newer climate models. We compare predictions of polar bear population response from all 3 models among four circumpolar Arctic ecoregions, using sea ice projections based on three IPCC greenhouse gas emissions scenarios (SSP2.6, 4.5, 8.5). Consistent with the previous two model generations, polar bears will continue to experience increasing probability of declining or greatly declining populations throughout the 21st century, varying by emission scenario. Populations within the Polar Basin Divergent Ice Ecoregion have the highest predicted probability of declines, but predictions were slightly less dire relative to the previous model generation. Most of the influence, denoted by model sensitivity analysis, is from expected degradation and loss of sea ice and reduced access to marine prey. The lack of terrestrial prey adequate to substitute for loss of access to marine prey, as well as human-caused bear morality associated with hunting and defense of life and property encountered when polar bears are increasingly forced ashore also contributed to predicted declines. Although some tidewater glacial fjords and other localized onshore resources may provide local refugia, their benefit is transient. Our findings continue to inform priorities for inventory, monitoring, and research needs, and suggest that similar updates to models of other at-risk species can capitalize on the comparison framework we present here.