Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This study provides a new conceptional framework to understand the life cycle of the floe size distribution of Arctic sea ice and the associated processes. We derived the floe size distribution from selected multi-scale satellite imagery data acquired from different locations and times in the Arctic. Our study identifies three stages of the floe size evolution during summer – 'fracturing', 'transition' and 'melt/wave fragmentation'. Fracturing defines the initial floe size distribution (N ∼ d−α, where d is floe size) formed from the spring breakup, characterized by the single power-law regime over d = 30–3000 m with α ≈ 2. The initial floe size distribution is then modified by various floe fragmentation processes during the transition period, which is characterized by 'selective' fragmentation of large floes (d > 200–300 m) with variable α = 2.5–3.5 depending on the degree of fragmentation. As ice melt intensifies, the melt fragmentation expands the single power-law regime into smaller floes (d = 70 m) with α = 2.4–3.8, while a significant reduction of small floes (d < 30–40 m) occurs due to lateral melt. The shape factor shows an overall progression from elongated floes into rounded floes. The effects of scaling and wave-fracture are also discussed. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'. [ABSTRACT FROM AUTHOR]

Shortwave cloud radiative effect (SWCRE), known as the cooling effect triggered by cloud, plays a vital role in adjusting the global radiation budget. As the Arctic gets warmer, it may become a more indispensable factor curbing this warming tendency. Research has pointed out a significant relationship between sea ice cover (SIC) and SWCRE over the Arctic during summer (June–August). Although no evidence has been found on cloud response to SIC during summer on the average of the Arctic, this study regards cloud as an inter-connection which can regulate SIC and SWCRE in a particular place: Barents and Kara Sea region (15°E–85°E, 70°N–80°N). Its SWCRE and SIC vary significantly, with their trends being 5.85 w∙m−2 and − 5.87% per decade compared to those of the Arctic mean (2.93 w∙m−2 and − 4.65% per decade). In this area, we find that the growing number of low-level cloud which is resulted from the loss on SIC may be accountable for the increase in SWCRE, as is shown in the correlation coefficient between low-level cloud and SIC reaches − 0.4. The correlation coefficient between low-level cloud and SWCRE is 0.6. It reflects a SIC-cloud-SWCRE negative feedback. Moreover, a regression fitting model is being established to quantify the contribution of Arctic cloud in the process of slowing down the Arctic warming. It reveals that this specific region would turn into an ice-free region with sea surface temperature (SST) 1.5 °C higher than reality during 2001 if we stop the increase in SWCRE. This result presents how fascinating the contribution cloud has been making in its way slowing down the warming pace. [ABSTRACT FROM AUTHOR]

The Arctic sea-ice extent has strongly declined over recent decades. A large inter-annual variability is superimposed on this negative trend. Previous studies have emphasised a significant warming effect associated with latent energy transport into the Arctic region, in particular due to an enhanced greenhouse effect associated with the convergence of the humidity transport over the Arctic. The atmospheric energy transport into the Arctic is mostly accomplished by waves such as Rossby waves and cyclones. Here we present a systematic study of the effect on Arctic sea ice of these atmospheric wave types. Through a regression analysis we investigate the coupling between transport anomalies of both latent and dry-static energy and sea-ice anomalies. From the state-of-the-art ERA5 reanalysis product the latent and dry-static transport over the Arctic boundary (70 ∘ N) is calculated. The transport is then split into transport by planetary and synoptic-scale waves using a Fourier decomposition. The results show that latent energy transport as compared to that of dry-static shows a much stronger potential to decrease sea ice concentration. However, taking into account that the variability of dry-static transport is of an order of magnitude larger than latent, the actual impact on the sea ice appears similar for the two components. In addition, the energy transport by planetary waves causes a strong decline of the sea ice concentration whereas the transport by synoptic-scale waves shows only little effect on the sea ice. The study emphasises the importance of the large-scale waves on the sea ice variability. [ABSTRACT FROM AUTHOR]

The thick multiyear sea ice that once covered large parts of the Arctic is increasingly being replaced by thinner and weaker first‐year ice, making it more vulnerable to breakup by winds. We use the neXtSIM sea ice model to investigate the driving mechanisms behind a large breakup event in the Beaufort Sea during winter 2013. Our simulations are the first to successfully reproduce the timing, location, and propagation of sea ice leads associated with wind‐driven breakup and highlight the importance of accuracy of the atmospheric forcing, sea ice rheology, and changes in sea ice thickness. We found that the breakup resulted in enhanced export of multiyear ice from the Beaufort Sea. Overall, this leads to a relatively thinner and weaker simulated ice cover that potentially preconditions earlier breakup in spring and accelerates sea ice loss. Finally, our simulations indicate that large breakup events could become more frequent as Arctic sea ice continues to thin. Plain Language Summary: The loss of thick multiyear sea ice in the Arctic leads to weaker sea ice that is more easily broken up by strong winds. As a consequence, extreme sea ice breakup events may become more frequent, even during the middle of winter when the sea ice cover is frozen solid. This can lead to an earlier onset of the melt season and potentially accelerate Arctic sea ice loss. Such extreme breakup events are generally not captured by climate models, potentially limiting our confidence in projections of Arctic sea ice. We investigated the driving forces behind sea ice breakup events during winter and how they change in a future climate. Our sea ice model is the first to reproduce such breakup events and reveals that the combination of strong winds and thin sea ice is a key factor for these breakups. We found that winter breakups have a large effect on local heat and moisture transfer and cause enhanced sea ice production, but also increase the overall movement of the sea ice cover, making it more vulnerable. Finally, we show that if the Arctic sea ice continues to thin, these extreme breakup events could become even more frequent. Key Points: An extreme winter breakup event in the Beaufort Sea is accurately simulated with the neXtSIM sea ice modelHigh‐resolution and skillful atmospheric forcing, as well as appropriate sea ice rheology, are necessary to simulate such breakupsIn the model, thinning Beaufort Sea ice is more vulnerable to breakup, which may accelerate loss of multiyear ice [ABSTRACT FROM AUTHOR]

Revealing the changes in the Fram Strait (FS) multiyear ice (MYI) export is crucial due to their climate relevance in the context of the loss rate of MYI being faster than that of the total ice in the Arctic. Here, we estimated winter (October–April) MYI area export through the FS over the last 2 decades by using updated MYI concentration data retrieved from active and passive microwave satellite observations. We divided the period into two regimes relative to the ice index: D1 (2002/03–2010/11) and D2 (2012/13–2019/20). The observed variations of winter MYI exports D2 were compared with those of the previous decade D1. The results show that the MYI area exports display strong interannual variability. A significant decrease in MYI export for the periods between D1 and D2 is noted. On average, the wintertime MYI area exports declined sharply by 22% from 3.82 × 105 km2 in D1 to 3.00 × 105 km2 in D2. In addition, the percentage of MYI in the total sea ice outflow through the FS (PCM) also decreased distinctly from 72% in D1 to 59% in D2. Statistics show that weekly sea ice drift across the strait can explain 76% of the MYI area export variability. Furthermore, the dominant atmospheric drivers contributing to the decline in MYI area export during D2 were examined. In the last decade (D2), the strengthened low pressure in the North Atlantic sector, combined with an eastward shift in the axis of dipole anomaly (DA), resulted in reduced MYI advection from the Beaufort Sea and Siberian Coast toward the FS. Moreover, weakened cyclonic activity south of the FS also contributed to the reduction in MYI export during D2. [ABSTRACT FROM AUTHOR]

Arctic sea ice has substantially changed over the past four decades, with a large decrease in sea‐ice area and volume. The exact causes of these changes are not entirely known. In our study, we make use of the Swedish Meteorological and Hydrological Institute Large Ensemble. This ensemble consists of 50 members realized with the EC‐Earth3 global climate model and covers the period 1970‐2100. We apply for the first time the Liang‐Kleeman information flow method to analyze the cause‐effect relationships between Arctic sea ice and its potential drivers. We show that recent and future changes in Arctic sea ice are mainly driven by air and sea‐surface temperatures and ocean heat transport. Conversely, changes in Arctic sea ice also considerably impact temperature and ocean heat transport. Finally, we find a progressive decrease in the influence of sea‐ice area and volume on air temperature and ocean heat transport through the twenty‐first century. Plain Language Summary: The Arctic has been warming at a larger rate than the rest of the world, resulting in a substantial loss of sea ice since the late 1970s. This has had and will continue to have an impact on our climate and societies. The exact causes of the ongoing sea‐ice loss are not entirely known, and understanding them is important in order to better prepare our societies for future climate changes. In our study, we apply a relatively novel approach that quantifies the cause‐effect relationships between Arctic sea ice and its potential drivers. We make use of a large range of model simulations performed with the EC‐Earth3 global climate model covering the period 1970‐2100. We find that air temperature, sea‐surface temperature, and the transport of heat by the ocean are important drivers of the ongoing and future retreat of Arctic sea ice. Conversely, changes in Arctic sea ice also affect the three former quantities. Our study demonstrates the performance of causal inference methods in the quest for a better understanding of relationships between climate variables. The geophysical and climate communities could greatly benefit from using these methods more intensively. Key Points: The Liang‐Kleeman rate of information transfer allows quantification of the directional dependence between Arctic sea ice and its driversRecent and future changes in Arctic sea ice are mainly driven by air and sea‐surface temperatures and ocean heat transportThe influence of Arctic sea ice on air temperature and ocean heat transport progressively decreases through the twenty‐first century [ABSTRACT FROM AUTHOR]

Extreme cold events (ECEs) on the Tibetan Plateau (TP) exert serious impacts on agriculture and animal husbandry and are important drivers of ecological and environmental changes. We investigate the temporal and spatial characteristics of the ECEs on the TP and the possible effects of Arctic sea ice. The daily observed minimum air temperature at 73 meteorological stations on the TP during 1980–2018 and the BCC_AGCM3_MR model are used. Our results show that the main mode of winter ECEs over the TP exhibits the same spatial variation and interannual variability across the whole region and is affected by two wave trains originating from the Arctic. The southern wave train is controlled by the sea ice in the Beaufort Sea. It initiates in the Norwegian Sea, and then passes through the North Atlantic Ocean, the Arabian Sea, and the Bay of Bengal along the subtropical westerly jet stream. It enters the TP from the south and brings warm, humid air from the oceans. By contrast, the northern wave train is controlled by the sea ice in the Laptev Sea. It originates from the Barents and Kara seas, passes through Lake Baikal, and enters the TP from the north, bringing dry and cold air. A decrease in the sea ice in the Beaufort Sea causes positive potential height anomalies in the Arctic. This change enhances the pressure gradient between the Artic and the mid-latitudes, leading to westerly winds in the northern TP, which block the intrusion of cold air into the south. By contrast, a decrease in the sea ice in the Laptev Sea causes negative potential height anomalies in the Artic. This change reduces the pressure gradient between the Artic and the mid-latitudes, leading to easterly winds to the north of the TP, which favors the southward intrusion of cold polar air. A continuous decrease in the amount of sea ice in the Beaufort Sea would reduce the frequency of ECEs over the TP and further aggravate TP warming in winter. [ABSTRACT FROM AUTHOR]

Three striking and impactful extreme cold weather events successively occurred across East Asia and North America during the mid-winter of 2020/21. These events open a new window to detect possible underlying physical processes. The analysis here indicates that the occurrences of the three events resulted from integrated effects of a concurrence of anomalous thermal conditions in three oceans and interactive Arctic-lower latitude atmospheric circulation processes, which were linked and influenced by one major sudden stratospheric warming (SSW). The North Atlantic warm blob initiated an increased poleward transient eddy heat flux, reducing the Barents-Kara seas sea ice over a warmed ocean and disrupting the stratospheric polar vortex (SPV) to induce the major SSW. The Rossby wave trains excited by the North Atlantic warm blob and the tropical Pacific La Nina interacted with the Arctic tropospheric circulation anomalies or the tropospheric polar vortex to provide dynamic settings, steering cold polar air outbreaks. The long memory of the retreated sea ice with the underlying warm ocean and the amplified tropospheric blocking highs from the midlatitudes to the Arctic intermittently fueled the increased transient eddy heat flux to sustain the SSW over a long time period. The displaced or split SPV centers associated with the SSW played crucial roles in substantially intensifying the tropospheric circulation anomalies and moving the jet stream to the far south to cause cold air outbreaks to a rarely observed extreme state. The results have significant implications for increasing prediction skill and improving policy decision making to enhance resilience in "One Health, One Future". [ABSTRACT FROM AUTHOR]

Significant changes in the Arctic climate, particularly a rapid decline of September Arctic sea ice has occurred over the past few decades. Though the exact reason for such drastic changes is still unknown, studies suggest anthropogenic drivers, natural variability of the climate system, and a combination of both as reasons. The present study focus on the influence of one of the natural variabilities of the climate system, the teleconnections associated with the Indian Summer Monsoon (ISM), and its relationship to September Arctic sea ice. Using 50 years (1951–2000) of National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) NCEP/NCAR reanalysis data, APHRODITE precipitation data, Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, V2, and HadISST sea-ice concentration data, it is shown that during many strong (weak) ISM years, the Arctic sea ice increased (decreased) predominantly over the Chukchi and Beaufort Seas. The ISM plays a significant role in causing a positive (negative) North Atlantic Oscillation (NAO) during strong (weak) ISM years through the monsoon-desert mechanism associated with monsoonal heating. Simultaneously, the NAO during a strong (weak) ISM causes weakening (strengthening) of the Beaufort Sea High (BSH). The strength of the BSH modulates the Arctic atmospheric circulation, advecting cold air and the direction of the transpolar drift stream, both leading to the generation of more (less) sea ice over the Chukchi-Beaufort Sea region during strong (weak) ISM years. The study illustrates a new atmospheric teleconnection between the tropics and the Arctic. [ABSTRACT FROM AUTHOR]

A severe persistent drought occurred in South China during autumn–winter 2020/2021, causing enormous economic losses and severe water shortage in South China. Focusing on this event, this study analyzed the climate anomalies associated with this event, and investigated the underlying mechanisms as well as their relationship with the leading modes of the autumn and winter precipitation in eastern China. The results suggest that the drought in South China during autumn 2020 is mainly attributed to the water vapor divergence anomalies induced by an anomalous anticyclone over eastern China and an anomalous cyclone over the South China Sea, which are caused by an anomalous Eurasian atmospheric wave‐train triggered by the negative sea ice anomalies in the Barents Sea‐Kara Sea region and a La Niña‐like condition in the tropical Pacific sea surface temperatures, respectively. The continued drought in South China during winter 2020/2021 is mainly attributed to the water vapor divergence anomalies induced by an anomalous cyclone over the western North Pacific, which is caused by the continued La Niña‐like condition. The mechanisms of the first two leading modes of autumn precipitation in eastern China both contributed to the drought in South China during autumn 2020, whereas the mechanisms of the first leading mode of winter precipitation in eastern China played an important role in causing the drought in South China during winter 2020/2021. Numerical experiments demonstrate important impact of the ENSO and Arctic sea ice anomalies on the East Asian climate anomalies during autumn–winter 2020/2021. Key Points: The drought in South China during autumn 2020 is attributed to combined effect of El Niño‐Southern Oscillation (ENSO) and Arctic sea ice anomaliesThe drought in South China during winter 2020/2021 is mainly attributed to the La Niña‐like conditionNumerical experiments demonstrate important impact of ENSO and Arctic sea ice on East Asian climate anomalies during autumn–winter 2020/2021 [ABSTRACT FROM AUTHOR]

Arctic sea ice has been declining over past several decades with the largest ice loss occurring in summer. This implies a strengthening of the sea ice seasonal cycle. Here, we examine global ocean salinity response to such changes of Arctic sea ice using simulations wherein we impose a radiative heat imbalance at the sea ice surface, inducing a sea ice decline comparable to the observed. The imposed perturbation leads to enhanced seasonal melting and a rapid retreat of Arctic sea ice within the first 5–10 years. We then observe a gradual freshening of the upper Arctic ocean that continues for about a century. The freshening is most pronounced within the central Arctic, including the Beaufort gyre, and is attributed to excess surface freshwater associated with the stronger seasonal sea ice melting, as well as a greater upper-ocean freshwater storage due to changes in ocean circulation. The freshening of the Nordic Seas can also occur via a distillation-like process in which denser saline waters with increased salinity are exported to the subtropical/tropical North Atlantic by meridional overturning circulation. Thus, enhanced seasonal sea ice melting in a warmer climate can lead to a persistent Arctic freshening with large impacts on the global salinity distribution. [ABSTRACT FROM AUTHOR]

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively. [ABSTRACT FROM AUTHOR]

The influence of early spring sea ice at Barents Sea on midsummer rainfall in Northeast China (NEC) is identified based on observational analyses and atmospheric modeling experiments in this study. Increased sea ice area (SIA) in the Barents Sea is ensued by positive rainfall anomalies at north of NEC and by negative anomalies at south, and vice verse. Specifically, due to a good seasonal persistence from spring to summer, the preceding sea ice anomalies exert an impact on midsummer surface air temperature anomalies and vertical stability over Barents Sea via the modulation on turbulent heat flux. The anomalous circulation is further triggered over Europe and the Mediterranean Sea through meridional vertical cells, with a barotropic structure. Accordingly, an effective Rossby wave source is excited over the eastern Mediterranean by the advection of vorticity by divergence wind, and causes an eastward propagation of Silk Road Pattern to East Asia. In addition, another SIA-related wave-like train can diffuse directly southeastward from Arctic to NEC in a polar path. Observations and numerical simulations indicate that, in response to increased sea ice at Barents Sea, an anomalous cyclone emerges over NEC, along with easterly or southeasterly over north of NEC and with northwesterly over south, leading to moisture convergence anomalies at north and divergence anomalies at south. Jointly, ascending (descending) motion anomalies favors a wet (dry) summer over north (south) of NEC. [ABSTRACT FROM AUTHOR]

Stratospheric aerosol geoengineering focused on the Arctic could substantially reduce local and worldwide impacts of anthropogenic global warming. Because the Arctic receives little sunlight during the winter, stratospheric aerosols present in the winter at high latitudes have little impact on the climate, whereas stratospheric aerosols present during the summer achieve larger changes in radiative forcing. Injecting SO2 in the spring leads to peak aerosol optical depth (AOD) in the summer. We demonstrate that spring injection produces approximately twice as much summer AOD as year‐round injection and restores approximately twice as much September sea ice, resulting in less increase in stratospheric sulfur burden, stratospheric heating, and stratospheric ozone depletion per unit of sea ice restored. We also find that differences in AOD between different seasonal injection strategies are small compared to the difference between annual and spring injection. Plain Language Summary: Scattering small particles called aerosols into the sky, "geoengineering", could reflect a small amount of sunlight in order to combat global warming. Doing this near the Arctic could help stop sea ice from melting, which would help preserve the Arctic climate. Our study shows that, for Arctic geoengineering, scattering particles in the spring is most efficient because the particles will be present throughout the summer, and the Arctic receives the most sunlight in the summer. Therefore, spring Arctic geoengineering could accomplish the same goals as year‐round Arctic geoengineering, but with fewer particles and thus fewer negative side‐effects. Key Points: Arctic focused stratospheric aerosol injection (SAI) concentrated in spring produces peak aerosol optical depth (AOD) in summer and therefore reflects sunlight more efficiently than year round SAISpring injection at 60°N restores twice as much September sea ice per unit of injection as year round injection at the same latitudeLimiting Arctic injection to spring also results in less ozone loss and stratospheric heating per unit of September sea ice recovered [ABSTRACT FROM AUTHOR]

The surface radiative imbalance has large impacts on the long-term trends and year-to-year variability of Arctic sea ice. Clouds are believed to be a key factor in regulating this radiative imbalance, whose underlying processes and mechanisms, however, are not well understood. Compared with observations, the Community Earth System Model version 1 (CESM1) is known to underestimate Arctic cloud liquid water. Here, the following hypothesis is proposed and tested: this underestimation is caused by an overactive Wegener–Bergeron–Findeisen (WBF) process in model as too many supercooled liquid droplets are scavenged by ice crystals via deposition. In this study, the efficiency of the WBF process in CESM1 was reduced to investigate the Arctic climate response, and differentiate the responses induced by atmosphere–ocean–sea ice coupling and global warming. By weakening the WBF process, CESM1 simulated liquid cloud fractions increased, especially in winter and spring. The cloud response resulted in increased downwelling longwave flux and decreased shortwave flux at the surface. Arctic clouds and radiation in simulations with reduced WBF efficiency show a better agreement with satellite retrievals. In addition, both coupling and global warming amplify the cloud response to a less efficient WBF process, due to increased relative humidity and enhanced evaporation, respectively. As a response, the sea ice tends to melt over the North Atlantic Ocean, most likely caused by a positive feedback process between clouds, radiation and sea ice during non-summer months. These results improve our understanding of large-scale effects of the WBF process and the role of cloud liquid water in the Arctic climate system. [ABSTRACT FROM AUTHOR]

The relationship between changes in Arctic sea ice and the mid‐latitude climate has been receiving increasing attention. As the highest and largest topography in Asia, the possible link between the west Tibetan Plateau snow depth (WTPSD) and the Arctic sea ice was investigated based on observational data and model simulations. The results indicate that a consistent variation between the WTPSD and the seesaw sea ice pattern in the Barents‐Nordic Sea and Labrador Sea (BLSIC) exists during boreal winter, and both the WTPSD and BLSIC are negatively correlated with the North Atlantic Oscillation (NAO) index. During the negative phases of the NAO, an anomalous Rossby wave train propagates from the North Atlantic to the north of the Arabian Sea, inducing cyclonic anomalies in the mid‐troposphere and enhancing water vapour transportation onto the TP, which is conducive to in situ snow accumulation. Furthermore, composite analysis and AGCM numerical experiments demonstrate that such an anomalous BLSIC pattern can in turn reinforce the negative phase of the NAO in strength, facilitating the propagation of the mid‐tropospheric Rossby waves to north of the Arabian Sea and hence an above normal WTPSD. Therefore, the variation in WTPSD is influenced by the direct effect of the NAO and additional feedback from BLSIC, which hints a potential cryospheric connection between the TP and the Arctic. [ABSTRACT FROM AUTHOR]

Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models' simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation. [ABSTRACT FROM AUTHOR]

Most global climate models, such as CMIP5 models, ignore the falling ice (snow) radiative effects (FIREs). Extended from our previous works, we explore the impacts of FIREs on the geographical distribution changes of sea ice concentration (SIC), sea ice thickness and skin temperature (Ts) under 1% per year increase of atmospheric CO2 concentration. We perform a pair of 140-year experiments including FIREs (SoN) and excluding FIREs (NoS) using CESM1-CAM5. These two simulations are compared with each other and against CMIP5 multi-model mean without FIREs (CMIP5-NoS). The results show that the changes of SIC, thickness and radiation fields in NoS minus SoN largely match the changes between CMIP5-NoS and SoN in winter but less so in summer and annual mean. Both NoS and CMIP5-NoS simulate less downward longwave and net radiative warming (~20 - 30 W m-2), resulting in colder Ts over Arctic and adjacent lands (~5 - 8 K colder) and stronger meridional temperature gradient, leading to more SIC and thicker sea ice (~30 - 40 cm) over the Arctic ocean. The inclusion of FIREs produces stronger changes in geographic patterns and magnitudes of Ts, SIC and thickness from the first to middle 20 years but less from the middle to last 20 years. The SIC and thickness changes in SoN are associated with warmer Ts, increasing downward surface longwave warming and thus net warming relative to NoS and CMIP5-NoS. With FIREs, the model shows faster warming-driven sea ice retreats and thinning over the entire Arctic ocean, resulting in a sea ice-free Arctic ocean 30 years earlier, as well as much warmer Ts (up to 5 K) over the adjacent lands than in NoS case. [ABSTRACT FROM AUTHOR]

This ethnographic study at the US National Snow and Ice Data Center (NSIDC) follows a group of scientists and communications specialists as they compose visualizations and analyses of near-real-time Arctic sea ice data. Research participants collectively make scientific judgments about near-real-time data in a highly visible public venue with 'relational agility'. They balance multiple phenomena including knowledge of how sceptics attack climate science, reflexivity about the conventions through which sea ice data is gathered, the needs of journalists working in a news cycle paced by Twitter, and the liveliness and vitality of sea ice itself. Relational agility, understood as a way of coordinating the social in relation to this plurality of contingent practices and processes, provides insight into the science and politics of nonlinear climate change. [ABSTRACT FROM AUTHOR]

During 1979-2015, the intensity of the Siberian high (SH) in November and December-January (DJ) is frequently shown to have an out-of-phase relationship, which is accompanied by opposite surface air temperature and circulation anomalies. Further analyses indicate that the autumn Arctic sea ice is important for the phase reversal of the SH. There is a significantly positive (negative) correlation between the November (DJ) SH and the September sea ice area (SIA) anomalies. It is suggested that the reduction of autumn SIA induces anomalous upward surface turbulent heat flux (SHF), which can persist into November, especially over the Barents Sea. Consequently, the enhanced eddy energy and wave activity flux are transported to mid and high latitudes. This will then benefit the development of the storm track in northeastern Europe. Conversely, when downward SHF anomalies prevail in DJ, the decreased heat flux and suppressed eddy energy hinder the growth of the storm track during DJ over the Barents Sea and Europe. Through the eddy-mean flow interaction, the strengthened (weakened) storm track activities induce decreased (increased) Ural blockings and accelerated (decelerated) westerlies, which makes the cold air from the Arctic inhibited (transported) over the Siberian area. Therefore, a weaker (stronger) SH in November (DJ) occurs downstream. Moreover, anomalously large snowfall may intensify the SH in DJ rather than in November. The ensemble-mean results from the CMIP5 historical simulations further confirm these connections. The different responses to Arctic sea ice anomalies in early and middle winter set this study apart from earlier ones. [ABSTRACT FROM AUTHOR]

The influence of Arctic sea ice concentration (SIC) on the inter‐annual variations of the frequency of summertime Ural blocking (UB) during the period of 1980–2013 is investigated using observational and reanalysis data sets and version 5.0 of the Community Atmospheric Model. The results reveal that the variations in the UB frequency display a statistically significant association with a persistent spring–summer SIC pattern in the Barents Sea. Related to high UB frequencies, heavy SICs exert a dynamic influence by increasing the meridional temperature gradient (MTG) in the lower troposphere and cause stronger (weaker) zonal winds in high‐latitude (mid‐latitude) areas through the thermal wind balance. This zonal wind pattern establishes the background conditions for the blocking activity and thus helps to initiate summertime UB events. Moreover, persistent heavy SICs tend to enhance the low‐level atmospheric baroclinicity to the south and decreases in mid‐latitude areas, inducing weakened synoptic‐scale transient eddy activity (STEA) that stretches from eastern Europe to the Ural Mountains. This reduced STEA is accompanied by a locally intensified eddy‐vorticity forcing that may exert a downstream influence on the onset of UB events. In terms of thermodynamic processes, heavy SICs‐induced water vapour content anomalies are expected to cause deficits in precipitation over the East European Plateau in late spring and subsequently desiccate the underlying soil. Both of these effects are expected to increase surface heat fluxes and thickness of the lower‐middle troposphere, thus favouring anomalous anticyclonic circulation over the Ural Mountains. On the other hand, the opposite dynamic and thermodynamic effects are expected to result from light SICs with respect to low UB frequencies. Therefore, these two effects identified in this study each contribute to an increased probability of more frequent (rare) UB events in summer as the spring–summer sea ice within the regions surrounding the Barents Sea expands (disappears). [ABSTRACT FROM AUTHOR]

With retreating sea ice and increasing human activities in the Arctic come a growing need for reliable sea ice forecasts up to months ahead. We exploit the subseasonal‐to‐seasonal prediction database and provide the first thorough assessment of the skill of operational forecast systems in predicting the location of the Arctic sea ice edge on these time scales. We find large differences in skill between the systems, with some showing a lack of predictive skill even at short weather time scales and the best producing skillful forecasts more than 1.5 months ahead. This highlights that the area of subseasonal prediction in the Arctic is in an early stage but also that the prospects are bright, especially for late summer forecasts. To fully exploit this potential, it is argued that it will be imperative to reduce systematic model errors and develop advanced data assimilation capacity. Plain Language Summary: The need for reliable forecasts for the sea ice evolution from weeks to months in advance has substantially grown in the last decade. Sea ice forecasts are of critical importance to manage the opportunities and risks that come with increasing socioeconomic activities in the rapidly changing Arctic, which, despite the reduction of the sea ice cover, remains an extreme environment. The position of the sea ice edge is a key parameter for potential forecast users, such as Arctic mariners. However, little is known about the ability of current operational subseasonal forecast systems to predict the evolution of the ice edge. Therefore, we assess for the first time the skill of state‐of‐the‐art forecast systems, using a new verification metric that quantifies the accuracy of the ice edge position in a meaningful way. Our results demonstrate that subseasonal sea ice predictions are in an early stage, although skillful predictions 1.5 months ahead are already possible. We argue that relatively modest investments into reducing initial state and model errors will lead to major returns in predictive skill. Key Points: The skill in predicting the location of the Arctic sea ice edge differs substantially among subseasonal forecasting systemsThe most skillful system beats climatological forecasts more than 1.5 months ahead, with then highest skills in late summerMajor improvements are possible by reducing errors in initial states and model formulation [ABSTRACT FROM AUTHOR]

In this study, the effects of the Northern Hemisphere atmospheric blocking circulation on Arctic sea ice decline at weekly time scales are examined by defining four key regions based on observational data analysis. Given the regression analysis, the frequently occurring atmospheric patterns related to the sea ice decline in four key sea regions (Baffin Bay, Barents-Kara Seas, Okhotsk Sea and Bering Sea) are found to be Greenland blocking (GB), Ural blocking (UB), western Pacific blocking (PB-W) and eastern Pacific blocking (PB-E), respectively. The results show that the regional blocking frequency is higher (lower) in lower (higher) sea ice winters for each key region. Moreover, composite analysis indicates that blocking evolution is usually accompanied by significant sea ice decline at weekly time scales during the blocking life cycle for each key region. In addition, the intensified surface downward infrared radiation (IR) anomaly and the precipitable water for the entire atmosphere (PWA) in each key region are found to make significant contributions to the positive surface air temperature (SAT) anomaly, which is beneficial for the reduction in sea ice. The approximate quantitative analysis of different surface energy fluxes induced by blocking is also applied. Further analysis shows that the blocking event and the associated changes in SAT and radiation anomalies for each key region lead the sea ice decline by approximately 3~6 days. This result indicates that regional blocking can contribute to regional sea ice decline at weekly time scales through surface warming associated with enhanced water vapor and associated IR variations. Further quantitative estimates indicate that regional blocking can reduce regional sea ice cover (SIC) by 49.6%, 49.4%, 52.2% and 49.5% for Baffin Bay, Barents-Kara Seas, Okhotsk Sea and Bering Sea, respectively, during the blocking life cycle. Finally, a physical process diagrammatic sketch is given to illustrate how blocking affects SIC decline. [ABSTRACT FROM AUTHOR]

Abstract: The thermal evolution of melt ponds on Arctic sea ice was investigated through a combination of autonomous observations and two‐dimensional high‐resolution fluid dynamics simulations. We observed one relatively fresh pond and one saline pond on the same ice floe, with similar depth. The comparison of observations and simulations indicates that thermal convection dominates in relatively fresh ponds, but conductive heat transfer dominates in salt‐stratified ponds. Using a parameterized surface energy balance, we estimate that the heat flux to the ice is larger under the saline pond than the freshwater pond when averaged over the observational period. The deviation is sensitive to assumed wind, varying between 3 and 14 W/m2 for winds from 0 to 5 m/s. If this effect persists as conditions evolve through the melt season, our results suggest that this imbalance potentially has a climatologically significant impact on sea‐ice evolution. [ABSTRACT FROM AUTHOR]

Sea ice in the Arctic grows during the winter and retreats in the summer. The highly reflective white surface of sea ice reflects solar energy, cooling the planet. However, when it melts, the darker ocean absorbs more solar radiation, reinforcing the cycle of melting sea ice. Since sea ice plays a critical role in regulating Earth's climate, changes in sea ice may influence global weather patterns and ocean circulations. In addition to the rise in greenhouse gases in the Arctic, upper tropospheric water vapor (UTWV) or the specific humidity (SH) is also increasing, while acting as an additional greenhouse gas trapping in heat released from the Earth's surface. While annual greenhouse gas concentrations in the Arctic are increasing rather smoothly over time, the interannual variability in sea ice is shown here to be linked to the variability of UTWV at 400 hPa in the Arctic. We propose that increasing thunderstorm activity in the Arctic in the last decade is increasing the water vapor in the upper troposphere, partially explaining the interannual variability, and long term decrease, in the Arctic summer sea ice extent. Both the temporal and spatial analysis between UTWV and Sea Ice changes show that the decline of the summer sea ice cover in the Arctic may be linked to the increasing thunderstorm activity in the Arctic in the last decades. • Summer Arctic Sea Ice is declining at an accelerating rate. • Interannual changes in Sea Ice are well correlated with upper tropospheric water vapor. • Thunderstorm activity appears to be increasing in the Arctic in the last decades. • Summer Arctic thunderstorms increase the water vapor in the upper troposphere. [ABSTRACT FROM AUTHOR]

The latest global mean surface temperature (GMST) indices from several datasets all show a significant warming trend during the so-called period of global-warming slowdown (i.e., 1998–2012), albeit at a more modest rise compared with the satellite era in the 20th century. Via the Mann-Kendall trend test, this study further examined the trend of GMST from a monthly perspective. It is surprising to find that a significant negative trend occurs only in February and the robust cooling is most evident in the mid–high latitudes of Eurasia. Then an empirical orthogonal function (EOF) based on multi-sources data (MS-EOF) was applied on the February surface air temperature (SAT) over Eurasia. Focus is given on the characteristics and factors of the leading MS-EOF mode (MS-EOF1), as in the trend pattern, which features a uniform cooling mode dominating the mid–high latitudes of Eurasian continent. The key circulation features a large-scale eastward-shift North Atlantic Oscillation (NAO)-like pattern, which is, in a negative phase, favourable for weakening the polar-front jet stream and transporting cold air from the Arctic regions to the mid-high latitudes of Eurasian continent. Relevant precursor signals can be traced back to the anomalous North Atlantic sea surface temperature (SST) and Barents–Kara sea ice in the preceding autumn, together contributing significantly to the cooling February over middle- to high-latitude Eurasia and is verified by a linear baroclinic model. This finding emphasizes the important contribution of cooling February to the famous global-warming slowdown. • A robust cooling trend is observed only in February during the so-called period of global-warming slowdown. • The robust cooling is most evident in the mid–high latitudes of Eurasia and is mainly driven by an eastward shift NAO within a negative phase. • Relevant precursor signals can be traced back to the anomalous North Atlantic SST and Barents–Kara sea ice in the preceding autumn. [ABSTRACT FROM AUTHOR]

A new seasonal and annual dataset describing Arctic sea ice extents for 1901–2015 was constructed by individually re-calibrating sea ice data sources from the three Arctic regions (North American, Nordic and Siberian) using the corresponding surface air temperature trends for the pre-satellite era (1901–1978), so that the strong relationship between seasonal sea ice extent and surface air temperature observed for the satellite era (1979-present) also applies to the pre-satellite era. According to this new dataset, the recent period of Arctic sea ice retreat since the 1970s followed a period of sea ice growth after the mid-1940s, which in turn followed a period of sea ice retreat after the 1910s. Arctic sea ice is a key component of the Arctic hydrological cycle, through both its freshwater storage role and its influence on oceanic and atmospheric circulation. Therefore, these new insights have significance for our understanding of Arctic hydrology.EDITOR D. KoutsoyiannisASSOCIATE EDITOR not assigned [ABSTRACT FROM PUBLISHER]

The purpose of this paper is twofold: 1) to propose an approach based on optical flow for the estimation of sea ice motion as an accurate, dense, and computationally efficient alternative to state-of-the-art pattern matching approaches and 2) to investigate the potential of moderate resolution imaging spectroradiometer (MODIS) optical satellite data for combined daily and high-resolution motion estimation. A series of MODIS image pairs for a selected region in Arctic Ocean is employed, and sparse pairwise correspondences between nonrigid patches are calculated. An edge-preserving sparse-to-dense interpolation is applied followed by variational energy minimization to compute the final optical flow. A state-of-the-art multiresolution pattern matching method based on phase correlation and normalized cross correlation is also implemented and evaluated for comparison. Thorough experimentation with different settings of input data preprocessing as well as varying scales of motion is performed. The derived motion vectors are compared with coarser resolution operational sea ice motion vector products from combined buoy and microwave satellite data. The proposed optical flow approach clearly outperforms the pattern matching method in most cases, both in terms of accuracy and motion vector consistency. The estimated motion vectors from the MODIS images highly correlate with the operational vector products. In addition, MODIS provides a vector field with spatial resolution two orders of magnitude higher than the operational products that are able to detect significantly smaller sea ice drifts. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates the relationship between the summer [June-July-August ( JJA)] Arctic dipole wind pattern and the following winter [December-January-February ( DJF)] Siberian High. It is found that the summer Arctic dipole wind pattern is not confined only to the Arctic region; it spans the large domain north of 20°N. The negative phase of this wind pattern depicts an anomalous anticyclone over the Arctic Ocean and its marginal seas, except for the Barents-Kara seas where an anomalous cyclone is dominant. This wind pattern is significantly correlated with the strength of the Siberian High during the following winter and with the frequency of extreme cold events over East Asia during the winters of 1979-2014. The relationship of this wind pattern with the winter Siberian High has strengthened over the past decades, particularly since the late 1980s. The more robust relationship coincides with significant changes in the winter atmospheric circulation and frequent occurrences of the negative phase of this wind pattern, which dynamically contributes to low September sea ice extent. The present study's results suggest that autumn Arctic sea ice provides a link between this wind pattern and climate variability over East Asia during the following winter. Results of simulation experiments suggest that (1) autumn sea ice loss favors the occurrence of a stronger East Asian winter monsoon; (2) the summer Arctic dipole wind pattern modulates winter atmospheric responses to sea ice loss, and the negative phase of this wind pattern enhances the negative feedback of Arctic sea ice loss on winter atmospheric variability over Eurasia and North America. These simulation experiments also imply that complex and varying summer circulation patterns obscure linkages between sea ice loss and large-scale circulation responses over Eurasia. Isolation of the summer Arctic dipole wind pattern, however, provides a potential precursor for the seasonal prediction of winter surface air temperature in a populous region of the world. [ABSTRACT FROM AUTHOR]

Two dominant modes of the winter temperature over East Asia, a northern mode and a southern mode, and their links with Arctic climate conditions are analyzed. The relationships of the two modes with Arctic sea ice are different. The northern mode is closely linked to variations in sea ice of the Arctic Barents-Laptev Sea in previous autumn and most of the Arctic in concurrent winter. The southern mode seems independent from the Arctic sea ice variations, but is associated with sea surface temperature (SST) anomalies in the equatorial central-eastern Pacific. Results suggest an effect of Arctic sea ice variation on the northern mode and an influence of tropical SST anomalies on the southern mode. Reduced sea ice over the Arctic increases 1000-500-hPa thickness over the high-latitudes of Eurasian continent, which reduces the meridional thickness gradient between the middle and high latitudes and thus weakens the extratropical upper-level zonal wind. The weakened zonal wind provides a favorable dynamic condition for the development of a high-latitude ridge around the Ural Mountain. Reduced Arctic sea ice also tends to enhance the Siberian high through both thermodynamic and dynamic processes. The above atmospheric circulation patterns provide a favorable condition for the intrusion of cold air to northern East Asia. [ABSTRACT FROM AUTHOR]

Solar radiation drives the melting of Arctic sea ice in summer, but its parameterization in thermodynamic modeling is difficult due to the large variability of the optical properties of sea ice in space and time. Here, a two-stream radiative transfer model was developed for the propagation of solar radiation in ponded sea ice to investigate the dependence of apparent optical properties (AOPs), particularly albedo and transmittance, on sky conditions, pond depth, ice thickness, and the inherent optical properties (IOPs) of ice and water. The results of numerical experiments revealed that decrease in melt-pond albedo during melting results not only from increase in pond depth but also from decrease in underlying ice thickness, and the latter is more important for thin ice with thickness less than 1.5 m. Hence, a parameterized pond albedo as a function of both pond depth and ice thickness is more suitable for thinning Arctic sea ice than the previously used exponential function of pond depth, which is valid for thicker ice. The increase in broadband transmittance during melting can be explained by the decrease in underlying ice thickness, because its dependence on ice thickness is nearly three times stronger than on pond depth. The spectral dependence of the pond albedo on depth is significant only in the 600–900-nm band, while it depends clearly on ice thickness in the 350–600-nm band. The uncertainty resulting from the absorption coefficient of ice is limited, while the effect of scattering in ice is more important, as determined by a sensitivity study on the influence of the IOPs on the AOPs of sea ice. The two-stream model provides a time-efficient parameterization of the AOPs for ponded sea ice, accounting for both absorption and scattering, and has potential for implementation into sea-ice thermodynamic models to explain the role of melt ponds in the summer decay of Arctic sea ice. [ABSTRACT FROM AUTHOR]

Herein we document findings from a unique scientific expedition north of Svalbard in the middle of the polar night in January 2012, where we observed an ice edge north of 82°N coupled with pronounced upwelling. The area north of Svalbard has probably been ice-covered during winter in the period from approximately 1790 until the 1980s, a period during which heavy ice conditions have prevailed in the Barents Sea and Svalbard waters. However, recent winters have been characterized by midwinter open water conditions on the shelf, concomitant with northeasterly along-shelf winds in January 2012. The resulting northward Ekman transport resulted in a strong upwelling of Atlantic Water along the shelf. We suggest that a reduction in sea ice and the upwelling of nutrient-rich waters seen in the winter of 2012 created conditions similar to those that occurred during the peak of the European whaling period (1690-1790) and that this combination of physical features was in fact the driving force behind the high primary and secondary production of diatoms and Calanus spp., which sustained the large historical stocks of bowhead whales ( Balaena mysticetus) in Arctic waters near Spitsbergen. [ABSTRACT FROM AUTHOR]

A study is carried out to understand whether the Indian summer monsoon rainfall variability is associated with the geophysical parameters over the Arctic region. The correlation analyses of the satellite-derived sea ice data for 29 years indicate that out of 9 sectors of the Arctic region, the Kara and Barents Seas sector's Sea Ice Extent (KBS SIE) during October has a strong relationship with the All-India Summer Monsoon Rainfall (AISMR) in the following year. This relationship is more pronounced for the extreme cases, which are identified as the drought or the excess monsoon years. Moreover, the composites of certain geophysical parameters over the Arctic also behave in tandem with the monsoon rainfall. In order to test the relationship of these geophysical parameters with the monsoon rainfall, a case of the recent drought of 2009 is independently evaluated. The results obtained in this study bring out that KBS SIE and some other parameters of the Arctic region can be used as potential predictors in the long-range forecasting of AISMR with a lead period of more than six months. A table indicating the qualitative forecast of the monsoon rainfall is presented on the basis of some parameters of the Arctic region. The mean sea level pressure anomaly tendency over northwest Europe during winter, which is one of the predictors used for forecasting the AISMR, is significantly correlated with the KBS SIE during the preceding October. As such, with the knowledge of October KBS SIE, this parameter can also be foreseen a few months in advance. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Associations between the spring Arctic sea ice concentration (SIC) and North American summer rainfall were discussed using the singular value decomposition analysis. Results show that a reduced SIC in the North Atlantic and Pacific sectors of the Arctic, and an enhanced SIC in much of the central Arctic Basin and Beaufort Sea, are accompanied by dry conditions over the western United States, the northern Great Plains, the Midwest, westernmost Canada, and central and eastern Greenland, and wet conditions over the southern United States, Alaska, northern Canada, and western Greenland. Atmospheric circulation anomalies associated with the SIC variability show two wave-train structures, which are persistent from spring to summer, leading to the identified relationship between the spring Arctic SIC and North American summer rainfall. This relationship indicates a potential long-term outlook for the North American summer rainfall as the decline of the spring sea ice in the north Atlantic and Pacific sectors of the Arctic is expected to continue as climate warms. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

We present a first assessment of airborne laser and radar altimeter data over snow-covered sea ice, gathered during the National Aeronautics and Space Administration Operation IceBridge Mission. We describe a new technique designed to process radar echograms from the University of Kansas snow radar to estimate snow depth. We combine IceBridge laser altimetry with radar-derived snow depths to determine sea ice thickness. Results are validated through comparison with direct measurements of snow and ice thickness collected in situ at the Danish GreenArc 2009 sea ice camp located on fast ice north of Greenland. The IceBridge instrument suite provides accurate measurements of snow and ice thickness, particularly over level ice. Mean IceBridge snow and ice thickness agree with in situ measurements to within ~ 0.01 and ~ 0.05 m, respectively, while modal snow and ice thickness estimates agree to within 0.02 and 0.10 m, respectively. IceBridge snow depths were correlated with in situ measurements (R = 0.7, for an averaging length of 55 m). The uncertainty associated with the derived IceBridge sea ice thickness estimates is 0.40 m. The results demonstrate the retrieval of both first-year and multiyear ice thickness from IceBridge data. The airborne data were however compromised in heavily ridged ice where snow depth, and hence ice thickness, could not be measured. Techniques developed as part of this study will be used for routine processing of IceBridge retrievals over Arctic sea ice. The limitations of the GreenArc study are discussed, and recommendations for future validation of airborne measurements via field activities are provided. [ABSTRACT FROM AUTHOR]

Arctic sea ice mass budgets for the twentieth century and projected changes through the twenty-first century are assessed from 14 coupled global climate models. Large inter-model scatter in contemporary mass budgets is strongly related to variations in absorbed solar radiation, due in large part to differences in the surface albedo simulation. Over the twenty-first century, all models simulate a decrease in ice volume resulting from increased annual net melt (melt minus growth), partially compensated by reduced transport to lower latitudes. Despite this general agreement, the models vary considerably regarding the magnitude of ice volume loss and the relative roles of changing melt and growth in driving it. Projected changes in sea ice mass budgets depend in part on the initial (mid twentieth century) ice conditions; models with thicker initial ice generally exhibit larger volume losses. Pointing to the importance of evolving surface albedo and cloud properties, inter-model scatter in changing net ice melt is significantly related to changes in downwelling longwave and absorbed shortwave radiation. These factors, along with the simulated mean and spatial distribution of ice thickness, contribute to a large inter-model scatter in the projected onset of seasonally ice-free conditions. [ABSTRACT FROM AUTHOR]

The article presents a study on the role of sea ice sediments in the seasonal development of sea ice biota in near-shore Arctic. It states that an assessment of the impact of sediments on the development of sea ice biota in Alaskan nearshore fast ice was conducted and ice, water and seafloor sediments were sampled in spring 2002 and 2003 in two locations. The study found out that the reduction of light in the sediment-loaded sea ice location is more than 99% compared to the clean ice location and the development of bottom ice algal blooms in fast ice reaches their maximum of 330 ug chl a 1-1. It adds that ice metazoans in clean ice increased from 17 700 to 276 200 ind. m-2 dominated by nematodes and polychaete juveniles.