Arctic Sea Ice Cover Research Articles

Seasonal evolution of the sea ice floe size distribution in the Beaufort Sea from 2 decades of MODIS data

Abstract. Arctic sea ice cover evolves seasonally from large plates separated by long, linear leads in the winter to a mosaic of smaller sea ice floes in the summer. The interplay between physical and thermodynamic mechanisms during this process ultimately creates the observed sea ice floe size distribution (FSD), which is an important metric for characterizing the sea ice cover and assessing model performance. Historically, the FSD has been studied at fixed locations over short periods, leaving a gap in our understanding of the spatial and temporal evolution of the FSD at large scales. Here, we present an automated framework for image segmentation, allowing the identification and labeling of individual ice floes in Moderate Resolution Imaging Spectroradiometer (MODIS) data. Using this algorithm, we automatically process and segment 4861 images, identifying more than 9.4 million floes over 23 years. The extracted characteristics of the floes – including area, perimeter, and orientation – evolve throughout the spring and summer in the Beaufort Sea. We find seasonal patterns of decreasing mean floe areas, increasing FSD power law slopes, and increasing variability in the floe orientation as the summer progresses.

Melt Pond Evolution along the MOSAiC Drift: Insights from Remote Sensing and Modeling

Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt ponds is essential for understanding changes in Arctic sea ice. In this study, we used a revised sea ice model to simulate the evolution of melt ponds along the MOSAiC drift at a resolution of 10 m. A novel melt pond parameterization scheme simulates the movement of meltwater under the influence of gravity over a realistic sea ice topography. We evaluated different melt pond parameterization schemes based on remote sensing observations. The absolute deviation of the maximum pond coverage simulated by the new scheme is within 3%, while differences among parameterization schemes exceed 50%. Errors were found to be primarily due to the calculation of macroscopic meltwater loss, which is related to sea ice surface topography. Previous studies have indicated that sea ice with a lower surface roughness has a larger catchment area, resulting in larger pond coverage during the melt season. This study has identified an opposing mechanism: sea ice with lower surface roughness has a larger catchment area connected to the macroscopic flaws of the sea ice surface, which leads to more macroscopic drainage into the ocean and thereby a decrease in melt pond coverage. Experimental simulations showed that sea ice with 46% higher surface roughness, resulting in 12% less macroscopic drainage, exhibited a 38% higher maximum pond fraction. The presence of macroscopic flaws is related to the fragmentation of sea ice cover. As Arctic sea ice cover becomes increasingly fragmented and mobile, this mechanism will become more significant.

Salinity and Stratification at the Sea Ice Edge (SASSIE): an oceanographic field campaign in the Beaufort Sea

Abstract. As our planet warms, Arctic sea ice coverage continues to decline, resulting in complex feedbacks with the climate system. The core objective of NASA's Salinity and Stratification at the Sea Ice Edge (SASSIE) mission is to understand how ocean salinity and near-surface stratification affect upper-ocean heat content and thus sea ice freeze and melt. SASSIE specifically focuses on the formation of Arctic Sea ice in autumn. The SASSIE field campaign in 2022 collected detailed observations of upper-ocean properties and meteorology near the sea ice edge in the Beaufort Sea using ship-based and piloted and drifting assets. The observations collected during SASSIE include vertical profiles of stratification up to the sea surface, air–sea fluxes, and ancillary measurements that are being used to better understand the role of salinity in coupled Arctic air–sea–ice processes. This publication provides a detailed overview of the activities during the 2022 SASSIE campaign and presents the publicly available datasets generated by this mission (available at https://podaac.jpl.nasa.gov/SASSIE, last access: 29 May 2024; DOIs for individual datasets in the “Data availability” section), introducing an accompanying repository that highlights the numerical routines used to generate the figures shown in this work.

The cooperative effects of November Arctic sea ice and Eurasian snow cover on the Eurasian surface air temperature in January–February

AbstractDue 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.

Multiscale mushy layer model for Arctic marginal ice zone dynamics

Perhaps the most dynamic component of the Arctic sea ice cover is the marginal ice zone (MIZ), the transitional region between dense pack ice to the north and open ocean to the south. It widens by a factor of four while seasonally migrating more than 1600 km poleward in the Bering–Chukchi Sea sector, impacting climate dynamics, ecological processes, and human accessibility to the Arctic. Here we showcase a transformative mathematical modeling approach to understanding changes in MIZ location and width, focusing on their seasonal cycles as observed by satellites. We view the MIZ as a liquid-solid phase transition region, or mushy zone, on the scale of the Arctic Ocean. Invoking the physics of phase changes, the MIZ is modeled as a dynamic, multiscale composite material layer; this model captures 96% of the annual cycle of MIZ location and 78% of the annual cycle of MIZ width. Temperature in the upper ocean is described by a nonlinear heat equation with effective parameters obtained using homogenization theory for a random medium of ice floes in a sea water host. Observations and simulations together indicate that MIZ location closely tracks the below-ice 273 K isotherm while the width of the MIZ follows vertical heat flux convergence, but with a three-week lag.

Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models

Abstract. We examine the past and projected changes in Arctic sea ice properties in six climate models participating in the High-Resolution Model Intercomparison Project (HighResMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Within HighResMIP, each of the experiments is run using a reference resolution configuration (consistent with typical CMIP6 runs) and using higher-resolution configurations. The role of horizontal grid resolution in both the atmosphere model component and the ocean model component in reproducing past and future changes in the Arctic sea ice cover is analysed. Model outputs from the coupled historical (hist-1950) and future (highres-future) runs are used to describe the multi-model, multi-resolution representation of the Arctic sea ice and to evaluate the systematic differences (if any) that resolution enhancement causes. Our results indicate that there is not a strong relationship between the representation of sea ice cover and the ocean/atmosphere grids; the impact of horizontal resolution depends rather on the sea ice characteristic examined and the model used. However, the refinement of the ocean grid has a more prominent effect compared to the refinement of the atmospheric one, with eddy-permitting ocean configurations generally providing more realistic representations of sea ice area and sea ice edges. All models project substantial sea ice shrinking: the Arctic loses nearly 95 % of sea ice volume from 1950 to 2050. The model selection based on historical performance potentially improves the accuracy of the model projections and predicts that the Arctic will turn ice-free as early as 2047. Along with the overall sea ice loss, changes in the spatial structure of the total sea ice and its partition in ice classes are noticed: the marginal ice zone (MIZ) will dominate the ice cover by 2050, suggesting a shift to a new sea ice regime much closer to the current Antarctic sea ice conditions. The MIZ-dominated Arctic might drive development and modification of model physics and parameterizations in the new generation of general circulation models (GCMs).

Increasing fluctuations in the Arctic summer sea ice cover are expected with future global warming

The loss of Arctic sea ice (ASI) represents a major transformation in the Arctic region, impacting regional and global climate, ecosystems, and socio-economic structures. Observational and reanalysis data have consistently shown a notable shift in polar environmental conditions over recent decades, marked by a substantial reduction in the ASI area and a rise in the variability in its coverage and distribution. Utilizing data from the latest Coupled Model Intercomparison Project phase, our study reveals a consistent pattern highlighting a fundamental shift in ASI dynamics preceding total loss. We observe increasing fluctuations in the September ASI area as the threshold for an ice-free Arctic is approached across various scenarios and models. This pattern is particularly concentrated in the Central Arctic (CA) sub-region. Spatial analyses reveal increasing variance along the CA’s northern coastlines, accompanied by a substantial increase in open water coverage, underscoring the shift from stable to highly variable ice conditions in this region. Additionally, our findings suggest a potential link between increased ASI fluctuations and variability in surface wind speeds. These specific results underscore the urgency of multidisciplinary approaches in addressing the challenges posed by ASI variability, with implications for marine ecosystems, Indigenous communities, and navigational safety.

Recent Thickening of the Barents Sea Ice Cover

AbstractThe Arctic sea ice cover has decreased rapidly over the last few decades both in extent and thickness. Here we present multi‐year (2013–2022) observations of sea ice thickness in the northwestern Barents Sea based on Upward Looking Sonar measurements and show that the winter sea ice has become thicker over the last decade. Sea ice thickness from the Pan‐Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) reproduces both the observed variability and recent 10‐year trend and shows that this thickening (0.24 m decade−1) has not been seen since the 1990s. Using PIOMAS we find that the recent increase in sea ice thickness can be explained by increased sea ice freezing as a result of lower temperatures in the ocean and in the atmosphere. The recent thickening is set in the context of a long‐term thinning trend, with PIOMAS showing much thinner ice now than in the 1980s.

Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals

Abstract. The net Arctic sea-ice area (SIA) can be estimated from the sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a truly useful metric, for example of the sensitivity of the Arctic sea-ice cover to global warming, we need, however, reliable estimates of its uncertainty. Here we retrieve this uncertainty by taking into account the spatial and temporal error correlations of the underlying local sea-ice concentration products. As 1 example year, we find that in 2015 the average observational uncertainties of the SIA are 306 000 km2 for daily estimates, 275 000 km2 for weekly estimates, and 164 000 km2 for monthly estimates. The sea-ice extent (SIE) uncertainty for that year is slightly smaller, with 296 000 km2 for daily estimates, 261 000 km2 for weekly estimates, and 156 000 km2 for monthly estimates. These daily uncertainties correspond to about 7 % of the 2015 sea-ice minimum and are about half of the spread in estimated SIA and SIE from different passive microwave SIC products. This shows that random SIC errors play a role in SIA uncertainties comparable to inter-SIC-product biases. We further show that the September SIA, which is traditionally the month with the least amount of Arctic sea ice, declined by 105 000±9000 km2 a−1 for the period from 2002 to 2017. This is the first estimate of a SIA trend with an explicit representation of temporal error correlations.

Near‐Inertial Wave Propagation in the Deep Canadian Basin: Turning Depths and the Homogeneous Deep Layer

AbstractThe internal wave climate in the deep Arctic Ocean, away from the shelves, is quiet because the ice cover shields the ocean from wind energy input, and tidal amplitudes are small. Hence, mixing due to internal wave breaking is small. The shrinking Arctic sea ice cover, however, exposes more open ocean areas to energy transfer by wind. Consequently, more energetic near‐inertial internal waves (NIWs) may carry energy to the bottom, potentially enhancing deep mixing. In the deep Canadian Basin, weakly stratified layers with local buoyancy frequencies smaller than the wave frequency may prevent NIW propagation to the seafloor. We estimate the distribution of these near‐inertial turning depths from temperature and salinity data of the years 2005–2014. Near‐inertial turning depths are ubiquitous in the deep Canadian Basin at ∼2,750 m depth, between 100 and 1,200 m above the bottom. A deep homogeneous layer below 3,300 m is characterized by small squared buoyancy frequencies N2 ∼ 0 with locally unstable layers (N2 < 0). The turning depths reflect NIWs and hence limit their contribution to deep mixing, but the waves create an evanescent perturbation with exponentially decreasing amplitude that can interact with the bathymetry, especially above slopes and ridges where the height of the turning depths above the seafloor is small. After reflection, the main part of the wave energy is trapped between turning depths and the surface, so that a potential increase of wave energy input mainly affects mixing of mid‐depth water masses like the Atlantic Water.

Why is summertime Arctic sea ice drift speed projected to decrease?

Abstract. Alongside declining Arctic sea ice cover during the satellite era, there have also been positive trends in sea ice Arctic average drift speed (AADS) during both winter and summer. This increasing sea ice motion is an important consideration for marine transportation as well as a potential feedback on the rate of sea ice area decline. Earlier studies have shown that nearly all modern global climate models (GCMs) produce positive March (winter) AADS trends for both the historical period and future warming scenarios. However, most GCMs do not produce positive September (summer) AADS trends during the historical period, and nearly all GCMs project decreases in September AADS with future warming. This study seeks to understand the mechanisms driving these projected summertime AADS decreases using output from 17 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) along with 10 runs of the Community Earth System Model version 2 Large Ensemble (CESM2-LE). The CESM2-LE analysis reveals that the projected summertime AADS decreases are due to changes in sea surface height (SSH) and wind stress which act to reduce sea ice motion in the Beaufort Gyre and Transpolar Drift. During March, changes in internal stress and wind stress counteract tilt force changes and produce positive drift speed trends. The simulated wintertime mechanisms are supported by earlier observational studies, which gives confidence that the mechanisms driving summertime projections are likely also at work in the real world. However, the precise strength of these mechanisms is likely not realistic during summer, and additional research is needed to assess whether the simulated summertime internal stress changes are too weak compared to changes in other forces. The projected summertime wind stress changes are associated with reduced sea level pressure north of Greenland, which is expected with the northward shift of the jet streams. The projected summertime SSH changes are primarily due to freshening of the Arctic Ocean (i.e., halosteric expansion), with thermal expansion acting as a secondary contribution. The associated ocean circulation changes lead to additional piling up of water in the Russian shelf regions, which further reinforces the SSH increase. Analysis of CMIP6 output provides preliminary evidence that some combination of wind stress and SSH changes is also responsible for projected AADS decreases in other models, but more work is needed to assess mechanisms in more detail. Altogether, our results motivate additional studies to understand the roles of SSH and wind stress in driving changes in Arctic sea ice motion.

Surface Cloud Warming Increases as Late Fall Arctic Sea Ice Cover Decreases

AbstractDuring 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.

High Arctic seawater and coastal soil microbiome co-occurrence and composition structure and their potential hydrocarbon biodegradation.

The accelerated decline in Arctic sea-ice cover and duration is enabling the opening of Arctic marine passages and improving access to natural resources. The increasing accessibility to navigation and resource exploration and production brings risks of accidental hydrocarbon releases into Arctic waters, posing a major threat to Arctic marine ecosystems where oil may persist for many years, especially in beach sediment. The composition and response of the microbial community to oil contamination on Arctic beaches remain poorly understood. To address this, we analyzed microbial community structure and identified hydrocarbon degradation genes among the Northwest Passage intertidal beach sediments and shoreline seawater from five high Arctic beaches. Our results from 16S/18S rRNA genes, long-read metagenomes, and metagenome-assembled genomes reveal the composition and metabolic capabilities of the hydrocarbon microbial degrader community, as well as tight cross-habitat and cross-kingdom interactions dominated by lineages that are common and often dominant in the polar coastal habitat, but distinct from petroleum hydrocarbon-contaminated sites. In the polar beach sediment habitats, Granulosicoccus sp. and Cyclocasticus sp. were major potential hydrocarbon-degraders, and our metagenomes revealed a small proportion of microalgae and algal viruses possessing key hydrocarbon biodegradative genes. This research demonstrates that Arctic beach sediment and marine microbial communities possess the ability for hydrocarbon natural attenuation. The findings provide new insights into the viral and microalgal communities possessing hydrocarbon degradation genes and might represent an important contribution to the removal of hydrocarbons under harsh environmental conditions in a pristine, cold, and oil-free environment that is threatened by oil spills.

Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data

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.

Strengthened East Asian Winter Monsoon Regulated by Insolation and Arctic Sea Ice Since the Middle Holocene

AbstractThe East Asian winter monsoon (EAWM) is a crucial climate system in Asia, with significant social and economic impacts. Orbital‐scale variability of the EAWM during the Holocene and its associated mechanisms, however, are still not fully understood. Based on a high‐resolution transient simulation by a coupled climate model, we present here a continuous climate evolution of the EAWM in response to orbital forcing. Our simulated springtime EAWM variations, consistent with grain size records of loess, exhibit an obvious strengthening trend since the mid‐Holocene. In winter, a similar increasing trend is also simulated although not statistically significant. Our results suggest that the Holocene EAWM is basically controlled by meridional temperature gradients between high and low latitudes in both seasons. Other than northern insolation forcing, the feedbacks from Arctic sea ice and Eurasian snow cover also modulate the meridional temperature gradients, highlighting their important roles in driving EAWM evolution during the Holocene.

Forced and internal components of observed Arctic sea-ice changes

Abstract. The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.

Evaluating Causal Arctic‐Midlatitude Teleconnections in CMIP6

AbstractTo analyze links among key processes that contribute to Arctic‐midlatitude teleconnections we apply causal discovery based on graphical models known as causal graphs. First, we calculate the causal dependencies from observations during 1980–2021. Observations show several robust connections from early to late winter, such as atmospheric blocking within central Asia via the Ural blocking and Siberian High, the North Atlantic Oscillation phase and the polar vortex (PV). The PV is affected by poleward eddy heat flux at 100 hPa, which is also directly connected with the Aleutian Low. We then evaluate climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) by comparing their causal graphs with those derived from observations. Compared to observations, CMIP6 historical and future simulations do not robustly capture Arctic‐midlatitude teleconnections arising from Arctic sea ice variability. This highlights the role of atmospheric internal variability in modulating the Arctic‐midlatitude teleconnections. However, we find several distinct patterns that are simulated by most of the analyzed climate models. For example, both historical and future model simulations robustly capture observed atmospheric blocking in central Asia. But contrary to observations, model simulations show a robust link between the Arctic temperature and sea ice cover over Barents and Kara seas. The analysis of future changes also reveals that the connection between the Aleutian Low and the poleward eddy heat flux at 100 hPa is expected to become more robust toward the end of the 21st century than in the analyzed past.

Broadband radiometric measurements from GPS satellites reveal summertime Arctic Ocean Albedo decreases more rapidly than sea ice recedes

New measurements from the Arctic ± 40 days around the summer solstice show reflected sunlight from north of 80°N decreases 20–35%. Arctic sea ice coverage decreases 7–9% over this same time period (as reported by the NSIDC) implying Arctic sea ice albedo decreases in addition to the sea ice receding. Similar Antarctic measurements provide a baseline to which Arctic measurements are compared. The Antarctic reflected sunlight south of 80°S is up to 30% larger than the Arctic reflectance and is symmetric around the solstice implying constant Antarctic reflectivity. Arctic reflected sunlight 20 days after solstice is > 100W/m2 less than Antarctic reflected sunlight. For perspective, this is enough heat to melt > 1 mm/hour of ice. This finding should be compared with climate models and in reanalysis data sets to further quantify sea ice albedo’s role in Arctic Amplification. The measurements were made with previously unpublished pixelated radiometers on Global Positioning System satellites from 2014 to 2019. The GPS orbits give each radiometer instantaneous and continuous views of 37% of the Earth, two daily full views of the Arctic and Antarctic. Furthermore, the GPS constellation gives full-time full-Earth coverage that may provide data that complements existing limited field of view instruments that provide a less synoptic Earth view.

The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

Abstract. In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from CryoSat-2 and NASA daily Bootstrap sea ice concentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the localised ensemble transform Kalman filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid-scale sea ice thickness distribution is of particular novelty. The sub-grid-scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. This study finds that assimilating CryoSat-2 products for the mean thickness and the sub-grid-scale thickness distribution can have significant consequences for the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid-scale sea ice thickness distribution together performed best when compared to a subset of CryoSat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Arctic Archipelago (CAA) is decreased, but the thickness of the ice in the central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid-scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid-scale thickness distribution alongside mean thickness.

Underrepresentation of the Linkage between the Barents–Kara Sea Ice and East Asian Rainfall in Early Summer by CMIP6 Models

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