Sea Ice Research Articles

Sea-ice loss in Eurasian Arctic coast intensifies heavy Meiyu-Baiu rainfall associated with Indian Ocean warming

Heavy Meiyu-Baiu rainfall can pose threat to the dense population in East Asia by catastrophic flooding. Although previous studies have identified Indian Ocean (IO) warming as the major cause of heavy Meiyu-Baiu rainfall, it failed to predict the record-breaking rainfall in July 2020. Synthesizing observational analysis, large-ensemble climate simulations, and atmospheric simulations, we show that sea-ice loss in the Kara Sea in May can intensify the IO warming-induced heavy Meiyu-Baiu rainfall and well explains the record-breaking rainfall in July 2020. In the precondition of IO warming, sea-ice loss tends to prolong Meiyu-Baiu season and strengthen convective activity over the Meiyu-Baiu region, thereby enhancing the IO warming-induced heavy Meiyu-Baiu rainfall by ~50% and doubling the risk of extreme events comparable to or greater than the one in 2020. A statistical model is further constructed to demonstrate that taking Arctic sea ice into consideration can significantly improve the seasonal prediction of extreme Meiyu-Baiu rainfall.

A dataset of the daily edge of each polynya in the Antarctic

Polynyas play a critical role in the formation of Antarctic Bottom Water and the enhancement of polar primary productivity. Accurate and exhaustive identification of Antarctic polynyas is fundamental to advancing in-depth research. However, due to methodological limitations, previous studies paid more attention to frequent polynyas and infrequent polynyas have not been investigated much despite that they could be vulnerable to climate change. Inspired by a cyclone tracking algorithm, we develop a novel method to overcome challenges identifying all types of polynyas satisfying spatiotemporal criteria and tracing their daily evolution, extracting from an extensive amount of sea ice concentration data. Based on it, we establish a dataset called “Daily Edge of Each Polynya in Antarctica” (DEEP-AA). Validation against remote sensing and ship-based observations confirms DEEP-AA’s reliability. Compared to existing maps, the DEEP-AA identifies a threefold number of polynyas and reveals the seasonal area recovery of infrequent polynyas is earlier than frequent ones.

Simulating Mixed‐Phase Open Cellular Clouds Observed During COMBLE: Evaluation of Parameterized Turbulence Closure

AbstractMarine cold‐air outbreaks, or CAOs, are airmass transformations whereby relatively cold boundary layer (BL) air is transported over relatively warm water. To more deeply understand BL and mixed‐phase cloud properties during CAO conditions, the Cold‐Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) took place from late 2019 into early 2020. During COMBLE, the U.S. Department of Energy's first Atmospheric Radiation Measurement Mobile Facility (AMF1) was deployed to Andenes, Norway, far downstream (∼1,000 km) from the Arctic pack ice. This study examines the two most intense CAOs sampled at the AMF1 site. The observed BL structures are open cellular with high (∼3–5 km) and cold (−30 to −50 C) cloud tops, and they often have pockets of high liquid water paths (LWPs; up to ∼1,000 g ) associated with strong updrafts and enhanced turbulence. We use a high‐resolution mesoscale model to explore how well four turbulence closure methods represent open cellular clouds. After applying a radar simulator to model outputs for direct evaluation, cloud top properties agree well with AMF1 observations (within ∼10%), but radar reflectivity and LWP agreement is more variable. Results suggest that the turbulent Prandtl number may play an important role for the simulated BL and cloud properties. All simulations produce enhanced precipitation rates that are well‐correlated with a cloud transition. Finally, the eddy‐diffusivity/mass‐flux approach produces the deepest cloud layer and therefore the largest and most coherent cellular structures. We recommend the use of a non‐local turbulence closure approach to better capture turbulent processes in intense CAOs.

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.

A Model‐Based Investigation of the Recent Rebound of Shelf Water Salinity in the Ross Sea

AbstractIntense atmosphere‐ocean‐ice interactions in the Ross Sea play a vital role in global overturning circulation by supplying saline and dense shelf waters. Since the 1960s, freshening of the Ross Sea shelf water has led to a decline in Antarctic Bottom Water formation. However, during 2012–2018, salinity of the western Ross Sea has rebounded. This study adopts a global ocean‐sea ice model to investigate the causes of this salinity rebound. Model‐based surface salinity budget analysis indicates that the salinity rebound was driven by increased brine rejection from sea ice formation, triggered by nearly equal effects of local anomalous winds and surface heat flux. The local divergent wind anomalies promoted local sea ice formation by creating a thin ice area, while cooling heat flux anomaly decreased the surface temperature, increasing sea ice production as well. This highlights the importance of understanding local climate variability in projecting future dense shelf water change.

Impacts of Atlantic meridional overturning circulation weakening on Arctic amplification

Enhanced warming of the Arctic region relative to the rest of the globe, known as Arctic amplification, is caused by a variety of diverse factors, many of which are influenced by the Atlantic meridional overturning circulation (AMOC). Here, we quantify the role of AMOC changes in Arctic amplification throughout the twenty-first century by comparing two suites of climate model simulations under the same climate change scenario but with two different AMOC states: one with a weakened AMOC and another with a steady AMOC. We find that a weakened AMOC can reduce annual mean Arctic warming by 2 °C by the end of the century. A primary contributor to this reduction in warming is surface albedo feedback, related to a smaller sea ice loss due to AMOC slowdown. Another major contributor is the changes in ocean heat uptake. The weakened AMOC and its associated anomalous ocean heat transport divergence lead to increased ocean heat uptake and surface cooling. These two factors are inextricably linked on seasonal timescales, and their relative importance for Arctic amplification can vary by season. The weakened AMOC can also abate Arctic warming via lapse rate feedback, creating marked cooling from the surface to lower-to-mid troposphere while resulting in relatively weaker cooling in the upper troposphere. Additionally, the weakened AMOC increases the low-level cloud fraction over the North Atlantic warming hole, causing significant cooling there via shortwave (sw) cloud feedback despite the overall effect of sw cloud feedback being a slight warming of the average temperature over the Arctic.

A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary

In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research.

Modulational instability of nonuniformly damped, broad-banded waves: Applications to waves in sea ice

Modulational instability of nonuniformly damped, broad-banded waves: Applications to waves in sea ice

Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations

Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations

Study on freezing separation process through observing microstructure of NaCl solution ice

Freezing desalination is a method to purify saline water. Researchers believe that the salt inclusions distributed inside ice affect the sea ice salinity, but there is not sufficient visualized proof that has been obtained via direct and non-destructive experiments to support this view. Even less study has been carried out on the salt inclusion distribution inside ice below the eutectic temperature. In this paper, a NaCl solution ice sample was prepared with the original mass concentration of 3 wt% using unidirectional heat transfer method. The freezing temperature was −24 ℃, which is lower than −21.2 ℃, the eutectic temperature of NaCl solution. Micro-CT device was used to scan the upper, middle and lower parts of a 3 wt% NaCl solution ice sample and the 5-layer U-Net architecture in DL (deep learning) module of the Dragonfly program was used to segment images to extract the information of interest. The image characteristics of salt inclusions and air bubbles inside the ice were obtained. Very high salt inclusion area proportion was found in the surface layer images of the sample with the maximum area proportion of about 43%. Experiments were also carried out to measure the salinity distribution inside the same solution ice samples. It was found that in the surface and bottom ice layers, the salinity is higher than that of the original solution. In the experiments, for the layers cut from the top and bottom of the 3 wt% NaCl solution ice sample, each of them possesses 10% of the solution ice sample mass, their salinities were 4.28 wt% and 5.41 wt%, respectively. Comparing the measurement results with the micro-CT image processing results, it was found that the variation tendency of the area proportion of salt inclusions is consistent with that of the ice layer salinity. The combination of micro-CT technology with DL image processing method has been proved an effective way to visualize and analyze quantitatively the internal structure of salt solution ice. Then, three NaCl solution samples that have the original mass concentrations of 3 wt%, 6 wt% and 9 wt%, and a pure water sample were frozen under the same conditions. The middle parts of the four ice samples were scanned and the obtained micro-CT images were processed. It can be found that in the middle parts of the three NaCl solution ice samples, the area proportions of salt inclusions increase from top to bottom and are positively correlated with the original salinities of the solution ice. The factors affecting air bubble distribution are more complex than that affecting salt inclusion distribution.

Multilateral collaborative governance of Arctic shipping safety: Examining the impact of the Arctic Council via collaborative network analysis

Global climate change has dramatically reduced Arctic sea ice, opening new potential shipping routes, which has garnered international attention. This study explores the role of the Arctic Council in the collaborative governance network focused on Arctic shipping safety. For this purpose, a collection of 49 collaborative events initiated by international organisations and the Arctic Council was used as the data sample. These events are divided into two groups, one containing all the events and the other excluding those initiated by the Arctic Council. As a result, two collaborative networks are developed and evaluated based on network density, average path length, network transitivity, centrality indices (degree, closeness, betweenness), and clustering analysis. The results demonstrate that the Arctic Council significantly enhances the network density and transitivity of the international collaboration network, and reshapes core-periphery dynamics and cohesive subgroup structures. For instance, under the influence of the Arctic Council, Canada, France, and Japan surpass the United Kingdom in centrality (degree, closeness, betweenness). Results of the clustering coefficients reveal the Arctic Council's impact on connections among states, leading to shifts in collaboration patterns. Additionally, subgroup cohesion and density coefficients confirm the Arctic Council's role in promoting collaboration among states and restructuring governance networks.

Coastal Sea Ice Concentration Derived from Marine Radar Images: A Case Study from Utqiaġvik, Alaska

We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities.

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.

Acoustic Velocity-Based Inversion of the Physical Properties of Sea Ice in the Central Arctic Region

Acoustic Velocity-Based Inversion of the Physical Properties of Sea Ice in the Central Arctic Region

Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness

Arctic sea ice volume (SIV) is a key climate indicator and memory source in sea ice predictions and projections, yet suffering from large observational and model uncertainty. Here, we test whether passive microwave (PMW) data constrain the long-term evolution of Arctic SIV, as recently hypothesized. We find many commonalities in Arctic SIV changes from a PMW sea ice thickness (SIT) 1992-2020 time series reconstructed with a neural network algorithm trained on lidar altimetry, and the reference PIOMAS reanalysis: relatively low differences in SIV mean (4615 km3, 37%), SIV trends (46 km3, 17%), and phased variability (r2=0.55). Key to reduced differences is the consistent evolution of many SIV contributors: seasonal and perennial ice coverage, their SIT contrast, whereas perennial SIT provides the largest remaining uncertainty source. We argue that PMW includes useful SIT information, reducing SIV uncertainty. We foresee progress from sea ice reanalyses combining dynamical models and data assimilation of PMW SIT estimates, in addition to the already assimilated PWM sea ice concentration.

Modeling freezing and BioGeoChemical processes in Antarctic sea ice

AbstractThe Antarctic sea ice, which undergoes annual freezing and melting, plays a significant role in the global climate cycle. Since satellite observations in the Antarctic region began, 2023 saw a historically unprecedented decrease in the extent of sea ice. Further ocean warming and future environmental conditions in the Southern Ocean will influence the extent and amount of ice in the Marginal Ice Zones (MIZ), the BioGeoChemical (BGC) cycles, and their interconnected relationships. The so‐called pancake floes are a composition of a porous sea ice matrix with interstitial brine, nutrients, and biological communities inside the pores. The ice formation and salinity are both dependent on the ambient temperature. To realistically model these multiphasic and multicomponent coupled processes, the extended Theory of Porous Media (eTPM) is used to develop Partial Differential Equations (PDEs) based high‐fidelity models capable of simulating the different seasonal variations in the region. All critical variables like salinity, ice volume fraction, and temperature, among others, are considered and have their equations of state. The phase transition phenomenon is approached through a micro‐macro linking scheme. In this paper, a phase‐field solidification model [4] coupled with salinity is used to model the microscale freezing processes and up‐scaled to the macroscale eTPM model. The evolution equations for the phase field model are derived following Landau‐Ginzburg order parameter gradient dynamics and mass conservation of salt allowing to model the salt trapped inside the pores. A BGC flux model for sea ice is set up to simulate the algal species present in the sea ice matrix. Ordinary differential equations (ODE) are employed to represent the diverse environmental factors involved in the growth and loss of distinct BGC components. Processes like photosynthesis are dependent on temperature and salinity, which are derived through an ODE‐PDE coupling with the eTPM model. Academic simulations and results are presented as validation for the mathematical model. These high‐fidelity models eventually lead to their incorporation into large‐scale global climate models.

Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea-Ice Loss using an Idealised Aquaplanet GCM

Abstract Coupled climate model simulations designed to isolate the effects of Arctic sea-ice loss often apply artificial heating, either directly to the ice or through modification of the surface albedo, to constrain sea ice in the absence of other forcings. Recent work has shown that this approach may lead to an overestimation of the climate response to sea-ice loss. In this study, we assess the spurious impacts of ice-constraining methods on the climate of an idealised aquaplanet general circulation model (GCM) with thermodynamic sea ice. The true effect of sea-ice loss in this model is isolated by inducing ice loss through reduction of the freezing point of water, which does not require additional energy input. We compare results from freezing point modification experiments with experiments where sea-ice loss is induced using traditional ice-constraining methods, and confirm the result of previous work that traditional methods induce spurious additional warming. Furthermore, additional warming leads to an overestimation of the circulation response to sea-ice loss, which involves a weakening of the zonal wind and storm track activity in midlatitudes. Our results suggest that coupled model simulations with constrained sea ice should be treated with caution, especially in boreal summer, where the true effect of sea-ice loss is weakest but we find the largest spurious response. Given that our results may be sensitive to the simplicity of the model we use, we suggest that devising methods to quantify the spurious effects of ice-constraining methods in more sophisticated models should be an urgent priority for future work.

Spatiotemporal evolution of air-sea CO2 flux in the Northwest Pacific and its response to ENSO

Global warming, driven by human activities since the Industrial Revolution, has significantly elevated atmospheric carbon dioxide (CO2) levels, leading to higher global temperatures and a rise in extreme weather events. The ocean, as a major carbon sink, has absorbed about 30% of human-induced carbon emissions, helping mitigate global warming’s impacts. This study examines the spatiotemporal distribution of air-sea CO2 flux in the Northwest Pacific from 1982 to 2021 and its response to El Niño-Southern Oscillation, using Empirical Orthogonal Function and composite analysis. The seasonal patterns of air-sea CO2 flux and the influence of environmental factors were further evaluated. The results show that air-sea CO2 flux in the Northwest Pacific exhibits clear seasonal fluctuations. In winter, high-latitude areas act as significant carbon sources. Strong winds deepen the mixed layer, promoting CO2 release from the ocean into the atmosphere. In contrast, in summer, longer daylight hours, rising SST, and melting sea ice lead to upwelling, which brings nutrients to the surface and stimulates phytoplankton growth. This process turns the region into a carbon sink as phytoplankton growth, driven by intense sunlight, enhances the ocean’s CO2 absorption. The mid-latitude region consistently acts as a carbon sink year-round. During El Niño events, more negative air-sea CO2 flux anomalies appear in the eastern Northwest Pacific, enhancing carbon uptake. La Niña events have the opposite effect in the eastern regions. These findings highlight the Northwest Pacific’s critical role in modulating regional and global carbon cycles under varying climatic conditions. Understanding these dynamics is crucial for improving predictions of future climate impacts and for developing effective strategies to mitigate global warming.

Submicron Organic Aerosol Types in the Summertime Arctic: Mixing State, Geographic Distribution, and Drivers

AbstractDuring the 2017 summertime Arctic cruise observation campaigns, we measured over 290,000 individual submicron particles and clustered them into two inorganic classes (dominated by sea salt, accounting for 38.6% by number fraction) and five organic classes (dominated by natural and anthropogenic organics, 61.4%), presenting a distinct difference in geographic distribution. In the high Arctic and marginal ice zone (81.1–84.6°N) compared with the low Arctic (Chukchi Sea, Svalbard, and Iceland, <80°N), ocean‐derived organic aerosols were more prevalent (73.6% vs. 37.1%). Specifically, we found sharp contrasts in the geographic distributions of OC‐Ca (organics internally mixed with calcium, 29.0% vs. 9.4%) and OC‐S (organics internally mixed with sulfate, 3.2% vs. 21.4%). Utilizing an explainable machine learning technique, we inferred that OC‐Ca was driven by wind‐blown sea ice and/or sea ice floes and/or bubble bursting within sea ice leads under low wind speed conditions in the high Arctic, while OC‐S tended to associate with elemental carbon, sulfate, and higher temperatures, potentially originating from combustion emissions at low latitude regions.

Summer snow on Arctic sea ice modulated by the Arctic Oscillation

Since the 1970s, Arctic sea ice has undergone unprecedented change, becoming thinner, less extensive and less resilient to summer melt. Snow’s high albedo greatly reduces solar absorption in sea ice and the upper ocean, which mitigates sea–ice melt and ocean warming. However, the drivers of summertime snow depth variability are unknown. The Arctic Oscillation is a mode of natural climate variability, influencing Arctic snowfall and air temperatures. Thus, it may affect summertime snow conditions on Arctic sea ice. Here we examine the role of the Arctic Oscillation in summer snow depth variability on Arctic sea ice in 1980–2020 using atmospheric reanalysis, snow modelling and satellite data. The positive phase leads to greater snow accumulation, ranging up to ~4.5 cm near the North Pole, and higher surface albedo in summer. There are more intense, frequent Arctic cyclones, cooler temperatures aloft and greater snowfall relative to negative and neutral phases; these conditions facilitate a more persistent summer snow cover, which may lessen sea-ice melt and ocean warming. The Arctic Oscillation influence on summertime snow weakens after 2007, which suggests that future warming and Arctic sea-ice loss might modify the relationship between the Arctic Oscillation and snow on Arctic sea ice.