Arctic Continental-Shelf Sediment Dynamics.

<p>Investigating the temporal and spatial changes in Arctic sea ice freeboard, thickness and volume is crucial for climate and environmental research. In this study, the freeboard, thickness and volume of the Arctic sea ice between 2011 and 2020 were acquired from CryoSat-2 data and it was compared with NASA Operation IceBridge and Alfred Wegener Institue AWI datasets. The effect of wind field and temperature and itscontribution on Arctic sea ice were also investigated. The main steps of our research are as follows. 1) Selecting data points above 66 degrees north latitude and filtering out sea ice by flag value and mask; Using OSI SAF sea ice concentration (SIC) data to further constrict the area; Interpolating the latest mean sea surface data DTU21 into CryoSat-2 data; Calculating the sea ice freeboard via spatial altimetry relationship. 2) Combining with snow density, snow depth, multi-year ice density and first-year ice density to estimate the sea ice thickness respectively from freeboard according to the assumption of hydrostatic equilibrium. 3) Using the area and extent provided by NSIDC to interpret the volume. 4) Utilizing Seasonal and Trend decomposition using Loess(STL) to analyse the seasonal and interannual variations of sea ice. 5) Dividing the Arctic Ocean by its marginal sea, coupled with the HY-2B microwave scatterometer data and NCEP/NCAR reanalysis data, the impact of the Beaufort Sea, the Chukchi Sea, the East Siberian Sea, the Laptev Sea, the Kara Sea, the Barents Sea, the Greenland Sea, and the Baffin Bay wind field on the sea ice were studied. 6) Exploring the correlation between the sea surface temperature and the sea ice freeboard, thickness and volume. The result indicated that (1) the freeboard and thickness was decreasing about 9.748% and 8.80% during 2011-2020,respectively; (2) there are interlunar variations in sea ice freeboard and thickness, the freeboard and thickness of sea ice reach the minimum in August to September each year and the maximum appears in March to April; (3) Arctic sea ice is affected by both thermodynamics and dynamics, the reduction of Arctic sea ice was largely due to the sea surface wind field, one of the dynamic factors is the wind field on the sea surface. The sea ice changes in the various sea areas of the Arctic Ocean are related to the wind field to varying degrees. The changes in the sea surface wind field in recent years have further promoted the reduction of sea ice, making it possible for the Arctic to open to navigation in the summer. Fully understanding the evolution of sea ice change in the Arctic Ocean is helpful for humans to better protect the earth.</p>

Wildfires pose an increasing threat to boreal forest and tundra ecosystems in boreal North America (Alaska and northwestern Canada), as their frequencies rise under global warming. These fires exhibit strong interannual variability that is influenced by regional atmospheric circulation. However, potential impacts of remote boundary forcings on regional fires and the underlying mechanisms remain unclear. This study provides a comprehensive analysis on the impacts of spring sea surface temperature (SST) and sea ice on interannual variability of burned area in this region during fire season (summer) from 1997 to 2020 using GFED5 burned area, SST and sea ice concentration data from the Met Office Hadley Centre, and ERA5 reanalysis data. Results show that in spring a warmer SST in the East Pacific and reduction of sea ice in the northern Chukchi Sea lead independently to an increase in burned area in boreal North America. The correlation coefficients between the SST and sea ice factors with the burned area in boreal North America are 0.43 and –0.44 respectively. The SST-fire relationships can be explained as follows: A warm SST anomaly in the East Pacific triggers a northeastward-propagated Rossby wave, inducing a high-pressure anomaly over boreal North America in spring. Consequently, this circulation anomaly causes a higher surface temperature and thus vegetation growth or drying. As temperatures rise and lightning activity intensifies in summer, burned area increases. On the other hand, the process of sea ice affecting burned area is different. A reduction in sea ice coverage in the northern Chukchi Sea leads to a decrease in surface albedo, resulting in an increase in heat flux. The heat release persists from spring to summer and causes a high-pressure circulation anomaly in boreal North America in summer, which suppresses regional water vapor convergence and precipitation, reducing soil moisture and surface air humidity and increasing vapor pressure deficit (VPD) thereby promoting fuel flammability.

Climate warming has enabled the Arctic region to achieve seasonal navigation, and sea ice concentration is an important factor affecting the navigation of the Arctic waterways. This article uses the Arctic sea ice concentration data of the three highest temperatures in 2016, 2019, and 2020, combined with the Arctic summer sea level pressure, wind field, temperature, temperature anomaly, ice age, and sea ice movement data to analyze the spatial and temporal variation of sea ice and connectivity in the Northeast Passage (NEP) of the Arctic in Summer in three hot years, and summarizes the causes of sea ice anomalies. The results show that: (1) the summer Arctic sea ice extent in 2016, 2019 and 2020 were all lower than the multi-year average sea ice extent, and the summer sea ice extent in 2020 had the largest change trend; (2) in October of these three years, the sea ice was all negative anomalies, extending the opening time of the NEP; (3) when the sea ice concentration was 30% as the threshold, the navigation period of the NEP in 2016 was from mid-August to late October, 2019 was from the beginning of August to mid-October, 2020 was from the end of July to the end of October, and 2020 was the longest year since the opening of the NEP; (4) when the sea ice concentration was 10% as the threshold, the navigation period of the NEP in 2016 was from the end of August to the end of October, 2019 was from early August to mid-October, and 2020 was from the beginning of August to the end of October; (5) the key navigable areas of the NEP in the past three years were the central waters of the East Siberian Sea, the New Siberian Islands and the Vilkitsky Strait; (6) the navigation period of the NEP in 2016, 2019 and 2020 was longer. The main reasons were that the temperature of the NEP in the past 3 years was relatively high, the wind was weak, the sea ice movement had little effect, and the sea ice age in the key navigable areas was first year ice, which was easy to melt, which greatly promoted the opening of the NEP.

The Arctic Ocean has suffered extreme reductions in sea ice in recent decades, and these observed changes suggest implications in terms of moisture transport. The Arctic region is a net sink of moisture in terms of the total hydrological cycle, however, its role as a moisture source for specific regions has not been extensively studied. Our results show that 80% of the moisture supply from the Arctic contributes to precipitation over itself, representing about 8% of the global moisture supply to the Arctic, the remaining 20% is distributed in the surrounding. A reduction in the sea ice extent could make the Arctic Ocean a slightly higher source of moisture to itself or to the surrounding areas. The analysis of the areas affected by Arctic moisture transport is important for establishing those areas vulnerable to change in a framework of a growing sea ice decline. To this end, the Lagrangian model FLEXPART was used in this work to establish the main sinks for the Arctic Ocean, focusing on the moisture transport from this region. The results suggest that most of the moisture loss occurs locally over the Arctic Ocean itself, especially in summer. Some moisture contribution from the Arctic Ocean to continental areas in North America and Eurasia is also noted in autumn and winter especially from Central Arctic, the East Siberian Sea, the Laptev, Kara, Barents, East Greenland and Bering Seas, and the Sea of Okhotsk.

Global, Arctic, and Antarctic sea ice volume predictions using score-driven threshold climate models

The Sea of Okhotsk, between northern Japan and Siberia, is the southernmost sea region of the Northern Hemisphere with seasonal sea ice cover. During early spring, occasional rapid reduction in sea ice cover observed in the southern Sea of Okhotsk can lead to anomalously early disappearance of sea ice along the Hokkaido coast (northern Japan). This study investigated the atmospheric and oceanic processes leading to rapid reduction in sea ice in the southern Sea of Okhotsk during early spring. We detected six events of subseasonal continued reduction in sea ice cover in March between 1993 and 2019. Strong southerly winds with marked thermal advection and moisture transport over northern Japan were observed in five of the six events. The passage of extratropical cyclones brought warm moist air from the south, which led to drifting and melting of sea ice by changing the surface wind speed and surface heat budget in the southern Sea of Okhotsk. The perturbation in sensible heat flux attributable to warm air advection contributed substantially to sea ice reduction, whereas longwave radiation played a secondary role. In addition to the atmospheric processes, an enhanced Soya Current also transports heat from the Sea of Japan, possibly contributing to the sea ice melt. The initial sea ice reduction leads to further sea ice loss through ice–albedo feedback, resulting in prolonged sea ice reductions for 1–2 weeks.

Arctic sea ice exhibits rapid reductions and large fluctuations during recent decades in conjunction with a warming climate. Arctic sea ice shows a prominent seasonal cycle with the maximum extent in spring and the minimum extent in fall. In this study a suite of modeling experiments, which were solely forced with observed time-varying sea-ice concentration for spring 1979 to 2008 was conducted using NCAR’s Community Atmosphere Model to identify sea ice impacts on extratropical storms and associated surface climate parameters when they at the peak of their seasonal variability. Storms are the linkage between the large-scale circulation changes, forced by Arctic sea ice and local extreme weather events through strong winds, high precipitation, and high/low air temperature. In this study, a storm identification and tracking algorithm indicates that reduced sea-ice cover enhances Arctic storm activity. As a consequence, surface climate parameters such as surface air temperature (SAT) and precipitation increases in spring over the Arctic. The changes in the Arctic have a direct and indirect effect on the extratropical storm activity over the mid-latitudes. In contrast to the Arctic, storm activity weakens over Eurasia in the years with less sea ice. Further analysis of the surface climate indicates a warmer and dryer Eurasia for years with reduced sea ice.

[1] The perennial (September) Arctic sea ice cover exhibits large interannual variability, with changes of over a million square kilometers from one year to the next. Here we explore the role of changes in Arctic cyclone activity, and related factors, in driving these pronounced year-to-year changes in perennial sea ice cover. Strong relationships are revealed between the September sea ice changes and the number of cyclones in the preceding late spring and early summer. In particular, fewer cyclones over the central Arctic Ocean during the months of May, June, and July appear to favor a low sea ice area at the end of the melt season. Years with large losses of sea ice are characterized by abnormal cyclone distributions and tracks: they lack the normal maximum in cyclone activity over the central Arctic Ocean, and cyclones that track from Eurasia into the central Arctic are largely absent. Fewer storms are associated with above-average mean sea level pressure, strengthened anticyclonic winds, an intensification of the transpolar drift stream, and reduced cloud cover, all of which favor ice melt. It is also shown that a strengthening of the central Arctic cyclone maximum helps preserve the ice cover, although the association is weaker than that between low cyclone activity and reduced sea ice. The results suggest that changes in cyclone occurrence during late spring and early summer have preconditioning effects on the sea ice cover and exert a strong influence on the amount of sea ice that survives the melt season.

The Arctic region has been warming at a rate significantly faster than the global average, leading to an accelerated decline in sea ice. This trend is expected to continue, potentially resulting in a "low-ice regime," which could make sea ice conditions more unpredictable. Anticipating changes in Arctic sea ice and climate states is therefore crucial for guiding various human activities, from natural resource management to risk assessment decisions. While global climate and Earth system models project continuous sea ice decline over decadal time scales, achieving reliable seasonal forecasts remains challenging. To address this, we apply dynamical downscaling with the state-of-the-art Regional Arctic System Model (RASM), which enables us to forecast Arctic sea ice on time scales ranging from weeks to six months. RASM is a fully coupled regional climate model that integrates components for the atmosphere, ocean, sea ice, and land, interconnected through the flux coupler of the Community Earth System Model. In our study, we simulate RASM at a horizontal resolution of 1/12 degree (approximately 9 km) for both the ocean and sea ice, with 45 vertical levels in the ocean and five thickness categories for sea ice. The atmosphere is configured on a 50-km grid with 40 vertical levels, dynamically downscaled from the NOAA/NCEP Climate Forecasting System version 2 (CFSv2) at 72-hour intervals for the upper half of the atmosphere. Monthly ensemble forecasts extending up to six months are generated using initial conditions derived from a fully-coupled RASM hindcast simulation without bias correction and assimilation. This presentation highlights results for September sea ice predictions initialized on April 1, May 1, June 1, July 1, August 1, and September 1, covering pan-Arctic and regional sea ice spatio-temporal conditions from 2012 to 2021. Specifically, we examine how lead time and initial conditions affect the quantitative skill of seasonal predictability for Arctic sea ice and demonstrate skillful predictions of September sea ice up to six months in advance. Overall, our study underscores that enhancing model physics and obtaining more realistic initial conditions are crucial for achieving skillful sub-seasonal to seasonal predictions.

Abstract. The largest pH decline and widespread undersaturation with respect to aragonite in this century due to uptake of anthropogenic carbon dioxide in the Arctic Ocean have been projected. The reductions in pH and aragonite saturation state in the Arctic Ocean have been caused by the melting of sea ice as well as by an increase in the concentration of atmospheric carbon dioxide. Therefore, future projections of pH and aragonite saturation in the Arctic Ocean will be affected by how rapidly the reduction in sea ice occurs. The observed recent Arctic sea-ice loss has been more rapid than projected by many of the climate models that contributed to the Intergovernmental Panel on Climate Change Fourth Assessment Report. In this study, the impact of sea-ice reduction rate on projected pH and aragonite saturation state in the Arctic surface waters was investigated. Reductions in pH and aragonite saturation were calculated from the outputs of two versions of an Earth system model with different sea-ice reduction rates under similar CO2 emission scenarios. The newer model version projects that Arctic summer ice-free condition will be achieved by the year 2040, and the older version predicts ice-free condition by 2090. The Arctic surface water was projected to be undersaturated with respect to aragonite in the annual mean when atmospheric CO2 concentration reaches 513 (606) ppm in year 2046 (2056) in new (old) version. At an atmospheric CO2 concentration of 520 ppm, the maximum differences in pH and aragonite saturation state between the two versions were 0.1 and 0.21 respectively. The analysis showed that the decreases in pH and aragonite saturation state due to rapid sea-ice reduction were caused by increases in both CO2 uptake and freshwater input. Thus, the reductions in pH and aragonite saturation state in the Arctic surface waters are significantly affected by the difference in future projections for sea-ice reduction rate. Our results suggest that the future reductions in pH and aragonite saturation state could be significantly faster than previously projected if the sea-ice reduction in the Arctic Ocean keeps its present pace.

The sea ice extent in the Arctic Ocean has reduced dramatically with the last 16 years (2007-2022) showing the 16 lowest September extents observed in the satellite era. Besides a declining sea ice cover and increase in ocean heat content in summer, the winter sea ice concentration and thickness have also become more vulnerable to changes. We present results from the Fram Strait Arctic Outflow Observatory showing that the upper ocean temperature in the East Greenland Current in the Fram Strait has increased significantly between 2003 and 2019. While the cold Polar Water now contains more heat in summer due to lower sea ice concentration and longer periods of open water upstream, the warmer returning Atlantic Water has shown a greater presence in winter the central Fram Strait, impacting the winter sea ice thickness and sea ice extent. These processes combined result in a reduced sea ice cover downstream along the whole east coast of Greenland both in summer and winter, which has consequences for winter-time ocean convection in the Greenland Sea.

The ability to classify and monitor Arctic multiyear sea ice cover using multispectral passive microwave data is studied. Sea ice concentration maps during several summer minima have been analyzed to obtain estimates of ice surviving the summer. The results are compared with multiyear ice concentrations derived from data the following winter, using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data is approximately 25 to 40% less than the summer ice cover minimum, suggesting that even during winter when the emissivity of sea ice is most stable, passive microwave data may account for only a fraction of the total multiyear ice cover. The difference of about 2×106 km2 is considerably more than estimates of advection through Fram Strait during the intervening period. It appears that as in the Antarctic, some multiyear ice floes in the Arctic, especially those near the summer marginal ice zone, have first‐year ice or intermediate signatures in the subsequent winter. A likely mechanism for this is the intrusion of seawater into the snow‐ice interface, which often occurs near the marginal ice zone or in areas where snow load is heavy. Spatial variations in melt and melt ponding effects also contribute to the complexity of the microwave emissivity of multiyear ice. Hence the multiyear ice data should be studied in conjunction with the previous summer ice data to obtain a more complete characterization of the state of the Arctic ice cover. The total extent and actual areas of the summertime Arctic pack ice were estimated to be 8.4×106 km2 and 6.2×106 km2, respectively, and exhibit small interannual variability during the years 1979 through 1985, suggesting a relatively stable ice cover.

Bacterial communities in Arctic sea ice play an important role in the regulation of nutrient and energy dynamics in the Arctic Ocean. Sea ice has vertical gradients in temperature, brine salinity and volume, and light and UV levels. Multiyear ice (MYI) has at least two distinct ice layers: old fresh ice with limited permeability, and new saline ice, and may also include a surface melt pond layer. Here, we determine whether bacterial communities (1) differ with ice depth due to strong physical and chemical gradients, (2) are relatively homogenous within a layer, but differ between layers, or (3) do not vary with ice depth. Cores of MYI off northern Ellesmere Island, NU, Canada, were subsectioned in 30-cm intervals, and the bacterial assemblage structure was characterized using 16S rRNA gene pyrotag sequencing. Assemblages clustered into three distinct groups: top (0-30 cm); middle (30-150 cm); and bottom (150-236 cm). These layers correspond to the occurrence of refrozen melt pond ice, at least 2-year-old ice, and newly grown first-year ice at the bottom of the ice sheet, respectively. Thus, MYI houses multiple distinct bacterial assemblages, and in situ conditions appear to play a less important role in structuring microbial assemblages than the age or conditions of the ice at the time of formation.

<p>Arctic sea ice has experienced dramatic changes for the past few decades. Recent changes in the properties of Arctic sea ice have posed significant challenges to the research community to provide sea ice predictions. To improve our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere-sea ice-ocean model configured for the Arctic with sufficient flexibility. The Los Alamos sea ice model is coupled with the Weather Research and Forecasting (WRF) Model and the Regional Ocean Modeling System (ROMS) within the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system. A series of sensitivity experiments with different physics options have been performed to determine the ‘optimal’ physics configuration that provides reasonable simulation of Arctic sea ice.</p><p>It is well known that dynamic models used to predict Arctic sea ice at short-term periods strongly depend on model initial conditions. Thus, a data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Parallel Data Assimilation Framework has been implemented into the new modeling system to assimilate SSMIS sea ice concentration, and CyroSat-2 and SMOS sea ice thickness using a localized error subspace transform ensemble Kalman filter (LESTKF). We have conducted Arctic sea ice predictions for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice states show reasonably accurate sea ice evolution and small biases in the minimum sea ice extent.</p><p>Storms-induced ocean surface waves are capable of breaking pack ice into smaller floes and changing the sea ice melting rate. We have also coupled the Simulating Wave Nearshore (SWAN) with above atmosphere-sea ice-ocean coupled system and examined the impacts of wave-ice interactions on sea ice simulation. Preliminary results suggest ocean waves have direct and indirect impacts on sea ice. Direct impacts are the fracturing of ice pack and indirect impacts the change of ocean thermo-structure through the wave breaking.</p>

Effects of recent decreases in arctic sea ice on an ice-associated marine bird