Search Results (34)

The Laptev Sea, as a marginal sea and a key source of sea ice for the Arctic Ocean, has a profound influence on the dynamic processes of sea ice evolution. Under a 2 °C global warming scenario, the accelerated ablation of Arctic sea ice is projected to greatly impact Arctic warming. The ocean regulates global climate through its interactions with the atmosphere, where sea surface temperature (SST) serves as a crucial parameter in exchanging energy, momentum, and gases. SST is also a key driver of sea ice concentration (SIC). In this paper, we analyze the spatiotemporal variability of SST and SIC, along with their interrelationships in the Laptev Sea, using daily optimum interpolation SST datasets from NCEI and daily SIC datasets from the University of Bremen for the period 2004–2023. The results show that: (1) Seasonal variations are observed in the influence of SST on SIC. SIC exhibited a decreasing trend in both summer and fall with pronounced interannual variability as ice conditions shifted from heavy to light. (2) The highest monthly averages of SST and SIC were in July and September, respectively, while the lowest values occurred in August and November. (3) The most pronounced trends for SST and SIC appeared both in summer, with rates of +0.154 °C/year and −0.095%/year, respectively. Additionally, a pronounced inverse relationship was observed between SST and SIC across the majority of the Laptev Sea with correlation coefficients ranging from −1 to 0.83. Full article

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. Full article

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute. Full article

The constant inherent optical properties (IOPs) for sea ice currently applied in sea ice models do not realistically represent the dividing of shortwave radiative fluxes in sea ice and the ocean below it. Here we implement a parameterization of variable IOPs based on ice microstructures in the Los Alamos sea ice model, version 6.0 (CICE6) and investigate its effects on the simulation of the dividing of shortwave radiation and sea ice in the Arctic. Our sensitivity experiments indicate that variable IOP parameterization results in strong seasonal variation for the IOP parameters, typically reaching the seasonal maximum in the boreal summer. With such large differences, variable IOP parameterization leads to increased absorbed solar radiation at the surface and in the interior of Arctic sea ice relative to constant IOPs, up to ~3 W/m², but decreased solar radiation penetrating into the ocean, up to ~5–6 W/m². The changes in the dividing of shortwave fluxes in sea ice and the ocean below it induced by the variable IOPs have significant influence on Arctic sea ice thickness by modulating surface and bottom melting and frazil ice formation (increasing surface melting by ~16% and reducing bottom melting by ~11% in summer). Full article

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. Full article

Mid-level clouds play a crucial role in the Arctic. Due to observational limitations, there is scarce research on the long-term evolution of Arctic mid-level clouds. From a satellite perspective, this study attempts to analyze the seasonal variations in Arctic mid-level clouds and explore the possible relationships with sea ice changes using observations from the hyperspectral Atmospheric Infrared Sounder (AIRS) over the past two decades. For mid-level clouds of three layers (648, 548, and 447 hPa) involved in AIRS, high values of effective cloud fraction (ECF) occur in summer, and low values primarily occur in early spring, while the seasonal variations are different. The ECF anomalies are notably larger at 648 hPa than those at 548 and 447 hPa. Meanwhile, the ECF values at 648 hPa show a clear reduced seasonal variability for the regions north of 80°N, which has its minimum coefficient of variation (CV) during 2019 to 2020. The seasonal CV is relatively lower in the regions dominated by Greenland and sea areas with less sea ice coverage. Analysis indicates that the decline in mid-level ECF’s seasonal mean CV is closely correlated to the retreat of Arctic sea ice during September. Singular value decomposition (SVD) analysis reveals a reverse spatial pattern in the seasonal CV anomaly of mid-level clouds and leads anomaly. However, it is worth noting that this pattern varies by region. In the Greenland Sea and areas near the Canadian Arctic Archipelago, both CV and leads demonstrate negative (positive) anomalies, probably attributed to the stronger influence of atmospheric and oceanic circulations or the presence of land on the sea ice in these areas. Full article

Sea ice regulates the overall energy exchange and radiation budget of the Arctic region, and understanding this relationship requires an accurate determination of snow depth. However, methods for deriving snow depth have a large error through the annual winter and early spring periods due to the potential complexity of surface melting during early summer. In this study, we explore the potential of retrieving snow depth during the early summer using optical satellite imagery of the sea-ice cover. Measurements using VIS/IR (visible and infrared) usually feature much higher spatial resolution than L-band satellite data and can provide additional surface melting and leads information; in addition, considering the snow grain size–snow surface temperature interaction, there is co-variability between the observed sea-ice surface broadband albedo using an optical satellite sensor, the sea-ice surface temperature, and the retrieval target of snow depth on the spatial scale of optical imagery samples. We applied a surface classification procedure to optical satellite imagery and introduce an approach to derive snow depth from optical satellite imagery and ice surface temperature data using two solar radiation transfer models: the Delta-Eddington solar radiation model, which is the shortwave radiative scheme of the Los Alamos sea-ice model, and a simplified snow albedo scheme, which is tuned to the observational data of buoys. The snow depth was inversed from the model simulation results using a lookup-table-based method. For comparison with the observational data, using the Delta-Eddington solar radiation model, about 55% of the differences are below 5 cm, and thicker snowpack has a larger bias; using the simplified snow albedo scheme, a mean difference of 4.1 cm between retrieval and measurements was found, with 93% of the differences being smaller than 5 cm. This approach can be applied to optical satellite imagery acquired under clear-sky conditions and can serve as an addition to overcome the limitations of existing methods. Full article

In polar regions, positive feedback of snow and ice albedo can intensify global warming. While recent significant decreases in Arctic surface ice albedo have drawn considerable attention, Antarctic surface albedo variability remains underexplored. Here, satellite albedo product CLARA-A2.1-SAL is first validated and then used to investigate spatial and temporal trends in the summer albedo over the Antarctic from 1982 to 2018, along with their association with Antarctic sea ice changes. The SAL product matches well surface albedo observations from eight stations, suggesting its robust performance in Antarctica. Summer surface albedo averaged over the entire ice sheet shows a downward trend since 1982, albeit not statistically significant. In contrast, a significant upward trend is observed in the sea ice region. Spatially, for ice sheet surface albedo, positive trends occur in the eastern Antarctica Peninsula and the margins of East Antarctica, whereas other regions exhibit negative trends, most prominently in the Ross and Ronne ice shelves. For sea ice albedo, positive trends are observed in the Ross Sea and the Weddell Sea, but negative trends are observed in the Bellingshausen and the Amundsen Seas. Between 2016 and 2018, an unusual decrease in the sea ice extent significantly affected both sea ice and Antarctic ice sheet (AIS) surface albedo changes. However, for the 1982–2015 period, while the effect of sea ice on its own albedo is significant, its impact on ice sheet albedo is less apparent. Air temperature and snow depth also contribute much to sea ice albedo changes. However, on ice sheet surface albedo, the influence of temperature and snow accumulation appears limited. Full article

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. Full article

The relative contribution of atmospheric forcing, oceanic preconditioning, and sea ice to Labrador Sea Deep Convection (LSDC) is investigated by conducting three ensemble experiments using a global coupled sea ice–ocean model for the first time. Simulated results show that the atmospheric activities dominate the interannual and decadal variability, accounting for 70% of LSDC. Oceanic preconditioning is more significant in the shallow LSDC years that the water column is stable, accounting for 21%, especially in the central Labrador Sea and Irminger Sea. Moreover, the sea ice contribution is negligible over the whole Labrador Sea, while its contribution is significant in the sea ice-covered slope regions, accounting for 20%. The increasingly importance of sea ice on LSDC and the water mass transformation will be found in the North Atlantic Ocean, if the Arctic sea ice declines continuously. Additionally, there is a 10 Sv increase (85%) in atmospheric forcing to the subpolar gyre in the North Atlantic Ocean, while oceanic preconditioning contributes a 7 Sv decrease (12%). These findings highlight the importance of summer oceanic preconditioning to LSDC and the subpolar gyre, and therefore it should be appropriately accounted for in future studies. Full article

The lagged relationship between Kara–Barents sea ice and summer precipitation in eastern China is evaluated for Chinese models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). A previous study revealed a dipole rainfall structure in eastern China related to winter Arctic sea ice variability. Almost all Chinese CMIP6 models reproduce the variability and climatology of the sea ice in most of the Arctic well except the transition regions with evident biases. Further, all Chinese CMIP6 models successfully simulate the decreasing trend for the Kara–Barents sea ice. The dipole centers located in the Yangtze–Huai River Valley (YHRV) and South China (SC) related to Kara–Barents sea ice variability are simulated with different degrees of success. The anomalous dipole rainfall structure related to the winter Kara–Barents sea ice variability can roughly be reproduced by two models, while other models reproduce a shifted rainfall anomaly pattern or with the sign reversed. The possible delayed influence of sea ice forcing on early summer precipitation in China is established via three possible processes: the long memory of ice, the long-lasting stratospheric anomalies triggered by winter sea ice forcing, and the downward impact of the stratosphere as the mediator. Most Chinese models can simulate the negative Northern Hemisphere Annular Mode (NAM) phase in early winter but fail to reproduce the reversal of the stratospheric anomalies to a positive NAM pattern in spring and early summer. Most models underestimate the downward impact from the stratosphere to the troposphere. This implies that the stratospheric pathway is essential to mediate the winter sea ice forcing and rainfall in early summer over China for CMIP6 models. Full article

The decadal variability of the summer extreme precipitation over southern China (EPSC) is remarkable, especially for the significant decadal enhancement after the 1990s. The study documented that the summer sea surface temperature (SST) over the North Atlantic and spring sea ice concentration (SIC) over the East Siberian Sea can significantly affect the EPSC. The summer SST over the North Atlantic influences the low-pressure cyclone in the western Pacific by modulating the SST over the tropical Pacific, thus affecting EPSC. A decrease in the SIC of the East Siberian Sea induces a negative Arctic Oscillation, which induces the increased SST over northwest Pacific and the anomalous cyclone over there, in turn, affecting EPSC. Both predictors have a quasi-period of 10–14 years, which provides useful predictive signals for EPSC. The leading 7-year SST and the leading 5-year SIC are chosen to establish the prediction model based on the decadal increment method, which can well predict the EPSC, especially for the shift in the early 1990s. These results provide a clue to the limited predictability of decadal-scale extreme climate events. Full article

Changes in Arctic sea ice since the middle of the last century are explored in this study. Both observations and climate model simulations show an overall sea ice expansion during 1953–1970 but a general sea ice decline afterward. Anthropogenic aerosols, nature forcing and atmospheric ozone changes are found to contribute to the sea ice expansion in the early period. Their effects are strong generally in late boreal summer. On the other hand, greenhouse gas warming has a dominant effect on diminishing Arctic sea ice cover during 1971–2005, especially in September. Internal climate variability also plays a role in the Arctic sea ice change during 1953–1970. However, it cannot solely explain the Arctic sea ice decline since the 1970s. Full article

The Arctic climate strongly affects phytoplankton production and biomass through several mechanisms, including warming, sea ice retreat, and global atmospheric processes. In order to detect the climatic changes in phytoplankton biomass, long-term variability of chlorophyll a (Chl-a) was estimated in situ with the changes in the surface sea temperature (SST) and salinity (SSS) in the Barents Sea and adjacent waters during the period of 1984–2021. Spatial differences were detected in SST, SSS, and Chl-a. Chl-a increased parallel to SST in the summer-autumn and spring periods, respectively. Chl-a peaks were found near the ice edge and frontal zones in the spring season, while the highest measures were observed in the coastal regions during the summer seasons. SST and Chl-a demonstrated increasing trends with greater values during 2010–2020. Generalized additive models (GAMs) revealed that SST and Chl-a were positively related with year. Climatic and oceanographic variables explained significant proportions of the Chl-a fluctuations, with six predictors (SST, annual North Atlantic Oscillation index, temperature/salinity anomalies at the Kola Section, and sea ice extent in April and September) being the most important. GAMs showed close associations between increasing Chl-a and a decline in sea ice extent and rising water temperature. Our data may be useful for monitoring the Arctic regions during the era of global changes and provide a basis for future research on factors driving phytoplankton assemblages and primary productivity in the Barents Sea. Full article

Quantified research on the Arctic Ocean carbon system is poorly understood, limited by the scarce available data. Measuring the associated phytoplankton responses to air–sea CO₂ fluxes is challenging using traditional satellite passive ocean color measurements due to low solar elevation angles. We constructed a feedforward neural network light detection and ranging (LiDAR; FNN-LID) method to assess the Arctic diurnal partial pressure of carbon dioxide (pCO₂) and formed a dataset of long-time-series variations in diurnal air–sea CO₂ fluxes from 2001 to 2020; this study represents the first time spaceborne LiDAR data were employed in research on the Arctic air–sea carbon cycle, thus providing enlarged data coverage and diurnal pCO₂ variations. Although some models replace Arctic winter Chl-a with the climatological average or interpolated Chl-a values, applying these statistical Chl-a values results in potential errors in the gap-filled wintertime pCO₂ maps. The CALIPSO measurements obtained through active LiDAR sensing are not limited by solar radiation and can thus provide ‘fill-in’ data in the late autumn to early spring seasons, when ocean color sensors cannot record data; thus, we constructed the first complete record of polar pCO₂. We obtained Arctic FFN-LID-fitted in situ measurements with an overall mean R² of 0.75 and an average RMSE of 24.59 µatm and filled the wintertime observational gaps, thereby indicating that surface water pCO₂ is higher in winter than in summer. The Arctic Ocean net CO₂ sink has seasonal sources from some continental shelves. The growth rate of Arctic seawater pCO₂ is becoming larger and more remarkable in sectors with significant sea ice retreat. The combination of sea surface partial pressure and wind speed impacts the diurnal carbon air–sea flux variability, which results in important differences between the Pacific and Atlantic Arctic Ocean. Our results show that the diurnal carbon sink is larger than the nocturnal carbon sink in the Atlantic Arctic Ocean, while the diurnal carbon sink is smaller than the nocturnal carbon sink in the Pacific Arctic Ocean. Full article