Cryospheric Impacts of Soviet River Diversion Schemes

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

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Natural variability of the Arctic Ocean sea ice during the present interglacial

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2008996117 ◽

2020 ◽

Vol 117 (42) ◽

pp. 26069-26075

Author(s):

Anne de Vernal ◽

Claude Hillaire-Marcel ◽

Cynthia Le Duc ◽

Philippe Roberge ◽

Camille Brice ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Natural Variability ◽

Arctic Sea Ice ◽

The Arctic ◽

Lomonosov Ridge ◽

Arctic Sea ◽

The Arctic Ocean ◽

Open Question ◽

The Impact

The impact of the ongoing anthropogenic warming on the Arctic Ocean sea ice is ascertained and closely monitored. However, its long-term fate remains an open question as its natural variability on centennial to millennial timescales is not well documented. Here, we use marine sedimentary records to reconstruct Arctic sea-ice fluctuations. Cores collected along the Lomonosov Ridge that extends across the Arctic Ocean from northern Greenland to the Laptev Sea were radiocarbon dated and analyzed for their micropaleontological and palynological contents, both bearing information on the past sea-ice cover. Results demonstrate that multiyear pack ice remained a robust feature of the western and central Lomonosov Ridge and that perennial sea ice remained present throughout the present interglacial, even during the climate optimum of the middle Holocene that globally peaked ∼6,500 y ago. In contradistinction, the southeastern Lomonosov Ridge area experienced seasonally sea-ice-free conditions, at least, sporadically, until about 4,000 y ago. They were marked by relatively high phytoplanktonic productivity and organic carbon fluxes at the seafloor resulting in low biogenic carbonate preservation. These results point to contrasted west–east surface ocean conditions in the Arctic Ocean, not unlike those of the Arctic dipole linked to the recent loss of Arctic sea ice. Hence, our data suggest that seasonally ice-free conditions in the southeastern Arctic Ocean with a dominant Arctic dipolar pattern, may be a recurrent feature under “warm world” climate.

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Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate

SOLA ◽

10.2151/sola.2011-010 ◽

2011 ◽

Vol 7 ◽

pp. 37-40 ◽

Cited By ~ 5

Author(s):

Takahiro Toyoda ◽

Toshiyuki Awaji ◽

Nozomi Sugiura ◽

Shuhei Masuda ◽

Hiromichi Igarashi ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

The Arctic ◽

Coupled Simulation ◽

Concentration Data ◽

Sea Ice Concentration ◽

Ocean Climate ◽

Ice Concentration ◽

The Arctic Ocean

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Arctic Ocean sea ice snow depth evaluation and bias sensitivity in CCSM

The Cryosphere ◽

10.5194/tc-7-1887-2013 ◽

2013 ◽

Vol 7 (6) ◽

pp. 1887-1900 ◽

Cited By ~ 12

Author(s):

B. A. Blazey ◽

M. M. Holland ◽

E. C. Hunke

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Blowing Snow ◽

Climate System Model ◽

The Arctic Ocean ◽

Future Work ◽

The Impact

Abstract. Sea ice cover in the Arctic Ocean is a continued focus of attention. This study investigates the impact of the snow overlying the sea ice in the Arctic Ocean. The impact of snow depth biases in the Community Climate System Model (CCSM) is shown to impact not only the sea ice, but also the overall Arctic climate. Following the identification of seasonal biases produced in CCSM simulations, the thermodynamic transfer through the snow–ice column is perturbed to determine model sensitivity to these biases. This study concludes that perturbations on the order of the observed biases result in modification of the annual mean conductive flux through the snow–ice column of 0.5 W m2 relative to an unmodified simulation. The results suggest that the ice has a complex response to snow characteristics, with ice of different thicknesses producing distinct reactions. Our results indicate the importance of an accurate simulation of snow on the Arctic sea ice. Consequently, future work investigating the impact of current precipitation biases and missing snow processes, such as blowing snow, densification, and seasonal changes, is warranted.

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Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function

Frontiers in Marine Science ◽

10.3389/fmars.2021.736429 ◽

2021 ◽

Vol 8 ◽

Author(s):

Quanhong Liu ◽

Ren Zhang ◽

Yangjun Wang ◽

Hengqian Yan ◽

Mei Hong

Keyword(s):

Deep Learning ◽

Sea Ice ◽

Loss Function ◽

Model Performance ◽

Arctic Sea Ice ◽

The Arctic ◽

Short Term ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Impact

The navigability potential of the Northeast Passage has gradually emerged with the melting of Arctic sea ice. For the purpose of navigation safety in the Arctic area, a reliable daily sea ice concentration (SIC) prediction result is required. As the mature application of deep learning technique in short-term prediction of other fields (atmosphere, ocean, and hurricane, etc.), a new model was proposed for daily SIC prediction by selecting multiple factors, adopting gradient loss function (Grad-loss) and incorporating an improved predictive recurrent neural network (PredRNN++). Three control experiments are designed to test the impact of these three improvements for model performance with multiple indicators. Results show that the proposed model has best prediction skill in our experiments by taking physical process and local SIC variation into consideration, which can continuously predict daily SIC for up to 9 days.

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Marine Heatwaves in Siberian Arctic Seas and Adjacent Region

Remote Sensing ◽

10.3390/rs13214436 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4436

Author(s):

Elena Golubeva ◽

Marina Kraineva ◽

Gennady Platov ◽

Dina Iakshina ◽

Marina Tarkhanova

Keyword(s):

Numerical Modeling ◽

Sea Ice ◽

Arctic Ocean ◽

Northern Region ◽

Arctic Sea Ice ◽

The Arctic ◽

Arctic Seas ◽

Siberian Arctic ◽

The Arctic Ocean ◽

The Impact

We used a satellite-derived global daily sea surface temperature (SST) dataset with resolution 0.25 × 0.25∘ to analyze interannual changes in the Arctic Shelf seas from 2000 to 2020 and to reveal extreme events in SST distribution. Results show that the second decade of the 21st century for the Siberian Arctic seas turned significantly warmer than the first decade, and the increase in SST in the Arctic seas could be considered in terms of marine heatwaves. Analyzing the spatial distribution of heatwaves and their characteristics, we showed that from 2018 to 2020, the surface warming extended to the northern deep-water region of the Laptev Sea 75∘ to 81∘N. To reveal the most important forcing for the northward extension of the marine heatwaves, we used three-dimensional numerical modeling of the Arctic Ocean based on a sea-ice and ocean model forced by the NCEP/NCAR Reanalysis. The simulation of the Arctic Ocean variability from 2000 to 2020 showed marine heatwaves and their increasing intensity in the northern region of the Kara and Laptev seas, closely connected to the disappearance of ice cover. A series of numerical experiments on the sensitivity of the model showed that the main factors affecting the Arctic sea-ice loss and the formation of anomalous temperature north of the Siberian Arctic seas are equally the thermal and dynamic effects of the atmosphere. Numerical modeling allows us to examine the impact of other physical mechanisms as well. Among them were the state of the ocean and winter sea ice, the formation of fast ice polynias and riverine heat influx.

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review of "Measuring sea ice concentration in the Arctic Ocean using SMOS" by Carolina Gabarro et al.

10.5194/tc-2016-175-rc1 ◽

2016 ◽

Author(s):

Anonymous

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

The Arctic ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Arctic Ocean

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Measuring sea ice concentration in the Arctic Ocean using SMOS

10.5194/tc-2016-175 ◽

2016 ◽

Author(s):

Carolina Gabarro ◽

Antonio Turiel ◽

Pedro Elosegui ◽

Joaquim A. Pla-Resina ◽

Marcos Portabella

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Radiative Properties ◽

The Arctic ◽

Sea Ice Concentration ◽

Wide Range ◽

Ice Concentration ◽

The Arctic Ocean ◽

Ice Conditions ◽

Microwave Imager

Abstract. We present a new method to estimate sea ice concentration in the Arctic Ocean using brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) interferometric satellite. The method, which employs a Maximum Likelihood Estimator (MLE), exploits the marked difference in radiative properties between sea ice and seawater, in particular when observed over the wide range of satellite viewing angles afforded by SMOS. Observations at L-band frequencies such as those from SMOS (i.e., 1.4 GHz, or equivalently 21-cm wavelength) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency. We find that sea ice concentration is well determined (correlations of about 0.75) as compared to estimates from other sensors such as the Special Sensor Microwave/Imager (SSM/I and SSMIS). We also find that the efficacy of the method decreases under thin sea ice conditions (ice thickness

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The impact of a warm moist air intrusion on dynamic and thermodynamic sea ice tendencies in the Arctic.

10.5194/egusphere-egu2020-8418 ◽

2020 ◽

Author(s):

Evelien Dekker

Keyword(s):

Sea Ice ◽

Radiative Forcing ◽

Surface Air Temperature ◽

Longwave Radiation ◽

Arctic Sea Ice ◽

The Arctic ◽

Moist Air ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Impact

Atmospheric blocking events in the Northern Hemishpere have been related to regional Arctic sea ice decline. During blocking events, pulses of warm and moist air enhance the radiative forcing on the sea ice in winter due to the increased longwave radiation associated with clouds. Several studies have shown that such events are related to regional sea ice concentration decline. Daily sea ice output with the latest version of CICE from the coupled Regional Arctic System model is used to study sea ice tendencies during January-February 2014. In this period there was a follow-up of a Atlantic warm moist air insturion and a Pacific warm moist air intrusion associated with surface air temperature perturbations up to 20 degrees locally.A decline in sea ice concentration during wintertime does not neccesarily mean that ice melt has occurred. The goal of this case study is to distinguish the sea ice response between a dynamic and a thermodynamic component. In this way, we learn how much of the sea ice is advected into another region during such an event and how much the sea ice is lost due to the enhanced forcing and temperature increase.&#160;&#160;&#160;

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Relationships between Sea Ice Concentration and Wind Speed over the Arctic Ocean during 1979–2015

Journal of Climate ◽

10.1175/jcli-d-19-0271.1 ◽

2019 ◽

Vol 32 (22) ◽

pp. 7783-7796 ◽

Cited By ~ 1

Author(s):

Liisi Jakobson ◽

Timo Vihma ◽

Erko Jakobson

Keyword(s):

Surface Roughness ◽

Wind Speed ◽

Sea Ice ◽

Arctic Ocean ◽

Time Scales ◽

The Arctic ◽

Sea Ice Concentration ◽

Atmospheric Stratification ◽

Ice Concentration ◽

The Arctic Ocean

Abstract NCEP CFSR reanalysis 6-hourly fields from 1979 to 2015 were used to investigate the relationships of sea ice concentration (SIC), atmospheric stratification, surface roughness, and wind speed at 10-m height (W10) and 850-hPa level (W850). We found that in autumn (September–November), winter (December–February), and spring (March–May) a lower SIC favors less-stable stratification and a higher W10. In autumn, the decrease in SIC is strongest, and SIC has its strongest correlation with the atmospheric stratification, W10, and the ratio of W10 and W850 (WSR). W10 and WSR have increased in autumn, and the negative trends in SIC typically are collocated with positive trends in W10 and WSR. In winter, W850 has negative trends over the Arctic Ocean, which, together with the lack of decrease of SIC in the central Arctic, has prevented W10 from increasing in winter. The winter trends are notably different from those for autumn, but the correlations are fairly similar. In autumn, winter, and spring, the negative correlation between SIC and W10 originated from the reduction of both stratification and aerodynamic surface roughness z0 with a reduction of SIC. The dependence of z0 on SIC is, however, weak in NCEP CFSR. In summer, the ratio of W10 and W850 has increased over large areas. The correlations between SIC and atmospheric variables were stronger on interannual time scales than on subseasonal time scales. The causal relationships are complicated by the two-way interaction between SIC and W10. In most cases, especially in summer, SIC increases after periods of W10 exceeding 5 m s−1.

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