scholarly journals MODERN DRIFTING ROBOTIZED DEVICES FOR CONTACT MEASUREMENTS OF PHYSICAL CHARACTERISTICS OF THE ARCTIC BASIN

2019 ◽  
Vol 47 (4) ◽  
pp. 5-31 ◽  
Author(s):  
S. V. Pisarev
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Personal Experience ◽  
Arctic Basin ◽  
Physical Characteristics ◽  
The Arctic ◽  
Gps Navigation ◽  
Air Temperatures ◽  
Observational Technique ◽  
Contact Measurements

A description of all available automatic devices that have constructed to carry out contact measurements of ice and ocean characteristics from drifting sea ice at low air temperatures is given. All the devices under consideration work according to the programmed schedules, transmit the results of measurements to the coastal centers in real time, have GPS navigation. The devices are manufactured individually or in limited batches and are not prototypes or operating models. In addition, all these types of devices have already made successful measurements in the Arctic for a year or more. The description of the devices is given not only according to information from the specialized literature, but also on the basis of the author’s many years of personal experience in working with this observational technique in the Arctic basin.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals On the effect of rheology on seasonal sea-ice simulations

1991 ◽  
Vol 15 ◽  
pp. 17-25 ◽  
Author(s):  
Chi F. Ip ◽  
William D. Hibler ◽  
Gregory M. Flato
Keyword(s):  
Shear Stress ◽  
Sea Ice ◽  
Correlation Coefficients ◽  
Arctic Basin ◽  
The Arctic ◽  
Flow Rule ◽  
Average Model ◽  
Ice Drift ◽  
Cavitating Fluid

A generalized numerical model which allows for a variety of non-linear rheologies is developed for the seasonal simulation of sea-ice circulation and thickness. The model is used to investigate the effects (such as the role of shear stress and the existence of a flow rule) of different rheologies on the ice-drift pattern and build-up in the Arctic Basin. Differences in local drift seem to be closely related to the amount of allowable shear stress. Similarities are found between the elliptical and square cases and between the Mohr-Coulomb and cavitating fluid cases. Comparisons between observed and simulated buoy drift are made for several buoy tracks in the Arctic Basin. Correlation coefficients to the observed buoy drift range from 0.83 for the cavitating fluid to 0.86 for the square rheology. The average ratio of buoy-drift distance to average model-drift distance for several buoys is 1.15 (square), 1.18 (elliptical), 1.30 (Mohr-Coulomb) and 1.40 (cavitating fluid).

scholarly journals Extreme Warming in the Kara Sea and Barents Sea during the Winter Period 2000–16

2017 ◽  
Vol 30 (22) ◽  
pp. 8913-8927 ◽  
Author(s):  
Svenja H. E. Kohnemann ◽  
Günther Heinemann ◽  
David H. Bromwich ◽  
Oliver Gutjahr
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Barents Sea ◽  
Kara Sea ◽  
The Arctic ◽  
Winter Period ◽  
Limited Area ◽  
Spatial And Temporal Variability ◽  
Large Variability ◽  
Air Temperatures

The regional climate model COSMO in Climate Limited-Area Mode (COSMO-CLM or CCLM) is used with a high resolution of 15 km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 20°C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice. Also, the 30-km version of the Arctic System Reanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 1°C for the ocean and sea ice area. Thus, ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.5°C yr−1 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 70°N; for CCLM the warming amounts to an average of almost 5°C for 2002/03–2011/12.

scholarly journals Genesis and Decay of Mesoscale Baroclinic Eddies in the Seasonally Ice-Covered Interior Arctic Ocean

2021 ◽  
Vol 51 (1) ◽  
pp. 115-129
Author(s):  
Gianluca Meneghello ◽  
John Marshall ◽  
Camille Lique ◽  
Pål Erik Isachsen ◽  
Edward Doddridge ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Seasonal Cycle ◽  
Climate Models ◽  
Baroclinic Instability ◽  
Temporal Distribution ◽  
Arctic Basin ◽  
Ocean Model ◽  
The Arctic ◽  
Origin And Evolution

AbstractObservations of ocean currents in the Arctic interior show a curious, and hitherto unexplained, vertical and temporal distribution of mesoscale activity. A marked seasonal cycle is found close to the surface: strong eddy activity during summer, observed from both satellites and moorings, is followed by very quiet winters. In contrast, subsurface eddies persist all year long within the deeper halocline and below. Informed by baroclinic instability analysis, we explore the origin and evolution of mesoscale eddies in the seasonally ice-covered interior Arctic Ocean. We find that the surface seasonal cycle is controlled by friction with sea ice, dissipating existing eddies and preventing the growth of new ones. In contrast, subsurface eddies, enabled by interior potential vorticity gradients and shielded by a strong stratification at a depth of approximately 50 m, can grow independently of the presence of sea ice. A high-resolution pan-Arctic ocean model confirms that the interior Arctic basin is baroclinically unstable all year long at depth. We address possible implications for the transport of water masses between the margins and the interior of the Arctic basin, and for climate models’ ability to capture the fundamental difference in mesoscale activity between ice-covered and ice-free regions.

scholarly journals Near Real Time Arctic sea ice thickness and volume from CryoSat-2

2016 ◽  
Author(s):  
R. L. Tilling ◽  
A. Ridout ◽  
A. Shepherd
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Ice Thickness ◽  
Satellite Orbits ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

Aerosols and their impacts on future Arctic climate change under different emission projections in the GISS-E2.1 Earth system model

2021 ◽  
Author(s):  
Ulas Im ◽  
Kostas Tsigaridis ◽  
Gregory S. Faluvegi ◽  
Peter L. Langen ◽  
Joshua P. French ◽  
...  
Keyword(s):  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Anthropogenic Aerosols ◽  
Emission Reductions ◽  
Air Temperatures ◽  
The Future ◽  
Emission Projections

<p>In order to study the future aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model. Different future anthrpogenic emission projections have been used from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Results showed that Arctic BC, OC and SO<sub>4</sub><sup>2-</sup> burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m<sup>-2</sup>, of which -0.24±0.01 W m<sup>-2</sup> were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m<sup>-2</sup> for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m<sup>-2</sup>), of which the anthropogenic aerosols accounted for -0.26 W m<sup>-2</sup>. The 2030-2050 mean surface air temperatures are projected to increase by 2.1 °C and 2.4 °C compared to the 1990-2010 mean temperature according to the Eclipse CLE and MFR ensembles, respectively, while the CMIP6 simulation calculated an increase of 1.9 °C (SSP1-2.6) to 2.2 °C (SSP3-7.0). Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios with no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss. </p>

scholarly journals A Model Simulation of 20 Years of Northern Hemisphere Sea-Ice Fluctuations

1984 ◽  
Vol 5 ◽  
pp. 170-176 ◽  
Author(s):  
John E. Walsh ◽  
William D. Hibler ◽  
Becky Ross
Keyword(s):  
Sea Ice ◽  
Northern Hemisphere ◽  
Model Simulation ◽  
Thickness Distribution ◽  
Arctic Basin ◽  
The Arctic ◽  
Data Set ◽  
Ice Dynamics ◽  
Thermodynamic Computations ◽  
Covered Area

A dynamic-thermodynamic sea-ice model (Hibler 1979) is used to simulate northern hemisphere sea ice for a 20-year period, 1961 to 1980. The model is driven by daily atmospheric grids of sea-level pressure (geo-strophic wind) and by temperatures derived from the Russian surface temperature data set. Among the modifications to earlier formulations are the inclusion of snow cover and a multilevel ice-thickness distribution in the thermodynamic computations.The time series of the simulated anomalies show relatively large amounts of ice during the early 1960s and middle 1970s, and relatively small amounts during the late 1960s and early 1970s. The fluctuations of ice mass, both in the entire domain and in individual regions, are more persistent than are the fluctuations of ice-covered area. The ice dynamics tend to introduce more high-frequency variability into the regional (and total) amounts of ice mass. The simulated annual ice export from the Arctic Basin into the East Greenland Sea varies interannually by factors of 3 to 4.

scholarly journals Modeling M2 tidal variability in Arctic Sea-ice drift and deformation

2006 ◽  
Vol 44 ◽  
pp. 418-428 ◽  
Author(s):  
W.D. Hibler ◽  
A. Roberts ◽  
P. Heil ◽  
A.Y. Proshutinsky ◽  
H.L. Simmons ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ocean Coupling ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift ◽  
Tidal Variability

AbstractSemi-diurnal oscillations are a ubiquitous feature of polar Sea-ice motion. Over much of the Arctic basin, inertial and Semi-diurnal tidal variability have Similar frequencies So that periodicity alone is inadequate to determine the Source of oscillations. We investigate the relative roles of tidal and inertial variability in Arctic Sea ice using a barotropic ice–ocean model with Sea ice embedded in an upper boundary layer. Results from this model are compared with ‘levitated’ ice–ocean coupling used in many models. In levitated models the mechanical buoyancy effect of Sea ice is neglected So that convergence of ice, for example, does not affect the oceanic Ekman flux. We use rotary Spectral analysis to compare Simulated and observed results. This helps to interpret the rotation Sense of Sea-ice drift and deformation at the Semi-diurnal period and is a useful discriminator between tidal and inertial effects. Results indicate that the levitated model generates an artificial inertial resonance in the presence of tidal and wind forcing, contrary to the embedded Sea-ice model. We conclude that Sea-ice mechanics can cause the rotational response of ice motion to change Sign even in the presence of Strong and opposing local tidal forcing when a physically consistent dynamic ice–ocean coupling is employed.

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