Impact of Aerosol Intrusions on Arctic Boundary Layer Clouds. Part II: Sea Ice Melting Rates

2005 ◽  
Vol 62 (9) ◽  
pp. 3094-3105 ◽  
Author(s):  
G. G. Carrió ◽  
H. Jiang ◽  
W. R. Cotton
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Cloud Condensation Nuclei ◽  
National Laboratory ◽  
Arctic Basin ◽  
Atmospheric Modeling ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Ice Melting ◽  
Arctic Boundary Layer

Abstract The potential impact of intrusions of polluted air into the Arctic basin on sea ice melting rates and the surface energy budget is examined. This paper extends a previous study to cloud-resolving simulations of the entire spring season during the 1998 Surface Heat Budget of the Arctic (SHEBA) field campaign. For that purpose, the Los Alamos National Laboratory sea ice model is implemented into the research and real-time versions of the Regional Atmospheric Modeling System at Colorado State University (RAMS@CSU). This new version of RAMS@CSU also includes a new microphysical module that considers the explicit nucleation of cloud droplets and a bimodal representation of their spectrum. Different aerosol profiles based on 4 May 1998 observations were used to characterize the polluted upper layer and the 2–3 daily SHEBA soundings were utilized to provide time-evolving boundary conditions to the model. Results indicate that entrainment of ice-forming nuclei (IFN) from above the inversion increases the sea ice melting rates when mixed-phase clouds are present. An opposite although less important effect is associated with cloud condensation nuclei (CCN) entrainment when liquid-phase clouds prevail.

scholarly journals Potential Consequences Of “Dirty” Arctic Sea Ice

1990 ◽  
Vol 14 ◽  
pp. 355
Author(s):  
Stephanie Pfirman ◽  
Manfred A. Lange ◽  
Tamara S. Ledley
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Surface Melting ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Melting ◽  
Large Area ◽  
Sea Ice Concentration ◽  
East Greenland Current ◽  
Ice Surface

Observations of high particulate loads on Eurasian Basin sea ice in 1987 raise questions of consequence for sediment budgets, ice melting, ice modeling and remote sensing. Biogenic and lithogenic particles were observed in concentrations high enough to color the ice surface brown over large area (greater than 15 × 15 km2) within the Siberian branch of the Transpolar Drift stream. The sediment is most likely incorporated when ice forms on the Siberian shelf seas, and is concentrated at the ice surface after several years of summer surface melting and biological growth within the Arctic basin. Much of the particle-laden multi-year ice appears to leave the Arctic basin via Fram Strait, depositing its sediment load along the axis of the East Greenland Current. To date, variation in sea-ice particle load has not been taken into consideration when modeling ice thickness or distribution for past or future environmental scenarios, with the exception of soot deposited from nuclear war. Naturally elevated surface-particle concentration may occur if there is increased deposition from long-range or coastal transport of aeolian material, increased sediment input into sea ice which is then exposed to surface melting, and/or increased biogenic productivity on the ice surface. Such conditions may have prevailed during the Younger Dryas. If particle loads become high enough to cause extensive sea-ice melting, changes may be expected in sea-ice concentration and distribution, sea-floor sedimentation rates, and oceanic productivity.

Impact of Aerosol Intrusions on Arctic Boundary Layer Clouds. Part I: 4 May 1998 Case

2005 ◽  
Vol 62 (9) ◽  
pp. 3082-3093 ◽  
Author(s):  
G. G. Carrió ◽  
H. Jiang ◽  
W. R. Cotton
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
National Laboratory ◽  
Atmospheric Modeling ◽  
Mixed Phase ◽  
Radiative Properties ◽  
Arctic Boundary Layer ◽  
Water Fraction ◽  
Boundary Layer Clouds ◽  
The Impact

Abstract The objective of this paper is to assess the impact of the entrainment of aerosol from above the inversion on the microphysical structure and radiative properties of boundary layer clouds. For that purpose, the Los Alamos National Laboratory sea ice model was implemented into the research and real-time versions of the Regional Atmospheric Modeling System at Colorado State University. A series of cloud-resolving simulations have been performed for a mixed-phase Arctic boundary layer cloud using a new microphysical module that considers the explicit nucleation of cloud droplets. Different aerosol profiles based on observations were used for initialization. When more polluted initial ice-forming nuclei (IFN) profiles are assumed, the liquid water fraction of the cloud decreases while the total condensate path, the residence time of the ice particles, and the downwelling infrared radiation monotonically increase. Results suggest that increasing the aerosol concentrations above the boundary layer may increase sea ice melting rates when mixed-phase clouds are present.

scholarly journals Potential Consequences Of “Dirty” Arctic Sea Ice

1990 ◽  
Vol 14 ◽  
pp. 355-355
Author(s):  
Stephanie Pfirman ◽  
Manfred A. Lange ◽  
Tamara S. Ledley
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Surface Melting ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Melting ◽  
Large Area ◽  
Sea Ice Concentration ◽  
East Greenland Current ◽  
Ice Surface

Observations of high particulate loads on Eurasian Basin sea ice in 1987 raise questions of consequence for sediment budgets, ice melting, ice modeling and remote sensing. Biogenic and lithogenic particles were observed in concentrations high enough to color the ice surface brown over large area (greater than 15 × 15 km2) within the Siberian branch of the Transpolar Drift stream. The sediment is most likely incorporated when ice forms on the Siberian shelf seas, and is concentrated at the ice surface after several years of summer surface melting and biological growth within the Arctic basin. Much of the particle-laden multi-year ice appears to leave the Arctic basin via Fram Strait, depositing its sediment load along the axis of the East Greenland Current.To date, variation in sea-ice particle load has not been taken into consideration when modeling ice thickness or distribution for past or future environmental scenarios, with the exception of soot deposited from nuclear war. Naturally elevated surface-particle concentration may occur if there is increased deposition from long-range or coastal transport of aeolian material, increased sediment input into sea ice which is then exposed to surface melting, and/or increased biogenic productivity on the ice surface. Such conditions may have prevailed during the Younger Dryas. If particle loads become high enough to cause extensive sea-ice melting, changes may be expected in sea-ice concentration and distribution, sea-floor sedimentation rates, and oceanic productivity.

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 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 Distribution and Driving Mechanism of N2O in Sea Ice and Its Underlying Seawater during Arctic Melt Season

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 145
Author(s):  
Jian Liu ◽  
Liyang Zhan ◽  
Qingkai Wang ◽  
Man Wu ◽  
Wangwang Ye ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ice Core ◽  
The Arctic ◽  
First Year ◽  
Driving Mechanism ◽  
Biogeochemical Processes ◽  
Ice Melting ◽  
Detailed Mechanism ◽  
The Arctic Ocean

Nitrous oxide (N2O) is the third most important greenhouse gas in the atmosphere, and the ocean is an important source of N2O. As the Arctic Ocean is strongly affected by global warming, rapid ice melting can have a significant impact on the N2O pattern in the Arctic environment. To better understand this impact, N2O concentration in ice core and underlying seawater (USW) was measured during the seventh Chinese National Arctic Research Expedition (CHINARE2016). The results showed that the average N2O concentration in first-year ice (FYI) was 4.5 ± 1.0 nmol kg−1, and that in multi-year ice (MYI) was 4.8 ± 1.9 nmol kg−1. Under the influence of exchange among atmosphere-sea ice-seawater systems, brine dynamics and possible N2O generation processes at the bottom of sea ice, the FYI showed higher N2O concentrations at the bottom and surface, while lower N2O concentrations were seen inside sea ice. Due to the melting of sea ice and biogeochemical processes, USW presented as the sink of N2O, and the saturation varied from 47.2% to 102.2%. However, the observed N2O concentrations in USW were higher than that of T-N2OUSW due to the sea–air exchange, diffusion process, possible N2O generation mechanism, and the influence of precipitation, and a more detailed mechanism is needed to understand this process in the Arctic Ocean.

scholarly journals Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Arctic Amplification ◽  
Ice Melting ◽  
Downward Longwave Radiation

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.

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 The role of cyclone activity in snow accumulation on Arctic sea ice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
M. A. Webster ◽  
C. Parker ◽  
L. Boisvert ◽  
R. Kwok
Keyword(s):  
Sea Ice ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Cyclone Activity ◽  
In Situ Data ◽  
Arctic Sea ◽  
Spatio Temporal

AbstractIdentifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

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