scholarly journals Observation-derived ice growth curves show patterns and trends in maximum ice thickness and safe travel duration of Alaskan lakes and rivers

2020 ◽  
Author(s):  
Christopher D. Arp ◽  
Jessica E. Cherry ◽  
Dana R.N. Brown ◽  
Allen C. Bondurant ◽  
Karen L. Endres
Keyword(s):  
Interannual Variability ◽  
Growth Curves ◽  
Cold Season ◽  
Ice Thickness ◽  
Observation Data ◽  
Regional Variability ◽  
Winter Climate ◽  
Ice Growth ◽  
Freshwater Ice ◽  
Northern Regions

Abstract. The formation, growth, and decay of freshwater ice on lakes and rivers are fundamental processes of northern regions with wide ranging implications for socio-ecological systems. Ice thickness at the end of winter is perhaps the best integration of cold-season weather and climate, while the duration of thick and growing ice cover is a useful indicator for the winter travel and recreation season. Both maximum ice thickness (MIT) and ice travel duration (ITD) can be estimated from temperature-driven ice growth curves fit to ice thickness observations. We simulated and analyzed ice growth curves based on ice thickness data collected from a range of observation programs throughout Alaska spanning the past 20–60 years to understand patterns and trends in lake and river ice. Results suggest reductions in MIT (thinning) in several northern, interior, and coastal regions of Alaska and overall greater interannual variability in rivers compared to lakes. Interior regions generally showed less variability in MIT and even slightly increasing trends in at least one river site. Average ITD ranged from 214 days in the northern-most lakes to 114 days across southern-most lakes with significant decreases in duration for half of sites. River ITD showed low regional variability, but high interannual variability, underscoring the challenges with predicting seasonally-consistent river travel. Standardization and analysis of these ice observation data provide a comprehensive summary for understanding changes in winter climate and its impact on freshwater ice services.

scholarly journals Observation-derived ice growth curves show patterns and trends in maximum ice thickness and safe travel duration of Alaskan lakes and rivers

2020 ◽  
Vol 14 (11) ◽  
pp. 3595-3609
Author(s):  
Christopher D. Arp ◽  
Jessica E. Cherry ◽  
Dana R. N. Brown ◽  
Allen C. Bondurant ◽  
Karen L. Endres
Keyword(s):  
Interannual Variability ◽  
Growth Curves ◽  
Cold Season ◽  
Ice Thickness ◽  
Observation Data ◽  
Regional Variability ◽  
Winter Climate ◽  
Ice Growth ◽  
Freshwater Ice ◽  
Northern Regions

Abstract. The formation, growth, and decay of freshwater ice on lakes and rivers are fundamental processes of northern regions with wide-ranging implications for socio-ecological systems. Ice thickness at the end of winter is perhaps the best integration of cold-season weather and climate, while the duration of thick and growing ice cover is a useful indicator for the winter travel and recreation season. Both maximum ice thickness (MIT) and ice travel duration (ITD) can be estimated from temperature-driven ice growth curves fit to ice thickness observations. We simulated and analyzed ice growth curves based on ice thickness data collected from a range of observation programs throughout Alaska spanning the past 20–60 years to understand patterns and trends in lake and river ice. Results suggest reductions in MIT (thinning) in several northern, interior, and coastal regions of Alaska and overall greater interannual variability in rivers compared to lakes. Interior regions generally showed less variability in MIT and even slightly increasing trends in at least one river site. Average ITD ranged from 214 d in the northernmost lakes to 114 d across southernmost lakes, with significant decreases in duration for half of sites. River ITD showed low regional variability but high interannual variability, underscoring the challenges with predicting seasonally consistent river travel. Standardization and analysis of these ice observation data provide a comprehensive summary for understanding changes in winter climate and its impact on freshwater ice services.

scholarly journals Interannual sea ice thickness variability in the Bay of Bothnia

2018 ◽  
Vol 12 (11) ◽  
pp. 3459-3476 ◽  
Author(s):  
Iina Ronkainen ◽  
Jonni Lehtiranta ◽  
Mikko Lensu ◽  
Eero Rinne ◽  
Jari Haapala ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Thickness Variability ◽  
Fast Ice ◽  
Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

scholarly journals Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

2006 ◽  
Vol 44 ◽  
pp. 281-287 ◽  
Author(s):  
Shotaro Uto ◽  
Haruhito Shimoda ◽  
Shuki Ushio
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Snow Depth ◽  
East Antarctica ◽  
Ice Thickness ◽  
First Year ◽  
Total Thickness ◽  
Sea Ice Thickness ◽  
Landfast Ice ◽  
Northeasterly Wind

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

scholarly journals The effects of snowfall on a snow-ice-thickness distribution

1997 ◽  
Vol 25 ◽  
pp. 287-291 ◽  
Author(s):  
J. L. Schramm ◽  
M. M. Holland ◽  
J. A. Curry
Keyword(s):  
Surface Albedo ◽  
Thickness Distribution ◽  
Ocean Model ◽  
Ice Thickness ◽  
Ice Growth ◽  
Melt Ponds ◽  
Winter Snow ◽  
Single Column ◽  
Time Of Year ◽  
Snow Fall

Snow falling uniformly on a distribution of ice thicknesses results in a distribution of snow-cover thicknesses. These snow depths depend on the amount of snow-fall, the time of year at which it falls, and the thickness of the underlying ice. The effect of snow fall on snow-ice-thickness distribution is examined using a single-column ice ocean model. The time at which snow begins to accumulate, and melt ponds and leads freeze, affects the surface albedo. The rate of snowfall affects ice-growth rates and, as a result, the ice-thickness distribution. During the period of rapid ice growth between the autumn freeze and mid-winter, snow falling on newly formed ice is rapidly depleted due to sublimation. Snow falling on thicker ice remains throughout the winter to create a source of melt-water for ponds and runoff into the ocean.

scholarly journals Compact Polarimetry Response to Modeled Fast Sea Ice Thickness

2020 ◽  
Vol 12 (19) ◽  
pp. 3240
Author(s):  
Mohammed Dabboor ◽  
Mohammed Shokr
Keyword(s):  
Sea Ice ◽  
Degree Of Polarization ◽  
Ice Thickness ◽  
Operational Mode ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Bay Area ◽  
Fast Ice ◽  
Preliminary Study ◽  
Sar Imagery

Compact Polarimetric (CP) Synthetic Aperture Radar (SAR) is expected to gain more and more ground for Earth observation applications in the coming years. This comes in light of the recently launched RADARSAT Constellation Mission (RCM), which uniquely provides CP SAR imagery in operational mode. In this study, we present observations about the sensitivity of CP SAR imagery to thickness of thermodynamically-grown fast sea ice during early ice growth (September–December 2017) in the Resolute Bay area, Canadian Central Arctic. Fast ice is most suitable to use for this preliminary study since it exhibits only thermodynamic growth in absence of ice mobility and deformation. Results reveal that ice thickness up to 30 cm can be retrieved using several CP parameters from the tested set. This ice thickness corresponds to the thickness of young ice. We found the surface scattering mechanism to be dominant during the early ice growth, exposing an increasing tendency up to 30 cm thickness with a correlation coefficient with the thickness equal to 0.86. The degree of polarization was found to be the parameter with the highest correlation up to 0.95. While thickness retrieval within the same range is also possible using parameters from Full Polarimetric (FP) SAR parameters as shown in previous studies, the advantage of using CP SAR mode is the much larger swath coverage, which is an operational requirement.

scholarly journals Seasonal changes of sea-ice characteristics off East Antarctica

1994 ◽  
Vol 20 ◽  
pp. 195-201 ◽  
Author(s):  
Ian Allison ◽  
Anthony Worby
Keyword(s):  
Sea Ice ◽  
Ice Cores ◽  
Open Water ◽  
Ice Formation ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Growth Season ◽  
Ice Growth ◽  
Antarctic Sea Ice ◽  
Ice Edge

Data on Antarctic sea‐ice characteristics, and their spatial and temporal variability, are presented from cruises between 1986 and 1993 for the region spanning 60°−150° E between October and May. In spring, the sea‐ice zone is a variable mixture of different thicknesses of ice plus open water and in some regions only 30−40% of the area is covered with ice >0.3 m thick. The thin‐ice and open‐water areas are important for air‐sea heat exchange. Crystallographic analyses of ice cores, supported by salinity and stable‐isotope measurements, show that approximately 50% of the ice mass is composed of small frazil crystals. These are formed by rapid ice growth in leads and polynyas and indicate the presence of open water throughout the growth season. The area‐averaged thickness of undeformed ice west of 120° E is typically less than 0.3 m and tends to‐increase with distance south of the ice edge. Ice growth by congelation freezing rarely exceeds 0.4 m, with increases in ice thickness beyond this mostly attributable to rafting and ridging. While most of the total area is thin ice or open water, in the central pack much of the total ice mass is contained in ridges. Taking account of the extent of ridging, the total area‐averaged ice thickness is estimated to be about 1m for the region 60°−90° E and 2 m for the region 120°−150° E. By December, new ice formation has ceased in all areas of the pack and only floes >0.3 m remain. In most regions these melt completely over the summer and the new season's ice formation starts in late February. By March, the thin ice has reached a thickness of 0.15 0.30 m, with nilas formation being an important mechanism for ice growth within the ice edge

Indentation and Penetration of Edge-Loaded Freshwater Ice Sheets in the Brittle Range

1987 ◽  
Vol 109 (3) ◽  
pp. 287-294 ◽  
Author(s):  
G. W. Timco
Keyword(s):  
Aspect Ratio ◽  
Ice Sheets ◽  
Ice Thickness ◽  
Test Results ◽  
Tabular Form ◽  
Test Procedures ◽  
Freshwater Ice ◽  
Edge Loading ◽  
Ice Failure ◽  
Penetration Tests

A series of indentation and penetration tests have been performed by edge loading of a vertical indentor into a floating sheet of columnar S2 freshwater ice. The load on the indentor was measured as a function of interaction speed (v = 0.1 – 60 cm-s−1), indentor width (D = 2.54 – 12.7 cm), ice thickness (h = 0.6 – 3.3 cm), strain rate (ε˙ = v/2D = 10−2 – 101 s−1) and aspect ratio (D/h = 0.5 − 22). In total, 66 tests were performed. In this paper, a description of the test procedures is given along with the full results in both graphical and tabular form. Five different ice fracture modes are identified and described. From the test results, an ice-failure mode map is derived which indicates the conditions in which each ice fracture mode predominates.

scholarly journals Laboratory study of initial sea-ice growth: properties of grease ice and nilas

2012 ◽  
Vol 6 (4) ◽  
pp. 729-741 ◽  
Author(s):  
A. K. Naumann ◽  
D. Notz ◽  
L. Håvik ◽  
A. Sirevaag
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Solid Fraction ◽  
Interstitial Water ◽  
Mass Conservation ◽  
Ice Formation ◽  
Ice Thickness ◽  
Ice Growth ◽  
Bulk Salinity ◽  
A Current

Abstract. We investigate initial sea-ice growth in an ice-tank study by freezing an NaCl solution of about 29 g kg−1 in three different setups: grease ice grew in experiments with waves and in experiments with a current and wind, while nilas formed in a quiescent experimental setup. In this paper we focus on the differences in bulk salinity, solid fraction and thickness between these two ice types. The bulk salinity of the grease-ice layer in our experiments remained almost constant until the ice began to consolidate. In contrast, the initial bulk-salinity evolution of the nilas is well described by a linear decrease of about 2.1 g kg−1 h−1 independent of air temperature. This rapid decrease can be qualitatively understood by considering a Rayleigh number that became maximum while the nilas was still less than 1 cm thick. Comparing three different methods to measure solid fraction in grease ice based on (a) salt conservation, (b) mass conservation and (c) energy conservation, we find that the method based on salt conservation does not give reliable results if the salinity of the interstitial water is approximated as being equal to the salinity of the underlying water. Instead the increase in salinity of the interstitial water during grease-ice formation must be taken into account. In our experiments, the solid fraction of grease ice was relatively constant with values of 0.25, whereas it increased to values as high as 0.50 as soon as the grease ice consolidated at its surface. In contrast, the solid fraction of the nilas increased continuously in the first hours of ice formation and reached an average value of 0.55 after 4.5 h. The spatially averaged ice thickness was twice as large in the first 24 h of ice formation in the setup with a current and wind compared to the other two setups, since the wind kept parts of the water surface ice free and therefore allowed for a higher heat loss from the water. The development of the ice thickness can be reproduced well with simple, one dimensional models that only require air temperature or ice surface temperature as input.

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