Structural analysis of thermokarst lake ice in Beiluhe Basin, Qinghai–Tibet Plateau

Huang, Wenfeng; Li, Zhijun; Han, Hongwei; Niu, Fujun; Lin, Zhijun; Leppäranta, Matti

doi:10.1016/j.coldregions.2011.11.005

Cited by 29 publications

(32 citation statements)

References 29 publications

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“…At present, scholars pay less attention to the ice duration in TP lakes. Only a few studies analyzed the changes in the freezing date of Nam Co Lake [34], as well as lake ice structure [35], thermal conductivity [36] and ice melting date [37] in a small TP thermokarst lake.…”

Section: Discussionmentioning

confidence: 99%

“…However, little attention has been paid to the ice albedo of TP lakes. Only a few studies have analyzed the changes in the freezing date of Nam Co Lake [35], as well as lake ice structure [36], thermal conductivity [37] and ice melting [38] in a small TP thermokarst lake. Although the observation of lake ice albedo is quite scarce in the TP, the MODIS albedo products, which have a relatively small error, can help us to obtain an in-depth understanding of the ice albedo characteristics of a large lake.…”

Section: Introductionmentioning

confidence: 99%

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An Investigation of Ice Surface Albedo and Its Influence on the High-Altitude Lakes of the Tibetan Plateau

Lang

Lyu

et al. 2018

Remote Sensing

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Most high-altitude lakes are more sensitive to global warming than the regional atmosphere. However, most existing climate models produce unrealistic surface temperatures on the Tibetan Plateau (TP) lakes, and few studies have focused on the influence of ice surface albedo on high-altitude lakes. Based on field albedo measurements, moderate resolution imaging spectrometer (MODIS) albedo products and numerical simulation, this study evaluates the ice albedo parameterization schemes in existing lake models and investigates the characteristics of the ice surface albedo in six typical TP lakes, as well as the influence of ice albedo error in the FLake model. Compared with observations, several ice albedo schemes all clearly overestimate the lake ice albedo by 0.26 to 0.66, while the average bias of MODIS albedo products is only 0.07. The MODIS-observed albedo of a snow-covered lake varies with the snow proportion, and the lake surface albedo in a snow-free state is approximately 0.15 during the frozen period. The MODIS-observed ice surface (snow-free) albedos are concentrated within the ranges of 0.14-0.16, 0.08-0.10 and 0.10-0.12 in Aksai Chin Lake, Nam Co Lake and Ngoring Lake, respectively. The simulated lake surface temperature is sensitive to variations in lake ice albedo especially in the spring and winter.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Investigation of Ice Surface Albedo and Its Influence on the High-Altitude Lakes of the Tibetan Plateau

Lang

Lyu

et al. 2018

Remote Sensing

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“…The following two types of gas bubbles were observed from field ice samples (Huang et al 2012): (1) dot-line-shaped bubbles (DB) in the peripheral lake ice cover (Figure 4(a) and (b)). Different from the tubular bubbles in other regions previously reported by (Jeffries et al 1994), DB are made up of tiny spherical pockets with diameters of 0.3-2.5 mm that are Figure 4.…”

Section: Field Observationsmentioning

confidence: 99%

“…According to the field observation, the lake ice in the Beiluhe basin may be classified as (Huang et al 2012): (a) Frazil ice: From late October to December, when air temperature decreases and falls below the freezing point of water and undergoes a slight super-cooling, the surface water starts to freeze rapidly (Kirillin et al 2012). Meanwhile, strong northwest wind is prevailing in the Beiluhe Basin, the surface layer is disturbed and small ice crystals do not have enough time to congeal, frazil ice then forms and progressively joins together to form a solid sheet that consists of tiny granular crystals in the surface.…”

Section: Field Observationsmentioning

confidence: 99%

“…Recently, vertical profiles of ice crystal crystal size, gas bubble shape and size, porosities, as well as ice density were investigated on December 9, 2010 and March 24, 2011, corresponding to fast ice growth and slow ice growth, respectively. Detailed descriptions of the lake ice sample are given by (Huang et al 2013;Huang et al 2012). It should be pointed out that the BLH-A is too small (just covers few pixels in SAR images) and it is difficult to extract its backscatter value accurately and reliably due to the effect of mix-pixels and speckle; however, according to our field investigation, there are no significant difference on ice crystals and fabrics, and gas bubbles morphology among these lakes distributed in the Beiluhe Basin (Huang et al 2012;Wu et al 2014).…”

Section: Datamentioning

confidence: 99%

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The backscattering characteristics of thermokarst lake ice in the qinghai-tibet plateau from SAR

Tian

Tang

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A thermokarst lake is an important indicator of changes in climate, which cause considerable thermal distribution to the surrounding permafrost. The imaging radar has demonstrated the capability to determine when and which lakes freeze or do not freeze. In this paper, the temporal variability of C-band backscattering of thermokarst lakes at Beiluhe test area, is located on the central QinghaiTibet Plateau (QTP), were examined by 45 ENVISAT-ASAR imageries acquired in freeze up, ice duration and break-up stages. The SAR behaviour response for lake ice change are analysed with ASAR observation in experiments area. The results showed that the ice layer volume scattering and ice-water surface scattering were the two major scattering components in C-band VV polarization, which is also affected by the increase of bubble size, ice density and roughness of ice-water interface. According to this study, the timing of lake ice-on in fall and ice-off in spring for this geographic region can be identified in radar images by comparing radar backscatter from lake ice to its surrounding alpine meadow. When ice duration, the radar signature proved to be able to monitor the ice thickness over lake and deformation around the lake.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Sea Ice Modelling

Leppäranta

Meleshko

Uotila

et al. 2019

Springer Polar Sciences

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This chapter presents the geophysical background of mesoscale to large-scale sea ice modelling, discusses the structure of existing advanced models, and shows results from model simulations on future sea ice conditions in the Arctic Ocean. The first and second section introduce the theory of material description of sea ice, sea ice thermodynamics, and the fundamental equations of sea ice dynamics -conservation laws of ice, momentum and heat, and sea ice rheology. Thermodynamics is a one-dimensional problem while dynamics when integrated across sea ice thickness becomes a twodimensional problem, and they are linked together by the ice conservation law. Forcing of sea ice dynamics and thermodynamics is via atmospheric and oceanic boundary layers, where parameterizations of exchange of momentum, heat, moisture, and salinity are of key importance.Sea ice properties relevant to climate are primarily sea ice extent, concentration and thickness.Mesoscale and large-scale sea ice models treat sea ice as a continuum by thickness distribution. These models consist of momentum equation, conservation laws of heat, salt and ice, and ice rheology. The main models used for climate investigations are the Los Alamos model CICE (Community Ice CodE) and LIM (The Louvain-la-Neuve Sea Ice Model), which are reviewed including their parameterizations.Moreover, the performance of these two main models are briefly assessed against observational reference data based on hindcasts and projections. Aspects of data assimilation are discussed where modelling is closely linked to sea ice remote sensing data. Specific attention is given to model performance in sea ice simulation using runs from CMIP3 (Coupled Model Intercomparison Project, Phase 3) and CMIP5 (Coupled Model Intercomparison Project, Phase 5). Sea ice extent and thickness are considered as the most important climate variables, which are needed for validation of current climate models. To validate model performance, comprehensive data sets are also required. Several important characteristics were considered and compared with observations: sea ice cover for seasons with maximum and minimum sea ice extent, sea ice trend in September, sea ice annual cycle, linear trend of sea ice area and volume, sea ice thickness distribution. A brief review is also provided for sea ice assimilation system and its current performance capability. 8.1Sea ice geophysics Sea ice fieldsA "sea ice landscape" consists of leads and ice floes with ridges, hummocks and other morphological characteristics. Mechanical behavior of sea ice fields depends largely on ice concentration, the relative It is common in the seasonal sea ice zone where snow accumulation is large. There are two more specific forms of ice: anchor ice and platelet ice. Anchor ice forms in the sea bottom in turbulent conditions in shallow regions, and it may rise to surface with bottom sediments due to its buoyancy. This is observed, e.g., in the Siberian shelf. Platelet ice forms in Antarctic waters next to floating ice shelves, which act as...

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Structural analysis of thermokarst lake ice in Beiluhe Basin, Qinghai–Tibet Plateau

Cited by 29 publications

References 29 publications

An Investigation of Ice Surface Albedo and Its Influence on the High-Altitude Lakes of the Tibetan Plateau

An Investigation of Ice Surface Albedo and Its Influence on the High-Altitude Lakes of the Tibetan Plateau

The backscattering characteristics of thermokarst lake ice in the qinghai-tibet plateau from SAR

Sea Ice Modelling

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