Snow and ice on Bear Lake (Alaska) – sensitivity experiments with two
                        lake ice models

Semmler, Tido; Cheng, Bin; Yu, Yang; Rontu, Laura

doi:10.3402/tellusa.v64i0.17339

Cited by 48 publications

(42 citation statements)

References 29 publications

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“…By combining the modeling with HIGHTSI and remote sensing data, an analysis that can better reveal the sea ice thickness and concentration information has been conducted (Karvonen et al, 2012). Moreover, HIGHTSI has also been used in lake ice studies , and its benefits from a detailed treatment of snow and ice thermodynamics were confirmed when compared with a simple lake model (Semmler et al, 2012).…”

Section: Hightsimentioning

confidence: 99%

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

et al. 2016

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Abstract. Sea ice plays an important role in the air-iceocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 • C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGH-TSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 • C and 20 W m −2 , respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

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Section: Hightsimentioning

confidence: 99%

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

et al. 2016

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“…Since its early applications for the Baltic Sea and Antarctic [69][70][71][72], the model has been further developed to investigate snow and ice thermodynamics in the Arctic Ocean [73] and lakes [74,75]. The snow and ice temperature regimes are solved by the partial-differential heat conduction equations applied for snow and ice layers, respectively.…”

Section: Thermodynamic Sea Ice Model Hightsimentioning

confidence: 99%

Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

et al. 2017

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Abstract:We estimate two essential sea ice parameters-namely, sea ice concentration (SIC) and sea ice thickness (SIT)-for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer. We compare the SIC and SIT estimation results with in-situ measurements conducted in the study area and estimates based on independent EO data from near-infrared/optical instruments. These comparisons suggest that the SAR-based discrimination between sea ice and open-water works well, and areas of thinner and thicker ice can be distinguished. A larger comprehensive training dataset is needed to set up an operational algorithm for the estimation of SIC and SIT.

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“…The model has been further developed and adapted to investigate lake snow and ice (Yang et al, , 2013Semmler et al, 2012;Cheng et al, 2014). The details of model physics for lake ice are summarised in Yang et al (2012) and Cheng et al (2014).…”

Section: Thermodynamic Snow and Ice Modelmentioning

confidence: 99%

The impact of meteorological conditions on snow and ice thickness in an Arctic lake

Wei¹,

Deng²,

Cheng³

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TInter-annual variation of meteorological conditions and their effects on snow and ice thickness in an Arctic lake Unari (67.148 N, 25.738 E) were investigated for winters 1980/1981Á2012/2013. The lake snow and ice thicknesses were modelled applying a thermodynamic model, and the results were compared with observations. Regression equations were derived for the relationships between meteorological parameters and modelled snow and ice properties. The composite differences of large-scale atmospheric circulation patterns between seasons of thin and thick ice were analysed. The air temperature had an increasing trend (statistical significance p B0.05) during the freezing season (1.08 C/decade), associated with an increasing trend of liquid precipitation (pB0.05) in winter. Both observed and modelled average and maximum ice thicknesses showed a decreasing trend (pB0.05). The model results were statistically more reliable (1) for lake ice than snow and (2) for seasonal means than maxima. Low temperature with less precipitation prompted the formation of columnar ice, whereas strong winds and heavy snowfall were in favour of granular ice formation. The granular (columnar) ice thickness was positively (negatively) correlated with precipitation. The seasonal mean and maximum modelled lake ice and snow thicknesses were controlled by precipitation and temperature history, with 58Á86 % of the inter-annual variance explained. Using regression equations derived from data from 1980 to 2013, snow and ice thickness for the following winter seasons was statistically forecasted, yielding errors of 9Á12 %. Among large-scale climate indices, the Pacific Decadal Oscillation was the only one that correlated with interannual variations in the seasonal average ice thickness in Lake Unari.

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Snow and ice on Bear Lake (Alaska) – sensitivity experiments with two lake ice models

Cited by 48 publications

References 29 publications

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

The impact of meteorological conditions on snow and ice thickness in an Arctic lake

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