Modelling of ice thermodynamics in natural water bodies

Launiainen, Jouko; Cheng, Bin

doi:10.1016/s0165-232x(98)00009-3

Cited by 144 publications

(161 citation statements)

References 41 publications

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“…For the ice thickness retrieval, an estimation of snow thickness on thin sea ice is needed. Since in situ measurements of snow thickness are seriously lacking in the Arctic, snow thickness from climatology (Warren et al, 1999) or from a thermodynamic sea ice model forced with numerical weather prediction model data (Launiainen and Cheng, 1998) can be used. Typically, snow thickness uncertainty is one of the main factors determining the uncertainty of the retrieved ice thickness (Yu and Rothrock, 1996;Wang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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Abstract. Following the launch of ESA's Soil Moisture andOcean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

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Section: Introductionmentioning

confidence: 99%

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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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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“…HIGHTSI was initially designed for seasonal ice simulation (Launiainen and Cheng, 1998;Vihma et al, 2002;Cheng et al, 2006), and it is also capable of being applied over perennial sea ice (Cheng et al, 2008b). Previous evaluations have shown that HIGHTSI can simulate the seasonal evolution of Arctic sea ice temperature and thickness well (Cheng et al, 2008b(Cheng et al, , 2013.…”

Section: Hightsimentioning

confidence: 99%

“…When the snow and ice thickness changes, an interpolation step which can ensure the conservation of heat is performed. More detailed technical information of HIGHTSI can be found in Launiainen and Cheng (1998).…”

Section: Hightsimentioning

confidence: 99%

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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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Modelling of ice thermodynamics in natural water bodies

Cited by 144 publications

References 41 publications

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

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

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

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