CVPM 1.1: a flexible heat-transfer modeling system for permafrost

Clow, Gary D.

doi:10.5194/gmd-11-4889-2018

Cited by 10 publications

(11 citation statements)

References 46 publications

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“…Water freezes to the streambed every winter (i.e., bedfast ice; Best et al, ). Thermal properties of the underlying sedimentary materials depend strongly on the water/ice content (Clow, ). Here these are assumed to be constant without consideration of water/ice inclusion, which would cause biases for both water temperature T w and soil temperature T s simulations.…”

Section: Discussionmentioning

confidence: 99%

“…To circumvent the data sparseness, we develop a one‐dimensional heat‐balance model to simulate T w driven by atmospheric and hydrologic observations. Over the inundation period, T w is substituted for the “dry land” ground surface temperatures T g and serve as the upper boundary condition for a control volume permafrost model (CVPM; Clow, ). We calculate permafrost temperature and active layer thickness (ALT) using CVPM.…”

Section: Methodsmentioning

confidence: 99%

“…To investigate the permafrost thermal state of seasonally flooded river and delta floodplains, we need to propagate the simulated water temperature T w as the upper boundary condition into a model that predicts permafrost temperature with depth (CVPM). CVPM is a nonlinear heat‐transfer model for permafrost terrain recently developed by Clow (). The model assumes advective heat flux is negligible compared to the diffusive heat flux, so that the enthalpy flux becomes a function of bulk thermal conductivity and temperature gradient.…”

Section: Methodsmentioning

confidence: 99%

“…Output variables include soil temperature T s , thermal diffusivity, volume fraction of ice, and liquid water content. For a detailed description of the physical basis of the CVPM model, we refer to Clow (). The model code and user's manual (Clow, ) are available at GitHub (https://github.com/csdms-contrib/CVPM/tree/v1.1).…”

Section: Methodsmentioning

confidence: 99%

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Changing Arctic River Dynamics Cause Localized Permafrost Thaw

et al. 2019

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Permafrost in the Arctic regions is degrading in response to decades of amplified warming.Advanced degradation of ice-rich permafrost could significantly alter the water balance by increasing runoff and flooding. How do the hydrological changes in river systems, in turn, affect the permafrost thermal state, specifically in floodplains? First, we develop a first-order heat budget approach to simulate evolving river-water temperature. The river-water thermal model includes heat exchanges at both the air-water and water-subsurface interfaces and can accurately estimate water temperature. Then, river-water temperature is employed as an upper boundary condition for the control volume permafrost model, which models the thermal state of shallow permafrost. The combined model is validated and applied in the Kuparuk River floodplain, Alaska. Results indicate that permafrost warms rapidly during inundation and that channelbelt active layer thickness can deepen by more than 1 m. We find that earlier arrival of the spring freshet and associated earlier inundation onset, as well as increase in river discharge, can significantly increase subsurface permafrost temperature and lead to a deepening of the active layer. In recent years Kuparuk River streamflow has arrived earlier, and mean annual river discharge has increased by 35% since the 1970s. New permanent water and seasonal water appeared throughout the river network of the Kuparuk River since the 1980s according to satellite observations. These hydrological changes likely have contributed to the expansion of riverbed thaw bulbs and the degradation of floodplain permafrost.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Changing Arctic River Dynamics Cause Localized Permafrost Thaw

et al. 2019

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“…In frozen soil, however, a significant proportion of water can remain liquid at temperatures well below 0°C. This depression of the melting temperature is due to the presence of solutes (Bouyoucos, 1913;Bouyoucos and McCool, 1915;Bouyoucos, 1920Bouyoucos, , 1923, surface effects in the interaction between water and soil particles as well as water and ice (Anderson and Tice, 1972;Clow, 2018), and the Gibbs-Thomson effect (Rempel et al, 2004;Watanabe and Mizoguchi, 2002). To some degree, also polycrystalline ice has a temperature-dependent liquid water content (Langham, 1974).…”

Section: Introductionmentioning

confidence: 99%

A method for solving heat transfer with phase change in ice or soil that allows for large time steps while guaranteeing energy conservation

Bottazzi¹,

Gruber²,

Rigon³

2020

Preprint

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Abstract. The accurate simulation of heat transfer with phase change is a central problem in cryosphere studies. This is because the nonlinear behaviour of enthalpy as function of temperature can prevent thermal models of snow, ice and frozen soil from converging to the correct solution. Existing numerical techniques rely on increased temporal resolution in trying to keep corresponding errors withing acceptable bounds. Here, we propose an algorithm, originally applied to solve water flow in soils, as a method to solve these integration issues with guaranteed convergence and conservation of energy for any time step size. We review common modeling approaches, focusing on the fixed-grid method and on frozen soil. Based on this, we develop a conservative formulation of the governing equation and outline problems of alternative formulations in discretized form. Then, we apply the nested Newton-Casulli-Zanolli (NCZ) algorithm to a one-dimensional finite-volume discretization of the energy-enthalpy formulation. Model performance is demonstrated against the Neumann and Lunardini analytical solutions and by comparing results from numerical experiments with integration time steps of one hour, one day, and ten days. Using our formulation and the NCZ algorithm, the convergence of the solver is guaranteed for any time step size. With this approach, the integration time step can be chosen to match the time scale of the processes investigated.

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