Non-iterative numerical model of soil freezing

Author(s):  
Tomas Vogel ◽  
Michal Dohnal ◽  
Jana Votrubova ◽  
Jaromir Dusek

<p>Increasingly, numerical models of varying complexity are used to simulate the thermal and water balance of soils exposed to freezing-thawing cycles. An important aspect of soil freezing modeling is the highly non-linear nature of the energy balance equation during phase transition. To handle the transformation between sensible and latent heat during freezing–thawing events, the majority of existing models employ the concept of apparent heat capacity. The main disadvantage of this approach is that the apparent heat capacity increases by several orders of magnitude at the freezing point, which complicates the numerical solution, possibly causing numerical oscillations and convergence problems.</p><p>An alternative approach was developed to facilitate the simulations of soil water flow and energy transport during sporadic freezing–thawing episodes, which are typical for the winter regime of humid temperate continental climate. The approach is based on an accurate non-iterative algorithm for solving highly non-linear energy balance equation during phase transitions. The suggested modeling approach abstracts from many complexities associated with the freezing phenomena in soils, yet preserves the principal physical mechanism of conserving the internal energy of the soil system during the phase transitions. When applied to simulate occasional freezing soil conditions, the model algorithm delivers the desired effect of slowing down the propagation of surface freezing temperatures into deeper soil horizons by converting water latent heat into sensible heat. The model also allows the evaluation of the extent and duration of frozen soil conditions – a crucial information for soil water flow modeling, as the frozen soil significantly reduces the soil hydraulic conductivity.</p><p>The proposed algorithm was successfully verified against analytical solutions for idealized freezing and thawing conditions and applied to both hypothetical and real field conditions.</p>

2009 ◽  
Vol 19 (06) ◽  
pp. 969-991 ◽  
Author(s):  
GIULIO SCHIMPERNA

The Penrose–Fife system for phase transitions is addressed. Dirichlet boundary conditions for the temperature are assumed. Existence of global and exponential attractors is proved. Differently from preceding contributions, here the energy balance equation is both singular at 0 and degenerate at ∞. For this reason, the dissipativity of the associated dynamical process is not trivial and has to be proved rather carefully.


Author(s):  
Shaun Lovejoy ◽  
Roman Procyk ◽  
Raphael Hébert ◽  
Lenin Del Rio Amador

2021 ◽  
Author(s):  
Roman Procyk ◽  
Shaun Lovejoy ◽  
Raphaël Hébert ◽  
Lenin Del Rio Amador

<p>We present the Fractional Energy Balance Equation (FEBE): a generalization of the standard EBE.  The key FEBE novelty is the assumption of a hierarchy of energy storage mechanisms: scaling energy storage.  Mathematically the storage term is of fractional rather than integer order.  The special half-order case (HEBE) can be classically derived from the continuum mechanics heat equation used by Budyko and Sellers simply by introducing a vertical coordinate and using the correct conductive-radiative surface boundary conditions (the FEBE is a mild extension).</p><div> <p>We use the FEBE to determine the temperature response to both historical forcings and to future scenarios.  Using historical data, we estimate the 2 FEBE parameters: its scaling exponent (<em>H</em> = 0.38±0.05; <em>H</em> = 1 is the standard EBE) and relaxation time (4.7±2.3 years, comparable to box model relaxation times). We also introduce two forcing parameters: an aerosol re-calibration factor, to account for their large uncertainty, and a volcanic intermittency exponent so that the intermittency volcanic signal can be linearly related to the temperature. The high frequency FEBE regime not only allows for modelling responses to volcanic forcings but also the response to internal white noise forcings: a theoretically motivated error model (approximated by a fractional Gaussian noise). The low frequency part uses historical data and long memory for climate projections, constraining both equilibrium climate sensitivity and historical aerosol forcings. <span>Parameters are estimated in a Bayesian framework using 5 global observational temperature series, and an error model which is a theoretical consequence of the FEBE forced by a Gaussian white noise.</span></p> <p>Using the CMIP5 Representative Concentration Pathways (RCPs) and CMIP6 Shared Socioeconomic Pathways (SSPs) scenario, the FEBE projections to 2100 are shown alongside the CMIP5 MME. The Equilibrium Climate Sensitivity = 2.0±0.4 <sup>o</sup>C/CO<sub>2</sub> doubling implies slightly lower temperature increases.   However, the FEBE’s 90% confidence intervals are about half the CMIP5 size so that the new projections lie within the corresponding CMIP5 MME uncertainties so that both approaches fully agree.   The mutually agreement of qualitatively different approaches, gives strong support to both.  We also compare both generations of General Circulation Models (GCMs) outputs from CMIP5/6 alongside with the projections produced by the FEBE model which are entirely independent from GCMs, contributing to our understanding of forced climate variability in the past, present and future.</p> <p>Following the same methodology, we apply the FEBE to regional scales: estimating model and forcing parameters to produce climate projections at 2.5<sup>o</sup>x2.5<sup>o</sup> resolutions. We compare the spatial patterns of climate sensitivity and projected warming between the FEBE and CMIP5/6 GCMs. </p> </div>


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