How well do permafrost models serve user needs? (Invited)
Abstract
There are indications that climatic change has already affected permafrost leading to deeper seasonal thawing and disappearance of the frozen ground in many locations. Particular concerns are associated with development of thermokarst, enhanced thermal erosion of the Arctic coasts, land sliding, environmental and economical risks due to the damage of constructions built upon the frozen ground, and potential enhancement of the global warming through emission of greenhouse gases from thawing permafrost. Comprehensive permafrost models are needed to predict such processes. Relatively simple permafrost models assume that the ground thermal regime is in equilibrium with the atmospheric climate. More advanced models simulate the dynamics of the ground temperature regime and seasonal thawing/freezing, so that transient response of permafrost to changing climatic and environmental conditions may be projected. Alternative approach is to model dynamics of the frozen ground directly within GCMs by improving some permafrost related ground processes. However few such studies presented in the recent publications demonstrate that more work is needed to make projections from the coupled climate-permafrost models realistic, in particular processes in the deeper soil layers have to be considered. Despite the growing skills of permafrost models the question how well they serve the user needs is still open. One of the key problems is inconsistency of the modeling methodologies with the stakeholders needs. Even the most comprehensive permafrost models are deterministic, while permafrost parameters are intrinsically stochastic and at local scales are largely governed by small-scale variability of soil, vegetation, snow parameters and topography. Many practical implications employ probabilistic metrics, such as the probability of permafrost bearing capacity to drop below the critical level necessary to support the structure built upon it, and appropriate permafrost models are needed to address them. The new type of stochastic permafrost models has recently come into existence. Unlike conventional models, they take into account the probabilistic nature of climatic projections and small-scale spatial variability of soil, snow, and biophysiographic parameters in the calculations of the statistical ensemble representing potential states of permafrost under the prescribed conditions. Aside from portraying the level of uncertainty on maps representing permafrost temperature and depth of seasonal thawing, output from a stochastic model can be used to construct series of maps depicting the probability of the parameters to exceed given thresholds within specified regions. This new methodology is fully harmonized with the ensemble approach that is used to construct probabilistic climatic projections on the basis of results derived from several GCMs. On the other hand, it provides important information that directly addresses the practical stakeholders needs and may be used in various applications such as the risk assessment of potential infrastructure damage and evaluation of other threshold-driven processes and impacts associated with thawing permafrost.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2009
- Bibcode:
- 2009AGUFM.C53A..02A
- Keywords:
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- 0702 CRYOSPHERE / Permafrost;
- 0706 CRYOSPHERE / Active layer;
- 0708 CRYOSPHERE / Thermokarst;
- 0798 CRYOSPHERE / Modeling