A New Method to Determine the Upper Boundary Condition for a Permafrost Thermal Model: An Example from the Qinghai‐Tibet Plateau

Abstract

Assessing possible permafrost degradation related to engineering projects, climate change and land use change is of critical importance for protecting the environment and in developing sustainable designs for vital infrastructure in cold regions. A major challenge in modelling the future degradation of permafrost is finding ways to constrain changes in the upper thermal boundary condition over time and space at appropriate scales. Here, we report on an approach designed to predict time series of air, ground surface and shallow ground temperatures at a spatial scale on the order of 102 m2 for engineering design of a railway or highway project. The approach uses a regional‐scale atmospheric model to downscale global climate model output, and then stepwise multiple regression to develop an equation that provides a best‐fit prediction of site‐specific observational data using bilinearly interpolated output from the atmospheric model. This approach bridges the scale difference between atmospheric climate models and permafrost thermal models, and allows for a wider range of factors to be used in predicting the thermal boundary condition. For a research site located in Beiluhe, China, close to the Qinghai‐Tibet Railway, a comparison of model predictions with observational data not used in the construction of the model shows that this method can be used with a high degree of accuracy to determine the upper boundary condition for a permafrost thermal model. Once a model is constructed, it can be used to predict future changes in boundary condition parameters under different greenhouse emission scenarios for climate change. Copyright © 2012 John Wiley & Sons, Ltd.

Effects of freeze-thaw cycles on soil mechanical and physical properties in the Qinghai-Tibet Plateau

“…With the rapid development of infrastructure in cold regions (Ma et al 2011;Lai et al 2012;Zhang et al 2012), the effects of freeze-thaw cycles on the physical and mechanical properties of soil requires more attention from the engineering and academic communities. The potential influence of future climate warming on the number of freezethaw cycles and associated geotechnical failures (Harris et al 2009;Kurylyk et al 2014) provides the impetus for seeking a better understanding of the influence of freeze-thaw cycles on soil physical and mechanical properties.…”

Atmosphere-Cryosphere Coupled Model for Regional Climate Applications

“…Accurate simulation of snowmelt runoff and infiltration is crucial for the mesoscale numerical simulation of atmosphere-land interactions [1,2]. Including detailed snow and frozen soil physics can improve not only seasonal cycle of snowmelt in climate simulations, but also the surface energy and water budgets in high and temperate latitudes [3,4]. Studies suggest that soil moisture, temperature, and snow exhibit persistence on seasonal to interannual time scales [5,6].…”

A Numerical Study of Mesoscale Model Initialization with Data Assimilation

“…The atmosphere-land as a coupled system, affects local weather and regional climate substantially and should be studied in-depth and with detail. Recent studies suggest that soil moisture, temperature, and snow, along with external forcing and internal land surface dynamics, exhibit persistence on seasonal to inter-annual time scales (Zhang et al, 2012). This persistence has important implications for the extended prediction of climatic and hydrologic extremes (Koster and Suarez, 1995;Kumar et al, 2008).…”

“…Warmer temperatures and increases in extreme weather events may affect the crop yields, the stability of engineering structures, and the economic costs of production (Rosenzweig et al, 2001;Zhang et al, 2012). Elevated rates of evapotranspiration and albedo may spur a reduction in water-retaining capacity, resulting in a water and energy imbalance.…”

“…Since surface energy and water storages of ground regulate atmospheric conditions, accurate initialization and representation of these variables in a coupled mesoscale model is essential for extended atmospheric and hydrologic prediction (Shukla and Mintz, 1982;Houser, 2000;Ryu et al, 2005;Zhang et al, 2012).…”