Numerical modeling of permafrost dynamics in Alaska using a high spatial resolution dataset

Abstract

Abstract. Climate projections for the 21st century indicate that there could be a pronounced warming and permafrost degradation in the Arctic and sub-Arctic regions. Climate warming is likely to cause permafrost thawing with subsequent effects on surface albedo, hydrology, soil organic matter storage and greenhouse gas emissions.To assess possible changes in the permafrost thermal state and active layer thickness, we implemented the GIPL2-MPI transient numerical model for the entire Alaska permafrost domain. The model input parameters are spatial datasets of mean monthly air temperature and precipitation, prescribed thermal properties of the multilayered soil column, and water content that are specific for each soil class and geographical location. As a climate forcing, we used the composite of five IPCC Global Circulation Models that has been downscaled to 2 by 2 km spatial resolution by Scenarios Network for Alaska Planning (SNAP) group.In this paper, we present the modeling results based on input of a five-model composite with A1B carbon emission scenario. The model has been calibrated according to the annual borehole temperature measurements for the State of Alaska. We also performed more detailed calibration for fifteen shallow borehole stations where high quality data are available on daily basis. To validate the model performance, we compared simulated active layer thicknesses with observed data from Circumpolar Active Layer Monitoring (CALM) stations. The calibrated model was used to address possible ground temperature changes for the 21st century. The model simulation results show widespread permafrost degradation in Alaska could begin between 2040-2099 within the vast area southward from the Brooks Range, except for the high altitude regions of the Alaska Range and Wrangell Mountains.

The importance of a surface organic layer in simulating permafrost thermal and carbon dynamics

“…The effect of the SOL has been well presented in several modeling studies. For example, Lawrence and Slater (2008) showed that soil organic matter affects the permafrost thermal state in the Community Land Model, and Jafarov et al (2012) discussed the effect of the SOL in the regional modeling study for Alaska, United States. Recently, Chadburn et al (2015a, b) incorporated an SOL in the Joint UK Land Environment Simulator (JULES) model to illustrate its influence on ALT and ground temperatures both at a site-specific study in Siberia, Russia, and globally.…”

Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost

“…Jafarov et al (2012) and Westermann et al (2013) produced a transient run of the ground thermal state in Alaska to assess permafrost dynamics under IPCC change scenarios. Another meso-scale modelling effort, that of Gisnå s et al (2013), provides an equilibrium model of permafrost distribution in Norway at a spatial resolution of 1 km 2 .…”

PIC: Comprehensive R package for permafrost indices computing with daily weather observations and atmospheric forcing over the Qinghai–Tibet Plateau

“…The probability of permafrost occurrence and most likely permafrost conditions are determined by computing the 16 indices. Although PIC quantitatively integrates most of these indices based on previous studies (Jafarov et al, 2012;Nelson et al, 1997;Riseborough 15 et al, 2008;Smith and Riseborough, 2010;Zhang et al, 2005;Zhang et al, 2014), it still has several limitations.…”

“…Permafrost is a subsurface feature that is difficult to directly observe and map. These methods integrate the effects of air and ground temperatures, topography, vegetation, and soil properties to map permafrost spatially and explicitly (Gisnå s et al, 2013;Jafarov et al, 2012;Zhang et al, 2014). Weather observation data, including air and soil temperatures with different depths, are the 15 main inputs for single-point simulation.…”

“…(RS), normalized RMSE (NRMSE), Nash-Sutchliffe efficiency (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), normalized average error (NAE), variance ratio (VR), and index of agreement (D) (Jafarov et al, 2012;Legates and McCabe, 1999). The sequential Mann-Kendall (MK) trend test was used to statistically assess whether there was a shift in trends of the climate factors and permafrost indices (Fraile, 1993).…”