Feedbacks Between Surface Deformation and Permafrost Degradation in Ice Wedge Polygons, Arctic Coastal Plain, Alaska

Abstract

In the past three decades, an abrupt, pan‐Arctic acceleration of ice wedge melting has transformed tundra landscapes, spurring the formation of hummock‐like features known as high‐centered polygons (HCPs). This rapid geomorphic transition profoundly alters regional hydrology and influences surface emissions of CO2 and CH4. In Arctic Alaska, most recent instances of ice wedge degradation have arrested within 15–20 years of inception, stabilizing HCP microtopography. However, feedbacks between ground surface deformation and permafrost stability are incompletely understood, limiting our capacity to predict trajectories of landscape evolution in a still warmer future. Here, we use field data from a site near Prudhoe Bay, Alaska, to develop a modeling‐based framework for assessing the strength of positive (i.e., exacerbating) feedbacks on ice wedge degradation, focusing on the importance of heterogeneity in surface drainage and microtopographic conditions. Our simulations suggest that, when troughs are narrow, positive feedbacks on ice wedge melting (associated with thermokarst pool formation) are relatively weak. Positive feedbacks are markedly stronger beneath wide troughs, such as those that form above older, larger ice wedges. Seasonal thaw abruptly accelerates once a talik begins to form beneath wide and deep thermokarst pools. Once a talik initiates, winter severity and snowpack thickness increase in importance as predictors of thaw intensity in summer. Our results indicate that meter‐scale heterogeneity in polygonal microtopography potentially exerts strong, nonlinear controls on thermokarst trajectories. These findings are useful for predicting future thermokarst dynamics and for interpreting the results from coarser‐resolution land surface models operating at greater spatial and temporal scales.

Impact of lateral groundwater flow on hydrothermal conditions of the active layer in a high-Arctic hillslope setting

“…ATS solves coupled conservation equations for energy and water mass transport, considering both above-and belowground processes, based on a multiphysics framework (Painter, 2011;Coon et al, 2016). The available energy at the surface-subsurface interface drives subsurface heat transport and is obtained by solving for a surface energy balance equation (Atchley et al, 2015). Snow and ice on the surface affect heat conduction by reducing or increasing thermal conductivity and subsequently impact heat transfer to the subsurface.…”

High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska

“…Commonly, features demarcating the edges of polygons-such as rims of soil in low-centered polygons (LCPs), or sunken troughs in high-centered polygons (HCPs)-represent less than 50 cm of relief from the surrounding terrain 1,2 . Nonetheless, variability in this microtopography often drives sharp spatial gradients in fluxes of energy [3][4][5] , water 6,7 , and carbon [8][9][10][11] between the atmosphere and the subsurface. The spatial distribution of polygons of different morphologies within a single landscape can be highly complex, as thousands of polygons may occupy a single square kilometer of terrain, and gradation between endmembers (such as LCPs and HCPs) may occur over distances that vary from meters to kilometers 12,13 .…”

Brief Communication: Effects of different saturation vapor pressure calculations on simulated surface-subsurface hydrothermal regimes at a permafrost field site

“…We used the Advanced Terrestrial Simulator (ATS v1.2) to explore how permafrost dynamics are affected by variability in RH associated with the choice of formulas for estimating SVP described in the previous section (Coon et al, 2019). ATS's permafrost configuration simultaneously solves for the surface and subsurface balance of energy and water mass; it is driven by meteorological forcing data (forcing variables include air temperature, longwave radiation, shortwave radiation, wind speed, rainfall, snowfall, and relative humidity) and allows for the accumulation of snow, ice, and liquid water at the surface, and has been extensively validated against field measurements in previous studies (e.g., Atchley et al, 2015;Abolt et al, 2020;Jan et al, 2020). We constructed a 1D permafrost column model (39 m depth) with a groundwater table close to the ground surface tailored for a tundra field site near Utqiagvik, Alaska.…”