The evolution of Arctic permafrost over the last three centuries

Abstract

Abstract. Understanding the future evolution of permafrost requires a better understanding of its climatological past. This requires permafrost models to efficiently simulate the thermal dynamics of permafrost over the past centuries to millennia, taking into account highly uncertain soil and snow properties. In this study, we present a computationally efficient numerical permafrost model which satisfactorily reproduces the current thermal state of permafrost in the Arctic and its recent trend over the last decade. Also, the active layer dynamics and its trend is realistically captured. The performed simulations provide insights into the evolution of permafrost since the 18th century and show that permafrost on the North American continent is subject to early degradation, while permafrost on the Eurasian continent is relatively stable over the investigated 300-year period. Permafrost warming since industrialization has occurred primarily in three "hotspot" regions in northeastern Canada, northern Alaska, and, to a lesser extent, western Siberia. The extent of near-surface permafrost has changed substantially since the 18th century. In particular, loss of continuous permafrost has accelerated from low (−0.10 × 105 km2 dec−1) to moderate (−0.77 × 105 km2 dec−1) rates for the 18th and 19th centuries, respectively. In the 20th century, the loss rate nearly doubled (−1.36 × 105 km2 dec−1), with the highest near-surface permafrost losses occurring in the last 50 years. Our simulations further indicate that climate disturbances due to large volcanic eruptions in the Northern Hemisphere, can only counteract near-surface permafrost loss for a relatively short period of a few decades. Despite some limitations, the presented model shows great potential for further investigation of the climatological past of permafrost, especially in conjunction with paleoclimate modeling.

First Quantification of the Permafrost Heat Sink in the Earth's Climate System

“…Despite these limitations, our model captures the essential physical mechanisms to assess the exchange of heat between the atmosphere and the land surface, its transport and storage within the subsurface, and its partitioning during the phase change of ground water. Indeed, our model is capable of reproducing recent borehole temperatures well (see Text S4 in Supporting Information S1 and Langer, Nitzbon, Groenke, et al (2022)).…”

“…We used an integration timestep of 1 day for solving the heat equation, which was found to be sufficient for numerical stability and accuracy. Mathematical details are provided in Supporting Information S1 (Text S1); details on the physical processes and the numerical solution scheme in Langer, Nitzbon, Groenke, et al (2022).…”

Simulated methane emissions from Arctic ponds are highly sensitive to warming

“…We used a simplified version of the CryoGrid permafrost model, called CryoGridLite (Langer et al, 2022), coupled to the FLake model to represent the transient temperature field in the sediments beneath the ponds. Unlike the standard CryoGrid model, this version employs an implicit finite difference scheme to solve the heat equation with phase change, originally established by Swaminathan and Voller (1992).…”

Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer

“…snow cover) and variability in soil thermal properties. These factors significantly affect energy uptake in the subsurface, and ultimately, the current and future thermal state of permafrost in Arctic regions (Smith et al, 2022;Langer et al, 2022). We believe that this can be at least partially attributed to the latent heat effect, in addition to soil thermal properties, both of which are a major source of uncertainty in making inferences about the subsurface thermal regime (Riseborough, 1990;Romanovsky and Osterkamp, 2000).…”

“…We also see a lot of potential in applying the inversion method outlined in this work at a larger, cirum-Arctic scale, perhaps by leveraging more efficient formulations of the heat conduction model such as those proposed by Tubini et al (2020) or Langer et al (2022). The greatest challenge here is the limited availability of high quality borehole data at a global scale, despite the ongoing efforts of collaborative data collection and publication projects such as GTN-P (Biskaborn et al, 2015).…”