Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization

• Published in: Geophysical research letters 2021-08, Vol.48 (16), p.n/a
• Main Authors: Bruin, Jelte G. H., Bense, Victor F., Ploeg, Martine J.
• Format: Article
• Language: English
• Subjects: active layer; permafrost; thermal properties
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2021GL093306

Permafrost has become increasingly unstable as a result of surface warming; therefore it is crucial to improve our understanding of permafrost spatiotemporal dynamics to assess the impact of active layer thickening on future hydrogeological processes. However, direct determinations of permafrost active‐layer thermal properties are few, resulting in large uncertainty in forecasts of active layer thickness. To assess how to reduce the uncertainty without expanding monitoring efforts, a total of 1,728 numerical 1D models were compared using three error measures against observed active layer temperature data from the Qinghai‐Tibetan Plateau. Resulting optimized parameter values varied depending on the error measure used, but agree with reported ones: bulk volumetric heat capacity is 1.82–1.94 ×106Jm3 K, bulk thermal conductivity 1.0–1.2 W/m K and porosity 0.25–0.45 (−). The active layer thickening rate varied significantly for the three error measures, as demonstrated by a ∼15 years thawing time‐lag between the error measures over a 100 years modeling period. Plain Language Summary In Arctic permafrost regions, the thickness of the active layer, the top soil layer subjected to seasonal freezing and thawing, is increasing as a result of increasing air temperatures. Consequently, increasing amounts of the greenhouse gasses of carbon dioxide and methane could be released into the atmosphere and the regional hydrology and vegetation will change. Models are used to study the development of the active layer, which are based upon parameters that represent properties found in the field. In this study, we compare observed temperature data from the active layer to results from 1,728 unique models with varying parameters using three error measures. We determined which parameters can best mimic the observations, without the need to collect samples in the field. This method can help improve the accuracy of models, enabling researchers to better understand the processes involved in increasing active layer thickness and to make more accurate forecasts of future active‐layer behavior. Key Points Active layer temperature observations are an effective means to infer thermal properties Soil porosity and thermal conductivity are the most sensitive active layer model parameters Long‐term predictions of active layer thaw thickening vary by up to 15 years due to parameter uncertainty