Projecting Permafrost Thaw of Sub-Arctic Tundra With a Thermodynamic Model Calibrated to Site Measurements

A. Garnello, S. Marchenko, D. Nicolsky, V. Romanovsky, J. Ledman, G. Celis, C. Schädel, Y. Luo, E. A.G. Schuur

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Northern circumpolar permafrost thaw affects global carbon cycling, as large amounts of stored soil carbon becomes accessible to microbial breakdown under a warming climate. The magnitude of carbon release is linked to the extent of permafrost thaw, which is locally variable and controlled by soil thermodynamics. Soil thermodynamic properties, such as thermal diffusivity, govern the reactivity of the soil-atmosphere thermal gradient, and are controlled by soil composition and drainage. In order to project permafrost thaw for an Alaskan tundra experimental site, we used seven years of site data to calibrate a soil thermodynamic model using a data assimilation technique. The model reproduced seasonal and interannual temperature dynamics for shallow (5–40 cm) and deep soil layers (2–4 m), and simulations of seasonal thaw depth closely matched observed data. The model was then used to project permafrost thaw at the site to the year 2100 using climate forcing data for three future climate scenarios (RCP 4.5, 6.0, and 8.5). Minimal permafrost thawing occurred until mean annual air temperatures rose above the freezing point, after which we measured over a 1 m increase in thaw depth for every 1 °C rise in mean annual air temperature. Under no projected warming scenario was permafrost remaining in the upper 3 m of soil by 2100. We demonstrated an effective data assimilation method that optimizes parameterization of a soil thermodynamic model. The sensitivity of local permafrost to climate warming illustrates the vulnerability of sub-Arctic tundra ecosystems to significant and rapid soil thawing.

Original languageEnglish (US)
Article numbere2020JG006218
JournalJournal of Geophysical Research: Biogeosciences
Volume126
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • Bayesian
  • climate
  • data model fusion
  • modeling
  • permafrost
  • projection

ASJC Scopus subject areas

  • Soil Science
  • Forestry
  • Water Science and Technology
  • Palaeontology
  • Atmospheric Science
  • Aquatic Science
  • Ecology

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