Permafrost Datasets: Observations and Modeling
Author: Tyler Herrington
Over large portions of the Arctic, and parts of the sub-Arctic, soils remain perennially frozen and are known as permafrost soils. One of the major aims of the Canada 1 Water project is to assess the impact of climate change in Canada’s North, which in large parts is dominate by permafrost. In these regions, the retreat of permafrost is expected to have significant impacts on infrastructure, water resources, and ecosystems.
Frozen ground acts as a barrier to infiltration and movement of water through the soil and can substantially influence the hydrology of Arctic and sub-Arctic environments. Warming of permafrost will lead to a deepening of the active layer (the portion of the soils that undergo seasonal freeze-thaw), and will lead to alterations in the surface geomorphology – both of which have substantial implications for drainage patterns in northern regions.
In addition, it is estimated that the amount of carbon stored in permafrost soils is roughly twice the amount that is currently present in the atmosphere. As permafrost melts, the previously frozen organic material becomes accessible to microbial breakdown and release of carbon dioxide or methane occurs (both greenhouse gases). This could potentially act as a feedback on climate change, with continued warming leading to further permafrost melt and release of greenhouse gases, which in turn leads to further warming.
Figure 1. Map of the ensemble mean soil temperature biases at 0.05° resolution. Panels A and C are the cold season biases, and panels B and D are the warm season biases. Note that the scale is from -5°C to +5°C since most biases are in this range, however there are some grid cells with much larger biases.
Owing to the remote nature of the Arctic, however, soil and land-surface observations are very sparsely distributed. Thus, in order to assess continental scale change to permafrost, we must rely on remotely sensed or model-derived estimates of soil temperatures, moisture and snow cover. Reanalysis products are a commonly used type of model-driven estimate that is constrained by observations on a regular spatial grid; however, little was previously known about the reliability of reanalysis products in permafrost regions.
Using a variety of observational (in-situ) soil temperature and permafrost databases, covering the Canadian and Eurasian Arctic, we are validating soil temperatures from several global and regional Reanalysis products across the Northern Hemisphere (including Canada’s Arctic). We evaluate 10 different reanalysis products at a 1° resolution and a subset of high-resolution products at 0.05° resolution. Readers are referred to Herrington et al. (2022) for further information about our methodology.
We find that most products are biased cold by about 2°C – 8°C across the Northern hemisphere Arctic and Sub-Arctic , on average, with the largest errors occurring across regions underlain by permafrost, and in the cold season. We also find that the ensemble mean soil temperature of the reanalysis products (i.e. the average of all products) generally outperforms any individual product.
Figure 1 shows the ensemble mean soil temperature biases at 0.05° resolution across the Northern hemisphere. The larger biases during the cold season (panels A and C) are readily apparent. It is also apparent that biases at depth (panels C and D) are generally smaller than near the surface (panels A and B).
Going forward, we plan to use the soil temperature data to drive freezing and thawing in hydrological simulations and to validate model-based simulations of active layer thickness and permafrost depth, with an aim to assess the impact of climate change on permafrost. Within the C1W project, this will also include the evaluation of high-resolution regional climate projections and land surface simulations that will be performed as part of the project.
References
Herrington, T. C., Fletcher, C. G., & Kropp, H. (2022). Validation of Pan-Arctic Soil Temperatures in Modern Reanalysis and Data Assimilation Systems. The Cryosphere Discussions, Preprint. https://doi.org/10.5194/tc-2022-5