Lead Authors: Stephan Gruber (Carleton University)
Even though variables related to ground temperature are available from climate projections, their application for informing decision-making related to future permafrost thaw is limited. This is a gap for which the development of new knowledge, methods and capacity is urgently needed in Canada. In the absence of established practice, this section outlines important considerations for using permafrost relevant projected model output in applications to inform adaptation or mitigation decisions.
Context: Climate change drives permafrost thaw globally and in the long term. The climate models used to estimate likely spatial and temporal patterns of climate changes employ grids with about 100–300 km horizontal resolution and the feedback of permafrost thaw to climate change is included only in some, and only partially, with physical processes sometimes represented [e.g., Burke et al., 2020] and permafrost carbon dynamics only rarely [e.g., Natali et al., 2021]. The timing and magnitude of permafrost thaw resulting from climate change, however, are determined by surface (vegetation, snow, drainage) and subsurface (ice content and other geotechnical variables) characteristics that vary strongly over distances of tens to hundreds of metres, and by processes that predominantly occur at these scales. The challenges for supporting decisions with climate model output that result from this mismatch of scales vary with the type of decision.
Mitigation: The magnitude and timing of permafrost carbon feedback are usually estimated with offline [e.g., de Vrese and Brovkin, 2021] or coupled [Burke et al., 2020, CMIP5/6] land-surface models. These are subject to significant shortcomings in representing permafrost carbon processes and feedbacks related to water and thermokarst. The scaling conflict here is a matter of how well model results can be compared with observations [Melton et al., 2019] and how well coarse grids can represent nonlinear processes in the sub-grid [Giorgi and Avissar, 1997; Gruber, 2012]. As the variable of interest here is the global amount of greenhouse gasses emitted from thawing permafrost, the scaling conflict can severely affect simulation quality, but it likely does not fundamentally challenge the usefulness of results. Regional warm and cold biases may balance to some degree.
Adaptation: To support adaptation, model output needs to inform decisions related to specific locations and specific ground conditions, usually involving variables such as temperature, ice content, and subsidence of the ground surface. These simulations are required at a resolution fine enough (grid or subgrid) that can represent landscape-scale processes [O’Neill et al., 2020, Cao et al., 2019, Schneider et al., 2021]. The models used for this need permafrost-specific capabilities [Endrizzi et al., 2014, Tubini et al., 2021] and require input on subsurface characteristics such as ice content, that are rarely available outside local applications. For driving such permafrost simulations, climate model output needs to be downscaled and/or debiased, for example using re-analysis. Debiasing permafrost variables such as ground temperatures or active-layer thickness directly is unsuitable due to the prominence of transient effects related to phase change near 0ºC.
Additional considerations: Permafrost is not readily observed remotely, and in-situ observations are sparse and biased to particular regions and terrain types [Biskaborn et al., 2015]. Almost no data is available for climatological periods (e.g., 30 years), for example, a recent global analysis only had about one hundred locations with one decade of observations [Biskaborn et al., 2019]. Consequently, testing the representation of permafrost in land-surface models needs to rely strongly on simulations driven by re- reanalysis [Cao et al., 2019, Fiddes et al., 2015].
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