Editing IPCC:AR6/WGI/Chapter-9 (section)

==== 9.5.2.2 Evaluation of Permafrost in Climate Models ====

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As stated in AR5 ( [[#Flato--2013|Flato et al., 2013]] ), coupled models contributing to CMIP5 showed large inter-model variability of permafrost extent due to deficiencies in reproducing surface characteristics and processes ( [[#Koven--2013|Koven et al., 2013]] ), particularly thermal properties of the ground and snow. These deficiencies led SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) to express only ''medium confidence'' in the models’ capacity to correctly project the magnitude of future permafrost changes, in spite of ''high confidence'' in the models’ projection of a general thaw depth increase and a substantial loss of shallow permafrost. The SROCC further noted that several types of physical ‘pulse’ disturbances, in particular fire and thermokarst formation, are usually not represented in coupled climate models. This has been discussed in detail in SROCC, which assessed that there is ''high confidence'' that permafrost degradation through fire ( [[#Jones--2015|Jones et al., 2015]] ; [[#Gibson--2018|Gibson et al., 2018]] ) is currently occurring faster in some well-studied regions than during the first half of the 20th century, and ''medium confidence'' that thermokarst formation, to which about 20% of the northern permafrost region is vulnerable ( [[#Olefeldt--2016|Olefeldt et al., 2016]] ), can lead to faster large-scale permafrost degradation in response to climate change.

Since SROCC, dedicated modelling of the evolution of ice- and organic-rich permafrost in the north-east Siberian lowlands ( [[#Nitzbon--2020|Nitzbon et al., 2020]] ) has shown that not representing thermokarst-inducing processes in ice-rich terrain leads to a systematic underestimation of the rapidity and magnitude of permafrost thaw. Simplified inventory-based modelling ( [[#Turetsky--2020|Turetsky et al., 2020]] ) points towards similar conclusions. Although these pulse disturbances still need to be represented in CMIP-type models, there have been many new developments to that type of model since CMIP5 and AR5. Soil freezing and its thermal and hydrological effects are now included in a large number of land-surface modules that are part of the CMIP6 ensemble (S. [[#Chadburn--2015|]] [[#Chadburn--2015|Chadburn et al., 2015]] ; [[#Hagemann--2016|Hagemann et al., 2016]] ; [[#Cuntz--2018|Cuntz and Haverd, 2018]] ; [[#Guimberteau--2018|Guimberteau et al., 2018]] ; [[#Yokohata--2020|Yokohata et al., 2020]] ), sometimes allowing for the effects of excess ice ( [[#Lee--2014|Lee et al., 2014]] ). Improved representation of snow insulation in models has led to more realistic simulated permafrost extents (e.g., [[#Paquin--2015|Paquin and Sushama, 2015]] ). In a post-CMIP5 ensemble of land-surface models driven by observed meteorological conditions ( [[#McGuire--2016|McGuire et al., 2016]] ), inter-model spread was substantially reduced when the ensemble was restricted to models that appropriately represented the effect of snow insulation on the underlying soil (W. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ). More detailed descriptions of high-latitude vegetation characteristics, vegetation dynamics, and snow-vegetation interactions have been included in several models since AR5 (S.E. [[#Chadburn--2015|]] [[#Chadburn--2015|Chadburn et al., 2015]] ; [[#Porada--2016|Porada et al., 2016]] ; [[#Druel--2017|Druel et al., 2017]] ).

A total soil column depth of at least about 10 m is required to adequately represent the dampening effect of seasonal-scale heat exchanges between shallow and deeper ground, and thus to correctly simulate ALT ( [[#Lawrence--2008|Lawrence et al., 2008]] ; [[#Ekici--2015|Ekici et al., 2015]] ). However, many CMIP6 models still have shallower total soil columns ( [[#Burke--2020|Burke et al., 2020]] ) and the proportion of models with deeper total soil columns has not increased since CMIP5 ( [[#Koven--2013|Koven et al., 2013]] ). Another recently identified process, usually not represented in the current (CMIP6) generation of climate models ( [[#Zhu--2019|Zhu et al., 2019]] ), is warming-driven decomposition and burning of organic material that provides strong thermal insulation of underlying ground. Decay of the insulating organic material can lead to increased permafrost thaw, creating a positive feedback loop.

In spite of the aforementioned structural improvements to many models, the simulated current permafrost extent from available CMIP6 models shows no substantial improvement with respect to CMIP5 (see Figure 9.22a). The extent of the region where permafrost is simulated within the top 15 m in the Northern Hemisphere for the 1979–1998 period is characterized by very large scatter in the coupled CMIP5 and CMIP6 historical simulations compared to estimates of the present permafrost extent based on multiple observational lines of evidence ( [[#Zhang--1999|Zhang et al., 1999]] ) and models based on satellite observations and reanalyses ( [[#Gruber--2012|Gruber, 2012]] ; [[#Obu--2019|Obu et al., 2019]] ). Outliers with very low simulated permafrost extent are models that have only a very shallow soil column (leading to an underestimate of thermal inertia at depth) and do not take into account soil water phase changes. These inadequacies lead to an overestimate of seasonal thaw depth, exceeding the total thickness of the models’ soil columns ( [[#Burke--2020|Burke et al., 2020]] ). Excessive simulated permafrost extent can in several cases be traced to insufficient thermal insulation by the winter snow cover ( [[#Burke--2020|Burke et al., 2020]] ).

Figure 9.22a also shows that the corresponding land-atmosphere simulations with prescribed observed sea surface temperatures and sea ice concentrations, and the land-only simulations with prescribed reanalysis-based meteorological forcing, do not provide an improved simulation of the current permafrost extent, although, by construction, they can be expected to exhibit lower land surface climate biases. This further points to deficiencies in the land modules as the main reason for biases, consistent with conclusions drawn from the analysis of CMIP5 output ( [[#Koven--2013|Koven et al., 2013]] ), as reported in SROCC and AR5.

In spite of more realistic description of permafrost-related processes in many coupled climate models, the CMIP6 models still produce a very scattered ensemble of estimates of current permafrost extent, and there is ''high confidence'' that this is strongly linked to deficiencies of the representation of soil processes. Furthermore, current-generation climate models tend to neglect several physical disturbances that can lead to faster permafrost thaw. Because of large uncertainties in the future evolution of these drivers (see SROCC), there is ''limited evidence'' that these shortcomings lead to an underestimate of permafrost degradation rates in response to climate change in the CMIP6 ensemble. In summary, there is ''high confidence'' that coupled models correctly simulate the sign of future permafrost changes linked to surface climate changes, but only ''medium confidence'' in the amplitude and timing of the transient response.

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'''Figure 9.22''' '''|''' '''Simulated versus observed permafrost extent and volume change by warming level. (a)''' Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) models, from the first ensemble member of the historical coupled run, and for CMIP6 Atmospheric Model Intercomparison Project (AMIP) (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. Estimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: [[#Obu--2018|Obu et al. (2018)]] ; star: [[#Zhang--1999|Zhang et al. (1999)]] ; circle: central value and associated range from [[#Gruber--2012|Gruber (2012)]] . '''(b)''' Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

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