Applicability of the ecosystem type approach to model permafrost dynamics across the Alaska North Slope

Nicolsky, Dmitry; Romanovsky, V. E.; Panda, Santosh K.; Marchenko, S. S.; Muskett, Reginald R.

doi:10.1002/2016jf003852

Cited by 76 publications

(75 citation statements)

References 78 publications

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“…A follow-on study estimated the mean ALT across Alaska to be between 42 and 49 cm with 95 % confidence (Mishra et al, 2016) main, which is larger than our model results and the previous study by Pastick et al (2015). Our model predicts relatively stable permafrost conditions in continuous permafrost areas during the study period, which is consistent with previous reports (Osterkamp, 2007;Jafarov et al, 2012;Nicolsky et al, 2017). Our estimate of the ALT trend in those areas (0.32 ± 1.18 cm yr −1 ) is also comparable with a regional modeling experiment in northern Alaska (Nicolsky et al, 2017).…”

Section: Discussionsupporting

confidence: 81%

“…Our model predicts relatively stable permafrost conditions in continuous permafrost areas during the study period, which is consistent with previous reports (Osterkamp, 2007;Jafarov et al, 2012;Nicolsky et al, 2017). Our estimate of the ALT trend in those areas (0.32 ± 1.18 cm yr −1 ) is also comparable with a regional modeling experiment in northern Alaska (Nicolsky et al, 2017). Our model results indicate widespread active layer deepening in the study domain from 2001 to 2015, with generally larger positive trends (> 3 cm yr −1 ) in discontinuous and sporadic permafrost areas, including central and southern Alaska, and smaller trends (∼ 0.32 cm yr −1 ) over colder and more continuous permafrost areas of northern Alaska (Fig.…”

Section: Discussionsupporting

confidence: 81%

“…The effects of soil organic matter on ground temperature evolution are also influenced by soil moisture content, which affects soil thermal conductivity and heat exchange processes (Hinkel and Nelson, 2003;Nicolsky et al, 2017). Our study may underestimate the modeled ALT uncertainties associated with SMAP SM data.…”

Section: Discussionmentioning

confidence: 71%

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Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Kimball

Chen

et al. 2018

The Cryosphere

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Abstract. An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ∼ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higherpermafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32 ± 1.18 cm yr −1 ) and much larger increases (> 3 cm yr −1 ) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionsupporting

confidence: 81%

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Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Kimball

Chen

et al. 2018

The Cryosphere

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“…The model represents the effects of unfrozen water, which is important in the modeling of phase change processes in frozen ground (Romanovsky & Osterkamp, ). A detailed description of this model and the unfrozen water function calculation can be found in Jafarov et al (), Marchenko, Romanovsky, and Tipenko (), Nicolsky, Romanovsky, and Tipenko (), Nicolsky, Romanovsky, and Panteleev (), Nicolsky et al (), and Romanovsky and Osterkamp (). The soil column is divided into several layers, and each soil layer has distinct thermal physical properties.…”

Section: Methods and Datamentioning

confidence: 99%

Numerical Modeling of the Active Layer Thickness and Permafrost Thermal State Across Qinghai‐Tibetan Plateau

et al. 2017

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The dynamics of permafrost (including the permafrost thermal state and active layer thicknesses (ALT)) across the Qinghai‐Tibetan Plateau (QTP) have not been well understood on a large scale. Here we simulate the ALT and permafrost thermal state using the Geophysical Institute Permafrost Lab version 2 (GIPL2) model across the QTP. Based on the single‐point simulations, the model is upscaled to the entire QTP. The upscaled model is validated with five investigated regions (IRs), including Wenquan (WQIR), Gaize (GZIR), Aerjin (AEJIR), Xikunlun (XKLIR), and Qinghai‐Tibetan Highway (G109IR). The results show that the modified GIPL2 model improves the accuracy of the permafrost thermal state simulations. Due to our simulated results on the QTP, the average ALT is of 2.30 m (2.21–2.40 m). The ALT decreases with an increase in the altitude and decreases from the southeast to the northwest. The ALT is thin in the central QTP, but it is thick in the high‐elevation mountain areas and some areas surrounding glaciers and lakes. The largest ALT is found in the border areas between permafrost and seasonally frozen ground regions. The simulated results of the MAGT (the mean annual ground temperature) indicate that most of the permafrost is substable, which is sensitive to climate warming. The simulated results would be of great significance on assessing the impacts of permafrost dynamics on local hydrology, ecology, and engineering construction.

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“…4,5 To evaluate permafrost distribution over large areas, landscape heterogeneity must be simplified using environmental datasets to represent ecosystem and geomorphic processes. [6][7][8] These datasets range from land cover class to surficial materials, and often differ in their spatial and temporal resolution. Numerical models used to assess permafrost responses to climate change also rely on the availability and quality of these environmental datasets for model parameterization.…”

Section: Introductionmentioning

confidence: 99%

Environmental controls on ground temperature and permafrost in Labrador, northeast Canada

Way

Lewkowicz

2018

Permafrost & Periglacial

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Field data from 83 environmental monitoring stations across Labrador, 17 with permafrost, were used to analyze the interrelationships of key variables considered in the temperature at the top of permafrost model. Snow depth, not mean annual air temperature, was the strongest climatic determinant of mean temperatures at the ground surface and at the base of the annual freeze–thaw layer, and its variability was most closely related to land cover class. A critical late‐winter snow depth of 70 cm or more was inferred to be sufficient to prevent the formation of permafrost at the monitoring sites, which meant that permafrost was absent beneath forest but present in some tundra, peatland and bedrock locations. Analyses showed no statistically significant relations identified between topographic indices and various station parameters, challenging their utility for regional modeling. Testing of several different land cover datasets for model parameterization gave errors in ground surface temperature ranging from ± 0.9 to 2.1°C. These results highlight the importance of local field data and emphasize the necessity of high‐quality national‐scale land cover datasets suitable for permafrost modeling.

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Applicability of the ecosystem type approach to model permafrost dynamics across the Alaska North Slope

Cited by 76 publications

References 78 publications

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Numerical Modeling of the Active Layer Thickness and Permafrost Thermal State Across Qinghai‐Tibetan Plateau

Environmental controls on ground temperature and permafrost in Labrador, northeast Canada

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