Standardized monitoring of permafrost thaw: a user-friendly, multi-parameter protocol

Climate change is destabilizing permafrost landscapes, affecting infrastructure, ecosystems and human livelihoods. The rate of permafrost thaw is controlled by surface and subsurface properties and processes, all of which are potentially linked with each other. Yet, no standardized protocol exists for measuring permafrost thaw and related processes and properties in a linked manner. The permafrost thaw action group of the Terrestrial Multidisciplinary distributed Observatories for the Study of the Arctic Connections (T-MOSAiC) project has developed a protocol, for use by non-specialist scientists and technicians, citizen scientists and indigenous groups, to collect standardized metadata and data on permafrost thaw. The protocol introduced here addresses the need to jointly measure permafrost thaw and the associated surface and subsurface environmental conditions. The parameters measured along transects are: snow depth, thaw depth, vegetation height, soil texture, and water level. The metadata collection includes data on timing of data collection, geographical coordinates, land surface characteristics (vegetation, ground surface, water conditions), as well as photographs. Our hope is that this openly available dataset will also be highly valuable for validation and parameterization of numerical and conceptual models, thus to the broad community represented by the T-MOSAIC project.

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Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme

The Cryosphere ◽

10.5194/tc-5-773-2011 ◽

2011 ◽

Vol 5 (3) ◽

pp. 773-790 ◽

Cited By ~ 59

Author(s):

R. Dankers ◽

E. J. Burke ◽

J. Price

Keyword(s):

Land Surface ◽

The Arctic ◽

Soil Parameters ◽

Active Layer Thickness ◽

Organic Carbon Content ◽

Land Surface Scheme ◽

Spatial Coverage ◽

Soil Temperatures ◽

Thaw Depth ◽

Surface Scheme

Abstract. Land surface models (LSMs) need to be able to simulate realistically the dynamics of permafrost and frozen ground. In this paper we evaluate the performance of the LSM JULES (Joint UK Land Environment Simulator), the stand-alone version of the land surface scheme used in Hadley Centre climate models, in simulating the large-scale distribution of surface permafrost. In particular we look at how well the model is able to simulate the seasonal thaw depth or active layer thickness (ALT). We performed a number of experiments driven by observation-based climate datasets. Visually there is a very good agreement between areas with permafrost in JULES and known permafrost distribution in the Northern Hemisphere, and the model captures 97% of the area where the spatial coverage of the permafrost is at least 50%. However, the model overestimates the total extent as it also simulates permafrost where it occurs sporadically or only in isolated patches. Consistent with this we find a cold bias in the simulated soil temperatures, especially in winter. However, when compared with observations on end-of-season thaw depth from around the Arctic, the ALT in JULES is generally too deep. Additional runs at three sites in Alaska demonstrate how uncertainties in the precipitation input affect the simulation of soil temperatures by affecting the thickness of the snowpack and therefore the thermal insulation in winter. In addition, changes in soil moisture content influence the thermodynamics of soil layers close to freezing. We also present results from three experiments in which the standard model setup was modified to improve physical realism of the simulations in permafrost regions. Extending the soil column to a depth of 60 m and adjusting the soil parameters for organic content had relatively little effect on the simulation of permafrost and ALT. A higher vertical resolution improves the simulation of ALT, although a considerable bias still remains. Future model development in JULES should focus on a dynamic coupling of soil organic carbon content and soil thermal and hydraulic properties, as well as allowing for sub-grid variability in soil types.

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Post-fire vegetation succession in the Siberian subarctic tundra over 45 years

10.1101/756163 ◽

2019 ◽

Author(s):

Ramona J. Heim ◽

Anna Bucharova ◽

Leya Brodt ◽

Johannes Kamp ◽

Daniel Rieker ◽

...

Keyword(s):

Soil Temperature ◽

Vegetation Cover ◽

The Arctic ◽

Vegetation Recovery ◽

Long Term Effects ◽

Forest Tundra ◽

Permafrost Thaw ◽

Legacy Effect ◽

Thaw Depth

AbstractWildfires are relatively rare in subarctic tundra ecosystems, but they can strongly change ecosystem properties. Short-term fire effects on subarctic tundra vegetation are well documented, but long-term vegetation recovery has been studied less. The frequency of tundra fires will increase with climate warming. Understanding the long-term effects of fire is necessary to predict future ecosystem changes.We used a space-for-time approach to assess vegetation recovery after fire over more than four decades. We studied soil and vegetation patterns on three large fire scars (>44, 28 and 12 years old) in dry, lichen-dominated forest tundra in Western Siberia. On 60 plots, we determined soil temperature and permafrost thaw depth, sampled vegetation and measured plant functional traits. We assessed trends in NDVI to support the field-based results on vegetation recovery.Soil temperature, permafrost thaw depth and total vegetation cover had recovered to pre-fire levels after >44 years, as well as total vegetation cover. In contrast, after >44 years, functional groups had not recovered to the pre-fire state. Burnt areas had lower lichen and higher bryophyte and shrub cover. The dominating shrub species, Betula nana, exhibited a higher vitality (higher specific leaf area and plant height) on burnt compared with control plots, suggesting a fire legacy effect in shrub growth. Our results confirm patterns of shrub encroachment after fire that were detected before in other parts of the Arctic and Subarctic. In the so far poorly studied Western Siberian forest tundra we demonstrate for the first time, long-term fire-legacies on the functional composition of relatively dry shrub- and lichen-dominated vegetation.

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Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme

The Cryosphere Discussions ◽

10.5194/tcd-5-1263-2011 ◽

2011 ◽

Vol 5 (2) ◽

pp. 1263-1309 ◽

Cited By ~ 5

Author(s):

R. Dankers ◽

E. J. Burke ◽

J. Price

Keyword(s):

Land Surface ◽

Soil Column ◽

The Arctic ◽

Soil Parameters ◽

Active Layer Thickness ◽

Organic Carbon Content ◽

Land Surface Scheme ◽

Soil Temperatures ◽

Thaw Depth ◽

Surface Scheme

Abstract. Land surface models (LSMs) need to be able to simulate realistically the dynamics of permafrost and frozen ground. In this paper we evaluate the performance of the LSM JULES (Joint UK Land Environment Simulator), the stand-alone version of the land surface scheme used in Hadley Centre climate models, in simulating the large-scale distribution of surface permafrost. In particular we look at how well the model is able to simulate the seasonal thaw depth or active layer thickness (ALT). We performed a number of experiments driven by observation-based climate datasets. Visually there is a very good agreement between areas with permafrost in JULES and known permafrost distribution in the Northern Hemisphere, and the model captures 97% of the area where the permafrost coverage is at least 50% of the grid cell. However, the model overestimates the total extent as it also simulates permafrost where it occurs sporadically or only in isolated patches. Consistent with this we find a cold bias in the simulated soil temperatures, especially in winter. However, when compared with observations on end-of-season thaw depth from around the Arctic, the ALT in JULES is generally too deep. Additional runs at three sites in Alaska demonstrate how uncertainties in the precipitation input affect the simulation of soil temperatures by affecting the thickness of the snowpack and therefore the thermal insulation in winter. In addition, changes in soil moisture content influence the thermodynamics of soil layers close to freezing. We also present results from three experiments in which the standard model setup was modified to improve physical realism of the simulations in permafrost regions. Extending the soil column to a depth of 60 m and adjusting the soil parameters for organic content had relatively little effect on the simulation of permafrost and ALT. A higher vertical resolution improves the simulation of ALT, although a considerable bias still remains. Future model development in JULES should focus on a dynamic coupling of soil organic carbon content and soil thermal and hydraulic properties, as well as allowing for sub-grid variability in soil types.

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Year-round simulated methane emissions from a permafrost ecosystem in Northeast Siberia

10.5194/bg-2017-310 ◽

2017 ◽

Author(s):

Karel Castro-Morales ◽

Thomas Kleinen ◽

Sonja Kaiser ◽

Sönke Zaehle ◽

Fanny Kittler ◽

...

Keyword(s):

Eddy Covariance ◽

Land Surface ◽

Large Scale ◽

Molecular Diffusion ◽

Russian Far East ◽

Far East ◽

Methane Emissions ◽

The Arctic ◽

Ch4 Emissions ◽

Permafrost Thaw

Abstract. Methane emissions to the atmosphere from natural wetlands are estimated to be about 25 % of the total global CH4 emissions. In the Arctic, these areas are highly vulnerable to the effects of global warming due to atmospheric warming amplification, leading to soil hydrologic changes involving permafrost thaw, formation of deeper active layers, and rising topsoil temperatures. As a result, projected increase in the degradation of permafrost carbon will likely lead to higher CO2 and CH4 emissions from these areas. Here we evaluate year-round model-simulated CH4 emissions to the atmosphere (for 2014 and 2015) from a region of northeastern Siberia in the Russian Far East. Four CH4 transport pathways are modeled with a revisit-ed version of the process-based JSBACH-methane model: plant-mediated transport, ebullition and molecular diffusion in the presence or absence of snow. This model also simulates the extent of wetlands as the fraction of inundated area in a model grid cell using a TOP-MODEL approach, and these are evaluated against a highly resolved wetland product from remote sensing data. The model CH4 emissions are compared against ground-based CH4 flux measurements using the eddy covariance technique and flux chambers in the same area of study. The magnitude of the summertime modeled CH4 emissions is comparable to those from eddy covariance and flux chamber measurements. However, wintertime modeled CH4 emissions are underestimated by one order of magnitude. The annual CH4 emissions are dominated by plant-mediated transport (61 %), followed by ebullition (~ 35 %). Molecular diffusion of CH4 from the soil into the atmosphere during summer is negligible (0.02 %) compared to the diffusion through the snow during the non-growing season (~ 4 %). We investigate the relationship between temporal changes in the CH4 fluxes, soil temperature, and soil moisture content. Our results highlight the heterogeneity in CH4 emissions at a landscape scale and suggest that further improvements to the representation of large-scale hydrological conditions in the model, especially at regional scales in Arctic ecosystems influenced by permafrost thaw, will allow us to arrive at a more process-oriented land surface scheme and better simulate CH4 emissions under climate change.

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Learning Soil Freeze Characteristic Curves with Universal Differential Equations

10.5194/egusphere-egu21-13409 ◽

2021 ◽

Author(s):

Brian Groenke ◽

Moritz Langer ◽

Guillermo Gallego ◽

Julia Boike

Keyword(s):

Differential Equations ◽

Phase Change ◽

Land Surface ◽

Large Scale ◽

Characteristic Curve ◽

Soil Freezing ◽

Climate Feedback ◽

The Arctic ◽

Permafrost Thaw

<p>Permafrost thaw is considered one of the major climate feedback processes and is currently a significant source of uncertainty in predicting future climate states. Coverage of in-situ meteorological and land-surface observations is sparse throughout the Arctic, making it difficult to track the large-scale evolution of the Arctic surface and subsurface energy balance. Furthermore, permafrost thaw is a highly non-linear process with its own feedback mechanisms such as thermokarst and thermo-erosion. Land surface models, therefore, play an important role in our ability to understand how permafrost responds to the changing climate. There is also a need to quantify freeze-thaw cycling and the incomplete freezing of soil at depth (talik formation). One of the key difficulties in modeling the Arctic subsurface is the complexity of the thermal regime during phase change under freezing or thawing conditions. Modeling heat conduction with phase change accurately requires estimation of the soil freeze characteristic curve (SFCC) which governs the change in soil liquid water content with respect to temperature and depends on the soil physical characteristics (texture). In this work, we propose a method for replacing existing brute-force approximations of the SFCC in the CryoGrid 3 permafrost model with universal differential equations, i.e. differential equations that include one or more terms represented by a universal approximator (e.g. a neural network). The approximator is thus tasked with inferring a suitable SFCC from available soil temperature, moisture, and texture data. We also explore how remote sensing data might be used with universal approximators to extrapolate soil freezing characteristics where in-situ observations are not available.</p>

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Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain

The Cryosphere ◽

10.5194/tc-6-51-2012 ◽

2012 ◽

Vol 6 (1) ◽

pp. 51-69 ◽

Cited By ~ 82

Author(s):

S. Hachem ◽

C. R. Duguay ◽

M. Allard

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Ground Surface ◽

Surface Heterogeneity ◽

The Arctic ◽

Near Surface ◽

The North ◽

Continuous Permafrost ◽

Spatial Estimates ◽

Promising Source

Abstract. Obtaining high resolution records of surface temperature from satellite sensors is important in the Arctic because meteorological stations are scarce and widely scattered in those vast and remote regions. Surface temperature is the primary climatic factor that governs the existence, spatial distribution and thermal regime of permafrost which is a major component of the terrestrial cryosphere. Land Surface (skin) Temperatures (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to ground-based near-surface air (Tair) and ground surface temperature (GST) measurements obtained from 2000 to 2008 at herbaceous and shrub tundra sites located in the continuous permafrost zone of Northern Québec, Nunavik, Canada, and of the North Slope of Alaska, USA. LSTs (temperatures at the surface materials-atmosphere interface) are found to be better correlated with Tair (1–3 m above the ground) than with available GST (3–5 cm below the ground surface). As Tair is most often used by the permafrost community, this study focused on this parameter. LSTs are in stronger agreement with Tair during the snow cover season than in the snow free season. Combining Aqua and Terra LST-Day and LST-Nigh acquisitions into a mean daily value provides a large number of LST observations and a better overall agreement with Tair. Comparison between mean daily LSTs and mean daily Tair, for all sites and all seasons pooled together yields a very high correlation (R = 0.97; mean difference (MD) = 1.8 °C; and standard deviation of MD (SD) = 4.0 °C). The large SD can be explained by the influence of surface heterogeneity within the MODIS 1 km2 grid cells, the presence of undetected clouds and the inherent difference between LST and Tair. Retrieved over several years, MODIS LSTs offer a great potential for monitoring surface temperature changes in high-latitude tundra regions and are a promising source of input data for integration into spatially-distributed permafrost models.

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Spatial Distribution of Permafrost in the Xing’an Mountains of Northeast China from 2001 to 2018

Land ◽

10.3390/land10111127 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1127

Author(s):

Yanyu Zhang ◽

Shuying Zang ◽

Miao Li ◽

Xiangjin Shen ◽

Yue Lin

Keyword(s):

High Resolution ◽

Land Surface ◽

Northeast China ◽

Climate Modeling ◽

Regional Scale ◽

Engineering Application ◽

Ground Surface ◽

Remote Sensing Data ◽

Surface Characteristics ◽

Significant Difference

Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing’an permafrost map (up to 1 km2), we employed the surface frost number (SFN) model and ground temperature at the top of permafrost (TTOP) model for the 2001–2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing’an permafrost distribution simulated by the SFN model and TTOP model were 6.88 × 105 km2 and 6.81 × 105 km2, respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale.

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Simultaneous retrieval of aerosol and surface optical properties from combined airborne- and ground-based direct and diffuse radiometric measurements

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-9-26491-2009 ◽

2009 ◽

Vol 9 (6) ◽

pp. 26491-26538 ◽

Cited By ~ 1

Author(s):

C. K. Gatebe ◽

O. Dubovik ◽

M. D. King ◽

A. Sinyuk

Keyword(s):

Optical Properties ◽

Land Surface ◽

Surface Characteristics ◽

Index Of Refraction ◽

The Arctic ◽

Aerosol Size Distribution ◽

Inversion Method ◽

Good Test ◽

Radiometric Measurements ◽

Surface Optical

Abstract. This paper presents a new method for simultaneously retrieving aerosol and surface reflectance properties from combined airborne and ground-based direct and diffuse radiometric measurements. The method is based on the standard Aerosol Robotic Network (AERONET) method for retrieving aerosol size distribution, complex index of refraction, and single scattering albedo, but modified to retrieve aerosol properties in two layers, below and above the aircraft, and parameters on surface optical properties from combined datasets (Cloud Absorption Radiometer, CAR, and AERONET data). A key advantage of this method is the inversion of all available spectral and angular data at the same time, while accounting for the influence of noise in the inversion procedure using statistical optimization. The wide spectral (0.34–2.30 μm) and angular range (180°) of the CAR instrument, combined with observations from an AERONET sunphotometer, provide sufficient measurement constraints for characterizing aerosol and surface properties with minimal assumptions. The robustness of the method was tested on observations made during four different field campaigns: (a) the Southern African Regional Science Initiative 2000 over Mongu, Zambia, (b) the Intercontinental Transport Experiment-Phase B over Mexico City, Mexico (c) Cloud and Land Surface Interaction Campaign over the Atmospheric Radiation Measurement (ARM) Central Facility, Oklahoma, USA, and (d) the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) over Elson Lagoon in Barrow, Alaska, USA. The four areas are dominated by different surface characteristics and aerosol types, and therefore provide good test cases for the new inversion method.

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Extremely wet summer events enhance permafrost thaw for multiple years in Siberian tundra

10.21203/rs.3.rs-512675/v1 ◽

2021 ◽

Author(s):

Rúna Magnússon ◽

Alexandra Hamm ◽

Sergey V. Karsanaev ◽

Juul Limpens ◽

David Kleijn ◽

...

Keyword(s):

Summer Precipitation ◽

Frozen Soil ◽

The Arctic ◽

Permafrost Thaw ◽

Air Temperatures ◽

Thaw Depth ◽

Controlled Irrigation ◽

Carry Over ◽

Thaw Period ◽

Carry Over Effect

Abstract Permafrost thaw can accelerate climate warming by releasing carbon from previously frozen soil in the form of greenhouse gases. Summer precipitation extremes have been proposed to increase permafrost thaw, but the magnitude and duration of this effect are poorly understood. Here we present empirical evidence showing that one extremely wet summer (+100mm; 120% increase relative to average summer precipitation) enhances thaw depth by up to 35% and prolonged the thaw period in a controlled irrigation experiment in an ice-rich Siberian tundra site. The effect persisted over two subsequent summers, demonstrating a carry-over effect of extremely wet summers. Using soil thermal hydrological modelling, we show that precipitation-induced increases in thaw are most pronounced during warm summers with mid-summer precipitation peaks. Our results suggest that, with summer precipitation and temperature both increasing in the Arctic, permafrost will likely degrade and disappear faster than is currently anticipated based on rising air temperatures alone.

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Simultaneous retrieval of aerosol and surface optical properties from combined airborne- and ground-based direct and diffuse radiometric measurements

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-2777-2010 ◽

2010 ◽

Vol 10 (6) ◽

pp. 2777-2794 ◽

Cited By ~ 17

Author(s):

C. K. Gatebe ◽

O. Dubovik ◽

M. D. King ◽

A. Sinyuk

Keyword(s):

Optical Properties ◽

Land Surface ◽

Surface Characteristics ◽

Index Of Refraction ◽

The Arctic ◽

Aerosol Size Distribution ◽

Inversion Method ◽

Good Test ◽

Radiometric Measurements ◽

Surface Optical

Abstract. This paper presents a new method for simultaneously retrieving aerosol and surface reflectance properties from combined airborne and ground-based direct and diffuse radiometric measurements. The method is based on the standard Aerosol Robotic Network (AERONET) method for retrieving aerosol size distribution, complex index of refraction, and single scattering albedo, but modified to retrieve aerosol properties in two layers, below and above the aircraft, and parameters on surface optical properties from combined datasets (Cloud Absorption Radiometer (CAR) and AERONET data). A key advantage of this method is the inversion of all available spectral and angular data at the same time, while accounting for the influence of noise in the inversion procedure using statistical optimization. The wide spectral (0.34–2.30 μm) and angular range (180°) of the CAR instrument, combined with observations from an AERONET sunphotometer, provide sufficient measurement constraints for characterizing aerosol and surface properties with minimal assumptions. The robustness of the method was tested on observations made during four different field campaigns: (a) the Southern African Regional Science Initiative 2000 over Mongu, Zambia, (b) the Intercontinental Transport Experiment-Phase B over Mexico City, Mexico (c) Cloud and Land Surface Interaction Campaign over the Atmospheric Radiation Measurement (ARM) Central Facility, Oklahoma, USA, and (d) the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) over Elson Lagoon in Barrow, Alaska, USA. The four areas are dominated by different surface characteristics and aerosol types, and therefore provide good test cases for the new inversion method.

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