A synthesis dataset of permafrost thermal state for the Qinghai–Tibet (Xizang) Plateau, China

Abstract. Permafrost has great influences on the climatic, hydrological, and ecological systems on the Qinghai–Tibet Plateau (QTP). The changing permafrost and its impact have been attracting great attention worldwide like never before. More observational and modeling approaches are needed to promote an understanding of permafrost thermal state and climatic conditions on the QTP. However, limited data on the permafrost thermal state and climate background have been sporadically reported in different pieces of literature due to the difficulties of accessing and working in this region where the weather is severe, environmental conditions are harsh, and the topographic and morphological features are complex. From the 1990s, we began to establish a permafrost monitoring network on the QTP. Meteorological variables were measured by automatic meteorological systems. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature (GT) was observed from boreholes. In this study, a comprehensive dataset consisting of long-term meteorological, GT, soil moisture, and soil temperature data was compiled after quality control from an integrated, distributed, and multiscale observation network in the permafrost regions of QTP. The dataset is helpful for scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will significantly promote the verification, development, and improvement of hydrological models, land surface process models, and climate models on the QTP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, last access: 24 August 2021, https://doi.org/10.11888/Geocry.tpdc.271107, Lin et al., 2021).

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A synthesis dataset of permafrost thermal state for the Qinghai-Xizang (Tibet) Plateau, China

10.5194/essd-2021-1 ◽

2021 ◽

Author(s):

Lin Zhao ◽

Defu Zou ◽

Guojie Hu ◽

Tonghua Wu ◽

Erji Du ◽

...

Keyword(s):

Soil Temperature ◽

Land Surface ◽

Process Model ◽

Climate Model ◽

Thermal State ◽

Ground Temperature ◽

Tibet Plateau ◽

Observation System ◽

Research Station ◽

Observation Network

Abstract. Permafrost is important for the climatic, hydrological, and ecological processes on the Qinghai-Xizang Plateau (QXP). The changing permafrost and its impact have been attracting great attention worldwide never before, and more observational and modeling approaches are need to promote an understanding of permafrost thermal state and climatic conditions on the QXP. However, there were almost no synthesis dataset on the permafrost thermal state and climate background on the QXP, but were sporadically reported in different literatures due to the difficulties to access to and work in this region, where the weather is severe, and environmental conditions are harsh and the topographic and morphological features are complex. In this study, a comprehensive dataset under quality controlled consisting of long-term meteorological, ground temperature, soil moisture and soil temperature data were compiled from an integrated, distributed and multiscale observation network in the permafrost regions of the Cryosphere Research Station on the QXP. Meteorological data were observation by automatic meteorological stations from a comprehensive observation network. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature was observed from boreholes. These datasets were helpfur for the scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will greatly promote the verification, development and improvement of the hydrological model, land surface process model and climate model on the QXP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, https://doi.org/10.11888/Geocry.tpdc.271107).

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Snow-Covered Soil Temperature Retrieval in Canadian Arctic Permafrost Areas, Using a Land Surface Scheme Informed with Satellite Remote Sensing Data

Remote Sensing ◽

10.3390/rs10111703 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1703

Author(s):

Nicolas Marchand ◽

Alain Royer ◽

Gerhard Krinner ◽

Alexandre Roy ◽

Alexandre Langlois ◽

...

Keyword(s):

Remote Sensing ◽

Thermal Conductivity ◽

Soil Temperature ◽

Land Surface ◽

Climate Models ◽

Thermal State ◽

Radiative Transfer Model ◽

Transfer Model ◽

Land Surface Scheme ◽

Surface Scheme

High-latitude areas are very sensitive to global warming, which has significant impacts on soil temperatures and associated processes governing permafrost evolution. This study aims to improve first-layer soil temperature retrievals during winter. This key surface state variable is strongly affected by snow’s geophysical properties and their associated uncertainties (e.g., thermal conductivity) in land surface climate models. We used infrared MODIS land-surface temperatures (LST) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperatures (Tb) at 10.7 and 18.7 GHz to constrain the Canadian Land Surface Scheme (CLASS), driven by meteorological reanalysis data and coupled with a simple radiative transfer model. The Tb polarization ratio (horizontal/vertical) at 10.7 GHz was selected to improve snowpack density, which is linked to the thermal conductivity representation in the model. Referencing meteorological station soil temperature measurements, we validated the approach at four different sites in the North American tundra over a period of up to 8 years. Results show that the proposed method improves simulations of the soil temperature under snow (Tg) by 64% when using remote sensing (RS) data to constrain the model, compared to model outputs without satellite data information. The root mean square error (RMSE) between measured and simulated Tg under the snow ranges from 1.8 to 3.5 K when using RS data. Improved temporal monitoring of the soil thermal state, along with changes in snow properties, will improve our understanding of the various processes governing soil biological, hydrological, and permafrost evolution.

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Effects of Gravel on Energy and Water Transport in the Soil of Qinghai-Tibet Plateau

10.5194/egusphere-egu2020-20983 ◽

2020 ◽

Author(s):

shihua lyu

Keyword(s):

Soil Temperature ◽

Soil Water ◽

Land Surface ◽

Land Surface Model ◽

Thermal Inertia ◽

Tibet Plateau ◽

Surface Model ◽

Gravel Content ◽

Water Move ◽

The Mean

<p>According to the shortcomings of the land surface model, the new scheme is developed and applied to the simulating soil process at Madoi and Nagqu. Simulations show that gravel tend to reduce soil water holding capacity and enhance soil hydraulic conductivity, surface infiltration and drainage. As a result, the upper layer of soil mixed with gravel tends to drier due to the soil water move to deeper layer. The mean biases of soil moisture between the simulation and observation reduced by 25- 48% at two sites. Soil thermal conductivity is increased with gravel content and the soil thermal inertia was decreased with gravel content increasing. Therefore the deeper layer temperature of soil containing gravel is rapid response to air temperature change. The mean biases of soil temperature between the simulation and observation reduced by 9.1-25% at two sites. From the simulation results at Madoi and Nagqu, we find that the new scheme performed better than the original scheme in simulating soil temperature and water content and the land model implemented the new scheme is suitable for simulating land process in the QTP.</p>

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Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau

Geoscientific Model Development ◽

10.5194/gmd-14-1753-2021 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1753-1771

Author(s):

Xiangfei Li ◽

Tonghua Wu ◽

Xiaodong Wu ◽

Jie Chen ◽

Xiaofan Zhu ◽

...

Keyword(s):

Soil Temperature ◽

Snow Cover ◽

Land Surface ◽

Tibet Plateau ◽

Cold Bias ◽

Surface Model ◽

Model Parameters ◽

Hydrothermal Regime ◽

Qinghai Tibet Plateau ◽

Permafrost Regions

Abstract. Extensive and rigorous model intercomparison is of great importance before model application due to the uncertainties in current land surface models (LSMs). Without considering the uncertainties in forcing data and model parameters, this study designed an ensemble of 55 296 experiments to evaluate the Noah LSM with multi-parameterization (Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil liquid water (SLW) simulation, and investigated the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai–Tibet Plateau (QTP). The results showed that Noah-MP systematically overestimates snow cover, which could be greatly resolved when adopting the sublimation from wind and a semi-implicit snow/soil temperature time scheme. As a result of the overestimated snow, Noah-MP generally underestimates ST, which is mostly influenced by the snow process. A systematic cold bias and large uncertainties in soil temperature remain after eliminating the effects of snow, particularly in the deep layers and during the cold season. The combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance contributes to resolving the cold bias in soil temperature. In addition, Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW simulation in comparison to the very limited impacts of all other physical processes. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes in the permafrost regions of the QTP as well as to further model improvements towards soil hydrothermal regime modeling using LSMs.

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Lifetime of soil moisture perturbations in a coupled land–atmosphere simulation

Earth System Dynamics ◽

10.5194/esd-7-1-2016 ◽

2016 ◽

Vol 7 (1) ◽

pp. 1-19 ◽

Cited By ~ 5

Author(s):

T. Stacke ◽

S. Hagemann

Keyword(s):

Soil Moisture ◽

Soil Temperature ◽

Land Surface ◽

Climate Models ◽

Root Zone ◽

Soil Layer ◽

Surface States ◽

Deep Soil ◽

Moisture Regimes ◽

Soil Moisture Initialization

Abstract. In order to evaluate whether the initialization of soil moisture has the potential to improve the prediction skill of earth system models (ESMs) on seasonal to decadal timescales, an elaborate experiment was conducted. For this task a coupled land–atmosphere model with prescribed ocean was utilized. The experiment design considered soil moisture initialization in different seasons and years and yielded information about the lifetime (memory) of extreme yet realistic soil moisture perturbations. Our analyses were focused on root zone soil moisture (RootSM) as it comprises the part of the soil that directly interacts with the atmosphere via bare-soil evaporation and transpiration. We found that RootSM memory differs not only spatially but also depends on the time of initialization. A long memory of up to 1 year is evident mostly for dry soil moisture regimes after heavy precipitation periods or prior to snow covered conditions. Short memory below 2 weeks prevails in wet soil moisture regimes and prior to distinct precipitation periods or snowmelt. Furthermore, RootSM perturbations affect other land surface states, e.g. soil temperature and leaf carbon content, and even induce anomalies with specific memory in these variables. Especially for deep-layer soil temperature, these anomalies can last for up to several years. As long as RootSM memory is evident, we found that anomalies occur periodically in other land surface states whenever climate conditions allow for interactions between that state and RootSM. Additionally, anomaly recurrence is visible for RootSM itself. This recurrence is related to the thickness of the soil layer below the root zone and can affect RootSM for several years. From our findings we conclude that soil moisture initialization has the potential to improve the predictive skill of climate models on seasonal scales and beyond. However, a sophisticated, multilayered soil hydrology scheme is necessary to allow for the interactions between RootSM and the deep-soil layer reservoir.

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Regularities of soil temperature changes during the period with snow cover in modern climatic conditions of the Eurasian Subarctic

Journal of the Belarusian State University. Geography and Geology ◽

10.33581/2521-6740-2021-1-13-22 ◽

2021 ◽

pp. 13-22

Author(s):

Lev M. Kitaev

Keyword(s):

Standard Deviation ◽

Soil Temperature ◽

Snow Cover ◽

Air Temperature ◽

Land Surface ◽

Environmental Changes ◽

Surface Air Temperature ◽

Climatic Conditions ◽

Winter Period

The influence of snow cover on the dynamics of soil temperature in the modern climatic conditions of the Eurasian Subarctic was investigated through a quantitative assessment of the features of the seasonal and long-term variation of parameters. Seasonal and long-term values of soil temperature for stable snow period decrease from west to east: a decrease of snow thickness and air temperature from west to east of Eurasia leads to a weakening of the heat-insulating properties of the snow cover with a significant decrease in regional air temperatures. With the emergence of a stable snow cover, the soil temperature seasonal and long-term standard deviation sharply decreases compared to the autumn and spring periods. With the appearance of snow cover, the soil temperature standard deviation drops sharply compared to the autumn and spring periods. An exception is the northeast of Siberia: here, a relatively small thickness of snow determines a noticeable dependence of the course of soil temperature on the dynamics of surface air temperature. There are no significant long-term trends in soil temperature due to its low variability during winter period. Analysis of the course of the studied characteristics anomalies showed an insignificant and non-systematic number of their coincidences. Currently, we have not found similar research results for large regions. The revealed patterns can be used in the analysis of the results of monitoring the state of the land surface, in the development of remote sensing algorithms, in the refinement of predictive scenarios of environmental changes.

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Life time of soil moisture perturbations in a coupled land-atmosphere simulation

Earth System Dynamics Discussions ◽

10.5194/esdd-6-1743-2015 ◽

2015 ◽

Vol 6 (2) ◽

pp. 1743-1788

Author(s):

T. Stacke ◽

S. Hagemann

Keyword(s):

Soil Moisture ◽

Soil Temperature ◽

Land Surface ◽

Climate Models ◽

Root Zone ◽

Soil Layer ◽

Surface States ◽

Life Time ◽

Moisture Regimes ◽

Soil Moisture Initialization

Abstract. In order to evaluate whether the initialization of soil moisture has the potential to improve the prediction skill of coupled climate models at seasonal to decadal time scales, an elaborated AMIP-type experiment was conducted. The experiment design considered soil moisture initialization in different seasons and years, and yields information about the life-time (memory) of extreme yet realistic soil moisture perturbations. Our analyses were focused on root zone soil moisture (RootSM) as it comprises the part of the soil that directly interacts with the atmosphere via bare soil evaporation and transpiration. We found that RootSM memory differs not only spatially but also depends on the time of initialization. Long memory up to one year is evident mostly for dry soil moisture regimes, after heavy precipitation periods or prior to snow covered conditions. Short memory below two weeks prevails in wet soil moisture regimes and prior to distinct precipitation periods or snow melt. Furthermore, RootSM perturbations affect other land surface states, e.g. soil temperature and leaf carbon content, and even induce anomalies with specific memory in these variables. Especially for deep layer soil temperature these anomalies can last up to several years. As long as RootSM memory is evident, we found that anomalies occur periodically in other land surface states whenever climate conditions allow for interactions between that state and RootSM. Additionally, anomaly recurrence is visible for RootSM itself. This recurrence is related to the thickness of the soil layer below the root zone and can affect RootSM for several years. From our findings we conclude that soil moisture initialization has the potential to improve the predictive skill of climate models on seasonal scales and beyond. However, a sophisticated, multi-layered soil hydrology scheme is necessary, to allow for the interactions between RootSM and the deep soil layer reservoir.

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Assessing the simulated soil thermal regime from Noah-MPLSM v1.1 for near-surface permafrost modeling on the Qinghai-Tibet Plateau

10.5194/gmd-2020-142 ◽

2020 ◽

Author(s):

Xiangfei Li ◽

Tonghua Wu ◽

Xiaodong Wu ◽

Xiaofan Zhu ◽

Guojie Hu ◽

...

Keyword(s):

Soil Moisture ◽

Soil Temperature ◽

Land Surface ◽

Stomatal Resistance ◽

Cold Season ◽

Canopy Gap ◽

Tibet Plateau ◽

Surface Model ◽

Near Surface ◽

Qinghai Tibet Plateau

Abstract. Land surface models (LSMs) are effective tools for near-surface permafrost modeling. Extensive and rigorous model inter-comparison is of great importance before application due to the uncertainties in current LSMs. This study designed an ensemble of 6912 experiments to evaluate the Noah land surface model with multi-parameterization (Noah-MP) for soil temperature (ST) simulation, and investigate the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai-Tibet Plateau. The results showed that Noah-MP generally underestimates ST, especially that during the cold season. In addition, the simulation uncertainty is greater in the cold season (October-April) and for the deep soil layers. ST is most sensitive to surface layer drag coefficient (SFC) while largely influenced by runoff and groundwater (RUN). By contrast, the influence of canopy stomatal resistance (CRS) and soil moisture factor for stomatal resistance (BTR) on ST is negligible. With limited impacts on ST simulation, vegetation model (VEG), canopy gap for radiation transfer (RAD) and snow/soil temperature time scheme (STC) are more influential on shallow ST, while super-cooled liquid water (FRZ), frozen soil permeability (INF) and lower boundary of soil temperature (TBOT) have greater impacts on deep ST. Furthermore, an optimal configuration of Noah-MP for permafrost modeling were extracted based on the connectivity between schemes, and they are: table leaf area index with calculated vegetation fraction, Jarvis scheme for CRS, Noah scheme for BTR, BATS model for RUN, Chen97 for SFC, zero canopy gap for RAD, variant freezing-point depression for FRZ, hydraulic parameters defined by soil moisture for INF, ST at 8 m for TBOT, and semi-implicit method for STC. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes on the QTP and further model improvements towards near-surface permafrost modeling using the LSMs.

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Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

Annals of Valahia University of Targoviste Geographical Series ◽

10.2478/avutgs-2018-0002 ◽

2018 ◽

Vol 18 (1) ◽

pp. 14-22 ◽

Cited By ~ 1

Author(s):

Georgiana Grigoraș ◽

Bogdan Urițescu

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Hot Spots ◽

Land Surface ◽

Hot Spot ◽

Monitoring Network ◽

Residential Areas ◽

Hotspot Analysis ◽

The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

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