Evaluation of the near-surface climate of the recent global atmospheric reanalysis for Qilian Mountains, Qinghai-Tibet Plateau

2021 ◽  
Vol 250 ◽  
pp. 105401
Author(s):  
Baojuan Huai ◽  
Junyao Wang ◽  
Weijun Sun ◽  
Yetang Wang ◽  
Wuying Zhang
Keyword(s):  
Qilian Mountains ◽  
Tibet Plateau ◽  
Near Surface ◽  
Surface Climate ◽  
Qinghai Tibet Plateau

scholarly journals Effects of Air Temperature and Precipitation on Soil Moisture on the Qinghai-Tibet Plateau during the 2015 Growing Season

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qingyan Xie ◽  
Jianping Li ◽  
Yufei Zhao
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Growing Season ◽  
Tibet Plateau ◽  
Ecosystem Stability ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
Qinghai Tibet Plateau

The Qinghai-Tibet Plateau (QTP) holds massive freshwater resources and is one of the most active regions in the world with respect to the hydrological cycle. Soil moisture (SM) plays a critical role in hydrological processes and is important for plant growth and ecosystem stability. To investigate the relationship between climatic factors (air temperature and precipitation) and SM during the growing season in various climate zones on the QTP, data from three observational stations were analyzed. The results showed that the daily average (Tave) and minimum air temperatures (Tmin) significantly influenced SM levels at all depths analyzed (i.e., 10, 20, 30, 40, and 50 cm deep) at the three stations, and Tmin had a stronger effect on SM than did Tave. However, the daily maximum air temperature (Tmax) generally had little effect on SM, although it had showed some effects on SM in the middle and deeper layers at the Jiali station. Precipitation was an important factor that significantly influenced the SM at all depths at the three stations, but the influence on SM in the middle and deep layers lagged the direct effect on near-surface SM by 5–7 days. These results suggest that environment characterized by lower temperatures and higher precipitation may promote SM conservation during the growing season and in turn support ecosystem stability on the QTP.

scholarly journals Using MODIS Land Surface Temperatures for Permafrost Thermal Modeling in Beiluhe Basin on the Qinghai-Tibet Plateau

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4200 ◽  
Author(s):  
Anyuan Li ◽  
Caichu Xia ◽  
Chunyan Bao ◽  
Guoan Yin
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Ground Surface ◽  
Tibet Plateau ◽  
Active Layer Thickness ◽  
Permafrost Region ◽  
Thermal Dynamics ◽  
Near Surface ◽  
Warm Bias ◽  
Qinghai Tibet Plateau

It is essential to monitor the ground temperature over large areas to understand and predict the effects of climate change on permafrost due to its rapid warming on the Qinghai-Tibet Plateau (QTP). Land surface temperature (LST) is an important parameter for the energy budget of permafrost environments. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are especially valuable for detecting permafrost thermal dynamics across the QTP. This study presents a comparison of MODIS-LST values with in situ near-surface air temperature (Ta), and ground surface temperature (GST) obtained from 2014 to 2016 at five sites in Beiluhe basin, a representative permafrost region on the QTP. Furthermore, the performance of the thermal permafrost model forced by MODIS-LSTs was studied. Averaged LSTs are found to strongly correlated with Ta and GST with R2 values being around 0.9. There is a significant warm bias (4.43–4.67 °C) between averaged LST and Ta, and a slight warm bias (0.67–2.66 °C) between averaged LST and GST. This study indicates that averaged MODIS-LST is supposed to be a useful data source for permafrost monitoring. The modeled ground temperatures and active-layer thickness have a good agreement with the measurements, with a difference of less than 1.0 °C and 0.4 m, respectively.

scholarly journals Air stagnation in China (1985–2014): climatological mean features and trends

2017 ◽  
Vol 17 (12) ◽  
pp. 7793-7805 ◽  
Author(s):  
Qianqian Huang ◽  
Xuhui Cai ◽  
Yu Song ◽  
Tong Zhu
Keyword(s):  
Eastern China ◽  
Central China ◽  
Tibet Plateau ◽  
Positive Trend ◽  
Shandong Peninsula ◽  
Near Surface ◽  
Wind Speeds ◽  
Main Driver ◽  
Southern Shaanxi ◽  
Qinghai Tibet Plateau

Abstract. Air stagnation is an important meteorological measure of unfavorable air pollution conditions, but little is known about it in China. We conducted a comprehensive investigation of air stagnation in China from January 1985 to December 2014 based on sounding and surface observations from 81 stations. The stagnation criteria were revised to account for the large topographical diversity in the country. It is found that the annual mean of air stagnation occurrences is closely related to general topography and climate features. Two basins in the northwest and southwest of China, the Tarim and Sichuan basins, exhibit the most frequent stagnation occurrence (50 % of days per year), whereas two plateaus (the Qinghai–Tibet Plateau and the Inner Mongolian plateau) and the eastern coastal areas experience the least (20 % of days per year). Over the whole country, air stagnation is at a maximum in summer and a minimum in winter, except for Urumchi, a major city in northwestern China where stagnation maintains a rather constant value year round with a minimum in spring. There is a nationwide positive trend in stagnation occurrence during 1985–2014, with the strongest increasing centers over Shandong Peninsula in eastern China and southern Shaanxi in central China. Changes in air stagnation occurrences are dependent on three components (upper- and lower-air winds and precipitation-free days). This shows that the behavior of upper-air wind speeds is the main driver of the spatial distribution and trends in air stagnation, followed by near-surface winds and dry days, which contribute the least.

scholarly journals Mapping Frozen Ground in the Qilian Mountains in 2004–2019 Using Google Earth Engine Cloud Computing

2021 ◽  
Vol 13 (1) ◽  
pp. 149
Author(s):  
Yuan Qi ◽  
Shiwei Li ◽  
Youhua Ran ◽  
Hongwei Wang ◽  
Jichun Wu ◽  
...  
Keyword(s):  
Google Earth ◽  
Qilian Mountains ◽  
Tibet Plateau ◽  
Spatial And Temporal Variation ◽  
Active Layer Thickness ◽  
Freezing And Thawing ◽  
Frozen Ground ◽  
Near Surface ◽  
Google Earth Engine ◽  
The Qilian Mountains

The permafrost in the Qilian Mountains (QLMs), the northeastern margin of the Qinghai–Tibet Plateau, changed dramatically in the context of climate warming and increasing anthropogenic activities, which poses significant influences on the stability of the ecosystem, water resources, and greenhouse gas cycles. Yet, the characteristics of the frozen ground in the QLMs are largely unclear regarding the spatial distribution of active layer thickness (ALT), the maximum frozen soil depth (MFSD), and the temperature at the top of the permafrost or the bottom of the MFSD (TTOP). In this study, we simulated the dynamics of the ALT, TTOP, and MFSD in the QLMs in 2004–2019 in the Google Earth Engine (GEE) platform. The widely-adopted Stefan Equation and TTOP model were modified to integrate with the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) in GEE. The N-factors, the ratio of near-surface air to ground surface freezing and thawing indices, were assigned to the freezing and thawing indices derived with MODIS LST in considerations of the fractional vegetation cover derived from MODIS normalized difference vegetation index (NDVI). The results showed that the GEE platform and remote sensing imagery stored in Google cloud could be quickly and effectively applied to obtain the spatial and temporal variation of permafrost distribution. The area with TTOP < 0 °C is 8.4 × 104 km2 (excluding glaciers and lakes) and accounts for 46.6% of the whole QLMs, the regional mean ALT is 2.43 ± 0.44 m, while the regional mean MFSD is 2.54 ± 0.45 m. The TTOP and ALT increase with the decrease of elevation from the sources of the sub-watersheds to middle and lower reaches. There is a strong correlation between TTOP and elevation (slope = −1.76 °C km−1, p < 0.001). During 2004–2019, the area of permafrost decreased by 20% at an average rate of 0.074 × 104 km2·yr−1. The regional mean MFSD decreased by 0.1 m at a rate of 0.63 cm·yr−1, while the regional mean ALT showed an exception of a decreasing trend from 2.61 ± 0.45 m during 2004–2005 to 2.49 ± 0.4 m during 2011–2015. Permafrost loss in the QLMs in 2004–2019 was accelerated in comparison with that in the past several decades. Compared with published permafrost maps, this study shows better calculation results of frozen ground in the QLMs.

Factors controlling near surface ground-ice characteristics in a region of warm permafrost, Beiluhe Basin, Qinghai-Tibet Plateau

Geoderma ◽  
2020 ◽  
Vol 376 ◽  
pp. 114540
Author(s):  
Zhanju Lin ◽  
Zeyong Gao ◽  
Xingwen Fan ◽  
Fujun Niu ◽  
Jing Luo ◽  
...  
Keyword(s):  
Tibet Plateau ◽  
Ground Ice ◽  
Near Surface ◽  
Warm Permafrost ◽  
Qinghai Tibet Plateau

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

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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