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Start Over Author "YOUHUA RAN" Topic atmospheric temperature
2 results on '"YOUHUA RAN"'

1. The start of frozen dates over northern permafrost regions with the changing climate.

Author

Jialing Li, Chaoyang Wu, Peñuelas, Josep, Youhua Ran, and Yongguang Zhang

Subjects
GLOBAL warming, PERMAFROST, CLIMATE change, SOIL heating, ATMOSPHERIC temperature, SNOW accumulation
Abstract

The soil freeze-thaw cycle in the permafrost regions has a significant impact on regional surface energy and water balance. Although increasing efforts have been made to understand the responses of spring thawing to climate change, the mechanisms controlling the global interannual variability of the start date of permafrost frozen (SOF) remain unclear. Using long-term SOF from the combinations of multiple satellite microwave sensors between 1979 and 2020, and analytical techniques, including partial correlation, ridge regression, path analysis, and machine learning, we explored the responses of SOF to multiple climate change factors, including warming (surface and air temperature), start date of permafrost thawing (SOT), soil properties (soil temperature and volume of water), and the snow depth water equivalent (SDWE). Overall, climate warming exhibited the maximum control on SOF, but SOT in spring was also an important driver of SOF variability; among the 65.9% significant SOT and SOF correlations, 79.3% were positive, indicating an overall earlier thawing would contribute to an earlier frozen in winter. The machine learning analysis also suggested that apart from warming, SOT ranked as the second most important determinant of SOF. Therefore, we identified the mechanism responsible for the SOT-SOF relationship using the SEM analysis, which revealed that soil temperature change exhibited the maximum effect on this relationship, irrespective of the permafrost type. Finally, we analyzed the temporal changes in these responses using the moving window approach and found increased effect of soil warming on SOF. In conclusion, these results provide important insights into understanding and predicting SOF variations with future climate change. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Enhancement of land surface information and its impact on atmospheric modeling in the Heihe River Basin, northwest China

Author

Youhua Ran, Yanhong Gao, Wei Liu, Hongchun Peng, Haiying Li, Michael Barlage, Mingguo Ma, Fei Chen, Ye Yu, Xin Li, and Guodong Cheng

Subjects
Hydrology, Atmospheric Science, Ecology, Land use, Elevation, Paleontology, Soil Science, Forestry, Terrain, Land cover, Atmospheric model, Aquatic Science, Oceanography, Atmospheric temperature, Atmospheric sciences, Geophysics, Space and Planetary Science, Geochemistry and Petrology, Earth and Planetary Sciences (miscellaneous), Environmental science, MM5, Precipitation, Earth-Surface Processes, Water Science and Technology
Abstract

[1] With increasing computational resources, atmospheric/environmental models continue to run at finer-grid spacing that can resolve land surface characteristics, such as topography, land use/land cover, and soil texture. This paper assesses the improvement in land surface information data sets and its impact on atmospheric modeling. The study focuses on the Heihe River Basin (HRB) in northwestern China. Fine-scale, remotely sensed, and in situ land surface data in HRB are derived and compared with the global data sets used in most mesoscale models. The incorporation of these fine-scale land surface data, compared to those currently used in MM5, yields substantially improved HRB land surface data sets. HRB local and regional data sets and the global land data set are used in a nonhydrostatic mesoscale model (MM5) to investigate the influences of land surface uncertainty on meteorological modeling in the lower atmosphere. Main results suggest the following: (1) enhanced land data sets have a stronger impact on atmospheric water vapor fields in the lower boundary layer than other meteorological fields. Soil texture data greatly impacts the local precipitation simulation and landuse data improves the air temperature simulation in the lower atmosphere; (2) generally, the average land surface temperature biases are reduced using the enhanced land surface information, but the low bias in zones with higher elevation and high bias in zones with lower elevation still persist; (3) the wet bias over rugged terrain and dry bias in the simulated water vapor in the flat plains are both reduced. Area mean bias of simulated accumulated monthly precipitation is greatly reduced using the enhanced soil data. Convective available potential energy was larger in the HRB mountain regions using the default land data, while it was decreased using the enhanced ones; (4) analysis of the correlation coefficient between simulation bias and the geographic features shows that there are some patterns in the simulation bias distribution. Generally, larger bias still exists in the foothills of the mountains.

Published
2008

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