Preprints
https://doi.org/10.5194/tc-2022-229
https://doi.org/10.5194/tc-2022-229
 
19 Dec 2022
19 Dec 2022
Status: this preprint is currently under review for the journal TC.

Review of snow phenology variation in the Northern Hemisphere and its relationship with climate and vegetation

Hui Guo1, Xiaoyan Wang1, Zecheng Guo1, Gaofeng Zhu1, Tao Che2, Jian Wang2,3, Xiaodong Huang4, Chao Han1, and Zhiqi Ouyang1 Hui Guo et al.
  • 1College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • 2Heihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
  • 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • 4Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China

Abstract. Snow phenology, recurrent seasonal patterns in snow cover and snowfall, has been significantly affected by global warming. Through the interaction with the climate, the dynamic variability of snow phenology affects the regional climate environment, vegetation ecosystem, soil properties, agricultural water resources, snow disasters and animal migration. First, this study compares the advantages, disadvantages and applicability of different sources of observation data and the principal research methods involved in studying snow phenology. Then, this work discusses the spatiotemporal variability and changing trends of snow phenology in the Northern Hemisphere, and summarizes the relationship between climate, vegetation and snow phenology. Finally, this review highlights the key areas related to snow phenology that require further study. Overall, during the past 50 years in the Northern Hemisphere, the snow cover end date (SCED) has shown a significantly advanced trend, the snow cover onset date (SCOD) has also been occurring slowly earlier, and the snow cover days (SCD) has shortened, but these two trends are not significant. The snow phenology variation is closely related to climate factors, atmospheric circulation, vegetation status and some spatial factors. Snow cover impacts climate change through interactions with atmospheric circulation systems. The rise in temperature will delay the SCOD, and the SCED is closely related to the temperature of the snowmelt season. The interaction between seasonal snow cover and climate will either stimulate or impede vegetation growth. With the change in snow cover, especially the decrease in snow cover in the melting stage can impact the climate change, the rise in temperature will change the growth conditions and extend the vegetation growth season. The relationship between snow cover and vegetation is inconsistent in different elevations and latitudes. Snow phenology variation is very complex and is the result of the combined action of many factors. Additionally, snow phenology will also have a great impact on the cryosphere. Therefore, we must understand snow phenology variation and prepare for future changes.

Hui Guo et al.

Status: open (until 05 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Hui Guo et al.

Data sets

Snow phenological parameters based on Northern Hemisphere EASE Grid 2.0 Weekly Snow and Sea Ice Extent (NHSCE) from 1972-2019 Guo Hui https://doi.org/10.5281/zenodo.7432273

Hui Guo et al.

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Short summary
Snow phenology is a seasonal pattern in snow cover and snowfall. In this review, we found that during the past 50 years in the Northern Hemisphere, the snow cover end date has shown a significantly advanced change trend. Eurasia contributes more to the snow phenology in the Northern Hemisphere than does North America. Snow phenology is related to climate and atmospheric circulation, and the response to vegetation phenology depends on geographical regions, temperature and precipitation gradients.