Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): organization and experimental design

• Published in: Geoscientific model development Vol. 14; no. 7
• Main Authors: Xue, Yongkang, Yao, Tandong, Boone, Aaron A., Diallo, Ismaila, Liu, Ye, Zeng, Xubin, Lau, William M., Sugimoto, Shiori, Tang, Qi, Pan, Xiaoduo, van Oevelen, Peter J., Klocke, Daniel, Koo, Myung-Seo, Sato, Tomonori, Lin, Zhaohui, Takaya, Yuhei, Ardilouze, Constantin, Materia, Stefano, Saha, Subodh K., Senan, Retish, Nakamura, Tetsu, Wang, Hailan, Yang, Jing, Zhang, Hongliang, Zhao, Mei, Liang, Xin-Zhong, Neelin, J. David, Vitart, Frederic, Li, Xin, Zhao, Ping, Shi, Chunxiang, Guo, Weidong, Tang, Jianping, Yu, Miao, Qian, Yun, Shen, Samuel P., Zhang, Yang, Yang, Kun, Leung, Ruby, Qiu, Yuan, Peano, Daniele, Qi, Xin, Zhan, Yanling, Brunke, Michael A., Chou, Sin Chan, Ek, Michael, Fan, Tianyi, Guan, Hong, Lin, Hai, Liang, Shunlin, Wei, Helin, Xie, Shaocheng, Xu, Haoran, Li, Weiping, Shi, Xueli, Nobre, Paulo, Pan, Yan, Qin, Yi, Dozier, Jeff, Ferguson, Craig R., Balsamo, Gianpaolo, Bao, Qing, Feng, Jinming, Hong, Jinkyu, Hong, Songyou, Huang, Huilin, Ji, Duoying, Ji, Zhenming, Kang, Shichang, Lin, Yanluan, Liu, Weiguang, Muncaster, Ryan, de Rosnay, Patricia, Takahashi, Hiroshi G., Wang, Guiling, Wang, Shuyu, Wang, Weicai, Zhou, Xu, Zhu, Yuejian
• Format: Journal Article
• Language: English
• Published: United States Copernicus Publications, EGU 21.07.2021
• Subjects: ENVIRONMENTAL SCIENCES
• ISSN: 1991-9603; 1991-9603

Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups.