Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to
Seasonal Prediction Project, Phase I (LS4P-I): Organization and Experimental design
Authors: Xue Yongkang , Yao Tandong , Boone Aaron A. , Diallo Ismaila , Liu Ye , Zeng Xubin , Lau William K.-M. , Sugimoto Shiori , Tang Qi , Pan Xiaoduo , van Oevelen Peter J. , Klocke Daniel , Koo Myung-Seo , Lin Zhaohui , Takaya Yuhei , Sato Tomonori , Ardilouze Constantin , Saha Subodh K. , Zhao Mei , Liang Xin-Zhong , Vitart Frederic , Li Xin , Zhao Ping , Neelin David , Guo Weidong , Yu Miao , Qian Yun , Shen Samuel S. P. , Zhang Yang , Yang Kun , Leung Ruby , Yang Jing , Qiu Yuan , Brunke Michael A. , Chou Sin Chan , Ek Michael , Fan Tianyi , Guan Hong , Lin Hai , Liang Shunlin , Materia Stefano , Nakamura Tetsu , Qi Xin , Senan Retish , Shi Chunxiang , Wang Hailan , Wei Helin , Xie Shaocheng , Xu Haoran , Zhang Hongliang , Zhan Yanling , Li Weiping , Shi Xueli , Nobre Paulo , 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 , Pan Yan , Peano Daniele , de Rosnay Patricia , Takahashi Hiroshi G. , Tang Jianping , Wang Guiling , Wang Shuyu , Wang Weicai , Zhou Xu , Zhu Yuejian 
Published: January 2021

Abstract. Sub-seasonal to seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging but 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 Sub-seasonal to Seasonal Prediction (LS4P), as the first international grass-root 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, hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than forty groups worldwide have participated in this effort, including 21 Earth System Models, 9 regional climate models, and 7 data groups. This paper overviews the history and objectives of LS4P, provides the first phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST in the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation and its S2S prediction. LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations which both tend to limit the soil memory; and ii) reanalysis data, that are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.