Preprints
https://doi.org/10.5194/gmd-2020-329
https://doi.org/10.5194/gmd-2020-329

Submitted as: model experiment description paper 07 Jan 2021

Submitted as: model experiment description paper | 07 Jan 2021

Review status: this preprint is currently under review for the journal GMD.

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

Yongkang Xue1, Tandong Yao2, Aaron A. Boone3, Ismaila Diallo1, Ye Liu1, Xubin Zeng4, William K.-M. Lau5, Shiori Sugimoto6, Qi Tang7, Xiaoduo Pan2, Peter J. van Oevelen8, Daniel Klocke9, Myung-Seo Koo10, Zhaohui Lin11, Yuhei Takaya12, Tomonori Sato13, Constantin Ardilouze3, Subodh K. Saha14, Mei Zhao15, Xin-Zhong Liang5, Frederic Vitart16, Xin Li2, Ping Zhao17, David Neelin1, Weidong Guo18, Miao Yu19, Yun Qian20, Samuel S. P. Shen21, Yang Zhang18, Kun Yang22, Ruby Leung20, Jing Yang23, Yuan Qiu11, Michael A. Brunke4, Sin Chan Chou24, Michael Ek25, Tianyi Fan23, Hong Guan26, Hai Lin27, Shunlin Liang28, Stefano Materia29, Tetsu Nakamura13, Xin Qi23, Retish Senan16, Chunxiang Shi30, Hailan Wang26, Helin Wei26, Shaocheng Xie7, Haoran Xu5, Hongliang Zhang31, Yanling Zhan11, Weiping Li32, Xueli Shi32, Paulo Nobre24, Yi Qin22, Jeff Dozier33, Craig R. Ferguson34, Gianpaolo Balsamo16, Qing Bao35, Jinming Feng11, Jinkyu Hong36, Songyou Hong10, Huilin Huang1, Duoying Ji23, Zhenming Ji37, Shichang Kang38, Yanluan Lin22, Weiguang Liu39,19, Ryan Muncaster27, Yan Pan18, Daniele Peano29, Patricia de Rosnay16, Hiroshi G. Takahashi40, Jianping Tang18, Guiling Wang39, Shuyu Wang18, Weicai Wang2, Xu Zhou2, and Yuejian Zhu26 Yongkang Xue et al.
  • 1University of California – Los Angeles, CA 90095, USA
  • 2Institute of Tibetan Plateau Research, Chinese Academy of Sciences, China
  • 3CNRM, University of Toulouse, Météo-France, CNRS, Toulouse, France
  • 4University of Arizona, Tucson, USA
  • 5Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, USA
  • 6Japan Agency for Marine Earth Science and Technology (JAMSTEC), Japan
  • 7Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
  • 8International GEWEX Project Office, George Mason University, USA
  • 9Hans Ertel Centre for Weather Research, Germany
  • 10Korea Institute of Atmospheric Prediction Systems, South Korea
  • 11Institute of Atmospheric Physics, Chinese Academy of Sciences, China
  • 12Meteorological Research Institute, Japan Meteorological Agency, Japan
  • 13Hokkaido University, Japan
  • 14Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, India
  • 15Bureau of Meteorology, Australia
  • 16European Centre for Medium-range Weather Forecasts (ECMWF), UK
  • 17Chinese Academy of Meteorological Sciences, China Meteorological Administration, China
  • 18School of Atmospheric Sciences, Nanjing University, China
  • 19Nanjing University of Information Science Technology, Nanjing 210044, China
  • 20Pacific Northwest National Laboratory, Richland, WA 99352, USA
  • 21San Diego State University, USA
  • 22Tsinghua University, China
  • 23Beijing Normal University, China
  • 24National Institute for Space Research (INPE), Brazil
  • 25National Center for Atmospheric Research (NCAR), USA
  • 26National Center for Environmental Prediction (NCEP)/National Weather Service/National Oceanic and Atmospheric Administration (NOAA), USA
  • 27Environment and Climate Change Canada, Canada
  • 28University of Maryland, College Park, USA
  • 29Euro-Mediterranean Centre on Climate Change Foundation (CMCC), Italy
  • 30National Meteorological Information Center, China Meteorological Administration, China
  • 31National Meteorology Center, China Meteorological Administration, China
  • 32National Climate Center, China Meteorological Administration, China
  • 33University of California, Santa Barbara, USA
  • 34Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, 12203
  • 35State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, China
  • 36Yonsei University, South Korea
  • 37Sun Yat-Sen University, China
  • 38Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, China
  • 39University of Connecticut, USA
  • 40Tokyo Metropolitan University, Japan

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.

Yongkang Xue et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of Xue et al. "Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): Organization and Experimental design"', Rene Orth, 18 Feb 2021
  • RC2: 'Comment on gmd-2020-329', Anonymous Referee #2, 11 Mar 2021

Yongkang Xue et al.

Data sets

LS4P-I evaluation datasets for the paper Organization and Experimental design (Version v1) Xue, Y. and Diallo, I. https://doi.org/10.5281/zenodo.4383284

Yongkang Xue et al.

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This paper overviews the history and research objectives of the Global Energy and Water Exchanges (GEWEX) initiative called Impact of initialized Land Surface temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) and provides the first phase experimental protocol (LS4P-I). The LS4P introduces spring land surface temperature/subsurface temperature anomalies over high mountain areas as a crucial factor that can lead to significant improvement in summer precipitation prediction.