Submitted as: model experiment description paper07 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 Zhu26Yongkang Xue et al.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
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
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
Received: 30 Sep 2020 – Accepted for review: 30 Dec 2020 – Discussion started: 07 Jan 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.
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.
This paper overviews the history and research objectives of the Global Energy and Water...