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Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation – Initialization and Improved Prediction

Yongkang XueUniversity of California – Los Angeles; Los Angeles, USA

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Ismaila DialloUniversity of California – Los Angeles; Los Angeles, USA

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Aaron A. BooneCNRM, Université de Toulouse, Météo-France, Toulouse, France

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Tandong YaoInstitute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS); Beijing, China

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Yang ZhangSchool of Atmospheric Sciences, Nanjing University; Nanjing, China

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Xubin ZengUniversity of Arizona; Tucson, USA

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J. David NeelinUniversity of California – Los Angeles; Los Angeles, USA

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William K.M. LauEarth System Science Interdisciplinary Center, University of Maryland; College Park, MD, USA

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Yan PanSchool of Atmospheric Sciences, Nanjing University; Nanjing, China

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Ye LiuUniversity of California – Los Angeles; Los Angeles, USA
Pacific Northwest National Laboratory, Richland, WA 99352, USA

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Xiaoduo PanInstitute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS); Beijing, China

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Qi TangLawrence Livermore National Laboratory; Livermore, USA

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Peter J. van OevelenInternational GEWEX Project Office, George Mason University; Fairfax, USA

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Tomonori SatoHokkaido University; Sapporo, Japan

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Myung-Seo KooKorea Institute of Atmospheric Prediction Systems; Seoul, South Korea

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Stefano MateriaClimate Simulation and Prediction, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici; Bologna, Italy

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Chunxiang ShiNational Meteorological Information Center, China Meteorological Administration (CMA); Beijing China

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Jing YangBeijing Normal University; Beijing, China

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Constantin ArdilouzeCNRM, Université de Toulouse, Météo-France, Toulouse, France

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Zhaohui LinInstitute of Atmospheric Physics, CAS; Beijing, China

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Xin QiBeijing Normal University; Beijing, China

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Tetsu NakamuraHokkaido University; Sapporo, Japan

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Subodh K. SahaIndian Institute of Tropical Meteorology, Pune, India

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Retish SenanEuropean Centre for Medium-range Weather Forecasts; Reading, UK

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Yuhei TakayaMeteorological Research Institute, Japan Meteorological Agency; Tsukuba, Japan

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Hailan WangNational Center for Environmental Prediction, College Park, MD, USA

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Hongliang ZhangNational Meteorology Center, CMA; Beijing, China

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Mei ZhaoBureau of Meteorology; Melbourne, Australia

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Hara Prasad NayakUniversity of California – Los Angeles; Los Angeles, USA

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Qiuyu ChenSchool of Atmospheric Sciences, Nanjing University; Nanjing, China
University of California – Los Angeles; Los Angeles, USA

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Jinming FengInstitute of Atmospheric Physics, CAS; Beijing, China

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Michael A. BrunkeUniversity of Arizona; Tucson, USA

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Tianyi FanBeijing Normal University; Beijing, China

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Songyou HongNOAA/ESRL and University of Colorado/CIRES; Boulder, Colorado, USA
Korea Institute of Atmospheric Prediction Systems; Seoul, South Korea

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Paulo NobreNational Institute for Space Research (INPE), Brazil

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Daniele PeanoClimate Simulation and Prediction, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici; Bologna, Italy

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Yi QinTsinghua University, China
Lawrence Livermore National Laboratory; Livermore, USA

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Frederic VitartEuropean Centre for Medium-range Weather Forecasts; Reading, UK

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Shaocheng XieLawrence Livermore National Laboratory; Livermore, USA

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Yanling ZhanInstitute of Atmospheric Physics, CAS; Beijing, China

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Daniel KlockeMax Planck Institute for Meteorology; Hamburg, Deutschland

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Ruby LeungPacific Northwest National Laboratory, Richland, WA 99352, USA

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Xin LiInstitute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS); Beijing, China

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Michael EkNational Center for Atmospheric Research; Boulder, USA

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Weidong GuoSchool of Atmospheric Sciences, Nanjing University; Nanjing, China

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Gianpaolo BalsamoEuropean Centre for Medium-range Weather Forecasts; Reading, UK

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Qing BaoLASG, Institute of Atmospheric Physics, CAS; Beijing, China

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Sin Chan ChouNational Institute for Space Research (INPE), Brazil

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Patricia de RosnayEuropean Centre for Medium-range Weather Forecasts; Reading, UK

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Yanluan LinTsinghua University, China

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Yuejian ZhuNational Center for Environmental Prediction, College Park, MD, USA

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Yun QianPacific Northwest National Laboratory, Richland, WA 99352, USA

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Ping ZhaoChinese Academy of Meteorological Sciences, China Meteorological Administration, China

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Jianping TangSchool of Atmospheric Sciences, Nanjing University; Nanjing, China

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Xin-Zhong LiangUniversity of Maryland; College Park, MD, USA

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Jinkyu HongYonsei University, South Korea

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Duoying JiBeijing Normal University; Beijing, China

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Zhenming JiSun Yat-Sen University, China

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Yuan QiuInstitute of Atmospheric Physics, CAS; Beijing, China

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Shiori SugimotoJapan Agency for Marine Earth Science and Technology (JAMSTEC), Japan

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Weicai WangInstitute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS); Beijing, China

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Kun YangTsinghua University, China

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Miao YuNanjing University of Information Science and Technology, Nanjing 210044, China

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Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project.

After using an innovative new land state initialization approach based on observed surface 2-meter temperature over the TP in the LS4P experiment, results from a multi-model ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hot spot” regions identified here; the ensemble means in some “hot spots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Corresponding author. Yongkang Xue, yxue@geog.ucla.edu

Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project.

After using an innovative new land state initialization approach based on observed surface 2-meter temperature over the TP in the LS4P experiment, results from a multi-model ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hot spot” regions identified here; the ensemble means in some “hot spots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Corresponding author. Yongkang Xue, yxue@geog.ucla.edu
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