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

DOI：

10.5194/gmd-14-4465-2021

发表时间：

2021-07

期刊：

Geoscientific Model Development

影响因子：

5.1

通讯作者：

中科院分区：

地球科学2区

文献类型：

作者：

Y. Xue;T. Yao;A. Boone;I. Diallo;Ye Liu;X. Zeng;W. Lau;S. Sugimoto;Q. Tang;Xiaoduo Pan;P. J. Oevelen;D. Klocke;Myung‐Seo Koo;Zhaohui Lin;Y. Takaya;Tomonori Sato;C. Ardilouze;S. Saha;Mei Zhao;Xin‐Zhong Liang;F. Vitart;Xin Li;P. Zhao;D. Neelin;W. Guo;Miao Yu;Y. Qian;S. Shen;Yang Zhang;Kun Yang;R. Leung;Jing Yang;Yuan Qiu;M. Brunke;S. Chou;M. Ek;T. Fan;H. Guan;Hai Lin;S. Liang;S. Materia;Tetsu Nakamura;Xin Qi;Retish Senan;C. Shi;Hailan Wang;Helin Wei;S. Xie;Haoran Xu;Hongliang Zhang;Yanling Zhan;Weiping Li;Xueli Shi;P. Nobre;Yi Qin;J. Dozier;C. Ferguson;G. Balsamo;Q. Bao;Jinming Feng;Jinkyu Hong;Songyoul Hong;Huilin Huang;D. Ji;Zhenming Ji;Shi-chang Kang;Yanluan Lin;Weiguang Liu;R. Muncaster;Yan Pan;D. Peano;P. Rosnay;Hiroshi G. Takahashi;Jianping Tang;G. Wang;Shuyu Wang;Weicai Wang;Xu Zhou;Yuejian Zhu

抽象的。分季节(S2S)预测，特别是对干旱和洪水等极端水文气候事件的预测，不仅在科学上具有挑战性，而且还具有重大的社会影响。在初步研究的推动下，全球能源和水交换(Gewex)/全球大气系统研究(GASS)发起了一项名为“初始地表温度和积雪对分季节到季节性预报的影响”(LS4P)的新倡议，这是第一个国际基层努力，将高山地区春季陆地表面温度(LST)/次表层温度(SUBT)异常作为一个关键因素，通过陆地-大气相互作用的远程影响，显著改善降水预测。LS4P侧重于过程理解和可预测性，因此它不同于其他侧重于可操作的S2S预测的国际项目，也是对这些项目的补充。全球有40多个小组参与了这项工作，其中包括21个地球系统模型、9个区域气候模型和7个数据小组。本文概述了LS4P的发展历史和目标，给出了针对青藏高原远程影响的第一阶段实验方案(LS4P-I)，讨论了LST/SUBT的初始化，并给出了初步结果。多模式集合试验和观测资料的分析表明，青藏高原春季LST的水文气候效应不仅限于长江流域，而且可能对东亚以外的夏季降水及其S2S预报产生显着的大尺度影响。初步研究和分析还表明，LS4P模式在产生观测异常时不能保留初始化的LST异常，主要是由于两个主要原因：(I)由于土壤总深度太浅而导致的陆地模式的不足，以及使用简化的参数化方案，两者都倾向于限制土壤记忆；(Ii)用于初始条件的再分析数据与青藏高原LST的观测平均状态和异常有很大的差异。已经开发出创新的方法来在很大程度上克服这些问题。

Abstract. 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. This paper provides an overview of 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 on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that 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; (ii) reanalysis data, which 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.

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

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