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Lei Wang, Tandong Yao, Chenhao Chai, Lan Cuo, Fengge Su, Fan Zhang, Zhijun Yao, Yinsheng Zhang, Xiuping Li, Jia Qi, Zhidan Hu, Jingshi Liu, and Yuanwei Wang

Abstract

Monitoring changes in river runoff at the Third Pole (TP) is important because rivers in this region support millions of inhabitants in Asia and are very sensitive to climate change. Under the influence of climate change and intensified cryospheric melt, river runoff has changed markedly at the TP, with significant effects on the spatial and temporal water resource distribution that threaten water supply and food security for people living downstream. Despite some in situ observations and discharge estimates from state-of-the-art remote sensing technology, the total river runoff (TRR) for the TP has never been reliably quantified, and its response to climate change remains unclear. As part of the Chinese Academy of Sciences’ “Pan-Third Pole Environment Study for a Green Silk Road,” the TP-River project aims to construct a comprehensive runoff observation network at mountain outlets (where rivers leave the mountains and enter the plains) for 13 major rivers in the TP region, thereby enabling TRR to be accurately quantified. The project also integrates discharge estimates from remote sensing and cryosphere–hydrology modeling to investigate long-term changes in TRR and the relationship between the TRR variations and westerly/monsoon. Based on recent efforts, the project provides the first estimate (656 ± 23 billion m3) of annual TRR for the 13 TP rivers in 2018. The annual river runoff at the mountain outlets varies widely between the different TP rivers, ranging from 2 to 176 billion m3, with higher values mainly corresponding to rivers in the Indian monsoon domain, rather than in the westerly domain.

Open access
Agus Santoso, Harry Hendon, Andrew Watkins, Scott Power, Dietmar Dommenget, Matthew H. England, Leela Frankcombe, Neil J. Holbrook, Ryan Holmes, Pandora Hope, Eun-Pa Lim, Jing-Jia Luo, Shayne McGregor, Sonja Neske, Hanh Nguyen, Acacia Pepler, Harun Rashid, Alex Sen Gupta, Andréa S. Taschetto, Guomin Wang, Esteban Abellán, Arnold Sullivan, Maurice F. Huguenin, Felicity Gamble, and Francois Delage

Abstract

El Niño and La Niña, the warm and cold phases of El Niño–Southern Oscillation (ENSO), cause significant year-to-year disruptions in global climate, including in the atmosphere, oceans, and cryosphere. Australia is one of the countries where its climate, including droughts and flooding rains, is highly sensitive to the temporal and spatial variations of ENSO. The dramatic impacts of ENSO on the environment, society, health, and economies worldwide make the application of reliable ENSO predictions a powerful way to manage risks and resources. An improved understanding of ENSO dynamics in a changing climate has the potential to lead to more accurate and reliable ENSO predictions by facilitating improved forecast systems. This motivated an Australian national workshop on ENSO dynamics and prediction that was held in Sydney, Australia, in November 2017. This workshop followed the aftermath of the 2015/16 extreme El Niño, which exhibited different characteristics to previous extreme El Niños and whose early evolution since 2014 was challenging to predict. This essay summarizes the collective workshop perspective on recent progress and challenges in understanding ENSO dynamics and predictability and improving forecast systems. While this essay discusses key issues from an Australian perspective, many of the same issues are important for other ENSO-affected countries and for the international ENSO research community.

Open access
Baoqiang Xiang, Lucas Harris, Thomas L. Delworth, Bin Wang, Guosen Chen, Jan-Huey Chen, Spencer K. Clark, William F. Cooke, Kun Gao, J. Jacob Huff, Liwei Jia, Nathaniel C. Johnson, Sarah B. Kapnick, Feiyu Lu, Colleen McHugh, Yongqiang Sun, Mingjing Tong, Xiaosong Yang, Fanrong Zeng, Ming Zhao, Linjiong Zhou, and Xiaqiong Zhou

Abstract

A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL SPEAR global coupled model. Based on 20-year hindcast results (2000-2019), the boreal wintertime (November-April) Madden-Julian Oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (15 days). The slow-propagating MJO detours southward when traversing the maritime continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases.

The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

Full access
Howard J. Diamond, Carl J. Schreck III, Emily J. Becker, Gerald D. Bell, Eric S. Blake, Stephanie Bond, Francis G. Bringas, Suzana J. Camargo, Lin Chen, Caio A. S. Coelho, Ricardo Domingues, Stanley B. Goldenberg, Gustavo Goni, Nicolas Fauchereau, Michael S. Halpert, Qiong He, Philip J. Klotzbach, John A. Knaff, Michelle L'Heureux, Chris W. Landsea, I.-I. Lin, Andrew M. Lorrey, Jing-Jia Luo, Kyle MacRitchie, Andrew D. Magee, Ben Noll, Richard J. Pasch, Alexandre B. Pezza, Matthew Rosencrans, Michael K. Tippet, Blair C. Trewin, Ryan E. Truchelut, Bin Wang, Hui Wang, Kimberly M. Wood, John-Mark Woolley, and Steven H. Young
Full access
Stephen Baxter, Gerald D Bell, Eric S Blake, Francis G Bringas, Suzana J Camargo, Lin Chen, Caio A. S Coelho, Ricardo Domingues, Stanley B Goldenberg, Gustavo Goni, Nicolas Fauchereau, Michael S Halpert, Qiong He, Philip J Klotzbach, John A Knaff, Michelle L'Heureux, Chris W Landsea, I.-I Lin, Andrew M Lorrey, Jing-Jia Luo, Andrew D Magee, Richard J Pasch, Petra R Pearce, Alexandre B Pezza, Matthew Rosencrans, Blair C Trewin, Ryan E Truchelut, Bin Wang, H Wang, Kimberly M Wood, and John-Mark Woolley
Free access