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Shi-Xin Wang, Hong-Chao Zuo, Fen Sun, Li-Yang Wu, Yixing Yin, and Jing-Jia Luo

Abstract

Dynamics of the East Asian spring rainband are investigated with a reanalysis dataset and station observations. Here, it is revealed that the rainband is anchored by external forcings. The midtropospheric jet core stays quasi-stationary around Japan. It has two branches in its entry region, which originate from the south and north flanks of the Tibetan Plateau and then run northeastward and southeastward, respectively. The southern branch advects warm air from the Tibetan–Hengduan Plateau northeastward, forming a rainband over southern China through causing adiabatic ascent motion and triggering diabatic feedback. The rainband is much stronger in spring than in autumn due to the stronger diabatic heating over the Tibetan–Hengduan Plateau, a more southward-displaced midtropospheric jet, and the resulting stronger warm advection over southern China. The northern jet branch forms a zonally elongated cold advection belt, which reaches a maximum around northern China, and then weakens and extends eastward to east of Japan. The westerly jet also steers strong disturbance activities roughly collocated with the cold advection belt via baroclinic instability. The high disturbance activities belt causes large cumulative warm advection (CWA) through drastically increasing extremely warm advection days on its eastern and south flanks, where weak cold advection prevails. CWA is more essential for monthly/seasonally rainfall than conventionally used time-average temperature advection because it is shown that strengthened warm advection can increase rainfall through positive diabatic feedback, while cold advection cannot cause negative rainfall. Thus, the rainband is collocated with the large CWA belt instead of the warm advection south of it. This rainband is jointed to the rainband over southern China, forming the long southwest–northeast-oriented East Asian spring rainband. Increasing moisture slightly displaces the rainband southeastward.

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Fang-Fang Li, Ying-Hui Jia, Guang-Qian Wang, and Jun Qiu

Abstract

Sound waves have proven to be effective in promoting the interaction and aggregation of droplets. It is necessary to theoretically study the motion of particles in a sound field to develop new acoustic technology for precipitation enhancement. In this paper, the motion of cloud droplets due to a traveling sound wave field emitted from the ground to the air is simulated using the motion equation of point particles. The force condition of the particles in the oscillating flow field is analyzed. Meanwhile, the effects of droplet size, sound frequency, and sound pressure level (SPL) on the velocity and displacement of the droplets are also investigated. The results show that Stokes force and gravity play a dominant role in the falling process of cloud droplets, and the effect of the sound wave is mainly reflected in the fluctuation of velocity and displacement, which also promotes the displacement of cloud droplets to a certain extent. The maximum displacement increments of cloud droplets of 10 µm can reach 9200 µm due to the action of sound waves of 50 Hz and 143.4 dB. The SPL required for a noticeable velocity fluctuation for droplets of 10 µm with frequency of 50 Hz is 88.2 dB. When SPL < 100 dB and frequency > 500 Hz, the effect is negligible. The cloud droplet size plays a significant role in the motion, and the sound action is weaker for larger particles. For a smaller sound frequency and higher SPL, the effect of the sound wave is more prominent.

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Jia Wang, Xuezhi Bai, Haoguo Hu, Anne Clites, Marie Colton, and Brent Lofgren

Abstract

In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of ~8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Niño–Southern Oscillation patterns.

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Jia Wang, James Kessler, Xuezhi Bai, Anne Clites, Brent Lofgren, Alexandre Assuncao, John Bratton, Philip Chu, and George Leshkevich

Abstract

In this study, decadal variability of ice cover in the Great Lakes is investigated using historical airborne and satellite measurements from 1963 to 2017. It was found that Great Lakes ice cover has 1) a linear relationship with the Atlantic multidecadal oscillation (AMO), similar to the relationship of lake ice cover with the North Atlantic Oscillation (NAO), but with stronger impact than NAO; 2) a quadratic relationship with the Pacific decadal oscillation (PDO), which is similar to the relationship of lake ice cover to Niño-3.4, but with opposite curvature; and 3) decadal variability with a positive (warming) trend in AMO contributes to the decreasing trend in lake ice cover. Composite analyses show that during the positive (negative) phase of AMO, the Great Lakes experience a warm (cold) anomaly in surface air temperature (SAT) and lake surface temperature (LST), leading to less (more) ice cover. During the positive (negative) phase of PDO, the Great Lakes experience a cold (warm) anomaly in SAT and LST, leading to more (less) ice cover. Based on these statistical relationships, the original multiple variable regression model established using the indices of NAO and Niño-3.4 only was improved by adding both AMO and PDO, as well as their interference (interacting or competing) mechanism. With the AMO and PDO added, the correlation between the model and observation increases to 0.69, compared to 0.48 using NAO and Niño-3.4 only. When November lake surface temperature was further added to the regression model, the prediction skill of the coming winter ice cover increased even more.

Open access
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

Capsule

The TP-River project is constructing a monitoring network for 13 major rivers at the Third Pole to quantify the total river runoff and its response to monsoon and westerly winds.

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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
Rongqing Han, Hui Wang, Zeng-Zhen Hu, Arun Kumar, Weijing Li, Lindsey N. Long, Jae-Kyung E. Schemm, Peitao Peng, Wanqiu Wang, Dong Si, Xiaolong Jia, Ming Zhao, Gabriel A. Vecchi, Timothy E. LaRow, Young-Kwon Lim, Siegfried D. Schubert, Suzana J. Camargo, Naomi Henderson, Jeffrey A. Jonas, and Kevin J. E. Walsh

Abstract

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.

Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

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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
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
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