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Atmosphere
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Climate Extremes in China (2nd Edition)
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Climate Extremes in China (2nd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: closed (27 March 2024) | Viewed by 1205

Special Issue Editors

Dr. Zhiqiang Gong

E-Mail Website
Guest Editor
Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
Interests: climate extreme events; climate prediction model prediction assessment; dynamic-statistic combined prediction; error correction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gang Huang

E-Mail Website
Guest Editor
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: monsoon; climate prediction; IOD; ENSO
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Li

E-Mail Website
Guest Editor
School of Geographic Sciences, East China Normal University, Shanghai 200062, China
Interests: detection and attribution; weather and climate extremes; physics of global warming; climatology statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second edition in a series of publications dedicated to “Climate Extremes in China” (https://0-www-mdpi-com.brum.beds.ac.uk/journal/atmosphere/special_issues/climate_extreme_China ).

Weather and climate extremes may cause meteorological disasters and have tremendous impacts on societies and economics. As Earth’s climate warms, more frequent and more intense extreme events have unfolded around the world. Critically evaluating the capability of state-of-the-art dynamic models for near-term extreme event predictions, which operate on sub-seasonal to decadal time scales, as well as for long-term extreme event projections, which operate on multidecadal to centurial scales, is important for identifying and addressing challenges in understanding and modeling physical mechanisms relevant to weather and climate extremes. This will in turn facilitate the diagnosis of the processes that have caused recent singular extreme events, such as atmospheric circulations, water vapor divergence, and teleconnections, as well as the development of more skillful prediction techniques for near-term extreme events. It will also benefit long-term projections of extreme events by improving our understanding of how much, how quickly, whether, and to what extent the recent changes in the frequency and intensity of different weather and climate extremes are associated with human climate warming. As such, a synthesis of recent progresses in forecasting China’s weather and climate extremes through sub-seasonal to multidecadal scales, diagnosing physical processes producing recent singular extreme events, and attributing the role of long-term human climate warming is important for China’s resilience and adaptation to climate extremes in a warming world.

Dr. Zhiqiang Gong
Prof. Dr. Gang Huang
Dr. Chao Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate extreme events
  • climate prediction
  • climate extreme events attribution
  • extreme rainfall event
  • extreme warm event
  • extreme drought event
  • diagnosing
  • external forces
  • model prediction assessment
  • dynamic-statistic combined prediction
  • error correction
  • ENSO

Published Papers (2 papers)

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Research

18 pages, 5137 KiB  
Article
Predicting Summer Precipitation Anomalies in the Yunnan–Guizhou Plateau Using Spring Sea-Surface Temperature Anomalies
by Ya Tuo, Panjie Qiao, Wenqi Liu and Qingquan Li
Atmosphere 2024, 15(4), 453; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15040453 - 05 Apr 2024
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Abstract
By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau were selected. It was found that spring SSTAs in the Bay of [...] Read more.
By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau were selected. It was found that spring SSTAs in the Bay of Bengal, southwestern Atlantic, and eastern Pacific are crucial for influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau. Setting SSTAs from these three regions as predictor variables 3 months in advance, we constructed multiple linear regression (MLR), ridge regression (RR), and lasso regression (LR) models to predict summer precipitation anomalies over the Yunnan–Guizhou region. The training phase involved data spanning from 1961 to 2005, which aimed to predict precipitation anomalies in the Yunnan–Guizhou Plateau for the period extending from 2006 to 2022. Based on MLR, RR, and LR models, the correlations between predicted values and observed summer precipitation anomalies in Yunnan–Guizhou were 0.48, 0.46, and 0.46, respectively. These values were all higher than the correlation coefficients of the NCC_CSM model’s predicted and observed values. Additionally, its performance in predicting summer precipitation anomalies over the Yunnan–Guizhou region, based on key SST regions, was assessed using performance metrics such as anomaly correlation coefficient (ACC), anomaly sign consistency rate (PC), and trend anomaly comprehensive score (PS score). The average ACC of MLR, RR, and LR models was higher than that of the NCC_CSM model’s predictions. For MLR, RR, LR, and NCC_CSM models, the PCs exceeding 50% of the year were 14, 14, 11, and 10, respectively. Furthermore, the average PS score for predicting summer precipitation anomalies over the Yunnan–Guizhou region using MLR, RR, and LR was approximately 73 points; 8 higher than the average PS score of the NCC_CSM model. Therefore, predicting summer precipitation anomalies over the Yunnan–Guizhou region based on key SST regions is of great significance for improving the prediction skills of precipitation anomalies in this region. Full article
(This article belongs to the Special Issue Climate Extremes in China (2nd Edition))
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14 pages, 8328 KiB  
Article
Characteristics of Hourly Extreme Precipitation over the Eastern Extension of the Tibetan Plateau
by Yuan Chen, Yang Zhu, Wei Luo, Ting Duan and Quanliang Chen
Atmosphere 2024, 15(2), 170; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15020170 - 29 Jan 2024
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Abstract
The eastern extension of the Tibetan Plateau (EETP) has complex terrain, unique climate characteristics, and significant regional differences. Based on the hourly precipitation data from 444 stations over the EETP, the characteristics of the extreme precipitation event (EPE) diurnal cycle over the EETP [...] Read more.
The eastern extension of the Tibetan Plateau (EETP) has complex terrain, unique climate characteristics, and significant regional differences. Based on the hourly precipitation data from 444 stations over the EETP, the characteristics of the extreme precipitation event (EPE) diurnal cycle over the EETP and their regional differences during the warm season (May–September) have been indicated and revealed in this study. The mean duration of EPEs at most stations over the EETP is over 6 h, except for some stations in the eastern part of the Tibetan Plateau and Yunnan province. In addition to the Qinba Mountain area, EPEs developed rapidly in most stations. EPEs with long (short) durations usually start at night (afternoon). But in the southwestern part of the Sichuan Basin (eastern part of the Tibetan Plateau), long-duration EPEs and short-duration EPEs often start at night (afternoon to early night). Meanwhile, the long-duration EPEs lead to the nocturnal diurnal peaks and eastward propagating features of extreme precipitation amount (EPA) over the EETP. In the Sichuan Basin (the eastern part of the Tibetan Plateau), the onset and peak moments of total EPEs show a single diurnal peak and appear at midnight (late afternoon to early night). The onset and peak moments of EPEs in the Yunnan–Guizhou Plateau and the Qinba Mountain area exhibit two diurnal peaks, one at midnight and the other from afternoon to early night. Over the EETP, for the long-duration EPEs, the peak moments are often delayed by 2–3 h compared to the start moment, while for the short-duration EPEs, the peak moment and the start moment almost coincide. Full article
(This article belongs to the Special Issue Climate Extremes in China (2nd Edition))
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