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Atmosphere
Special Issues
Climate Modeling and Dynamics
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Climate Modeling and Dynamics

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 6293

Special Issue Editors

Dr. Chengfei He

E-Mail Website
Guest Editor
Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL 33149, USA
Interests: climate dynamics; climate modeling; paleoclimate
Dr. Jun Cheng

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: dynamics and climatic impact of thermohaline circulation; climate modelling; paleoclimate
Special Issues, Collections and Topics in MDPI journals
Dr. Yishuai Jin

E-Mail Website
Guest Editor
Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao 266100, China
Interests: climate prediction; climate dynamics

Special Issue Information

Dear Colleagues,

Climate models are critical tools to improve our understanding of climate dynamics varying from seasonal, annual, and decadal, to even longer timescales. Climate models allow us to quantify the role of different climate features by performing sensitivity experiments that cannot be conducted in real observations. Climate models provide us with a projection of future climate change. Yet, great challenges remain within areas such as global rainfall simulation, the double-ITCZ, and regional-to-global-scale climate variability (ENSO, NAO, AMV, etc.) due primarily to limited observations, deficient theories, and imperfect representations in climate models.

To further our understanding of our climate system and climate modeling, we are calling for original research papers related to climate modeling and dynamics in this Special Issue. For example, studies could focus on a quantitative understanding of the characteristics, variability, and underlying mechanisms of a wide range of climate variability ENSO, NAO, AMV, PDO, etc.) through climate modeling; studies could also address their regional and global influences and adaptions, or could evaluate climate model performances on, for example, global monsoon rainfall and circulation or extreme weather events. Additionally, studies using machine learning to improve climate models would be of great interest. Paleoclimate modeling and direct data–model comparison are also encouraged, in particular with explicated geophysical and geochemical tracers.

Dr. Chengfei He
Dr. Jun Cheng
Dr. Yishuai Jin
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 modeling
  • climate dynamics
  • climate change
  • climate variability
  • paleoclimate

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Published Papers (3 papers)

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Research

18 pages, 21532 KiB  
Article
Predictability of the Wintertime Western Pacific Pattern in the APEC Climate Center Multi-Model Ensemble
by Eung-Sup Kim, Vladimir N. Kryjov and Joong-Bae Ahn
Atmosphere 2022, 13(11), 1772; https://doi.org/10.3390/atmos13111772 - 27 Oct 2022
Cited by 2 | Viewed by 1621
Abstract
The predictability of the wintertime Western Pacific (WP) pattern is evaluated based on seasonal predictions from five models participating in the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) for the winters from 1982/1983 to 2021/2022. The temporal correlation coefficient (TCC) [...] Read more.
The predictability of the wintertime Western Pacific (WP) pattern is evaluated based on seasonal predictions from five models participating in the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) for the winters from 1982/1983 to 2021/2022. The temporal correlation coefficient (TCC) between the observed and MME-predicted WP indices was 0.61 (0.37–0.54 for individual models) for the entire series. However, when only three Super El Niño (SEN) years (Niño3.4 ≥ 2.0) out of the 40-year series were excluded, the TCC dropped down to 0.54 (0.27–0.42). During the SEN years, the WP was strongly affected by the SEN-excited anomalies via the PNA. In observations from non-SEN years, the WP pattern was strongly related to the dipole pattern in Northwestern Pacific SST (TCC = 0.8), for the description of which we suggested a Northwestern Pacific (NWP) index, and it was significantly weakly related to the ENSO and IOD, whereas in the model simulations, the main role was played by the ENSO (TCC = 0.6). The NWP index was well predictable in MME (TCC = 0.73) and individual models (0.56–0.71). We showed that the prediction of the WP index polarity is reliable when both predicted WP and NWP anomalies are significant and indicate the same WP sign that has implications for the seasonal forecasting. Full article
(This article belongs to the Special Issue Climate Modeling and Dynamics)
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18 pages, 4317 KiB  
Article
Dimensionality Reduction by Similarity Distance-Based Hypergraph Embedding
by Xingchen Shen, Shixu Fang and Wenwen Qiang
Atmosphere 2022, 13(9), 1449; https://doi.org/10.3390/atmos13091449 - 7 Sep 2022
Viewed by 1868
Abstract
Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be reflected through a simple graph or Euclidean distance, is worth paying [...] Read more.
Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be reflected through a simple graph or Euclidean distance, is worth paying attention to. For this purpose, we propose a novel similarity distance-based hypergraph embedding method (SDHE) for hyperspectral images DR. Unlike conventional graph embedding-based methods that only consider the affinity between two samples, SDHE takes advantage of hypergraph embedding to describe the complex sample relationships in high order. Besides, we propose a novel similarity distance instead of Euclidean distance to measure the affinity between samples for the reason that the similarity distance not only discovers the complicated geometrical structure information but also makes use of the local distribution information. Finally, based on the similarity distance, SDHE aims to find the optimal projection that can preserve the local distribution information of sample sets in a low-dimensional subspace. The experimental results in three hyperspectral image data sets demonstrate that our SDHE acquires more efficient performance than other state-of-the-art DR methods, which improve by at least 2% on average. Full article
(This article belongs to the Special Issue Climate Modeling and Dynamics)
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24 pages, 4335 KiB  
Article
Effects of Low-Frequency Oscillation at Different Latitudes on Summer Precipitation in Flood and Drought Years in Southern China
by Lu Liu, Liping Li and Guanhua Zhu
Atmosphere 2022, 13(8), 1277; https://doi.org/10.3390/atmos13081277 - 11 Aug 2022
Viewed by 2002
Abstract
Based on the daily precipitation data from 753 meteorological stations provided by the National Meteorological Information Center (China) and the daily reanalysis data from NCEP/NCAR and ERA5 during the period from 1980 to 2020, the low-frequency (LF) precipitation characteristics of the typical summer [...] Read more.
Based on the daily precipitation data from 753 meteorological stations provided by the National Meteorological Information Center (China) and the daily reanalysis data from NCEP/NCAR and ERA5 during the period from 1980 to 2020, the low-frequency (LF) precipitation characteristics of the typical summer flood and drought years in southern China and their relation to the LF atmospheric circulation at different latitudes are compared and analyzed, and extended-range forecasting signals are given. The results show that: (a) In both flood and drought years, summer precipitation in southern China is controlled by 10–20 day oscillation (quasi-biweekly oscillation, QBWO); (b) LF convection is active in southern China in both flood and drought years, but the convective center is southward in flood years, and the vertical meridional circulation is stronger. The key circulation systems of 500 hPa LF height field in flood and drought years include LF “two ridges and one trough” and LF “+”, “−”, “+” East Asia Pacific (EAP) teleconnection wave train in mid-high latitudes of Eurasia. However, the “two ridges and one trough” in flood years are more westward and meridional than in drought years, and the LF Subtropical High is stronger and more extensive, with more significant westward extension; (c) In flood (drought) years, there is northerly and then westerly (central westerly) dry-cold, northeasterly wet-cold, southwesterly (none), and southeasterly (including southerly across the equator) wet-warm water vapor channels. The sources of dry and wet cold air in flood (drought) years are located near Novaya Zemlya (the eastern West Siberian Plain), the Yellow Sea, and the Bohai Sea (Sea of Japan). Additionally, the sources of wet-warm water vapor include the Arabian Sea, the Bay of Bengal, the western Pacific Ocean, and the sea area of northeastern Australia (the western Pacific Ocean and the northern sea area of Australia); and (d) The LF predictive signals of outgoing longwave radiation (OLR) appear on −11 days, while the signals of the 500 hPa height field are on −9 days. There are both westward and eastward propagation predictive signals in flood years, and only westward spread signals in drought years. Full article
(This article belongs to the Special Issue Climate Modeling and Dynamics)
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