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Atmosphere
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Recent Advances in Research on Ocean Climate Variability (2nd Edition)
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Recent Advances in Research on Ocean Climate Variability (2nd Edition)

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

Deadline for manuscript submissions: closed (25 October 2024) | Viewed by 4973

Special Issue Editors

Dr. Jifeng Qi

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Guest Editor
CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Interests: ocean dynamics and mixed layer and thermocline dynamics; air-sea interaction; water mass; ENSO; tropics and extra-tropics interaction; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yinghao Qin

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Guest Editor
National Marine Environmental Forecasting Center, Beijing 100081, China
Interests: ocean forecasting; data assimilation; ocean dynamics; ENSO
Special Issues, Collections and Topics in MDPI journals
Dr. Shanliang Zhu

E-Mail Website
Guest Editor
School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
Interests: artificial intelligence ocean technology; thermohaline structure; climate modelling; extreme events; data mining technology; data-driven control

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of a previous Special Issue entitled "Recent Advances in Research on Ocean Climate Variability" (https://www.mdpi.com/journal/atmosphere/special_issues/96ZYKC16ZF) published in Atmosphere in 2023.

Ocean climate variability is a core component of ocean climate dynamics, which will lead to alterations in climate patterns around the world. Recent advances in ocean climate have improved our understanding of global climate change by introducing some innovative theories and methods in detecting, diagnosing, analyzing, and predicting the ocean climate variability on various time scales ranging from seasonal, interannual, and decadal time scales.

The objective of this Special Issue is to focus on recent advances in research on ocean climate variability. We invite all interested researchers to submit original research articles as well as review articles that will stimulate the continuing efforts to understand and predict ocean climate variability on various time scales (years to decades to centuries), such as the El Niño/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), North Atlantic Oscillation (NAO), etc. Theoretical, observational, modelling and machine learning studies focusing on elucidating specific physical processes and their contribution to understanding ocean climate variability are all welcome. Especially welcome are regional and global ocean studies, methods and results concerning ocean thermohaline structure and water masses variability for present and future climates, methods and challenges in understanding ocean circulation variability and its influence in future decades, applications of machine learning/deep learning techniques in ocean climate variability, and any other innovative contributions.

Dr. Jifeng Qi
Dr. Yinghao Qin
Dr. Shanliang Zhu
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

  • ocean circulation
  • air–sea interactions
  • thermohaline structure
  • water masses
  • marine heat waves
  • El Niño/southern oscillation
  • machine learning/deep learning
  • climate change

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Published Papers (3 papers)

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Research

18 pages, 15556 KiB  
Article
Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes
by Melly Ariska, Suhadi, Supari, Muhammad Irfan and Iskhaq Iskandar
Atmosphere 2024, 15(9), 1036; https://doi.org/10.3390/atmos15091036 - 28 Aug 2024
Cited by 1 | Viewed by 838
Abstract
The analysis of rainfall patterns in the Indonesian region utilized the Empirical Orthogonal Function (EOF) method to identify spatial and temporal variations. The study evaluated the dynamic influence of the Tropical Indian Ocean (TIO) and the Tropical Pacific Ocean (TPO) on Indonesian rainfall [...] Read more.
The analysis of rainfall patterns in the Indonesian region utilized the Empirical Orthogonal Function (EOF) method to identify spatial and temporal variations. The study evaluated the dynamic influence of the Tropical Indian Ocean (TIO) and the Tropical Pacific Ocean (TPO) on Indonesian rainfall using monthly data from the Southeast Asian Climate Assessment and Dataset (SACA&D) spanning from January 1981 to December 2016 and encompassing three extreme El Niño events in 1982/1983, 1997/1998 and 2015/2016. Using combined reanalysis and gridded-observation data, this study evaluates the potential impact of the two primary modes in the tropical Indo-Pacific region, namely the Indian Ocean Dipole (IOD) and the El Niño-Southern Oscillation (ENSO) on Indonesian rainfall. The analysis using the EOF method revealed two main modes with variances of 35.23% and 13.07%, respectively. Moreover, the results indicated that rainfall in Indonesia is highly sensitive to sea surface temperatures (SST) in the southeastern tropical Indian Ocean and the central Pacific Ocean (Niño3.4 and Niño3 areas), suggesting that changes in SST could significantly alter rainfall patterns in the region. This research is useful for informing government policies related to anticipating changes in rainfall variability as part of Indonesia’s preparedness for hydrometeorological disasters. Full article
(This article belongs to the Special Issue Recent Advances in Research on Ocean Climate Variability (2nd Edition))
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15 pages, 7430 KiB  
Article
Phase-Locking of El Niño and La Niña Events in CMIP6 Models
by Yu Yan and De-Zheng Sun
Atmosphere 2024, 15(8), 882; https://doi.org/10.3390/atmos15080882 - 24 Jul 2024
Cited by 1 | Viewed by 964
Abstract
El Niño–Southern Oscillation (ENSO) usually peaks in the boreal winter—November to January of the following year. This particular feature of ENSO is known as the seasonal phase locking of ENSO. In this study, based on 34 climate models from the Coupled Model Intercomparison [...] Read more.
El Niño–Southern Oscillation (ENSO) usually peaks in the boreal winter—November to January of the following year. This particular feature of ENSO is known as the seasonal phase locking of ENSO. In this study, based on 34 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the seasonal phase-locking characteristics of the model-simulated El Niño and La Niña events are evaluated in terms of the evolution of the SST anomalies associated with ENSO and the probability distribution of the peak month—the time at which ENSO peaks. It is found that CMIP6 models underestimate the phase-locking strength of ENSO for both El Niño and La Niña events. The ensemble mean peak month matches the observations, but the inter-model spread is large. The models simulate the phase locking of El Nino events better than that of La Niña events, and the large simulation bias of CMIP6 for La Niña phase-locking in the models may have an impact on the simulation of seasonal phase-locking in the ENSO. Full article
(This article belongs to the Special Issue Recent Advances in Research on Ocean Climate Variability (2nd Edition))
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20 pages, 9286 KiB  
Article
Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data
by Bowen Xie, Jifeng Qi, Shuguo Yang, Guimin Sun, Zhongkun Feng, Baoshu Yin and Wenwu Wang
Atmosphere 2024, 15(1), 86; https://doi.org/10.3390/atmos15010086 - 9 Jan 2024
Cited by 3 | Viewed by 2340
Abstract
Accurate sea surface temperature (SST) prediction is vital for disaster prevention, ocean circulation, and climate change. Traditional SST prediction methods, predominantly reliant on time-intensive numerical models, face challenges in terms of speed and efficiency. In this study, we developed a novel deep learning [...] Read more.
Accurate sea surface temperature (SST) prediction is vital for disaster prevention, ocean circulation, and climate change. Traditional SST prediction methods, predominantly reliant on time-intensive numerical models, face challenges in terms of speed and efficiency. In this study, we developed a novel deep learning approach using a 3D U-Net structure with multi-source data to forecast SST in the South China Sea (SCS). SST, sea surface height anomaly (SSHA), and sea surface wind (SSW) were used as input variables. Compared with the convolutional long short-term memory (ConvLSTM) model, the 3D U-Net model achieved more accurate predictions at all lead times (from 1 to 30 days) and performed better in different seasons. Spatially, the 3D U-Net model’s SST predictions exhibited low errors (RMSE < 0.5 °C) and high correlation (R > 0.9) across most of the SCS. The spatially averaged time series of SST, both predicted by the 3D U-Net and observed in 2021, showed remarkable consistency. A noteworthy application of the 3D U-Net model in this research was the successful detection of marine heat wave (MHW) events in the SCS in 2021. The model accurately captured the occurrence frequency, total duration, average duration, and average cumulative intensity of MHW events, aligning closely with the observed data. Sensitive experiments showed that SSHA and SSW have significant impacts on the prediction of the 3D U-Net model, which can improve the accuracy and play different roles in different forecast periods. The combination of the 3D U-Net model with multi-source sea surface variables, not only rapidly predicted SST in the SCS but also presented a novel method for forecasting MHW events, highlighting its significant potential and advantages. Full article
(This article belongs to the Special Issue Recent Advances in Research on Ocean Climate Variability (2nd Edition))
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