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Atmosphere
Special Issues
Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability
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Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability

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

Deadline for manuscript submissions: 28 June 2024 | Viewed by 3921

Special Issue Editors

Dr. Wei Zhang

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Guest Editor
University of Miami and NOAA Global Systems Laboratory, Miami, FL 33149, USA
Interests: climate Variability and prediction; extreme weather and climate; air-sea interaction; machine learning
Dr. Duo Chan

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Guest Editor
Woods Hole Oceanographic Institution, Falmouth, MA 02543, USA
Interests: climate data and reconstruction; climate dynamics; air–sea coupling; land–air coupling; machine learning
Dr. Jie Feng

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Guest Editor
Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
Interests: atmospheric predictability; data assimilation; ensemble forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Yulong Yao

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Guest Editor
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanography, Chinese Academy of Sciences, Guangzhou 510301, China
Interests: extreme climate; ocean’s role in climate; land-sea interaction

Special Issue Information

Dear Colleagues,

Air–sea interaction is an active area of research that is crucial for reducing uncertainties in weather and climate predictions.  Exchanges of momentum, heat, and mass across the marine boundary layer involve a variety of dynamic, thermodynamic, and biogeochemical processes, and hence play an important role in the variability and predictability of weather and climate. Recent studies have shown advances in many respects, including, but not limited to:

(1) Improving air–sea coupling and exchange observations;
(2) Refining the representation of relevant processes in coupled climate models;
(3) Developing statistical representations using data-driven/ machine learning techniques;
(4) Understanding relevant physical processes from the submesoscale to mesoscale to synoptic scales and, further, to large-scale modes of climate variability;
(5) Addressing air–sea interaction in the context of climate change predictions at global and regional scales.

We hope to follow along these lines in this Special Issue. Therefore, we are inviting contributions covering the following topics: 

  • Air–sea interaction at the submeso, meso, and synoptic scales from the tropics to high latitudes;
  • Recent advances in the observation and modeling of air–sea coupling and exchange;
  • Large-scale modes of climate variability, such as ENSO, IOD, PDO, NAO, and AMO, and teleconnections;
  • High-resolution modeling of marine boundary layer processes;
  • Global and regional estimates of air–sea fluxes, including, but not limited to: heat, moisture, and momentum;
  • The influence of air–sea coupling on climate variability and predictability, including extreme weather and climate events;
  • Noval techniques involving air–sea interaction and coupling, including data-driven and machine learning approaches;
  • Other topics on air–sea interaction, climate dynamics, and predictability.

Dr. Wei Zhang
Dr. Duo Chan
Dr. Jie Feng
Dr. Yulong Yao
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

  • air–sea interactions
  • submesoscale and mesoscale processes
  • climate dynamics and modeling
  • climate variability and predictability
  • extreme weather and climate
  • large-scale climate and teleconnections
  • observations and coupled modeling
  • high-resolution modeling
  • machine learning methods

Published Papers (3 papers)

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Research

15 pages, 5518 KiB  
Article
Ross–Weddell Dipole Critical for Antarctic Sea Ice Predictability in MPI–ESM–HR
by Davide Zanchettin, Kameswarrao Modali, Wolfgang A. Müller and Angelo Rubino
Atmosphere 2024, 15(3), 295; https://doi.org/10.3390/atmos15030295 - 28 Feb 2024
Viewed by 596
Abstract
We use hindcasts from a state-of-the-art decadal climate prediction system initialized between 1979 and 2017 to explore the predictability of the Antarctic dipole—that is, the seesaw between sea ice cover in the Weddell and Ross Seas, and discuss its implications for Antarctic sea [...] Read more.
We use hindcasts from a state-of-the-art decadal climate prediction system initialized between 1979 and 2017 to explore the predictability of the Antarctic dipole—that is, the seesaw between sea ice cover in the Weddell and Ross Seas, and discuss its implications for Antarctic sea ice predictability. Our results indicate low forecast skills for the Antarctic dipole in the first hindcast year, with a strong relaxation of March values toward the climatology contrasting with an overestimation of anomalies in September, which we interpret as being linked to a predominance of local drift processes over initialized large-scale dynamics. Forecast skills for the Antarctic dipole and total Antarctic sea ice extent are uncorrelated. Limited predictability of the Antarctic dipole is also found under preconditioning around strong warm and strong cold events of the El Niño-Southern Oscillation. Initialization timing and model drift are reported as potential explanations for the poor predictive skills identified. Full article
(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability)
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20 pages, 17629 KiB  
Article
Mesoscale Convective Systems and Extreme Precipitation on the West African Coast Linked to Ocean–Atmosphere Conditions during the Monsoon Period in the Gulf of Guinea
by Sandrine Djakouré, Joël Amouin, Kouassi Yves Kouadio and Modeste Kacou
Atmosphere 2024, 15(2), 194; https://doi.org/10.3390/atmos15020194 - 02 Feb 2024
Viewed by 625
Abstract
This study investigates the importance of convective systems for extreme rainfall along the northern coast of the Gulf of Guinea (GG) and their relationship with atmospheric and oceanic conditions. Convective system data (MCSs), daily precipitation, sea surface temperature (SST) and moisture flux anomalies [...] Read more.
This study investigates the importance of convective systems for extreme rainfall along the northern coast of the Gulf of Guinea (GG) and their relationship with atmospheric and oceanic conditions. Convective system data (MCSs), daily precipitation, sea surface temperature (SST) and moisture flux anomalies from June to September 2007–2016 are used. The results show that 2/3 of MCSs crossing Abidjan are produced in June, which is the core of the major rainy season. Likewise, 2/3 of MCSs originate from continental areas, while 1/3 come from the ocean. Oceanic MCSs are mostly initiated close to the coast, which also corresponds to the Marine Heat Waves region. Continental MCSs are mostly initiated inland. The results also highlight the moisture flux contribution of three zones which have an impact on the onset and the sustaining of MCSs: (i) the seasonal migration of the intertropical convergence zone (ITCZ), (ii) the GG across the northern coastline, and finally (iii) the continent. These contributions of moisture fluxes coincide with oceanic warming off Northeast Brazil and the northern coast of the GG both two days before and the day of extreme rainfall events. The ocean contributes to moisten the atmosphere, and therefore to supply and sustain the MCSs during their lifecycle. Full article
(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability)
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26 pages, 10597 KiB  
Article
NAO Seasonal Forecast Using a Multivariate Air–Sea Coupled Deep Learning Model Combined with Causal Discovery
by Bin Mu, Xin Jiang, Shijin Yuan, Yuehan Cui and Bo Qin
Atmosphere 2023, 14(5), 792; https://doi.org/10.3390/atmos14050792 - 26 Apr 2023
Cited by 2 | Viewed by 1735
Abstract
The North Atlantic Oscillation (NAO) is a major climatic phenomenon in the Northern Hemisphere, but the underlying air–sea interaction and physical mechanisms remain elusive. Despite successful short-term forecasts using physics-based numerical models, longer-term forecasts of NAO continue to pose a challenge. In this [...] Read more.
The North Atlantic Oscillation (NAO) is a major climatic phenomenon in the Northern Hemisphere, but the underlying air–sea interaction and physical mechanisms remain elusive. Despite successful short-term forecasts using physics-based numerical models, longer-term forecasts of NAO continue to pose a challenge. In this study, we employ advanced data-driven causal discovery techniques to explore the causality between multiple ocean–atmosphere processes and NAO. We identify the best NAO predictors based on this causality analysis and develop NAO-MCD, a multivariate air–sea coupled model that incorporates causal discovery to provide 1–6 month lead seasonal forecasts of NAO. Our results demonstrate that the selected predictors are strongly associated with NAO development, enabling accurate forecasts of NAO. NAO-MCD significantly outperforms conventional numerical models and provides reliable seasonal forecasts of NAO, particularly for winter events. Moreover, our model extends the range of accurate forecasts, surpassing state-of-the-art performance at 2- to 6-month lead-time NAO forecasts, substantially outperforming conventional numerical models. Full article
(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability)
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