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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13730

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Special Issue Editors

Dr. Jifeng Qi

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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
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Dr. Lei Liu

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College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China
Interests: multi-satellite altimeter mapping; ocean dynamic topography; typhoon-ocean interaction; typhoon numerical simulation
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Dr. Yinghao Qin

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National Marine Environmental Forecasting Center, Beijing 100081, China
Interests: ocean forecasting; data assimilation; ocean dynamics; ENSO
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Dr. Fengxiang Guo

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Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao 266100, China
Interests: ocean observation; observing system evaluation; ocean thermohaline structure

Special Issue Information

Dear Colleagues,

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 researches of ocean climate variability. We invite all interested researchers to show their 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-decades to centuries), such as the El Niño/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM) and North Atlantic Oscillation (NAO), etc. Theoretical, observational, modelling as well as 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. Lei Liu
Dr. Yinghao Qin
Dr. Fengxiang Guo
Guest Editors

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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 (7 papers)

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Research

17 pages, 2249 KiB

Open AccessArticle

Seasonal Water Mass Transformation in the Eastern Indian Ocean from In Situ Observations

by Noir P. Purba, Mohd Fadzil Akhir, Widodo S. Pranowo, Subiyanto and Zuraini Zainol

Atmosphere 2024, 15(1), 1; https://doi.org/10.3390/atmos15010001 - 19 Dec 2023

Cited by 1 | Viewed by 1592

Abstract

The Eastern Indian Ocean (EIO) is one of the eastern boundary areas, which control currents circulation and atmospheric dynamics. This research mainly aimed to identify and analyze the water mass transformation in the EIO. The investigated physical properties of the ocean are the temperature, salinity, seasonal temperature–salinity, and water column stability. An extensive amount of in situ data measurements from 1950 to 2018 was downloaded from the global datasets inventory. The visualization and analysis of the data were defined in monthly spatial and vertical profiles. The result showed the mixed layer is shallower during the northwest monsoon relative to the southwest monsoon. The surface water in the EIO is documented to be warmer due to the interaction with the atmosphere. Furthermore, low-salinity surface water around the Java Seas area is caused by a mixing with fresh water from the eastern Indonesia rivers. The data also confirmed that, at latitude 16° S, the maximum salinity occurred at a depth between 150 and 350 m. There are ten types of water masses found in the EIO, which originate from several regions, including the Indonesia Seas, Pacific Ocean, Indian Ocean, and Antarctic. During the northwest and southeast monsoons, a stable layer is found at a depth of 40 to 150 m and 80 to 150 m, respectively. For further research, it is recommended to focus on the coastal region, particularly the Timor Sea and Northwestern Australia, to investigate the dynamics between the Indonesian Throughflow, Holloway Currents, and Leeuwin Currents. Additionally, deep water observations below 800 m are crucial for a comprehensive understanding of the oceanographic variability in the deep layers of the EIO. Full article

(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)

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16 pages, 6978 KiB

Open AccessArticle

Analysis of Marine Heatwaves in China’s Coastal Seas and Adjacent Offshore Waters

by Zhijie Li, Liying Wan, Yang Liu, Zhaoyi Wang and Lunyu Wu

Atmosphere 2023, 14(12), 1738; https://doi.org/10.3390/atmos14121738 - 25 Nov 2023

Cited by 2 | Viewed by 1468

Abstract

Marine heatwaves (MHWs) are changing global ecosystems and bearing profound socio-economic impacts, yet our understanding of the spatial features, temporal evolution characteristics, and regional differences in China’s marginal seas remains insufficient. In this study, the spatio-temporal variation characteristics of the frequency, mean intensity, maximum intensity, cumulative intensity, duration and total days of MHWs are systematically analyzed based on daily sea surface temperature data from Operational Sea Surface Temperature and Ice Analysis (OSTIA) for the period of 1983–2020. The results show the following: The annual mean frequency of MHWs in China’s coastal seas is 1.4–4.6 counts per year and increases gradually from north to south. The annual mean of mean intensity, maximum intensity and cumulative intensity are, respectively, in the ranges of 0.3–2.9 °C, 0.4–3.7 °C and 3.9–41.7 °C days, all of which show a significant decreasing trend from north to south. The annual mean of duration and total days of MHWs are in the respective ranges 8.7–19.7 and 18.9–69.1 days. The annual mean of frequency, cumulative intensity, duration and total days all show a clear increasing trend, with respective linear increases of 1.03 counts, 3.57 °C days, and 1.98 and 17.58 days per decade. The annual means of the mean intensity and maximum intensity have no obvious upward trend, with the exceptions of the Bohai and Yellow Seas. Finally, MHWs were divided into the four categories of moderate, strong, severe and extreme, with the results showing that moderate ones account for more than 70% of all heatwave events, while strong, severe and extreme ones, respectively, account for about 25%, 2% and 0.02%. Full article

(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)

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17 pages, 11580 KiB

Open AccessArticle

Heat Budget Analysis for the Extended Development of the 2014–2015 Warming Event

by Yinghao Qin, Huier Mo, Liying Wan, Yi Wang, Yang Liu, Qinglong Yu and Xiangyu Wu

Atmosphere 2023, 14(6), 954; https://doi.org/10.3390/atmos14060954 - 30 May 2023

Viewed by 1629

Abstract

In order to figure out the associated underlying dynamical processes of the 2014–2015 warming event, we used the ECCO (Estimating the Circulation and Climate of the Ocean) reanalysis from 1993 to 2016 and two combined scatterometers, QuikSCAT and ASCAT, to analysis hydrodynamic condition and ocean heat budget balance process in the equatorial tropical pacific. The spatiotemporal characteristics of that warming event were revealed by comparing the results with a composite El Niño. The results showed that the significant differences between the 2014 and 2015 warming periods were the magnitudes and positions of the equatorial easterly wind anomalies during the summer months. The abruptly easterly wind anomalies of 2014 that spread across the entire equatorial Pacific triggered the upwelling of the equatorial Kelvin waves and pushed the eastern edge of the warm pool back westward. These combined effects caused abrupt decreases in the sea surface temperatures (SST) and upper ocean heat content (OHC) and damped the 2014 warming process into an El Niño. In addition, the ocean budget of the upper 300 m of the El Niño 3.4 region showed that different dynamical processes were responsible for different warming phases. For example, at the beginning of 2014 and 2015, the U advection and subsurface processes played dominant roles in the positive ocean heat content tendency. During the easterly wind anomalies period of 2014, the U advection process mainly caused a negative tendency and halted the development of the warming phase. In regard to the easterly wind anomalies of 2015, the U advection and subsurface processes were weaker negatively when compared with that in 2014. However, the V advection processes were consistently positive, taking a leading role in the positive trends observed in the middle of 2015. Full article

(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)

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15 pages, 2006 KiB

Open AccessArticle

The Impacts of the Application of the Ensemble Optimal Interpolation Method in Global Ocean Wave Data Assimilation

by Mengmeng Wu, Hui Wang, Liying Wan, Juanjuan Wang, Yi Wang and Jiuke Wang

Atmosphere 2023, 14(5), 818; https://doi.org/10.3390/atmos14050818 - 30 Apr 2023

Cited by 2 | Viewed by 1705

Abstract

The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China’s offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China’s ocean satellite data in ocean wave data assimilation and prediction. Full article

(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)

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16 pages, 4123 KiB

Open AccessArticle

Assessment of the Sea Surface Salinity Simulation and Projection Surrounding the Asian Waters in the CMIP6 Models

by Shanshan Jin, Haidong Pan and Tengfei Xu

Atmosphere 2023, 14(4), 726; https://doi.org/10.3390/atmos14040726 - 17 Apr 2023

Cited by 5 | Viewed by 1936

Abstract

Sea surface salinity (SSS) is a crucial indicator that is used to monitor the hydrological cycle in the ocean system. In this study, we evaluated the simulation skill of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in reproducing the SSS in the Asian Marginal Seas (AMSs). The results show that the AMSs’ SSS simulated by most CMIP6 models is generally in good agreement with the observations in terms of spatial patterns and seasonal variability. However, these models tend to overestimate the SSS in the Eastern Arabian Sea and the Bay of Bengal by up to 1.3 psu, while they underestimate the SSS in the Bohai Sea, the Yellow Sea, the Southern South China Sea, and the Indonesian Seas, with the bias exceeding −1.5 psu. Additionally, the seasonal variations in the Sea of Okhotsk, the Bay of Bengal, and the Arabian Sea exhibit large biases with phase shift or reversal in some CMIP6 models. Notably, the observed magnitudes in the AMSs are significantly higher than the global average of 0.2 psu, ranging from 0.22 to 1.19 psu. Furthermore, we calculated the projected trends in sea surface salinity under different future scenarios by using the CMIP6 models. The results reveal relatively larger SSS freshening trends in the second half of the 21st century compared to the first half. Specifically, the freshening trends for the Shared Socio-Economic Pathway (SSP) of low- (global radiative forcing of 2.6 W/m² by the year 2100), medium- (global radiative forcing of 4.5 W/m² by 2100), and high-end (8.5 W/m² by 2100) pathways are 0.05–0.21, 0.12–0.39, and 0.28–0.78 psu/century, respectively. The most rapid freshening trends of SSS are observed in the East China Seas and the Indonesian Seas, which are over two times greater than the global mean. On the other hand, the SSS freshening trends in the Arabian Sea are slightly lower than the global mean SSS freshening trend. Full article

(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)

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17 pages, 6556 KiB

Open AccessArticle

The Atlantic Meridional Mode and Associated Wind–SST Relationship in the CMIP6 Models

by Fannyu Xia, Jinqing Zuo, Chenghu Sun and Ao Liu

Atmosphere 2023, 14(2), 359; https://doi.org/10.3390/atmos14020359 - 11 Feb 2023

Cited by 5 | Viewed by 2574

Abstract

The Atlantic Meridional Mode (AMM) is the dominant mode of interannual climate variability in the tropical Atlantic, maintained primarily by the positive wind–evaporation–sea surface temperature (SST) feedback in which the wind anomalies lead the SST anomalies by ~2 months. A previous study revealed that climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) show poor performance in simulating the AMM-related wind–SST relationship, but the possible causes remain unclear. This study assesses the representation of the AMM and associated wind–SST relationship in the climate models from CMIP6. Results show that most of the CMIP6 models can reasonably reproduce the observed spatial pattern of the AMM, with significant SST and wind anomalies in the northern tropical Atlantic and weak anomalies in the equatorial–southern oceans. However, the simulated wind–SST relationship associated with the AMM varies among the models. In particular, several models fail to capture the observed wind–SST relationship; that is, the simulated wind anomalies peak in boreal spring as in the observations, but no obvious peak occurs in the corresponding SST anomalies. Further analysis suggests the models that fail to capture the observed wind–SST relationship tend to simulate a stronger mean trade wind and a thicker mixed layer in the northern tropical Atlantic, leading to a weaker ocean–atmosphere coupling and, thus, a weaker SST response to the wind forcing. Moreover, there exists a significant out-of-phase relationship between the strength of ocean–atmosphere coupling and mean mixed layer depth among the models, supporting the impact of mean state biases on the AMM variability in the models. Full article

(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)

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22 pages, 5796 KiB

Open AccessArticle

Numerical Study on Storm Surge Level Including Astronomical Tide Effect Using Data Assimilation Method

by Junli Xu, Kai Ma, Yuling Nie, Chuanyu Liu, Xin Bi, Wenqi Shi and Xianqing Lv

Atmosphere 2023, 14(1), 38; https://doi.org/10.3390/atmos14010038 - 25 Dec 2022

Cited by 2 | Viewed by 1725

Abstract

In the storm surge model, the wind drag coefficient C_d is a critical parameter that has a great influence on the forecast of the storm surge level. In the present study, the effect of various wind drag coefficient parameterizations on the storm surge level is investigated in the Bohai Sea, Yellow Sea and East China Sea for Typhoons 7203 and 7303. Firstly, the impacts of initial values of a and b in the linear expression C_d = (a + b × U₁₀) × 10⁻³ on the pure storm surge model are evaluated based on the data assimilation method. Results indicate that when a and b (i.e., the wind drag coefficients given by Smith, Wu, Geernaert et al. and Mel et al.) are non-zeros, the performance of the model has little difference, and the result from Wu is slightly better. However, they are superior to the performance of the model adopting zero initial values. Then, we discuss the influences of diverse ways of calculating wind drag coefficients, which are inverted by data assimilation method (including both linear and constant C_d) and given in the form of linear formulas, on simulating pure storm surge level. They show that the data assimilation-based coefficients greatly exceed those of the ordinary coefficient formulas. Moreover, the wind drag coefficient in the linear form is a little better than that in constant form when the data assimilation method is used. Finally, the assessment of the impact of astronomical tides on the storm surge level is conducted, and the simulation demonstrates that the storm surge model, which has the combination of four constituents (M₂, S₂, K₁, O₁) and wind drag coefficient inverted by the data assimilation method with the linear C_d, exhibits the best performance. Full article

(This article belongs to the Special Issue Recent Advances in Researches of Ocean Climate Variability)

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