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Atmosphere
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Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change
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Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 7766

Special Issue Editors

Prof. Dr. Biyun Guo

E-Mail Website
Guest Editor
School of Marine Science and Technology, Zhejiang Ocean University, Zhoushan 316022, China
Interests: estuarine dynamics; coastal and estuarine circulations; sediment transport; marine remote sensing; environment remote sensing; watershed hydrological processes; riverbed evolution; geographic information system
Prof. Dr. Chunyan Li

E-Mail Website
Guest Editor
Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: physical oceanography; transport processes; sediment transport; flushing of bays; coastal and estuarine circulations; innovative observations; modeling of coastal ocean processes; weather induced oceanographic and estuarine response and impact to the coast; storm surges; cold front induced oceanic and coastal processes; arctic estuarine dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ocean, land and atmosphere are important components of the climate system, and the ocean–atmosphere–land interactions act as integrated driving forces behind climate change. Due to the complexity of the interactions, the mechanisms are yet to be untangled. The skills and predictabilities of climate models are still limited due to a lack of understanding of the mechanisms of these interactions. Therefore, in the context of global climate change, studying the mechanisms of ocean–atmosphere–land interactions is key to understanding climate anomalies, properly responding to global climate change, improving climate predictions, and enabling for disaster prevention and mitigation.    

This Special Issue invites contributions describing ocean–atmosphere–land interactions and their response to climate change. Of special interest are the processes of ocean–atmosphere dynamics and numerical simulation methods, extreme weather events caused by climate change, relevant mechanisms, and the response of the marine environment to climate change. The subjects can also include the coupling mechanisms between land surface hydrology and climate (including the impacts of climate change on hydrology and water resources; river geomorphological processes in response to global climate changes; and changes in river runoff, water and sediment under the influence of climate change). Observations, analyses, and numerical experiments and predictions of the ocean, atmosphere, and land surface processes (hydrology, soil, ecology, etc.) are also welcome.

Prof. Dr. Biyun Guo
Prof. Dr. Chunyan Li
Guest Editors

Manuscript Submission Information

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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 change
  • ocean–atmosphere–land interactions
  • extreme weather
  • ecosystem
  • hydrology
  • river runoff
  • marine environment
  • geomorphic process

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Published Papers (6 papers)

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Research

21 pages, 7948 KiB  
Article
Response of Sea Surface Temperature and Chlorophyll-a to Typhoon Lekima (2019)
by Yaowei Shi, Biyun Guo, Yuqian Niu, Venkata Subrahmanyam Mantravadi, Jushang Wang, Zhaokang Ji, Yingliang Che and Menglu Ye
Atmosphere 2024, 15(8), 919; https://doi.org/10.3390/atmos15080919 - 31 Jul 2024
Viewed by 739
Abstract
Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) [...] Read more.
Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) concentration variation. SST is also an important factor affecting marine fishery resources. Chl-a is closely related to the marine ecosystem and primary productivity. In this study, we analyzed the response of SST and Chl-a to Typhoon Lekima (2019) process. The result indicates that the response of temperature to typhoon decreases from the center to the outer edge, which has a good correlation with the location, path and influence area of the typhoon center. The mean SST in the study area (14°~40° N, 116°~136° E) decreased during the typhoon’s passage, from 28.97 °C at the beginning (5 August) to 28.22 °C (15 August). The concentration of Chl-a was high in the northwest and coastal areas; its mean value in the study area decreased from 2 to 8 August (on 2 and 8 August, the concentration was 0.484 mg/m3 and 0.405 mg/m3, respectively). From 8 to 14 August, Chl-a decreased with the increase in SST, and 10 and 14 August were the two peak values of Chl-a (while SST was low). Chl-a concentration increased after the typhoon’s landfall (from 15 to 31 August); the Chl-a trend was the same as that of SST. The stronger the typhoon and the longer the residence time, the greater the contribution to the increase in Chl-a concentration at sea surface. Typhoon-induced rainfall over the ocean surface, increased evaporation of seawater, enhanced mixing within the mixed layer and upwelling of the pycnocline resulted in an increase in Chl-a quantity. This study describes the spatial response of the upper ocean to typhoons. It provides a general method for the comprehensive assessment of typhoons in marginal seas and upper open oceans, which has wide applicability and good scientific application prospects. Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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19 pages, 8687 KiB  
Article
Contribution of Atmospheric Factors in Predicting Sea Surface Temperature in the East China Sea Using the Random Forest and SA-ConvLSTM Model
by Qiyan Ji, Xiaoyan Jia, Lifang Jiang, Minghong Xie, Ziyin Meng, Yuting Wang and Xiayan Lin
Atmosphere 2024, 15(6), 670; https://doi.org/10.3390/atmos15060670 - 31 May 2024
Viewed by 865
Abstract
Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be [...] Read more.
Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be tested. This study presents a prediction model for SST in the East China Sea (ECS) using two machine-learning methods: Random Forest and SA-ConvLSTM algorithms. According to the Random Forest feature importance scores and correlation coefficients R, 2 m air temperature and longwave radiation were selected as the two most important key atmospheric factors that can affect the SST prediction performance of machine-learning methods. Four datasets were constructed as input to SA-ConvLSTM: SST-only, SST-T2m, SST-LWR, and SST-T2m-LWR. Using the SST-T2m and SST-LWR, the prediction skill of the model can be improved by about 9.9% and 9.43% for the RMSE and by about 8.97% and 8.21% for the MAE, respectively. Using the SST-T2m-LWR dataset, the model’s prediction skill can be improved by 10.75% for RMSE and 9.06% for MAE. The SA-ConvLSTM can represent the SST in ECS well, but with the highest RMSE and AE in summer. The findings of the presented study requires much more exploration in future studies. Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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14 pages, 26531 KiB  
Article
Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Tibetan Plateau from 1979 to 2018
by Mingwang Li, Qiong Li and Mingxing Xue
Atmosphere 2024, 15(5), 579; https://doi.org/10.3390/atmos15050579 - 9 May 2024
Cited by 2 | Viewed by 1149
Abstract
The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net [...] Read more.
The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C·m2·yr−1) over the past 40 years, with a continuously increasing trend of 2.38 (g C·m2·yr−1). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change. Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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18 pages, 12820 KiB  
Article
Determination of Transport Pathways and Mutual Exchanges of Atmospheric Moisture between Source Regions of Yangtze and Yellow River Basins
by Beiming Kang, Jiahua Wei, Olusola O. Ayantobo and Haijiao Yang
Atmosphere 2024, 15(5), 524; https://doi.org/10.3390/atmos15050524 - 25 Apr 2024
Viewed by 954
Abstract
Knowledge of the quantitative importance of the moisture transport pathways and mutual moisture exchange of the source regions of the Yangtze (SYZR) and Yellow (SYR) rivers’ basins, the adjacent origins of China’s two longest rivers, can provide insights into the regional atmospheric branch [...] Read more.
Knowledge of the quantitative importance of the moisture transport pathways and mutual moisture exchange of the source regions of the Yangtze (SYZR) and Yellow (SYR) rivers’ basins, the adjacent origins of China’s two longest rivers, can provide insights into the regional atmospheric branch of the hydrological cycle over the source regions. The method with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and a Lagrangian moisture source diagnostic to identify the major moisture transport pathways quantifies their importance to two types of daily precipitation events—daily precipitation more than 10 mm (PM) events and daily precipitation less than 10 mm (PL) events—for the two rivers’ regions during the summer (June–August, 1986–2015) and finds the characteristics of mutual moisture exchange. The results indicated that both the Bay of Bengal group pathway and the northwest China group pathway play significant roles in PM and PL events over the SYZR, contributing 41.87% and 39.12% to PM events and 41.33% and 33.16% to PL events, respectively. The SYR has five main moisture path groups; the Bay of Bengal group pathway, the northwest China group pathway, and the southeast China group pathway play significant roles in PM and PL events over the SYR, contributing 32.34%, 23.28%, and 34.36% to PM events and 34.84%, 36.18%, and 19.83% to PL events, respectively. The volume of moisture passing from the SYZR to the SYR is approximately 60 times that of the reverse, constituting about 6.9% of the total moisture released in SYR precipitation. It is worth noting that the moisture release was concentrated in the nearer west group pathway, and the main moisture uptake locations were beyond the source region of the two rivers (remote sources) in the PM events. The aggregate moisture release high-frequency moisture transport path groups are found in the southeastern parts of Zhiduo County and the southeast of Zaduo County. Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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16 pages, 6197 KiB  
Article
Lake Surface Temperature Retrieval Study Based on Landsat 8 Satellite Imagery—A Case Study of Poyang Lake
by Xudong Kong, Yajun Li, Lingli Wang and Huijie Liu
Atmosphere 2024, 15(4), 428; https://doi.org/10.3390/atmos15040428 - 29 Mar 2024
Viewed by 1151
Abstract
Poyang Lake is the largest freshwater lake in China and forms an essential component of the hydrological, nutrient, and carbon cycles, providing various ecosystem services to the local environment. Since changes in Poyang Lake’s water temperature can significantly affect the surrounding environment and [...] Read more.
Poyang Lake is the largest freshwater lake in China and forms an essential component of the hydrological, nutrient, and carbon cycles, providing various ecosystem services to the local environment. Since changes in Poyang Lake’s water temperature can significantly affect the surrounding environment and social development, continuous monitoring of lake temperature changes is required. Traditional water monitoring methods are resource intensive and cannot simultaneously conduct extensive water monitoring. Remote sensing of temperature inversion has the advantages of all-weather, efficient, and large-scale real-time monitoring. Six Landsat 8 images from August to October in 2020 and 2021 were utilized to extract lake surface temperature (LST), and the variations in LST over the two years were analyzed to determine the impact of global climate anomalies on inland lakes. The results indicate that the LST in August and October 2021 was significantly higher than that in the same periods of the previous year, and the temperature difference in October reached 8 °C. In contrast to the overall normal distribution pattern of the water temperature in 2020, 2021 exhibited a relatively concentrated, unimodal distribution pattern. A trend analysis of the driving factors suggests that the LST of Poyang Lake is influenced by the global climate, and the artificial heat sources around the lake clearly alter the distribution characteristics of the LST simultaneously. Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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19 pages, 6732 KiB  
Article
Atmospheric CO2 Isotopic Variations, with Estimation of Ocean and Plant Source Contributions
by Tom Quirk and Michael Asten
Atmosphere 2023, 14(11), 1623; https://doi.org/10.3390/atmos14111623 - 29 Oct 2023
Viewed by 1538
Abstract
This analysis uses both atmospheric CO2 concentrations and the accompanying δ13C isotopic measurements of CO2 over 40 years from 1978 to 2015 observed at ten different latitudes from 90° S to 82 °N. Atmospheric CO2 is separated into [...] Read more.
This analysis uses both atmospheric CO2 concentrations and the accompanying δ13C isotopic measurements of CO2 over 40 years from 1978 to 2015 observed at ten different latitudes from 90° S to 82 °N. Atmospheric CO2 is separated into two components of CO2 attributable to deep ocean and to plant (including fossil fuel) sources. The isotopic values assigned to the two components are δ13C = 0‰ and −26‰, respectively. The latitude variations in residual source component CO2 show the ocean source component peaking at the equator. This contrasts with the residual plant source component that peaks in the Arctic Circle region. Seasonal comparisons show no change in the ocean component peaking at the equator and no significant changes in its variation with latitude, while the plant component shows seasonal changes of the order of 15 ppm at high latitudes. The ocean component shows clear anomalous behavior in the three years following the 1989 Pacific Ocean regime shift (a shift independently identified from the changed biological time series). By contrast, the residual plant component shows a correlation in the timing of maxima in its annual variations with the timing of El Nino events over the time span of 1985–2015. It also shows a discontinuity in annual variation coinciding with the 1995 AMO phase change. We conclude that the ocean and plant components of atmospheric CO2 relate to independent sources of atmospheric CO2 and have approximately equal magnitudes. The observations are consistent with a hypothesis that variations in the ocean components have an origin from upwelling water from deep ocean currents, and variations in plant components are dominated by a combination of fossil fuel CO2, phytoplankton productivity, and forest and peat fires, which primarily occur in the northern hemisphere. Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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