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Atmosphere
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New Approaches to Complex Climate Systems
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New Approaches to Complex Climate Systems

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

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 20132

Special Issue Editors

Dr. Jingfang Fan

E-Mail Website
Guest Editor
1. School of Systems Science, Beijing Normal University, Beijing 100875, China
2. Earth System Analysis, Potsdam Institute for Climate Impact Research, 14412 Potsdam, Germany
Interests: climate change; climate networks; statistical physics; complexity science; extreme climate events; data analysis
Dr. Sridhara Nayak

E-Mail Website
Guest Editor
Severe Storm Research Section, Disaster Prevention Research Institute, Kyoto University, Kyoto 611 0011, Japan
Interests: climate modeling; extreme events; dynamical downscaling; land use and land cover change; numerical weather prediction; statistical methods applications; Remote Sensing applications and GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Meng

E-Mail Website
Guest Editor
School of Sciences, Beijing University of Posts and Telecommunications, Beijing, China
Interests: system science; complex science; climate science; statistical physics; theoretical physics

Special Issue Information

Dear Colleagues,

Climate change has the potential to threaten our society through impacts on health, economics, conflict, migration, and demographics. As a result of the strong complexity and nonlinearity of the Earth system, the understanding and, in particular, the forecasting of such events represent formidable challenges for the scientific community. Sophisticated modern techniques, from machine learning and network theory to nonlinear data analysis, can provide crucial predictive power for mitigating the global-warming crisis and other socio-climatic challenges. This is currently a hot research topic in all branches of science and technology. This is a timely Special Issue in which outstanding scientists from around the world and from all related areas can gather together and exchange ideas and concerns from different aspects.

Dr. Jingfang Fan
Dr. Sridhara Nayak
Dr. Jun Meng
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.

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Keywords

  • Climate networks 
  • Complex Earth systems 
  • Critical phenomena 
  • Nonlinear data analysis 
  • Machine learning 
  • Tipping elements 
  • Climate resilience

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Published Papers (6 papers)

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Research

10 pages, 3400 KiB  
Communication
Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change
by Haiming Kuai, Ping Yu, Wenqi Liu, Yongwen Zhang and Jingfang Fan
Atmosphere 2023, 14(4), 692; https://doi.org/10.3390/atmos14040692 - 7 Apr 2023
Cited by 1 | Viewed by 2349
Abstract
Anthropogenic climate change poses a significant threat to both natural and social systems worldwide. In this study, we aim to identify regions most impacted by climate change using the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR) reanalysis [...] Read more.
Anthropogenic climate change poses a significant threat to both natural and social systems worldwide. In this study, we aim to identify regions most impacted by climate change using the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR) reanalysis of near-surface daily air temperature data spanning 73 years (1948–2020). We develop a novel climate network framework to identify “hot spots”, regions that exhibit significant impact or impacted characteristics. Specifically, we use the node degree, a fundamental feature of the network, to measure the influence of each region and analyze its trend over time using the Mann–Kendall test. Our findings reveal that the majority of land areas experiencing increasing degrees are more closely connected to other regions, while the ocean shows the opposite trend due to weakened oceanic circulations. In particular, the degree in the central Pacific Ocean’s El Niño region is significantly reduced. Notably, we identify three “hot spots” in East Asia, South America, and North Africa, respectively, with intensive increasing network degree fields. Additionally, we find that the hot spot in East Asia is teleconnected to remote regions, such as the South Pacific, Siberia, and North America, with stronger teleconnections in recent years. This provides a new perspective for assessing the planetary impacts of anthropogenic global warming. By using a novel climate network framework, our study highlights regions that are most vulnerable to the effects of climate change and emphasizes the importance of understanding network structures to assess the global impacts of anthropogenic climate change. Full article
(This article belongs to the Special Issue New Approaches to Complex Climate Systems)
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17 pages, 9561 KiB  
Article
Arboreal Urban Cooling Is Driven by Leaf Area Index, Leaf Boundary Layer Resistance, and Dry Leaf Mass per Leaf Area: Evidence from a System Dynamics Model
by Harold N. Eyster and Brian Beckage
Atmosphere 2023, 14(3), 552; https://doi.org/10.3390/atmos14030552 - 14 Mar 2023
Cited by 5 | Viewed by 2468
Abstract
Heat waves are becoming more frequent due to climate change. Summer heat waves can be particularly deadly in cities, where temperatures are already inflated by abundant impervious, dark surfaces (i.e., the heat island effect). Urban heat waves might be ameliorated by planting and [...] Read more.
Heat waves are becoming more frequent due to climate change. Summer heat waves can be particularly deadly in cities, where temperatures are already inflated by abundant impervious, dark surfaces (i.e., the heat island effect). Urban heat waves might be ameliorated by planting and maintaining urban forests. Previous observational research has suggested that conifers may be particularly effective in cooling cities. However, the observational nature of these studies has prevented the identification of the direct and indirect mechanisms that drive this differential cooling. Here, we develop a systems dynamics representation of urban forests to model the effects of the percentage cover of either conifers or broadleaf trees on temperature. Our model includes physiological and morphological differences between conifers and broadleaf trees, and physical feedback among temperature and energy fluxes. We apply the model to a case study of Vancouver, BC, Canada. Our model suggests that in temperate rainforest cities, conifers may by 1.0 °C cooler than broadleaf trees; this differential increases to 1.2 °C when percentage tree cover increases from 17% to 22% and to 1.7 °C at 30% cover. Our model suggests that these differences are due to three key tree traits: leaf area index, leaf boundary layer resistance, and dry mass per leaf area. Creating urban forests that optimize these three variables may not only sequester CO2 to mitigate global climate change but also be most effective at locally minimizing deadly urban heat waves. Full article
(This article belongs to the Special Issue New Approaches to Complex Climate Systems)
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12 pages, 2281 KiB  
Article
Network Analysis Measuring the Impact of Volcanic Eruptions
by Yu Sun, Yuelong Zhang, Jun Meng and Jingfang Fan
Atmosphere 2022, 13(11), 1910; https://doi.org/10.3390/atmos13111910 - 16 Nov 2022
Viewed by 2150
Abstract
Volcanoes can be extremely damaging to the environment, human society, and also impact climate change. During volcanic eruption, massive amounts of gases and dust particles are thrown into the atmosphere and propagated instantaneously by the stratospheric circulation, resulting in a huge impact on [...] Read more.
Volcanoes can be extremely damaging to the environment, human society, and also impact climate change. During volcanic eruption, massive amounts of gases and dust particles are thrown into the atmosphere and propagated instantaneously by the stratospheric circulation, resulting in a huge impact on the interactive pattern of the atmosphere. Here, we develop a climate network-based framework to study the temporal evolution of lower stratospheric atmosphere conditions in relation to a volcanic eruption, the Hunga Tonga-Hunga Ha’apai (HTHH) volcano, which erupted on 20 December 2021. Various spatial-temporal topological features of the climate network are introduced to analyze the nature of the HTHH. We show that our framework has the potential to identify the dominant eruption events of the HTHH and reveal the impact of the HTHH eruption. We find that during the eruption periods of the HTHH, the correlation behaviors in the lower stratosphere became much stronger than during normal periods. Both the degree and clustering coefficients increased significantly during the dominant eruption periods, and could be used as indications for the eruption of HTHH. The underlying mechanism for the observed cooperative mode is related to the impact of a volcanic eruption on global mass circulations. The study on the network topology of the atmospheric structure during a volcanic eruption provides a fresh perspective to investigate the impact of volcanic eruptions. It can also reveal how the interactive patterns of the atmosphere respond to volcanic eruptions and improve our understanding regarding the global impacts of volcanic eruptions. Full article
(This article belongs to the Special Issue New Approaches to Complex Climate Systems)
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16 pages, 33483 KiB  
Article
Sensitivity of Land Surface Processes and Its Variation during Contrasting Seasons over India
by Hara Prasad Nayak, Sridhara Nayak, Suman Maity, Nibedita Patra, Kuvar Satya Singh and Soma Dutta
Atmosphere 2022, 13(9), 1382; https://doi.org/10.3390/atmos13091382 - 28 Aug 2022
Cited by 4 | Viewed by 1935
Abstract
The study investigates the influence of near-surface atmospheric parameters on land surface processes at the land–atmosphere interface through the offline simulation of the 2D Noah Land Surface Model-based High-Resolution Land Data Assimilation System (HRLDAS). The HRLDAS is used to conduct sensitive experiments by [...] Read more.
The study investigates the influence of near-surface atmospheric parameters on land surface processes at the land–atmosphere interface through the offline simulation of the 2D Noah Land Surface Model-based High-Resolution Land Data Assimilation System (HRLDAS). The HRLDAS is used to conduct sensitive experiments by introducing perturbation in the atmospheric parameters, and the experiments were conducted for the period 2011–2013 in India. In each sensitive experiment, a single parameter is perturbed at a time, keeping the rest of the forcing parameters unchanged, and the procedure is followed for all the forcing parameters. The results revealed that the downward longwave radiation and T2 are highly sensitive to land surface processes, while wind speed is the least sensitive. The land surface process sensitivity varies with soil moisture content. The annual mean soil moisture at the surface layer is increased (decreased) by 8% when long wave radiation is decreased (increased) by 20%. Similarly, the annual mean soil temperature increased (decreased) by 2.2 °C when T2 increased (decreased) by 1%. The latent heat flux is highly sensitive to longwave radiation over the wetter soil, while its sensitivity to rainfall is higher over the drier soil. This is attributed to evapotranspiration’s sensitivity to the preferred soil moisture state. Further, the land surface sensitivity varies with contrasting seasons. The sensitivity of soil moisture and latent heat flux is high in OND and JJA seasons, respectively, and are least sensitive in the MAM season. In contrast, the sensible heat flux is highly sensitive to solar radiation in the MAM season and comparatively less sensitive in the JJA season. The study suggests that the antecedent soil moisture state plays a critical role in modulating land surface process sensitivity, and, therefore, a realistic soil moisture state is important for land surface feedback processes. Full article
(This article belongs to the Special Issue New Approaches to Complex Climate Systems)
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19 pages, 7985 KiB  
Article
Evaluation of Observed and Future Climate Change Projection for Uttarakhand, India, Using CORDEX-SA
by Neetu Tyagi, Tripti Jayal, Mukesh Singh, Vipan Mandwal, Atul Saini, Nirbhav, Netrananda Sahu and Sridhara Nayak
Atmosphere 2022, 13(6), 947; https://doi.org/10.3390/atmos13060947 - 10 Jun 2022
Cited by 9 | Viewed by 4681
Abstract
The climate change scenarios, especially global warming, have raised significant concerns, and the Himalayan regions such as Uttarakhand are highly vulnerable to such climatic shifts. Considering 10 Coordinated Regional Climate Downscaling Experiments in South Asia (CORDEX-SA), experiments with 3 regional climate models (RCMs), [...] Read more.
The climate change scenarios, especially global warming, have raised significant concerns, and the Himalayan regions such as Uttarakhand are highly vulnerable to such climatic shifts. Considering 10 Coordinated Regional Climate Downscaling Experiments in South Asia (CORDEX-SA), experiments with 3 regional climate models (RCMs), driven by 13 global climate models, historical estimates and future projections are analyzed from the mid-century (MC) i.e., from 2021–2050 to the end of the century (EC) i.e., from 2070–2099 to characterize annual and seasonal variations in precipitation and temperature. The analysis shows a decrease in the annual average precipitation by 5.92% at MC and an increase of 5.97% at EC for the Representative Climate Pathway (RCP) 4.5, while precipitation may likely increase from 2.83% to 15.89% towards MC and EC in the RCP 8.5. The maximum temperature may likely increase from 0.42 °C to 3.07 °C from MC to EC in the RCP 4.5 and from 0.83 °C to 5.49 °C in the RCP 8.5. In addition, the minimum temperature may increase from 0.80 °C to 3.25 °C from MC to EC in the RCP 4.5 and from 0.30 °C to 5.86 °C from MC to EC in the RCP 8.5. Notably, a decrease in the pre-monsoon precipitation at EC and a higher increase in the maximum temperature during the monsoon season are observed. An increase in the maximum temperature along with precipitation may lead to an increase in the frequency of the monsoon season’s extreme rainfall events. Full article
(This article belongs to the Special Issue New Approaches to Complex Climate Systems)
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13 pages, 5659 KiB  
Article
Complex Networks Reveal Teleconnections between the Global SST and Rainfall in Southwest China
by Panjie Qiao, Wenqi Liu, Yongwen Zhang and Zhiqiang Gong
Atmosphere 2021, 12(1), 101; https://doi.org/10.3390/atmos12010101 - 12 Jan 2021
Cited by 5 | Viewed by 3045
Abstract
Droughts and floods have frequently occurred in Southwest China (SWC) during the past several decades. Yet, the understanding of the mechanism of precipitation in SWC is still a challenge, since the East Asian monsoon and Indian monsoon potentially influence the rainfall in this [...] Read more.
Droughts and floods have frequently occurred in Southwest China (SWC) during the past several decades. Yet, the understanding of the mechanism of precipitation in SWC is still a challenge, since the East Asian monsoon and Indian monsoon potentially influence the rainfall in this region. Thus, the prediction of precipitation in SWC has become a difficult and critical topic in climatology. We develop a novel multi-variable network-based method to delineate the relations between the global sea surface temperature anomalies (SSTA) and the precipitation anomalies (PA) in SWC. Our results show that the out-degree patterns in the Pacific, Atlantic and Indian Ocean significantly influence the PA in SWC. In particular, we find that such patterns dominated by extreme precipitation change with the seasons. Furthermore, we uncover that the teleconnections between the global SSTA and rainfall can be described by the in-degree patterns, which dominated by several vital nodes within SWC. Based on the characteristics of these nodes, we find that the key SSTA areas affect the pattern of the nodes in SWC with some specific time delays that could be helpful to improve the long-term prediction of precipitation in SWC. Full article
(This article belongs to the Special Issue New Approaches to Complex Climate Systems)
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