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Atmosphere
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Regional Climate Predictions and Impacts
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Regional Climate Predictions and Impacts

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 1466

Special Issue Editors

Prof. Dr. Jens Hesselbjerg Christensen

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Guest Editor
Climate Physics at the Niels Bohr Institute, Copenhagen University, Nørregade 10, 1165 København, Denmark
Interests: climate phenomena and their role in the climate system; regional climate modeling; regional climate change; arctic teleconnections with lower latitutes; arctic climate change and variability
Special Issues, Collections and Topics in MDPI journals
Dr. Martin Drews

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Guest Editor
Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Interests: climate modelling; atmospheric sciences; mathematical statistics; experimental physics
Special Issues, Collections and Topics in MDPI journals
Dr. Tugba Ozturk

E-Mail Website
Guest Editor
Faculty of Engineering and Natural Sciences, Department of Physics, Isik University, Istanbul, Turkey
Interests: climate change; CMIP; regional climate; extreme climate

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "Regional Climate Predictions and Impacts", explores the role of climate phenomena within the broader climate system. It focuses on employing regional climate modeling and other down-scaling techniques to predict and understand localized climate changes. Special emphasis is placed on investigating regional climate change, with a particular focus on teleconnections and their role in controlling such changes. This research may also delve into the complexities of the role of extreme events under climate change and as a consequence of natural variability, aiming to uncover the broader implications and impacts on society and ecosystems.

Prof. Dr. Jens Hesselbjerg Christensen
Dr. Martin Drews
Dr. Tugba Ozturk
Guest Editor

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

  • regional climate predictions
  • climate phenomena
  • regional climate modeling
  • downscaling techniques
  • localized climate changes
  • teleconnections
  • extreme events
  • climate change impacts
  • natural variability
  • societal impacts
  • ecosystem impacts

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Published Papers (2 papers)

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Research

20 pages, 4691 KiB  
Article
Modeling the Effect of Climate Change on Evapotranspiration in the Thrace Region
by Huzur Deveci and Fatih Konukcu
Atmosphere 2024, 15(10), 1188; https://doi.org/10.3390/atmos15101188 - 3 Oct 2024
Viewed by 399
Abstract
The aim of this study is to determine the effect of climate change on reference evapotranspiration (ETo) and sunflower and wheat evapotranspiration (ETs and ETw, respectively) in the Trakya Region of Türkiye. ETo Calculator (version 3.2) and CROPWAT 8.0 were used to compute [...] Read more.
The aim of this study is to determine the effect of climate change on reference evapotranspiration (ETo) and sunflower and wheat evapotranspiration (ETs and ETw, respectively) in the Trakya Region of Türkiye. ETo Calculator (version 3.2) and CROPWAT 8.0 were used to compute ETo and ET in the reference period (1970–1990), short- (2016–2025), mid- (2046–2055), and long- (2076–2085) terms. Additionally, ETo was tested in 2012 and ETo was simulated for every 1 °C temperature increase up to 5 °C in the reference period. Calculated ETo and ET values for the future were compared with the reference period. For the future, climate data estimated by RegCM3 Regional Climate Model, A2 scenario were used. While the average ETo value of the reference period was 3.3 mm day−1, it was 3.0 mm day−1 in 2012. Compared to the reference period, ETo values change by −3% (3.2 mm day−1), 9% (3.6 mm day−1), and 21% (4.0 mm day−1) in the short-, mid-, and long-term, respectively. The 575 mm ET deficit calculated during the vegetation period of sunflower in the model reference period was forecasted to change by −11% (514 mm), +15% (660 mm), and +25% (721 mm) in the short-, mid-, and long-term, respectively. For wheat, while 59 mm of excess water was calculated in the reference period, it became 193 mm (+227%) in the short-term and a water deficit of 8 mm (−113%) and 6 mm (−110%) in the mid- and long-term, respectively. In addition, it is estimated that there will be an increase of 0.1 mm day−1 (4%) in ETo values for each 1 °C temperature increase compared to the reference period (1970–1990). It was concluded that climate change in the Trakya Region will not significantly affect wheat farming; however, it will cause a serious water deficit in sunflower production. Full article
(This article belongs to the Special Issue Regional Climate Predictions and Impacts)
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16 pages, 3948 KiB  
Article
A Downscaling Method of TRMM Satellite Precipitation Based on Geographically Neural Network Weighted Regression: A Case Study in Sichuan Province, China
by Ge Zheng, Nan Zhang, Laifu Zhang, Yijun Chen and Sensen Wu
Atmosphere 2024, 15(7), 792; https://doi.org/10.3390/atmos15070792 - 30 Jun 2024
Cited by 1 | Viewed by 677
Abstract
Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, the existing methods often fail to adequately consider the spatial heterogeneity and complex nonlinearity between precipitation and surface parameters, resulting in poor downscaling performance and inaccurate expression of [...] Read more.
Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, the existing methods often fail to adequately consider the spatial heterogeneity and complex nonlinearity between precipitation and surface parameters, resulting in poor downscaling performance and inaccurate expression of regional details. In this study, we propose a precipitation downscaling model based on geographically neural network weighted regression (GNNWR), which integrates normalized difference vegetation index, digital elevation model, land surface temperature, and slope data to address spatial heterogeneity and complex nonlinearity. We explored the spatiotemporal trends of precipitation in the Sichuan region over the past two decades. The results show that the GNNWR model outperforms common methods in downscaling precipitation for the four distinct seasons, achieving a maximum R2 of 0.972 and a minimum RMSE of 3.551 mm. Overall, precipitation in Sichuan Province exhibits a significant increasing trend from 2001 to 2019, with a spatial distribution pattern of low in the northwest and high in the southeast. The GNNWR downscaled results exhibit the strongest correlation with observed data and provide a more accurate representation of precipitation spatial patterns. Our findings suggest that GNNWR is a practical method for precipitation downscaling considering its high accuracy and model performance. Full article
(This article belongs to the Special Issue Regional Climate Predictions and Impacts)
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