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Understanding the Spatio-Temporal Characteristics of Climatic Extremes: Insights from Regional...
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Understanding the Spatio-Temporal Characteristics of Climatic Extremes: Insights from Regional Climate Modeling

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

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 5219

Special Issue Editors

Dr. Namendra Kumar Shahi

E-Mail Website
Guest Editor
Department Troposphere Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Interests: climate modeling; climate variability
Dr. Armelle Reca C. Remedio

E-Mail Website
Guest Editor
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Chilehaus, Eingang B Fischertwiete 1, 20095 Hamburg, Germany
Interests: regional climate modeling; climate extremes; climate services

Special Issue Information

Dear Colleagues,

Climatic extremes such as droughts, heavy precipitation events, and heat waves exhibit significant spatio-temporal variability and have a profound impact on humans and ecosystems across various regions of the world. Therefore, a deeper understanding of these climatic extremes and their drivers is crucial for assessing and mitigating risks to economic and societal activities. In recent years, there has been growing scientific interest in investigating the mechanisms leading to these climatic extremes. Additionally, immense efforts have been made, especially in model development, to predict and project such events in the context of a changing climate.

Over the past two to three decades, regional climate models (RCMs) have emerged as a powerful tool for understanding and predicting climatic extremes at regional and local scales, often overlooked by global models. Although RCMs have made significant advancements in representing the spatio-temporal characteristics of climatic extremes, understanding their underlying physical mechanisms has not received as much attention, an aspect specifically addressed by this Special Issue.

This issue welcomes papers that address and advance our understanding of regional climate processes and the challenges and opportunities associated with high-resolution climate simulations, as well as research on enhancing these modeling techniques. Special attention will be given to papers that understand/explore the physical mechanisms leading to climatic extremes.

Thank you very much for your consideration. We are looking forward to your contributions!

Dr. Namendra Kumar Shahi
Dr. Armelle Reca C. Remedio
Guest Editors

Manuscript Submission Information

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Keywords

  • regional climate modeling
  • mesoscale and mountain meteorology
  • weather and climate extremes/hazards
  • regional hydrological cycle
  • climate sensitivity
  • monsoon variability and predictability
  • synoptic-scale meteorology
  • aerosol–climate interaction
  • land–atmosphere–ocean interactions
  • regional climate change

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Published Papers (1 paper)

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Research

28 pages, 7435 KiB  
Article
Climatological and Hydrological Extremes of the Andaman and Nicobar Islands, India, and Its Database for Public Users
by Abhilash, Anurag Satpathi, Talaviya Harshangkumar, Thangavel Subramani, Iyyappan Jaisankar and Namendra Kumar Shahi
Atmosphere 2025, 16(3), 301; https://doi.org/10.3390/atmos16030301 - 4 Mar 2025
Viewed by 4790
Abstract
The Andaman and Nicobar Islands experience a climate characterized by consistently high humidity, substantial annual precipitation, and moderate temperature fluctuations. The region’s susceptibility to extreme weather events—such as cyclones, heavy precipitation, and rising sea levels - highlights the need for a thorough understanding [...] Read more.
The Andaman and Nicobar Islands experience a climate characterized by consistently high humidity, substantial annual precipitation, and moderate temperature fluctuations. The region’s susceptibility to extreme weather events—such as cyclones, heavy precipitation, and rising sea levels - highlights the need for a thorough understanding of its climatic patterns. In light of this, this study provides a comprehensive analysis of spatiotemporal variability and trends in mean and extreme precipitation across the Andaman and Nicobar Islands using long-term (i.e., 1981–2023) high-resolution Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Our findings indicate a significant increase in monsoonal precipitation, particularly in South Andaman, where the mean precipitation trend is 11.10 mm/year, compared to 6.54 mm/year in Nicobar. Light-to-moderate precipitation events occur more frequently than heavy precipitation across all districts, although heavy precipitation is more frequent in Andaman than in Nicobar. Significant decadal increases in light-to-moderate precipitation events are found across most of Nicobar, while parts of Andaman showed a rise in the frequency of moderate-to-heavy precipitation events. Trend analysis of the highest single-day precipitation annually reveals mixed patterns, with increases noted in North and Middle Andaman (3.66 mm per decade) and South Andaman (1.13 mm per decade), while Nicobar shows a slight decrease (−0.63 mm per decade). Maximum consecutive five-day precipitation trends indicate significant annual increases in North and Middle Andaman (14.98 mm per decade) and South Andaman (3.49 mm per decade), highlighting the variability in extreme precipitation events. The observed trends in precipitation and its extremes highlight the heterogeneity of precipitation patterns, which are critical for water resource management, agriculture, and disaster risk mitigation in the region, particularly in the context of increasing precipitation variability and intensity driven by climate change. Further investigation is needed to understand the physical mechanisms driving the increase in frequency and intensity of precipitation, which will be addressed in a separate paper. Full article
(This article belongs to the Special Issue Understanding the Spatio-Temporal Characteristics of Climatic Extremes: Insights from Regional Climate Modeling)
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