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Special Issue "Climate Extremes in the Pannonian Basin (2nd Edition)"

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

Deadline for manuscript submissions: 2 July 2023 | Viewed by 5748

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Special Issue Editor

Dr. Blanka Bartok

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Guest Editor

Department of Geography in Hungarian, Faculty of Geography, Babeș-Bolyai University, Cluj-Napoca, Romania
Interests: climate change; climate modelling; surface solar radiation; renewable energy

Special Issue Information

Dear Colleagues,

In recent decades, we have found more and more evidence that climate change is making extreme events more likely or more intense. As these events often cause significant damages in human and natural systems, they are considered to be among the potentially most harmful consequences of a changing climate. Recently, we have seen an explosion of interest in extreme event attribution providing local-scale perspectives that people in affected communities can use to develop recovery and resilience plans that match their future risk.

This Special Issue is open to all publications on climate extremes (research or review papers) in the Pannonian Basin, which is the focus area of the Pannonian Basin Experiment (PannEx) Regional Hydroclimate Project of the Global Energy and Water Exchanges Project of the World Meteorological Organisation (GEWEX). This Special Issue covers all topics regarding practices and challenges related to the detection and attribution of changes in climate extremes. Our intention is to understand and predict climate extremes by analysing: historical records or estimated based on the climate model data; synoptic and seasonal conditions generating climate extremes; social, economic, and environmental impacts of climate extremes; perception; and public policies and strategies to be implemented at urban/local or regional levels.

Dr. Blanka Bartok
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 2000 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 extremes
extreme events attribution
climate extremes modelling
climate extremes impact
climate extremes risk assessment
Pannonian Basin

Related Special Issue

Climate Extremes in the Pannonian Basin: Current Approaches and Challenges in Atmosphere (14 articles)

Published Papers (5 papers)

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Research

Open AccessArticle

Assessment of Climate Indices over the Carpathian Basin Based on ALADIN5.2 and REMO2015 Regional Climate Model Simulations

Otília A. Megyeri-Korotaj

Beatrix Bán

Réka Suga

Gabriella Allaga-Zsebeházi

and

Gabriella Szépszó

Atmosphere 2023, 14(3), 448; https://doi.org/10.3390/atmos14030448 - 23 Feb 2023

Viewed by 760

Abstract

The Hungarian Meteorological Service has been conducting climate model simulations in order to assess the effects of climate change in the Carpathian Basin and provide data for impact research and stakeholders. Two regional climate models are used: ALADIN-Climate 5.2 (hereafter ALADIN5.2) and REMO2015. They were tested for the past when the lateral boundary conditions were taken from two sources. ERA-Interim reanalysis was used in the evaluation experiment, while the CNRM-CM5 and the MPI-ESM-LR global climate models (GCM) provided the forcings in the control experiments. The model outputs were compared with the CarpatClim-HU observational dataset for the 1981–2000 period. Future projections were carried out with the RCP4.5 and RCP8.5 scenarios, and the results were analyzed for 2021–2050 and 2071–2100. The evaluation of the results focused mainly on climate indices calculated from temperature and precipitation. The validation results showed that REMO2015 assessed the mean temperature well, but the indices based on the minimum and maximum temperature had a significant bias which has to be taken into account when interpreting future changes. The model overestimated the minimum temperature in summer, which might affect the number of tropical nights. Moreover, the maximum temperature was underestimated; thus, the derived indices, such as the occurrence of summer days and hot days, were profoundly underestimated. In comparison, ALADIN5.2 had smaller biases for the high temperature indices; moreover, the number of hot days and extremely cold days was overestimated. Taking future projections into account, we can clearly see that the results of REMO2015 show a much more moderate increase in temperature than ALADIN5.2. The reasons are yet unknown and require further investigation. In spring and summer, the number of wet days was slightly overestimated, while the number of heavy precipitation days was marginally underestimated. The projections showed the highest uncertainty in the changes in mean summer precipitation and other precipitation indices. Although the REMO2015 model assessed a decrease in precipitation, ALADIN5.2 projected an increase in precipitation with a similar magnitude. Full article

(This article belongs to the Special Issue Climate Extremes in the Pannonian Basin (2nd Edition))

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Open AccessArticle

Extreme Value Analysis and Modelling of Wet Snow Accretion on Overhead Lines in Hungary

Katalin Somfalvi-Tóth

and

André Simon

Atmosphere 2023, 14(1), 81; https://doi.org/10.3390/atmos14010081 - 31 Dec 2022

Viewed by 955

Abstract

Wet snow events in Hungary can occasionally cause damage to overhead power lines and serious power supply failures. Return period calculation of such high snow mass events was determined upon a 50-year long data series available at 12 meteorological stations. Wet snow masses were estimated with a model of cylindrical accretion around wire of 3.1 cm diameter. Trends in the return periods and ice classes (according to the ISO 12494 standard) were assessed from division of the dataset into two periods (1965–1990 and 1991–2016). Ice classes in the range of R2 to R5 (mass of 0.7–6.1 kg m⁻¹) were identified. The wet snow risk decreased in southern or central Hungary, while an increase was found in the western part of Hungary. Ice classes R6–R8 (up to 40 kg m⁻¹) could occur in mountain areas of Hungary as indicated by numerical simulations in case studies. However, their return period is unknown due to the lack of long observation series. Full article

(This article belongs to the Special Issue Climate Extremes in the Pannonian Basin (2nd Edition))

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Figure 1

Open AccessArticle

Extreme Months: Multidimensional Studies in the Carpathian Basin

and

Atmosphere 2022, 13(11), 1908; https://doi.org/10.3390/atmos13111908 - 15 Nov 2022

Viewed by 724

Abstract

In addition to the one-dimensional mathematical statistical methods used to study the climate and its possible variations, the study of several elements together is also worthwhile. Here, a combined analysis of precipitation and temperature time series was performed using the norm method based on the probability distribution of the elements. This means, schematically speaking, that each component was transformed into a standard normal distribution so that no element was dominant. The transformed components were sorted into a vector, the inverse of the correlation matrix was determined and the resulting norm was calculated. Where this norm was at the maximum, the extreme vector, in this case the extreme month, was found. In this paper, we presented the results obtained from a joint analysis of the monthly precipitation and temperature time series for the whole territory of Hungary over the period 1871–2020. To do this, multidimensional statistical tests that allowed the detection of climate change were defined. In the present analysis, we restricted ourselves to two-dimensional analyses. The results showed that none of the tests could detect two-dimensional climate change on a spatial average for the months of January, April, July and December, while all the statistical tests used indicated a clear change in the months of March and August. As for the other months, one or two, but not necessarily all tests, showed climate change in two dimensions. Full article

(This article belongs to the Special Issue Climate Extremes in the Pannonian Basin (2nd Edition))

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Open AccessArticle

Using Long-Term Historical Meteorological Data for Climate Change Analysis in the Carpathian Region

Ágoston Vilmos Tordai

and

Tamás Weidinger

Atmosphere 2022, 13(11), 1751; https://doi.org/10.3390/atmos13111751 - 24 Oct 2022

Viewed by 838

Abstract

In this study, we consider the historical climatological time series available in the meteorological yearbooks of the Royal Hungarian Central Institute of Meteorology and Earth Magnetism, first published in 1871. Data quality improvement of historical data includes the homogenization process with outlier checks and data gap filling by applying the MASH software. We investigated 13 stations from the Carpathian Region having the most complete monthly temperature and precipitation time series for the period of 1871–1918 and 8 stations with fog observations (1886–1916). First, statistical tests were conducted to compare the main statistics of the historical datasets (1871–1918) with current data (1971–2020). The sources of the current data are the National Meteorological Administration of Romania and the European Climate Assessment & Dataset. The results show significant changes between the two periods. In the whole region, the mean temperature in the last five decades (1971–2020) was 0.77 °C higher than in 1871–1918. Changes in the frequency of foggy situations were also detected. On an annual scale, in the last 31-year period (1990–2020), the number of foggy days increased by 16.2 compared with 1886–1916. Even if some local trends can be detected in the historical periods (e.g., Cluj-Napoca), significant changes are much more characteristic in the recent period. Full article

(This article belongs to the Special Issue Climate Extremes in the Pannonian Basin (2nd Edition))

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Open AccessArticle

A GIS-Based Methodology to Combine Rain Gauge and Radar Rainfall Estimates of Precipitation Using the Conditional Merging Technique for High-Resolution Quantitative Precipitation Forecasts in Țibleș and Rodnei Mountains

and

Atmosphere 2022, 13(7), 1106; https://doi.org/10.3390/atmos13071106 - 14 Jul 2022

Cited by 1 | Viewed by 1378

Abstract

Rain gauges provide accurate rainfall amount data; however, the interpolation of their data is difficult, especially because of the high spatial and temporal variability. On the other hand, a high-resolution type of information is highly required in hydrological modeling for discharge calculations in small catchments. This problem is partially solved by meteorological radars, which provide precipitation data with high spatial and temporal distributions over large areas. The purpose of this study is to validate a conditional merging technique (CMT) for 15 rainfall events that occurred on the southern slope of the Tibleș and Rodnei Mountains (Northern Romania). A Geographic Information System (GIS) methodology, based on three interpolation techniques—simple kriging, ordinary kriging, and cokriging—were utilized to derive continuous precipitation fields based on discrete rain gauge precipitation data and to derive interpolated radar data at rain gauge locations, and spatial analysis tools were developed to extract and analyze the optimal information content from both radar data and measurements. The dataset contains rainfall events that occurred in the period of 2015–2018, having 24 h temporal resolution. The model performance accuracy was carried out by using three validation metrics: mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE). The validation stage showed that our model, based on conditional merging technique, performed very well in 11 out of 15 rainfall events (approximate 78%), with an MAE under 0.4 mm and RMSE under 0.7 mm. Full article

(This article belongs to the Special Issue Climate Extremes in the Pannonian Basin (2nd Edition))

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