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Theoretical and Applied Climatology
Journal Prestige (SJR): 0.867
Citation Impact (citeScore): 2
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1434-4483 - ISSN (Online) 0177-798X
Published by Springer-Verlag Homepage  [2468 journals]
  • MJO modulation of air temperature and associated circulation in years with
           extreme frequency of generalized frosts over southeastern South America

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      Abstract: Abstract Extreme cold events can have several consequences in different sectors, including human health, infrastructure, agriculture, and the economy. The frost events that affect central and northeastern Argentina are associated with stationary Rossby waves triggered by tropical heat sources and convection, which result in intense polar air advection. However, the processes responsible for these cold events include complex interactions between these wave trains and mechanisms at different atmospheric scales, such as the Madden–Julian Oscillation (MJO), which can alter the temperature patterns. This study investigates the impacts of the MJO on air temperature and associated circulation in years with extreme occurrence frequency of cold events in central southern South America during the winter. The results showed that the MJO convection can enhance or weaken the temperature anomalies during winters with a maximum and minimum frequency of generalized frosts (GF) occurrence due to Rossby wave train propagation triggered in the tropical region. Moreover, the effect on the anomaly patterns in these events depends on the MJO phase. Our analysis shows that individual MJO phases can modulate the temperature anomalies even in an unfavorable basic state (as in winters with an extreme frequency of GF and emphasizes the importance of considering the MJO in the predictions of the temperature anomalies associated with GF over southeastern South America.
      PubDate: 2024-02-24
       
  • Parameterization model of soil thermal conductivity and its application in
           the permafrost region of the Qinghai-Tibet Plateau

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      Abstract: Abstract Soil thermal conductivity (STC), which describes the ability of soil to transfer heat, is critical to understanding the thermal regime. Simulations of the heat of the permafrost regions on the Qinghai-Tibet Plateau (QTP) are currently inaccurate. This is partly because the current STC models used in the land surface models are not adequate to accurately reflect the characteristics of the ice-water phase change. Here, the new STC model was developed by dividing three different stages. Our analyses revealed that the soil moisture ( \({\theta }_{\mathrm{w}}\) ) undergoes a rapid phase change in the − 2.5 ~ 0 ℃, where minor temperature changes could cause larger \({\theta }_{\mathrm{w}}\) changes. When the temperature is below − 2.5 ℃, the \({\theta }_{\mathrm{w}}\) mostly remains stable. Considering the influence of various factors in different temperature ranges, an improved STC model was proposed by piecewise fitting at 0 ℃ and − 2.5 ℃ for the depths of 10–50 cm. Independent test results showed that the new model significantly improved simulation accuracy of STC in permafrost regions and was better able to reflect it changing characteristics, especially in the 50 cm depth. Lastly, the daily STC product in the permafrost region of the QTP was estimated with the new model. The average STC during 1982 to 2020 was about 0.495 Wm−1K−1, showing a spatial pattern of low in the northwest and high in the southeast. In addition, the STC showed a tiny increasing trend at a rate of 0.008 Wm−1K−1/10a. Spatially, the regions with the highest rates of increase were concentrated in the eastern, southeastern, and south-western regions, which comprise mostly unstable and extremely unstable permafrost. This study deepened our understanding of the STC during the freeze–thaw cycle and provides data products for further studies on the soil thermal state in permafrost regions.
      PubDate: 2024-02-23
       
  • A comparison of Indian and South American monsoon variability and likely
           causes

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      Abstract: Abstract The main goal of the present work is to compare and contrast the characteristics of distinct monsoon systems like the Indian monsoon system (IMS) and South American monsoon system (SAMS) for the period (1979–2022). In addition, we discuss in some detail the theoretical aspects of the two monsoon systems by examining the energetics and the applicability of “convective quasi-equilibrium, (CQE).” We have also analyzed the precipitation interannual variability of SAMS and IMS considering neutral, El Niño, and La Niña years. Then, a discussion of the applicability of CQE along with the recent changes in surface entropy is presented. In our analysis, we found that interannual variability in the case of SAMS is less than that of IMS, and rainfall of SAMS is not drastically affected by ENSO when compared to IMS. We observed that rainfall characteristics over IMS are more complex than SAMS. The annual cycle of the vertically integrated total kinetic energy (KE) over SAMS is maximum in Austral spring, while the precipitation is higher in Austral summer. This is in sharp contrast to the IMS, where the maximum KE and rainfall coincide, both occurring in July and August. Further analysis showed that the conversion from the mean available potential energy PZ to the eddy available potential energy PE and conversion from PE to KE are important over SAMS. This shows that in South America, baroclinic conversions associated with baroclinic instability are important in austral summer, while over IMS, baroclinic conversions are not important in boreal summer. Our results support CQE for the IMS, but in the case of El Niño, we found that CQE is invalid. For SAMS, the applicability of CQE is climatologically doubtful, but during El Niño, the applicability of CQE is robustly visible.
      PubDate: 2024-02-22
       
  • Trends and correlation between deforestation and precipitation in the
           Brazilian Amazon Biome

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      Abstract: Abstract This paper analyzes the correlation between precipitation and deforestation and trends in the Brazilian Amazon Biome from 1985 to 2021 using rain gauge measurements. A total of 187 stations were selected after filtering time series with less than 10% of missing data. Deforestation data was acquired using the MapBiomas dataset. Results show that deforestation rates vary across different locations and may be influenced by various factors. The analysis also found significant negative correlations between precipitation and deforestation, with a range of − 0.49 to − 0.33, as well as significant positive correlations, with a range of 0.34 to 0.57. These findings underscore the importance of continued monitoring of precipitation patterns in the region and the urgent need for measures to mitigate deforestation, which is critical for the hydrological cycle of the region. The highest deforestation rates and values were found between latitude 0 and − 12.5, where the arc of deforestation lies. Additionally, this study found a significant negative correlation between precipitation patterns and deforestation, indicating that deforestation led to a reduction in rainfall in the region.
      PubDate: 2024-02-21
       
  • Machine learning–based prediction of agricultural drought using global
           climatic indices for the Palakkad district in India

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      Abstract: Abstract Agricultural drought refers to soil moisture deficit, which causes adverse effects on the crop production and economy of a nation. This work compared the capability of artificial neural network (ANN) and support vector machine (SVM) algorithm in predicting agricultural drought in the Palakkad district of Kerala, India. Also, the influence of various global climatic indices on soil moisture stress in the study area is assessed. Two models were developed to investigate the impact of global climatic indices. Model 1 considered only local meteorological variables as predictors, and model 2 included global climatic indices along with meteorological variables. The results showed that ENSO has commendable influence on the early prediction of agricultural drought in Palakkad and are more evident at higher lead times (2 to 4 months). For the first model of ANN and SVM, the R2 values at a 4-month lead range from 0.56 to 0.76 and 0.62 to 0.77, respectively. Similarly, for model 2, the R2 varies from 0.61 to 0.77 and 0.75 to 0.82 for ANN and SVM models, respectively. Further, the results indicated that the SVM model shows clear advancement in prediction over ANN especially at higher lead times, even though both show a comparable performance at 1-month lead time. The study provided useful information regarding the potential predictors of agricultural drought in the study area and suggest suitable models for the early prediction. This will support the decision makers in drought prevention and water resource management.
      PubDate: 2024-02-19
       
  • Spatiotemporal sensitivity analysis of surface soil moisture to
           precipitation and temperature variations: a case study of the Cheliff
           Basin in Algeria

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      Abstract: Abstract Since a trend towards aridification is expected in Algeria, as in the countries of the Maghreb and the Mediterranean basin, changes in precipitation and temperature can have a significant impact on various water and energy balance processes and, consequently, on surface soil moisture (SSM). The latter is an important measure for monitoring and even forecasting meteorological and hydrological droughts, as well as irrigation for water resource optimization. This study examines the spatiotemporal sensitivity of SSM to variations in precipitation and temperature in different climatic zones of the Cheliff basin. The monthly mean data from four datasets of SSM, extracted from two reanalysis products (ERA-Land and ERA5), active and passive microwave satellite observations (ESA CCI SM), GLEAM model, along with precipitation and temperature records over the period from 1980 to 2018 from 104 weather stations, were analyzed by using the Modified Mann–Kendall test (MMK) and canonical correlation; the study revealed that (1) SSM generally increased from September to December or March, depending on the dataset; (2) monthly precipitation increased from September to November; (3) SSM decreased from June to August, coinciding with rising temperatures; (4) the results of the canonical analysis revealed that monthly SSM is positively correlated with delayed precipitation from 1 to 3 months, depending on the climatic zone; (5) SSM is negatively correlated with temperatures over the previous 2 months for the stations in Csa and Bsk climate zones and with temperatures over the previous 1 and 2 months for the stations in Bsh climate zone from 1 to 2 months prior, also depending on the climatic zone. These results highlight the crucial role of SSM in monitoring and forecasting weather events such as droughts and provide insights for irrigation strategies aiming to optimize water resources.
      PubDate: 2024-02-19
       
  • Investigating monthly geopotential height changes and mid-latitude
           Northern Hemisphere westerlies

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      Abstract: Abstract The investigation of the height variations of the westerly in the mid-latitudes of the Northern Hemisphere is crucial for monitoring changes in the intricate atmospheric system. This study examined the hypothesis of changes in the thickness of the westerly domain by analyzing the height of the 500 hPa pressure level. To this end, monthly data from the European Centre for Medium-Range Weather Forecasts (ECMWF) spanning from 1959 to 2021 was utilized to investigate three components: geopotential height (GH), zonal, and meridional wind components. The dataset underwent statistical tests, including trend analysis using Least Squares Error linear regression (LSE-LR) and shift (jump) analysis using the Standard Normal Homogeneity Test (SNHT). The results revealed a significant increasing trend in the height of the troposphere and the westerlies in the mid-latitudes of the Northern Hemisphere, accompanied by significant fluctuations, indicating an increase in the height of the 500 hPa pressure level and a decrease in the thickness of the westerly domain. The extent of these trends was observed to be greater in cold months, particularly in January and February, while the changes were minimal in November. Notably, the spatial distribution of upward jumps, indicative of change, was highest in January, July, and August in the eastern and southeastern regions of Asia. Furthermore, the convergence section of the westerlies trough exhibited a substantial number of jumps, particularly in March, spanning Central America and southern parts of North America. Regarding decadal coincidence onset, the highest number of jumps occurred in February, encompassing the western Pacific Ocean, western Atlantic Ocean, Eastern Europe, and western Asia. The end of these jumps coincided in February, and the spatial proximity of the end times was closer compared to the onset times in February, March, April, July, and August.
      PubDate: 2024-02-17
       
  • A systematic review of regional and global climate extremes in CMIP6
           models under shared socio-economic pathways

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      Abstract: Abstract Climate extremes pose significant risks to human health, agriculture, and water resources. These extremes are defined as long-term, unusual events that fall into the 10th or 90th percentile of a probability density function derived from observations at a certain location (e.g., drought and wildfire). The quantification of future climate risks is based on climate model predictions. Here, we present a review of literature focusing on extreme climate projections in the latest generation of climate models, namely, Coupled Model Intercomparison Project Phase 6 (CMIP6) from 2020 to the present. We highlight the extreme events that could cause potential societal risks, including precipitation (the 90th percentile of the cumulative frequency distribution of daily precipitation), temperature (the 10th or 90th percentile of daily temperature within a reference period), droughts (meteorological, hydrological, and agricultural), floods, heat waves, and compound/concurrent extremes. Regionally, the precipitation extremes are projected to increase in North Africa (Ethiopia, Uganda, and Kenya), followed by drying in South Africa. Heatwaves will increase in a warming scenario (SSP3-7.0) in Asia (Indo-Gangetic Plain) and Afghanistan. The rise in heat stress intensity in Asia will augment the climate risks to agriculture under the SSP2-4.5 and SSP5-8.5 scenarios. On a global scale, land areas are projected to face severe drought specifically in higher biomass regions, under the SSP5-8.5 scenario. Future droughts bring hazards to Europe and the Amazon River basin with severe aridification over Australia, the Middle East, South and North Africa, and Central Asia. The CMIP6 model projections on a regional and global scale over the US Southwest predict intense drought and hot dry summers. The study supplements the discussion section by providing insights on sources of uncertainty in extreme event projections, the role of emergent constraints in uncertainty reduction, and the impact of extremes on water resources, agriculture, and human health.
      PubDate: 2024-02-17
       
  • Spatial and temporal variations of precipitation in Northwest China during
           1973-2019

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      Abstract: Abstract The Shaanxi-Gansu-Ningxia (SGN) region in Northwest China has been challenged by drought and water shortage for decades. Based on the precipitation data from 30 weather stations in the SGN region during 1973–2019, the Empirical Orthogonal Function (EOF) and the Wavelet analysis (WA) were applied to analyze the precipitation spatio-temporal variations at different time scales. The results showed that precipitation at annual scale had decreased significantly (P < 0.01) at the rate of 1.46 mm/10a during 1973–2019, and 56.7% of stations showed a decreasing trend, which was mainly concentrated in the Jing River basin. In terms of seasonal precipitation, more than 65% of the stations showed an increasing trend in summer and winter, with increasing at 2.06 mm/10a and 0.56 mm/10a, respectively, but most significantly, 86.7% of the stations showed a decreasing trend in the spring, and only less than 15% of the stations in the Hulu River basin showed an increasing trend. This study will help to further improve understanding of climate change, and provide reference to cope with future climate change and solve the problem of water shortage.
      PubDate: 2024-02-17
       
  • Unriddle the complex associations among urban green cover, built-up index,
           and surface temperature using geospatial approach: a micro-level study of
           Kolkata Municipal Corporation for sustainable city

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      Abstract: Abstract The research article delves into the background of urban land use and land cover (LULC) change, specifically focusing on built-up expansion, and underscores its significant implications on land surface temperature (LST) and the urban heat island (UHI) phenomenon. This research aims to unravel the intricate associations among urban green cover, built-up index, and surface temperature, specifically within the spatial confines of the Kolkata Municipal Corporation. The primary objective is to comprehensively understand how the conversion of green spaces into built-up areas influences land surface temperature and, consequently, the urban heat island effect. Employing a geospatial approach, the study utilizes normalized differential vegetation index (NDVI), normalized differential built-up index (NDBI), and land surface temperature (LST) data extracted from Landsat imagery spanning four temporal points (1990, 2000, 2010, and 2020). The borough-level analysis offers a micro-level perspective within the limited urban space of Kolkata Municipal Corporation. Correlation analyses and scatter diagrams are employed as tools to scrutinize the complex relationships between these variables, providing a robust methodology for the investigation. The research underscores the significant impact of urbanization on the study area, revealing a consistent trend of converting green spaces into built-up areas over the studied decades. This transformation has led to a reduction in green coverage and a concurrent increase in surface temperatures. The study reveals compelling correlations and patterns through NDVI, NDBI, and LST analyses, emphasizing the urgency for serious attention from urban planners, environmentalists, and ecologists. The findings highlight the pressing need for the development of appropriate policy frameworks to ensure the future sustainability and health of cities.
      PubDate: 2024-02-16
       
  • Historical variability of Coupled Model Intercomparison Project Version 6
           (CMIP6)-driven surface winds and global reanalysis data for the Eastern
           Mediterranean

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      Abstract: Abstract Comparing the near-surface wind speeds obtained from the most recent global circulation model (GCM) simulations to well-known benchmark datasets like the European Centre for Medium-Range Weather Forecasts reanalysis Version 5 (ERA5) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), is necessary to make a critical assessment. Using 28 Coupled Model Intercomparison Project Phase 6 (CMIP6)-based monthly surface wind predictions, the multi-model ensemble (MME) approach in this study generates these predictions using random forest (RF) and multiple linear regression (MLR) methods over seven geographical regions in Türkiye with varying topographic complexity between 1980 and 2014, along with an offshore region. Benchmark datasets, station observations, and individual GCM predictions are used to compare the performances of MME predictions. The analysis showed that individual and the simple mean of GCM simulations are highly biased in spatial and temporal wind means. On the other hand, the MMEs formed by using groups of GCMs have significant skill for representing temporal variability in wind speed as well as for producing annual climatology and anomaly range for topographically complex regions. In MME predictions, the correlation improvements are 38–45% for RF and 22–34% for MLR. Moreover, the effect of the model group with dynamic vegetation growth on improvement remains only marginal.
      PubDate: 2024-02-16
       
  • Investigating the relationship between urban sprawl and urban heat island
           using remote sensing and machine learning approaches

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      Abstract: Abstract Urbanization is triggering the expansion in an unplanned and unrestricted manner, growth of outward expansion for some areas. One such important environmental and ecological hazard in recent decades is urban heat island (UHI) and urban thermal field variation index (UTFVI). Studies employing temporal satellite imagery from 1991 to 2021 focused on the appraisal of spatiotemporal patterns of urbanization and UHI in West Bengal’s mid-sized cities (Medinipur and Kharagpur city). This study examines competencies of remote sensing (RS) and GIS procedures in empathetic heat effects and urban thermal conditions using Landsat datasets and GEE cloud software-based time-consuming conventional method. The change detection analysis revealed that built-up lands developed since 7.88–26.94% (1991–2021). The UHI’s highest value in 1991 is 4.57 and in 2021 it increased by 6.87%. The highest 0.34% UTFVI value is found in the year 1991, but in 2021, the highest value was shown at 0.35% in this study area. The mean NDVI value increased from 1991 (0.29%) to 2021 (0.44%). The land surface temperature (LST) maps have been prepared using Landsat 5 and Landsat 8 surface reflectance datasets. Anthropogenic-related ecological changes in urbanized areas are concerning these days since they have the potential to negatively impact both the environment and human health. The investigation has shown that while LULC alterations were sufficiently large, unplanned changes to it can have a detrimental effect on the environment. Investigation established an operative systematic methods application to measure urbanization appearances and urban thermal conditions in Medinipur and Kharagpur city.
      PubDate: 2024-02-16
       
  • Trend, driving factors, and temperature-humidity relationship of the
           extreme compound hot and humid events in South China

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      Abstract: Abstract With global warming and frequent occurrence of extreme hot events, the accompanying heat stress, which is believed to be the combined effects of temperature and humidity on human health, is also expected to increase. The temperature and precipitation both increased recently, which indicates a high incidence of extreme compound hot and humid events (CHHEs) in South China, while its trend and mechanism still need further research. Through the comparison of three types of extreme events (extreme hot events (HOEs), extreme humid events (HUEs), and extreme compound hot and humid events (CHHEs)), this study revealed the spatiotemporal characteristics, driving factors, and temperature-humidity relationships of CHHEs in South China. What’s more, the temperature-humidity relationships of CHHEs with different dominant types and intensities were further explored. The research results are as follows: (1) HOEs and CHHEs significantly increased, while HUEs decreased, which is consistent with the trend of temperature and relative humidity. (2) The driving factors of HOEs were opposite to those of HUEs, and strong net thermal radiation, evaporation, and water vapor transport were favorable meteorological conditions for CHHEs in South China. (3) Most of CHHEs in South China were temperature dominant type, which covered longer duration and higher intensity than humidity dominant and non-dominant types. (4) There was a strong linear relationship between temperature and humidity during CHHEs. And for the same temperature range, the higher the intensity of CHHEs was, the lower the humidity difference would be.
      PubDate: 2024-02-14
       
  • Abating water storage and associated hydrological processes in Indian
           Himalayan river basins

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      Abstract: Abstract In recent decades, the hydrological balance/budget over Himalayan river basins has imperatively become crucial for decision-making in flood risk, water resource management, identifying water-sensitive areas etc. In the present study, assessment of the abating total water storage (TWS) in the three river basins viz. Indus (IRB), Ganga (GRB) and Brahmaputra (BRB) is carried out. TWS contributory factors, viz. precipitation, evaporation, runoff, snow water equivalent (SWE), soil moisture, groundwater etc., are arguably assessed. TWS anomaly and other variables are considered, and corresponding statistical seasonal (winter, premonsoon, monsoon and postmonsoon) trends are calculated using the Mann–Kendall test and the Theil-Sen estimator. Dominant monsoon precipitation over GRB and BRB and winter precipitation over IRB contribute to the replenishment of TWS with almost a month lag. Still, there are decreasing TWS trends. Most of the basins are drying, though slower during monsoon. Maximum decrease in TWS is observed in postmonsoon over IRB, while GRB and BRB show it in premonsoon. The highest intraseasonal variability is shown by precipitation, followed by runoff. Evaporation shows less variability and is less dependent. Present work will be of utmost importance for the policy or planning for governance at the state level for societal benefit.
      PubDate: 2024-02-14
       
  • Effects of climate trends and variability on tree health responses in the
           Black Sea and Mediterranean forests of Türkiye

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      Abstract: Abstract To adapt forest ecosystems and forest management to climate change, it is essential to know which forest regions and which tree species are resilient to climate variability and which ones are possibly affected most by past and anticipated future changes. In this contribution, for the main forest regions of Türkiye and six tree species, recent climate variability and trends were quantified and statistically correlated to record tree defoliation and vitality. Climate variables considered are maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and total precipitation (Prcp), which are compared to forest health responses recorded as part of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) on 277 plots across forests along the Black Sea and Mediterranean regions. In addition, long-term data on satellite measurements of the normalized difference vegetation index (NDVI) were extracted for the same 277 plots for the period 2008–2020. Firstly, 30 years (1991–2020) of reanalysis of climate variables from ECMWF were extracted for all plots; secondly, individual correlations and cross-correlations of climate variables and tree health and vitality were computed for the period 2008–2020 (significance level of 95%) for the four most dominant species from the Black Sea forests (F. orientalis, Q. cerris, P. sylvestris, P. orientalis) and two species from Mediterranean forests (P. brutia and C. libani). Temperature showed a stronger effect on most species than precipitation. Finally, time-lagged correlations were analyzed for seven-time lags (significance level of 95%) to evaluate legacy effect. The analysis revealed that different tree species from the two regions show different responses to climate variables. Species in the Mediterranean region are more resistant to droughts and climatic variations. Legacy effects of defoliation and NDVI have lasted for at least 2 years.
      PubDate: 2024-02-14
       
  • South Atlantic Convergence Zone as Rossby wave source

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      Abstract: Abstract We investigate the possibility for the South Atlantic Convergence Zone (SACZ) to act as a Rossby wave source for global scale teleconnections. A simple heating perturbation is applied to numerical simulations using a baroclinic model with differing basic states. Sixteen days after the perturbation is applied, wave propagation is diagnosed within the subtropical latitudes of the Northern Hemisphere for the basic states corresponding to El Niño and neutral years, which have normal and strong westerly flow in the upper troposphere over the Equatorial Atlantic Ocean. For La Niña basic states, there is very little propagation over the Equatorial Atlantic Ocean because the westerly flow is weak, confining Rossby waves to the Southern Hemisphere. We conclude that the SACZ may act as interhemispheric Rossby wave source, providing the tropical westerly flow is strong enough to permit wave propagation across the Equatorial Atlantic Ocean.
      PubDate: 2024-02-14
       
  • Investigation of thermodynamics and dynamic factors affecting the
           development, strength, and longevity of mesoscale convective systems

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      Abstract: Abstract The aim of this study is to elucidate the primary factors influencing the development, strength, and longevity of mesoscale convective systems (MCSs) in southwest Iran. Focusing on dynamic and thermodynamic factors, this research investigates their impact on MCSs’ maximum area, longevity, and precipitation characteristics. The study reveals that MCS characteristics are intricately linked to environmental factors such as humidity, convective available potential energy (CAPE), and low-level wind shear, predominantly within the Red Sea convergence zone. These factors are, in turn, influenced by larger atmospheric phenomena like Sudan’s low, Saudi Arabia’s high, and the Azores high. Multiple linear regression analysis identifies low-level wind shear along the Red Sea convergence zone and the 300 hPa wind speed along the subtropical jet stream as significant predictors for both the maximum and mean precipitation of MCSs. Notably, CAPE over the west of the Red Sea emerges as crucial for maximum precipitation, while sensible heat flux over Eastern Europe is key for mean precipitation estimation. The findings also underscore that humidity variables and 850 hPa wind speed are vital in determining the longevity and area of MCSs. This study contributes to a better understanding of the environmental conditions influencing MCS occurrence, aiding in the prediction of heavy precipitation events in subtropical regions like southwest Iran.
      PubDate: 2024-02-13
       
  • An agro-meteorological hazard analysis for risk management in a
           Mediterranean area: a case study in Southern Italy (Campania Region)

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      Abstract: Abstract Agriculture is highly dependent on environmental, climate and weather conditions and on extreme weather events leading to natural disasters. Such events are more and more frequent in Italy, and in the last decades huge public investments were dedicated to risk management policies in agriculture. In order to set an adequate weather-related risk assessment, a robust analysis of the hazard is needed, which requires an agro-meteorological approach to detect the potential impacts of weather extremes on agricultural activities. With the aim of assessing the effectiveness of the current risk management policy in catching the main hazards, specific agro-meteorological indices were applied to highlight occurrence, trends, and spatial patterns of extreme events. The analysis was based on reanalysis datasets and focused on a study area in Southern Italy (Campania region) during the 1981–2021 period. The findings are reported in terms of maps and statistics aggregated at administrative unit level (5 provinces) and show a general intensification of weather extremes in the last decades, both in frequency and intensity of the events. The main indications refer to growth rates of heavy precipitation, potentially leading to flood, locally exceeding 3–4 mm/year, an increasing number of months with severe/extreme droughts, mainly concentrated during the growing season. An upward trend was also observed for days with extreme maximum temperatures, which already exceeded or approached 50% between June and September in the 1981–2021 period in most areas. Maximum temperatures above 35 °C are becoming more frequent and in the inner areas they were reached in 10 days in the 2021 summer quarter. On the other hand, no significant trends were detected for late frosts. In terms of policy implications, the results seem to suggest that some extreme weather events can no longer be considered as exceptional at the present time and in a trend perspective, making them less suitable to be addressed through the risk management tools based almost exclusively on the strategy of transferring risks (insurances and more recent mutual funds) both for farmers and for the allocation of public resources. Therefore, the need is underlined for improving the design of the risk management policies to increase farms’ resilience and adaptation to climate change. Moreover, the study highlights the information potential of agro-meteorological indices in supporting evidence-based decision making in agriculture.
      PubDate: 2024-02-10
       
  • Changes in rainfall seasonality in Serbia from 1961 to 2020

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      Abstract: Abstract Rainfall seasonality in Serbia is examined trough the analysis of several indices: seasonality index (SI), individual seasonality index ( \({SI}_{i})\) , and replicability index (RI). Based on data from 14 synoptic stations that are well distributed over Serbian territory, spatial distribution of general seasonality index \(\overline{SI}\) , mean individual seasonality index \(\overline{{SI }_{ind}}\) , and \(RI\) are analyzed in two subperiods (1961–1990 and 1991–2020). The modified Mann–Kendall test (MMK) and Sen’s slope methods are used to investigate the possible trends and its magnitudes in time series of \({SI}_{i}\) . The values of \(\overline{SI}\) show that precipitation regime in Serbia is very equitable or equitable with definite wetter season. For the entire Serbian territory, the values of RI are low, indicating that the month of maximum rainfall occurs over large spread of months along the studied periods. The existence of significant negative correlation between RI and longitude is found. Results show a highest influence of the North Atlantic Oscillation and East Atlantic/West Russia pattern on rainfall seasonality in Serbia.
      PubDate: 2024-02-10
       
  • Improving flood forecasts capability of Taihang Piedmont basin by
           optimizing WRF parameter combination and coupling with HEC-HMS

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      Abstract: Abstract Based on numerical weather prediction model Weather Research and Forecasting (WRF) and Hydrologic Modeling System (HEC-HMS), a coupling model is constructed in Taihang Piedmont basin. The WRF model parameter scheme combinations composed of microphysics, planetary boundary layers, and cumulus parameterizations suitable for the study area are optimized. In both time and space, we tested the effects of the WRF model by a multi-index evaluation system composed of relative error, root meantime square error, probability of detection, false alarm ratio, and critical success index and established this system in two stages. A multi-attribute decision-making model based on Technique for Order Preference by Similarity to an Ideal Solution and grey correlation degree is proposed to optimize each parameter scheme. Among 18 parameter scheme combinations, Mellor-Yamada-Janjic, Grell-Devinji, Purdue-Lin, Betts-Miller-Janjić, and Single-Moment6 are ideal choices according to the simulation performance in both time and space. Using the unidirectional coupling method, the rolling rainfall forecast results of the WRF model in the 24 h and 48 h forecast periods are input to HEC-HMS hydrological model to simulate three typical floods. The coupling simulation results are better than the traditional forecast method, and it prolongs the flood forecast period of the Taihang Piedmont basin.
      PubDate: 2024-02-10
       
 
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  Subjects -> METEOROLOGY (Total: 106 journals)
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