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  Subjects -> METEOROLOGY (Total: 106 journals)
Showing 1 - 36 of 36 Journals sorted alphabetically
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 4)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 45)
Advances in Climate Change Research     Open Access   (Followers: 50)
Advances in Meteorology     Open Access   (Followers: 27)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 11)
Aeolian Research     Hybrid Journal   (Followers: 7)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 20)
American Journal of Climate Change     Open Access   (Followers: 37)
Atmósfera     Open Access   (Followers: 2)
Atmosphere     Open Access   (Followers: 33)
Atmosphere-Ocean     Full-text available via subscription   (Followers: 16)
Atmospheric and Oceanic Science Letters     Open Access   (Followers: 13)
Atmospheric Chemistry and Physics (ACP)     Open Access   (Followers: 43)
Atmospheric Chemistry and Physics Discussions (ACPD)     Open Access   (Followers: 15)
Atmospheric Environment     Hybrid Journal   (Followers: 72)
Atmospheric Environment : X     Open Access   (Followers: 3)
Atmospheric Research     Hybrid Journal   (Followers: 73)
Atmospheric Science Letters     Open Access   (Followers: 40)
Boundary-Layer Meteorology     Hybrid Journal   (Followers: 32)
Bulletin of Atmospheric Science and Technology     Hybrid Journal   (Followers: 5)
Bulletin of the American Meteorological Society     Open Access   (Followers: 63)
Carbon Balance and Management     Open Access   (Followers: 6)
Ciencia, Ambiente y Clima     Open Access   (Followers: 1)
Climate     Open Access   (Followers: 8)
Climate and Energy     Full-text available via subscription   (Followers: 6)
Climate Change Economics     Hybrid Journal   (Followers: 44)
Climate Change Responses     Open Access   (Followers: 23)
Climate Dynamics     Hybrid Journal   (Followers: 45)
Climate Law     Hybrid Journal   (Followers: 6)
Climate of the Past (CP)     Open Access   (Followers: 6)
Climate of the Past Discussions (CPD)     Open Access   (Followers: 1)
Climate Policy     Hybrid Journal   (Followers: 51)
Climate Research     Hybrid Journal   (Followers: 9)
Climate Resilience and Sustainability     Open Access   (Followers: 21)
Climate Risk Management     Open Access   (Followers: 10)
Climate Services     Open Access   (Followers: 4)
Climatic Change     Open Access   (Followers: 69)
Current Climate Change Reports     Hybrid Journal   (Followers: 17)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 6)
Dynamics of Atmospheres and Oceans     Hybrid Journal   (Followers: 19)
Earth Perspectives - Transdisciplinarity Enabled     Open Access   (Followers: 1)
Economics of Disasters and Climate Change     Hybrid Journal   (Followers: 13)
Energy & Environment     Hybrid Journal   (Followers: 25)
Environmental and Climate Technologies     Open Access   (Followers: 3)
Environmental Dynamics and Global Climate Change     Open Access   (Followers: 21)
Frontiers in Climate     Open Access   (Followers: 4)
GeoHazards     Open Access   (Followers: 2)
Global Meteorology     Open Access   (Followers: 20)
International Journal of Atmospheric Sciences     Open Access   (Followers: 25)
International Journal of Biometeorology     Hybrid Journal   (Followers: 3)
International Journal of Climate Change Strategies and Management     Hybrid Journal   (Followers: 29)
International Journal of Climatology     Hybrid Journal   (Followers: 28)
International Journal of Environment and Climate Change     Open Access   (Followers: 20)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
Journal of Agricultural Meteorology     Open Access  
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 42)
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 33)
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 133)
Journal of Atmospheric Chemistry     Hybrid Journal   (Followers: 23)
Journal of Climate     Hybrid Journal   (Followers: 56)
Journal of Climate Change and Health     Open Access   (Followers: 4)
Journal of Climatology     Open Access   (Followers: 4)
Journal of Hydrology and Meteorology     Open Access   (Followers: 39)
Journal of Hydrometeorology     Hybrid Journal   (Followers: 10)
Journal of Integrative Environmental Sciences     Hybrid Journal   (Followers: 4)
Journal of Meteorological Research     Full-text available via subscription   (Followers: 2)
Journal of Meteorology and Climate Science     Full-text available via subscription   (Followers: 21)
Journal of Space Weather and Space Climate     Open Access   (Followers: 30)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 83)
Journal of the Meteorological Society of Japan     Partially Free   (Followers: 7)
Journal of Weather Modification     Full-text available via subscription   (Followers: 4)
Mediterranean Marine Science     Open Access   (Followers: 2)
Meteorologica     Open Access   (Followers: 2)
Meteorological Applications     Open Access   (Followers: 4)
Meteorological Monographs     Hybrid Journal   (Followers: 1)
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 4)
Meteorology and Atmospheric Physics     Hybrid Journal   (Followers: 29)
Mètode Science Studies Journal : Annual Review     Open Access  
Michigan Journal of Sustainability     Open Access   (Followers: 1)
Modeling Earth Systems and Environment     Hybrid Journal   (Followers: 1)
Monthly Notices of the Royal Astronomical Society     Hybrid Journal   (Followers: 13)
Monthly Weather Review     Hybrid Journal   (Followers: 30)
Nature Climate Change     Full-text available via subscription   (Followers: 145)
Nature Reports Climate Change     Full-text available via subscription   (Followers: 40)
Nīvār     Open Access   (Followers: 1)
npj Climate and Atmospheric Science     Open Access   (Followers: 6)
Open Atmospheric Science Journal     Open Access   (Followers: 6)
Open Journal of Modern Hydrology     Open Access   (Followers: 5)
Revista Iberoamericana de Bioeconomía y Cambio Climático     Open Access   (Followers: 1)
Russian Meteorology and Hydrology     Hybrid Journal   (Followers: 4)
Space Weather     Full-text available via subscription   (Followers: 27)
Studia Geophysica et Geodaetica     Hybrid Journal   (Followers: 1)
Tellus A     Open Access   (Followers: 21)
Tellus B     Open Access   (Followers: 20)
The Cryosphere (TC)     Open Access   (Followers: 8)
The Quarterly Journal of the Royal Meteorological Society     Hybrid Journal   (Followers: 32)
Theoretical and Applied Climatology     Hybrid Journal   (Followers: 14)
Tropical Cyclone Research and Review     Open Access   (Followers: 1)
Urban Climate     Hybrid Journal   (Followers: 5)
Weather     Hybrid Journal   (Followers: 20)
Weather and Climate Dynamics     Open Access   (Followers: 1)
Weather and Climate Extremes     Open Access   (Followers: 18)
Weather and Forecasting     Hybrid Journal   (Followers: 43)
Weatherwise     Hybrid Journal   (Followers: 18)
气候与环境研究     Full-text available via subscription   (Followers: 2)

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Theoretical and Applied Climatology
Journal Prestige (SJR): 0.867
Citation Impact (citeScore): 2
Number of Followers: 14  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1434-4483 - ISSN (Online) 0177-798X
Published by Springer-Verlag Homepage  [2469 journals]
  • Spatiotemporal analysis of drought and rainfall in Pakistan via
           Standardized Precipitation Index: homogeneous regions, trend, wavelet, and
           influence of El Niño-southern oscillation

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      Abstract: Abstract The phenomenon of drought is common in the world, especially in Pakistan. El Niño-Southern Oscillation (ENSO) influences the spatial and temporal variability of drought and rainfall in Pakistan. Therefore, the objectives of this study are to identify homogeneous rainfall regions and their trend regions, as well as the impact of ENSO phases. In this study, monthly rainfall data from 44 weather stations are used during 1980–2019. Moreover, descriptive and exploratory statistics tests (e.g., Pettitt and Mann-Kendall—MK), Sen method, and cluster analysis (CA) are evaluated along with the annual Standardized Precipitation Index (SPI) on spatiotemporal scales. ENSO occurrences were classified based on the Oceanic Nino Index (ONI) for region 3.4. Using the cophenetic correlation coefficient (CCC) and a significance level of 5%, seven methods were applied to the rainfall series, with the complete method (CCC > 0.9082) being the best. According to the CA method, Pakistan has four groups of homogeneous rainfall (G1, G2, G3, and G4). Descriptive and exploratory statistics showed that G1 differs from the other groups in size and spatial distribution. Pettitt’s technique identified the most extreme El Niño years in terms of spatial and temporal drought variability, along with the wettest months (March, August, September, June, and December) in Pakistan. Non-significant increases in Pakistan’s annual precipitation were identified via the MK test, with exceptions in the southern and northern regions, respectively. No significant increase in rainfall in Pakistan was found using the Sen method, especially in regions G2, G3, and G4. The severity of the drought in Pakistan is intensified by El Niño events, which demand attention from public managers in the management of water resources, agriculture, and the country’s economy.
      PubDate: 2022-05-18
       
  • Climate change vulnerability in Bangladesh based on trend analysis of some
           extreme temperature indices

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      Abstract: Abstract Extreme temperature is the key indicator of extreme climatic events. The goal of this research was to better understand the long-term trends and shifting behaviors associated with Bangladesh’s record-breaking high temperatures in the country’s atmosphere. Data from 26 Bangladeshi meteorological stations collected between 1981 and 2018 was analyzed with RClimDex. The annual count of warm (cold) spell duration increased, according to the findings (decrease). In the coastal regions, this rising temperature trend is more pronounced. There were longer (shorter) periods of warm (cold) weather in the twentieth century than there were in the previous decade. As a result, the length of warm (cold) spells has become longer since the beginning of the twenty-first century, as compared to the last quarter of the twentieth century. There is little fluctuation in diurnal temperatures, but they are getting smaller and smaller. There is a 13% decrease in the Cold Spell Duration Indicator (CSDI), which indicates that we are in for a long, cold winter. At a rate of 14% per year, the Warm Spell Duration Indicator (WSDI) annual count suggests an extremely hot summer is imminent. Diurnal temperature range (DTR) values decreased by 1.1% year-round, raising the specter of climate extremes like the CSDI and WSD. An increasing (decreasing) trend in indicators of how long hot (cold) weather lasts indicates an increase (decrease) in Bangladesh’s warm atmosphere. As a result, an increase in the number of extreme weather events, particularly along the coasts, should be expected across the country.
      PubDate: 2022-05-17
       
  • Uncertainties in assessing climate change impacts and adaptation options
           with wheat crop models

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      Abstract: Abstract Mechanistic and process-oriented crop models are important tools to quantify the potential impacts of climate change on crop production and yields and to evaluate the efficacy of management strategies, policies, or actions developed by the stakeholders. This review focusses on the epistemic uncertainty associated with the use of crop models. It firstly identifies the main sources of uncertainties from the perspectives of crop model inputs, methods for estimating crop parameters, crop model structure/complexity/process scale, and the underpinning experimental datasets. Pathways for managing those uncertainties are identified and future research directions are discussed. The conclusion is that strengthening experimental studies on the effects of extreme temperatures including their interaction with enhanced atmospheric CO2 concentration on crop production and further improvement, evaluation, and inter-comparison of crop models based on new experimental datasets will contribute to the reduction of uncertainties in projected climate change impacts and evaluated adaptation options. It is envisaged that crop models will continue to serve as an important research tool in addressing climate change in the agricultural sector specifically and in general with respect to global food security. Therefore, this review will provide the agroclimate impact modelling community with information on the sources of uncertainties and the ways forward to tackle this critical issue.
      PubDate: 2022-05-16
       
  • Cooling island effect of urban lakes in hot waves under foehn and climate
           change

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      Abstract: Abstract The central region of Vietnam has a tropical monsoon climate but often undergoes heat waves due to uncontrolled urbanization, foehn winds, and climate change. Water bodies are considered effective candidates for heat mitigation in cities through the water cooling island (WCI) effect. Quantifying the WCI capacity of water areas and related factors is necessary for sites with advantages of surface water. The current attempt used the WCI effect range (Lmax), temperature drop amplitude (ΔTmax), and temperature gradient (Gtemp) to investigate the cooling effect of 20 lakes in the Thanh Noi region, Hue City. Data derived from high-resolution Google Earth, Landsat-8 Satellite Imagery Data, and ground truth. The results show that the average water temperature of the 20 studied lakes was about 36.61 °C, lower than the average temperature in the area with an urban heat island (UHI) of about 2.82 °C. The mean Lmax was 150 m, ΔTmax was 1.52 °C, and Gtemp was 10.16 °C /km or 0.01 °C/m. Climate characteristics and human impacts had reduced the ability of the lakes to create WCI during the period when the lake water level was low. The factors that influenced the WCI significantly were the landscape shape index (LSI), the proportion of green (PG), and the percentage of impervious surfaces (PI). Most lakes with relatively simple LSI, high PG, and low PI obtained high WCI, suggesting that structural and landscape characteristics played a critical role in urban cooling.
      PubDate: 2022-05-16
       
  • Analyzing spatial–temporal change of multivariate drought risk based on
           Bayesian copula: Application to the Balkhash Lake basin

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      Abstract: In the past century, drought events were likely to be frequent and severe in the arid areas under climate change impact. In this study, a Bayesian copula multivariate analysis (BCMA) method is developed for assessing the impact of spatial–temporal variation on drought risk, through coupling Bayesian copula with multivariate analysis. BCMA can reveal the dynamic characteristics of droughts and deal with the uncertainty caused by copula parameters when modeling the dependent structures of variable pairs (duration-severity-affected area). BCMA is applied to the Balkhash Lake basin in Central Asia for assessing multivariate drought risk during 1901–2020. Some major findings can be summarized: (1) in 1901–2020, the basin suffered 53 droughts; the most severe drought occurred from October 1973 to January 1977 (39 months), and 95% of the basin was affected (335,800 km2); (2) droughts usually develop in the direction of “east–west,” and Ili River delta and alluvial plain are the most frequent areas (47.2%) in the basin; (3) droughts show significant seasonality and frequently occur in spring and summer (64.2%), and drought risks of the middle and lower reaches of Ili River are the highest in spring and summer; (4) multivariate characteristics significantly affect drought risk, and drought risk ranges from 1.9 to 18.1% when the guarantee rate is 0.99; (5) the possible causes of drought risk dynamics are meteorological factors (e.g., precipitation and evapotranspiration) and underlying surface factors (e.g., runoff and soil moisture). The findings suggest that droughts in the Balkhash Lake basin are affected by climatic factors, and BCMA can provide methodological support for the studies of drought in other arid regions. Graphical abstract
      PubDate: 2022-05-14
       
  • Characteristics and mechanisms study of abnormal meridional movement of
           the Western Pacific Subtropical High in July 2020

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      Abstract: Abstract The climate in China is extremely abnormal in July 2020, which is largely attributed to the anomalous activities of the Western Pacific Subtropical High (WPSH). In the present study, statistical analysis and diagnostic calculation of individual terms of the complete form of vorticity equation are conducted to investigate features of meridional movement of WPSH in July 2020, and the mechanism for the anomalous meridional movement of the WPSH is also explored. Results indicate that in July 2020, the western segment of the WPSH ridge line persistently remained to the south of its climatological position, especially in the lower troposphere. Meanwhile, the eastern segment of the ridge line continuously stayed to the north of its normal position. Such a configuration leads to a large meridional span of the WPSH ridge line and is directly responsible for the super long Meiyu season in eastern China in the summer of 2020. The persistent southward shift of the WPSH ridge line in its western segment is attributed to the maintenance of strong positive vorticity anomalies to the north of the WPSH ridge line. Whereas the abnormally frequent southward invasion of cold air and the activities of troughs in the mid- and high-latitude regions are the main reasons for the maintenance of the vorticity anomalies to the north of the WPSH ridge line. In addition, the anomalously southward WPSH combined with the Indian ocean SST anomalies inhibited the development of tropical convection in the South China Sea—Philippines, which subsequently weakened the vorticity anomalies to the south of the WPSH ridge line and made the P-J teleconnection pattern exhibiting in abnormally negative phase, and resulting in anomalous cyclonic circulation and negative geopotential height developed in the middle and lower troposphere over southern Japan, which further caused the WPSH centroid locating southward and restricted the northward movement of the WPSH.
      PubDate: 2022-05-13
       
  • Coupling relationship between meteorological factors and precipitation: an
           empirical study from Xinjiang Province, China

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      Abstract: Abstract The present study examines the spatio–temporal trend analysis of rainfall and temperature over time (annual, warm season, and cold season) from 1981 to 2010 in Xinjiang Province, China. The Mann–Kendall test, least square linear regression, and Spearman rank correlation were used to detect the direction and magnitude of change in rainfall and temperature. A panel data set of 8 meteorological factors during 1981–2010 was used to detect the statistical relationship between rainfall and other meteorological factors in Sect. 4.4. The results showed that annual rainfall and seasonal rainfall increased steadily, and the increasing rainfall trend during the cold season was more significant compared to the warm season. For temperature, average, high, and low temperatures increased on the annual and seasonal scales. Spatially, the northern parts of Xinjiang received more rainfall and showed higher variability. Based on the rainfall trend, the province was clustered into three parts using the K–means method. Based on this clustering, the statistical relationships between rainfall and other meteorological factors were investigated. The results showed that temperature and relative humidity were significantly correlated with rainfall in the three clusters, whereas sunshine, wind speed, and cloud cover had small influences on rainfall.
      PubDate: 2022-05-12
       
  • Spatiotemporal analysis of precipitation and temperature concentration
           using PCI and TCI: a case study of Khuzestan Province, Iran

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      Abstract: Abstract Temperature and precipitation are the basic elements of climate, and their variation can change the water demands of different uses. In this study, the trend of temperature and precipitation of Iran’s largest agricultural products hub (i.e., Khuzestan Province) was examined in monthly, seasonal, and annual timescales in the period 1988–2018 (30 years) in six synoptic stations. The effect of long-term persistence was eliminated, considering the effect of the Hurst coefficient using the fourth version of the Mann–Kendall nonparametric test. The time of occurring sudden change in the time series and concentration of precipitation and the temperature of the study area were also analyzed using the Pettitt test, precipitation concentration index (PCI), and temperature concentration index (TCI), respectively. The results showed that there is a direct relationship between increasing temperature and decreasing precipitation in the study area. The annual temperature has experienced a significant increasing trend, while the annual precipitation has decreased significantly in all stations. Due to the significant trend in the studied series, the Pettitt test detected a total of 94 significant failure points (year of failure) and it was found that sudden changes in air temperature time series began in November 1993 at Ramhormoz station and continued to January 2009. The results of investigating the temperature and precipitation trends in the two sub-periods (1988–2000 and 2001–2018) showed that most of the significant increasing trends in temperature time series were experienced in the first period and most of the significant decreasing trends in precipitation time series were experienced in the second period. In addition to the trend and sudden changes in precipitation and temperature series of the study area, PCI and TCI showed that the climate of the study area is changing and the tendency to climatic irregularities is increasing. Therefore, the trend evaluation of temperature and precipitation at different time and space scales has great importance in planning and managing water resources.
      PubDate: 2022-05-12
       
  • Analysis and farmers’ perception of climate change in the Kashmir
           Valley, India

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      Abstract: Abstract Analysis of climatic variables is important for the detection and attribution of climate change trends and has received considerable attention from researchers across the globe including in India. The mountains surrounding the Kashmir Valley in northwestern India are often glacierized. Hence, the area will react strongly to even small changes in temperature and precipitation. To this end, the current study analyzes the changing patterns in precipitation and temperature for various elevation zones of the Kashmir Valley and also investigates farmers’ perceptions of climate change. The results revealed that during the last 40 years (1980–2019), the annual minimum and maximum temperatures have increased by 0.02 and 0.017 °C/year. With some numerical variations, the warming trends were observed in all seasons of different elevation zones. The rate of increasing temperature in plains and mountains was more than Karewas and foothill regions. Conversely, a downward trend of annual precipitation at − 5.01 mm/year has been recorded due to declining precipitation during spring, winter, autumn, and summer seasons at − 4.95, − 0.30, − 0.28, and − 0.06 mm/year, respectively. Higher rates of declining precipitation around the mountainous area may be detrimental to the crops of Kashmir Valley by disturbing the water supply and groundwater recharge. Focused on the farmers’ perception of climate change, the majority of farmers (> 65%) perceived the changes in temperature and precipitation in line with the above results of historical meteorological data analysis, conforming with an upward and downward trend of temperature and precipitation respectively over time. Farmers’ knowledge coupled with the actual data analysis may concertedly give a clearer understanding of climate change–related instability and patterns in weather variables, which is critically important for planning and implementing appropriate adaptation measures in their farming against climate change in the Kashmir Valley.
      PubDate: 2022-05-08
       
  • Optimal selection of representative climate models and statistical
           downscaling for climate change impact studies: a case study of Rhode
           Island, USA

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      Abstract: Abstract The future climate impact studies rely on future projections obtained from downscaling of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. The main challenge is large, yet growing pool of CMIP5 models, posing a high computational cost for analyzing the climate model ensemble. The optimal model selection from the large pool is hence necessary. It is critical that the selected climate models represent the uncertainty range of possible future and have high skill in past performance. In this study, we use a multi-criteria decision process based on the coupling of the envelope and past performance approach to identify potential future climate stresses for RI, USA. The selection is based on projected changes in annual climatic means (precipitation and temperature) followed by a range of projected changes in climatic extremes and past performance among these models. From a pool of 109 models from RCP4.5 and 79 models from RCP8.5, a final subset of 4 models was selected for RCP4.5 and RCP8.5 respectively. The change in annual climatic means for temperature varied + 1.7 to + 3.0 °C in RCP4.5 and + 2.4 to + 5.8 °C in RCP8.5, and the range in climate mean of annual precipitation varied from 0.2 to 12.7% in RCP4.5 and − 4.1 to 15.9% in RCP8.5. The selected climate models are statistically downscaled to produce reliable local-scale climate estimates. Various variants of quantile mapping are studied, and quantile delta mapping is applied to systematically reduce biases and preserve raw GCM signals.
      PubDate: 2022-05-07
       
  • Relative performance of CMIP5 and CMIP6 models in simulating rainfall in
           Peninsular Malaysia

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      Abstract: Abstract This study evaluated the skills of global climate models (GCMs) of the fifth and sixth Coupled Model Intercomparison Project (CMIP5 and CMIP6) in simulating observed rainfall climatology, seasonal variability, and probability distribution function (PDF) in Peninsular Malaysia. Monthly rainfall records of eighty stations for 1975 − 2005 were employed for this purpose. The Kling-Gupta efficiency was applied to estimate GCMs’ skill to reconstruct rainfall climatology and seasonal variability, while Perkins skill score to replicate PDF. The GCMs of individual CMIP were initially ranked based on the individual metric, and then a compromise rating matric was then employed for the grading. Finally, the highest-ranking CMIP6 GCMs were identified and employed for rainfall projections over Peninsular Malaysia for different shared socioeconomic pathways (SSPs). Results revealed higher bias in CMIP6 GCMs than CMIP5 GCMs but the better association in replicating rainfall climatology and seasonal variability. The EC-ERATH was the best performing model in CMIP5, followed by MPI-ESM-LR, FGOALS-g2, and CanESM2. In contrast, MPI-ESM-MR showed the highest skill among CMIP6 models, followed by MPI-ESM-LR, MIROC-ESM, and GFDL-ESM2M. The employment of the most skilled four GMIP6 GCMs in projecting rainfall in the peninsula revealed a non-linear rainfall change for SSPs—an increase in rainfall for SSP1-26 and SSP5-85 and a decrease for SSP2-45 and SSP3-70. Overall, rainfall was projected to increase in the northwest and central south by 10 − 20% and decrease in the northeast and far south by 1 to 30%.
      PubDate: 2022-05-07
       
  • Frequency-intensity-distribution bias correction and trend analysis of
           high-resolution CMIP6 precipitation data over a tropical river basin

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      Abstract: Abstract Advancements in computational power have enabled general circulation models (GCMs) to simulate climate variables at a higher resolution. However, GCM outputs often deviate from the observed climatological data and therefore need bias correction (BC) before they are used for impact studies. While there are several BC methods, BCs considering frequency, intensity and distribution of rainfall are few. This study proposes a BC method which focuses on separately correcting the frequency, intensity and distribution of precipitation. This BC was performed on high-resolution daily precipitation simulations of Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 with a 20-km grid size (MRI-AGCM3-2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6) on Netravati basin, a tropical river basin in India. The historical rain gauge station data was considered for testing the effectiveness of the BC method applied. The quantile–quantile (Q–Q) plot, Taylor diagram, Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS) and correlation coefficient (R) are employed for the evaluation of the BC method. Higher R and R2 and lower RMSE, MAE and PBIAS values were observed for the bias-corrected GCM data than raw simulation. The PBIAS reduced from 15.6 to 6% when BC was applied. The analysis suggested that the proposed method effectively corrects the bias in rainfall over the basin. Furthermore, an attempt has been made to analyse the trend of historical and future rainfall in the basin. The analysis revealed a declining trend of precipitation in monsoon months with the magnitude of 12.44 mm and 56.7 mm in the historical and future periods respectively. This study demonstrates that BC should be applied before the use of GCM simulated precipitation for any analysis or impact studies to improve the predictions.
      PubDate: 2022-05-06
       
  • Understanding the influence of climate elements on traffic: the wind
           impact approach

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      Abstract: Abstract Due to intense highway congestion in Europe, increased percentage of highway accidents, as well as mortality rate, safety is an imperative in highway planning and design. Highway design safety standards have been researched extensively, but not enough attention has been paid to the surrounding environmental impacts, foremost climate elements. Therefore, this research attempts to understand the least researched climate element — the wind, and its impact on highway safety. The highway landscape falls under the category of the wind impacts that can cause significant problems for the drivers throughout the year. The values for wind direction, frequency, and intensity were taken from the CARPATCLIM database and Meteorological Annuals. The evaluation of homogenized and harmonized set of data on a daily basis for a 20-year period documented a variety of high-intensity wind impacts on the researched highway. By using the ArcGIS and the interpolation method, it has been clearly observed at which points the effect of intense winds was present the most. In order to understand the overall problem of the impacts of winds on the researched highway, the fieldwork was conducted in various weather conditions. The checklists photo-documented and qualitatively described the observed extreme wind events (alone or combined with one more climate element). All calculated and collected data were observed and the image of the current situation was provided, and the proposal for control of the impact of wind using an adequate vegetation assembly (windbreaks) and landscape design has been offered.
      PubDate: 2022-05-06
       
  • Meteorological drought analysis using copula theory for the case of upper
           Tekeze river basin, Northern Ethiopia

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      Abstract: Abstract Meteorological drought is among the key climate-related risks affecting Ethiopia. It indicates a shortage of precipitation over a long period, usually for a season or a year. This study uses copula theory to analyze meteorological drought in the upper Tekeze river basin, Northern part of Ethiopia. Meteorological drought analysis using copula theory provides a promising opportunity to deal with such risks in advance and to improve sectoral resilience. In this study, long-year (1982–2020) rainfall and soil moisture data were used to analyze standardized precipitation index (SPI) and standardized soil moisture index (SSI), respectively. The best-fit copula family was selected to construct the joint probability distribution (JPD) of SPI and SSI. Multivariate standardized drought index (MSDI) at 3-, 6-, and 12-month timescales was analyzed using the MSDI toolbox. The nonparametric Mann–Kendall (M–K) statistical test was used for trend detection. We found that the newly developed MSDI captures all drought events during the observation period compared with SPI and SSI. MSDI particularly showed the most recent drought of 2015, with the drought duration and severity of 4 months and 6.4, respectively, and its joint return period was 5.24 years. The M–K and Sen’s Slope estimator statistical tests indicated a positive trend for all drought timescales in the basin. The spatial extent of MSDI shows most frequently extreme drought occurred in the basin. Meteorological drought analysis using multiple indices is better than a single drought index. This approach can better inform adaptation policies and interventions that are aimed at monitoring and reducing drought risk in the basin and beyond.
      PubDate: 2022-05-04
       
  • Urban flood vulnerability assessment in a densely urbanized city using
           multi-factor analysis and machine learning algorithms

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      Abstract: Abstract Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland, using three machine learning models, such as the Bayesian logistic regression (BLR), the artificial neural networks (ANN), and the deep learning neural networks (DLNNs). The perfomance of these three methods was assessed in order to select the best method for flood vulnerability mapping in densely urbanized city. Thus, initially, thirteen flood predictors were evaluated using the information gain ratio (IGR), and eight most important predictors were considered from model training and testing. The performance of the applied models and accuracy of the result was evaluated through the area under the curve (AUC) and statistical measures. By using the testing dataset, the result reveals that DLNN (AUC = 0.877) is the more performant model in comparison to ANN (AUC = 0.851) and BLR (AUC = 0.697). However, the BLR model has the lowest predictive capability. The results of the present study could be effectively used for the urban flood management strategies.
      PubDate: 2022-05-04
       
  • New method for estimating reference evapotranspiration and comparison with
           alternative methods in a fruit-producing hub in the semi-arid region of
           Brazil

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      Abstract: Abstract Analysing alternative methods for estimating reference evapotranspiration (ET0) is essential for improving the management of water resources, especially where meteorological data are not fully available for application of the standard Penman–Monteith (PM) method. In this study, the aim was (a) to propose a model for estimating ET0 and (b) to investigate its application together with alternative methods in an area of fruit production in the semi-arid region of Brazil. The Souza-Silva model was generated to estimate ET0 and together with other original methods based on thermal-water variables and global incident solar radiation was tested relative to the PM method. The Souza-Silva method (ET0SS =  − 0.00728 + 1.356325DPV + 0.174658Qg, where VPD is the water vapour-pressure deficit and Qg the global incident solar radiation) showed excellent performance in estimating ET0 with a low level of error (RMSE = 0.467 mm day−1 and MBE =  − 0.034 mm day−1). Among the original alternative methods, those for Hargreaves (ET0HS) and Camargo (ET0C) models did not perform well in the region under study. The Benevides and Lopes (ET0BL) and Valiantzas1 (ET0V1) methods gave excellent results; these were however even lower than methods based on the solar radiation. Compared to each of the original methods, the result of the Souza-Silva method showed greater precision and accuracy, with smaller estimation errors. It is concluded that this method, based on the water vapour-pressure deficit and solar radiation, can be used to estimate ET0 in places where data collection is limited.
      PubDate: 2022-05-03
       
  • Uncertainty of climate change impact on crop characteristics: a case study
           of Moghan plain in Iran

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      Abstract: Abstract Crop yield is one of the most critical factors in the food security chain. Climate plays a crucial role in crop water productivity in rainfed and irrigated crop productions. Climate changes would significantly impact crop characteristics, especially in Iran, where water is the major constraint of crop production. This study assessed the impact of climate change on crop water productivity with related uncertainty. The global climate model simulations of rainfall and temperature were statistically downscaled using LARS-WG6 for climate projection. The projected climate was used in the FAO AquaCrop model to simulate the variability of crop characteristics (crop cycle length, crop yield, and water productivity) for the assessment of climate change effect on major crops for three future horizons (2021–2040, 2041–2060, 2061–2080). Results revealed an increase in wheat yield by 14 − 54% and a decrease of growth duration by 1 − 12%, leading to an increase in water productivity by 9 − 96% in the future compared to the base period (1985–2016). In contrast, reduction in corn and soybean yield by 1 − 5% and 2 − 6% and growth period by 1 − 5% and 3 − 12%, and thus, an increase in water productivity by 1 − 9% and 2 − 24%, respectively, were projected. The growth duration of all the major crops was projected to decrease due to a rise in temperature and an increase in crop water productivity in the study area. The results indicate a more favorable condition for crop agriculture in the study area under the projected climate.
      PubDate: 2022-05-03
       
  • Observed and projected changes in temperature and precipitation extremes
           based on CORDEX data over Iran

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      Abstract: Abstract Due to increasing greenhouse gases, Iran is experiencing changes in patterns and trends of extreme climate events. Future climate extremes are one of the hottest topics affecting agricultural, economical, and environmental balances in many regions. Here, changes in temperature and precipitation extremes are investigated for the observational (1976–2005) and future (2025–2054) periods over Iran. For this, temperature and precipitation data of the CORDEX project acquired from three regional climate models with RCP4.5 and RCP8.5 scenarios are simulated using a developed multiscale bias correction downscaling method. For identifying trends and changes in 14 extreme climate indices used in this study from 54 climatic stations, the Mann–Kendall test and Sen’s slope estimator are used. Results show that notable changes can be seen in the climate extremes across Iran for both observed and projected data. Overall, more warming and increasing trends are found for both maximum and minimum values of daily maximum and minimum temperatures, which rise from RCP4.5 to RCP8.5. The number of cold days and nights indicate decreasing trends; alternatively, the number of warm days and nights show opposite behaviors. Changes in the monthly maximum 1-day and consecutive 5-day precipitation illustrate an increasing trend in most model-scenarios for the projected data in the future compared to the observed one. The spatial analysis results reveal that changes in the climate extremes are more noticeable in the northwestern and western regions of Iran. These areas will probably experience heavy disastrous rainfall because the number of very wet days and heavy precipitation days will increase in the future. Changes in climate extremes will likely increase the risk of severe extreme events in the future in these areas and, consequently, make society more vulnerable to natural disasters. The findings of this study can help decision-makers consider appropriate management in the face of climate change consequences.
      PubDate: 2022-05-02
       
  • Regionalization and association with global climate drivers of rainfall in
           the Rift Valley Lakes Basin of Ethiopia

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      Abstract: Abstract Regionalization and evaluation of associations of hydro-climatic variables with indices of global climate drivers are helpful for local-scale seasonal forecasting of weather patterns and planning water resources management. The main objective was to regionalize the Ethiopian Rift Valley Lakes Basin into homogenous sub-regions based on monthly and seasonal rainfalls and investigate the influence of some global climate drivers at the scale of the sub-regions to devise predictive tools. The dataset used monthly rainfall and global and regional climate indices anomalies from 1983 to 2014. Principal component analysis (PCA), Pearson correlation, and multivariate linear regression methods were applied using SPSS and R software. Based on PCA analysis, three principal components were identified which have a significant association with global climate indices. Over the study period, there were nine moderates to strong El Niño and six La Niña events; the warming phases received more rainfall and less in the cooling phase. Lagged sea surface temperature (SST) and atmospheric variables were selected as predictors based on significant associations with regional rainfall. The multiple linear regression analysis revealed the possibilities of deriving seasonal forecasts at the local level. The study showed that the model derived an excellent and scientifically robust seasonal rainfall prediction skill in a short lead time of the different seasons at a range of 30 to 80% in the sub-regional level. The capabilities of rainfall prediction skills help reduce climate-induced hazards in planning and decision-making processes by providing timely and specific climate information.
      PubDate: 2022-05-01
       
  • Correction to: WRF sensitivity simulations of a dense advection fog event
           in Istanbul

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      PubDate: 2022-04-13
       
 
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