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  Subjects -> METEOROLOGY (Total: 113 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: 39)
Advances in Meteorology     Open Access   (Followers: 28)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 10)
Aeolian Research     Hybrid Journal   (Followers: 6)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 20)
American Journal of Climate Change     Open Access   (Followers: 34)
Atmósfera     Open Access   (Followers: 3)
Atmosphere     Open Access   (Followers: 29)
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: 48)
Atmospheric Chemistry and Physics Discussions (ACPD)     Open Access   (Followers: 16)
Atmospheric Environment     Hybrid Journal   (Followers: 75)
Atmospheric Environment : X     Open Access   (Followers: 3)
Atmospheric Research     Hybrid Journal   (Followers: 71)
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: 51)
Carbon Balance and Management     Open Access   (Followers: 5)
Ciencia, Ambiente y Clima     Open Access   (Followers: 3)
Climate     Open Access   (Followers: 6)
Climate and Energy     Full-text available via subscription   (Followers: 7)
Climate Change Economics     Hybrid Journal   (Followers: 33)
Climate Change Responses     Open Access   (Followers: 18)
Climate Dynamics     Hybrid Journal   (Followers: 44)
Climate of the Past (CP)     Open Access   (Followers: 5)
Climate of the Past Discussions (CPD)     Open Access  
Climate Policy     Hybrid Journal   (Followers: 51)
Climate Research     Hybrid Journal   (Followers: 6)
Climate Resilience and Sustainability     Open Access   (Followers: 21)
Climate Risk Management     Open Access   (Followers: 7)
Climate Services     Open Access   (Followers: 3)
Climatic Change     Open Access   (Followers: 68)
Current Climate Change Reports     Hybrid Journal   (Followers: 10)
Developments in Atmospheric Science     Full-text available via subscription   (Followers: 31)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 5)
Dynamics of Atmospheres and Oceans     Hybrid Journal   (Followers: 19)
Earth Perspectives - Transdisciplinarity Enabled     Open Access  
Economics of Disasters and Climate Change     Hybrid Journal   (Followers: 9)
Energy & Environment     Hybrid Journal   (Followers: 24)
Environmental and Climate Technologies     Open Access   (Followers: 4)
Environmental Dynamics and Global Climate Change     Open Access   (Followers: 17)
Frontiers in Climate     Open Access   (Followers: 3)
GeoHazards     Open Access   (Followers: 2)
Global Meteorology     Open Access   (Followers: 18)
International Journal of Atmospheric Sciences     Open Access   (Followers: 23)
International Journal of Biometeorology     Hybrid Journal   (Followers: 1)
International Journal of Climate Change Strategies and Management     Hybrid Journal   (Followers: 27)
International Journal of Climatology     Hybrid Journal   (Followers: 30)
International Journal of Environment and Climate Change     Open Access   (Followers: 12)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 2)
Journal of Agricultural Meteorology     Open Access  
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 36)
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 34)
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 210)
Journal of Atmospheric Chemistry     Hybrid Journal   (Followers: 22)
Journal of Climate     Hybrid Journal   (Followers: 57)
Journal of Climate Change     Full-text available via subscription   (Followers: 16)
Journal of Climatology     Open Access   (Followers: 3)
Journal of Hydrology and Meteorology     Open Access   (Followers: 36)
Journal of Hydrometeorology     Hybrid Journal   (Followers: 11)
Journal of Integrative Environmental Sciences     Hybrid Journal   (Followers: 4)
Journal of Meteorological Research     Full-text available via subscription   (Followers: 1)
Journal of Meteorology and Climate Science     Full-text available via subscription   (Followers: 17)
Journal of Space Weather and Space Climate     Open Access   (Followers: 28)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 84)
Journal of the Meteorological Society of Japan     Partially Free   (Followers: 6)
Journal of Weather Modification     Full-text available via subscription   (Followers: 2)
Large Marine Ecosystems     Full-text available via subscription   (Followers: 1)
Mediterranean Marine Science     Open Access   (Followers: 1)
Meteorologica     Open Access   (Followers: 2)
Meteorological Applications     Hybrid Journal   (Followers: 4)
Meteorological Monographs     Hybrid Journal   (Followers: 2)
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 3)
Meteorology and Atmospheric Physics     Hybrid Journal   (Followers: 27)
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: 16)
Monthly Weather Review     Hybrid Journal   (Followers: 33)
Nature Climate Change     Full-text available via subscription   (Followers: 144)
Nature Reports Climate Change     Full-text available via subscription   (Followers: 39)
Nīvār     Open Access  
npj Climate and Atmospheric Science     Open Access   (Followers: 6)
Open Atmospheric Science Journal     Open Access   (Followers: 4)
Open Journal of Modern Hydrology     Open Access   (Followers: 7)
Revista Brasileira de Meteorologia     Open Access  
Revista Iberoamericana de Bioeconomía y Cambio Climático     Open Access  
Russian Meteorology and Hydrology     Hybrid Journal   (Followers: 3)
Space Weather     Full-text available via subscription   (Followers: 25)
Studia Geophysica et Geodaetica     Hybrid Journal  
Tellus A     Open Access   (Followers: 22)
Tellus B     Open Access   (Followers: 21)
The Cryosphere (TC)     Open Access   (Followers: 6)
The Quarterly Journal of the Royal Meteorological Society     Hybrid Journal   (Followers: 28)
Theoretical and Applied Climatology     Hybrid Journal   (Followers: 13)
Tropical Cyclone Research and Review     Open Access  
Urban Climate     Hybrid Journal   (Followers: 4)
Weather     Hybrid Journal   (Followers: 18)
Weather and Climate Dynamics     Open Access  
Weather and Climate Extremes     Open Access   (Followers: 16)
Weather and Forecasting     Hybrid Journal   (Followers: 27)
Weatherwise     Hybrid Journal   (Followers: 4)
气候与环境研究     Full-text available via subscription   (Followers: 1)

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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  [2658 journals]
  • Simulation of rainfall-runoff process using an artificial neural network
           (ANN) and field plots data

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      Abstract: Rainfall-runoff modeling is necessary for many hydrological studies, such as estimating peak discharges and designing hydraulic structures. The intensity and frequency of extreme climatic events necessitate the use of advanced approaches that incorporate different climatic and landscape parameters for rainfall-runoff modeling. The majority of small basins around the world lack hydrometric data. This study applied an artificial neural network (ANN) to simulate the rainfall-runoff process using data from field sampling plots in conjunction with rainfall and hydrometric data. For this purpose, similarly sized field plots were established among different land uses to determine the amounts of initial loss and infiltration during rainfall occurrences at the Talar basin in the north of Iran. The modeling process was carried out using a multi-layer perceptron network where the network inputs were rainfall time series, initial loss, soil antecedent moisture condition (A.M.C), and the time to peak of the basin, and the output was runoff time series. The data from rain gauge and hydrometric stations and field plots were collected for three consecutive months. The threefold exercises of training (R-sqr = 0.96, MSE = 0.005), cross-validation (R-sqr = 0.95, MSE = 0.006), and test (R-sqr = 0.81, MSE = 0.05) have yielded favorable results. The modeling results also indicated the significance of the cumulative rainfall data and initial loss in the modeling process. Results show that runoff time series and flood hydrograph can be simulated using the optimal inputs and an appropriate neural network structure for the basins without active hydrometric stations.
      PubDate: 2021-10-17
       
  • A simple model of blocking action over a hemisphere

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      Abstract: A two-zone model of the atmospheric circulation over a hemisphere is considered. The latitude φ of the boundary between the zone of the Rossby circulation regime at mid and high latitudes and the zone of the Hadley circulation regime at low latitudes serves as a model variable. The principle of maximum of the (information) entropy of the eddy regime within the Rossby regime zone is used to determine a statistical (climatic) equilibrium value of φ. Based on the proposed model, the question of atmospheric blocking action over the hemisphere is addressed. An attempt has been made to represent a blocking phenomenon as a necessary attribute of the atmospheric circulation in statistical equilibrium. The model suggests that long-term climate change related either to the (significant) global warming or to the (significant) global cooling, both respective to the current climate state, and quantified in terms of changes in latitude φ, leads to an increase in the probability of blocking action.
      PubDate: 2021-10-16
       
  • Applicability and improvement of different evapotranspiration methods of
           reference crops in Jiangxi Province

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      Abstract: To achieve accurate evaluation of evapotranspiration of reference crops (ET0) in Jiangxi, China, in the absence of systematic climatological data, with reference to the FAO-56 Penman–Monteith (P-M) equation, the Priestley-Taylor (P–T) method, the Makkink method, the Hargreaves-Samani (H–S) method, the Irmak-Allen (I-A) method, the Penman1948 (48PM) method, the Penman-Van Bavel (PVB) method, the Baier-Robertson (B-R) method, the improved Baier-Robertson (M-B-R) method, the Schendel (Sch) method, the Turc method, the Jensen-Haise (J-H) method, and the Brutsaert-Stricker (B-S) method were used to evaluate the daily climatological data collected by 26 weather stations in Jiangxi, China, and 17 weather stations in adjacent provinces. The results were compared with each other and parameter rate determination was conducted. The results indicated that the Turc method exhibited optimized applicability before parameter rate determination and the average root mean square error (RMSE) and the average normalized root mean square error (NRMSE) by this method were 0.39 mm/d and 0.157 mm, respectively. However, parameter rate determination led to negligible improvement in accuracy for this method. The Turc method could be directly applied in Jiangxi (except Nanchang). For special distribution of error after parameter rate determination, all methods exhibited significant errors in Northern Jiangxi. Herein, the 48PM method and the B-S method showed good applicability after parameter rate determination and RMSE and NRMSE of data by these methods ranged in 0.06 ~ 0.34 mm/d and 0.08 ~ 0.27, 8 ~ 27%, respectively, and their d-indices were close to 1. The annual over-estimations in weather stations in Jiangxi were below 30 mm. In the absence of data about relative humidity and wind speed, the P–T method was an appropriate simplified method for Jiangxi. In this case, α was slightly lower than the default value (1.05 ~ 1.18), RMSE was within 0.21 ~ 0.66 mm/d, and NRMSE was within 0.08 ~ 0.308 ~ 30%. Accuracy of RMSE, d-index, and NRMSE of data by the P–T method, the I-A method, and the PVB method was consistent with all stations, while that by the Mak method was slightly lower, which could be attributed to severe over-estimation in July and August. RMSE of the H–S method, the B-R method, the M-B-R method, the J-H method, and the Sch method were above 0.75 mm/d and these methods were not suitable for accurate evaluation of ET0 in Jiangxi, China. The annual ET0 was calculated by various methods (except the 48PM method and the B-S method) exhibited significant variation around 2003. This may be attributed to significant changes in certain meteorological factors over recent years.
      PubDate: 2021-10-16
       
  • New climatic zones in Iran: a comparative study of different empirical
           methods and clustering technique

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      Abstract: Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This study aims to compare four empirical approaches to climate classification (Thornthwaite and Mather, De Martonne, the Extended De Martonne, and the IRIMO (I.R. of Iran Meteorological Organization)) with Ward’s hierarchical agglomerative clustering applied to the climate of Iran. The dataset used in this study comprises maximum and minimum temperatures and precipitation data of 356 weather stations extracted from IRIMO’s databases. Thirty-five synoptic weather stations are selected among 356 stations. These stations are selected regarding the best uniform distribution, elevation, windward and leeward sides of the mountain ranges, and availability of a continuous 50-year data (1966–2015). Compared with the other three empirical reference methods of climate classification, the Thornthwaite and Mather method emphasizes the role of water bodies and air masses in determining the climate type of a region. Highlighting these two factors is identified as the main advantage of this method over the other three. This advantage is the most noticeable for the highlands/mountainous regions, in the vicinity of the Zagros Mountains, and in the western regions of Iran. As a case in point, while in the De Martonne and the Extended De Martonne methods, the Zagros storm cell is climatically classified similar to patchy areas in Caspian Sea coastal zone, this cell is correctly identified as a separate zone in the Thornthwaite and Mather method. The results also reveal that the clusters obtained from Ward’s algorithm are comparable to those of empirical climate classifications, particularly Thornthwaite and Mather method.
      PubDate: 2021-10-13
       
  • Daily precipitation concentration in Central Coast Vietnam

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      Abstract: Empirical frequency distribution of daily precipitation amounts can be fitted by a negative exponential distribution, because anywhere there are many small daily totals and few large ones. Therefore, the cumulative percentages of days with precipitation, sorted in increasing order according to their amounts, against the cumulative percentage of the rainfall amounts that they contribute are fitted by positive exponential curves Y = aXebx, a and b constants. Based on these curves, the Concentration Index (CI) evaluates the contribution of the rainiest days to the total amount. In this study the CI has been calculated for 15 meteorological stations in Da Nang city and Quang Nam province in Central Coast Vietnam, for the 1979–2016 period. The results show high values of CI, ranging from 0.62 to 0.72. Conversely, the linear correlation between altitude and CI is negative (R = -0.60, p < 0.01). There are no correlations between the latitude nor the annual mean number of precipitation days and the CI. CI change for the sub-periods of 1979–1997 and 1998–2016 is also analyzed.
      PubDate: 2021-10-13
       
  • Extreme temperature return level mapping for northwest Turkey

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      Abstract: Risk management and design strategies for extreme temperatures are generally based on a 50-year return level in many national and international legislations. Recent studies have reported significant increasing trends in extreme temperature characteristics all over the world including Turkey. For such an analysis, return level mapping of climatic variables under non-stationarity conditions is a new topic. For this reason, the goal of this study was to introduce a mapping methodology for a 50-year return level of the extreme temperatures for northwest Turkey by considering the non-stationarity conditions. Generalized Extreme Value distribution was fitted for this purpose to de-trended extreme temperature time series. Multiple regression models were developed accordingly based on the physiographic parameters to interpolate point-based return levels over the study area. Observed annual maximum and minimum temperatures as well as the 50-year return levels indicated a high spatial variability over the region. According to the results of the study, Continentality Index, elevation, and latitude are determined to be the appropriate predictors for obtaining annual minimum temperature return level maps while the first two parameters are optimal for a statistical model of the maximum temperatures. Return level extreme temperature maps were produced with 2.5 × 2.5-km grids. Under climate change and its variability, it is of utmost importance for any method to consider the non-stationarity in calculations when modeling the changing conditions. The maps that are produced can be a tool for adapting to intensifying heat waves under climate change and its variability while the applied methodology can be transferred to any other region.
      PubDate: 2021-10-13
       
  • Spatiotemporal analysis of the annual rainfall in the Kingdom of Saudi
           Arabia: predictions to 2030 with different confidence levels

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      Abstract: Predictions of future water resources are essential for the strategic plans of any country especially in arid regions such as Saudi Arabia (SA). This paper presents a modeling study of the temporal annual rainfall variability over SA, evaluating the best model for future rainfall predictions and mapping spatiotemporal rainfall over SA. Common time series models of orders p and q such as autoregressive, AR(p), moving average, MA(q), and the combined autoregressive-moving average, ARMA(p,q), models are utilized. The models are applied to 28 metrological stations distributed over SA. Spatiotemporal statistical analysis of rainfall data is performed over a period between 1970 and 2012. The minimum and maximum fitted parameters of the models are φ1 =  − 0.55, 0.46 for ARMA (1,0), θ1 =  − 0.66, 0.17 for ARMA (0,1) and φ1 =  − 0.84, 0.94, θ1 =  − 0.87, 0.78 for ARMA (1,1), respectively. It has been shown that ARMA (1,0) is the best to model the temporal variability based on the Akaike information criterion (AIC), the correlation coefficient (R), and the root mean square error (RMSE). The Monte Carlo method is used to make future predictions (100 realizations) with the confidence levels (CIs) based on ARMA (1,0). Spatial distribution of the ensemble predictions and their CIs are presented graphically at the upper limit of 95%, 97.5%, and 99% and the lower limit of 5%, 2.5%, and 1% confidence, respectively, for the year 2030 to help decision-makers for future water resources planning of the country. Abha city has the highest annual rainfall prediction in 2030 (221 mm) with upper confidences (436, 533, and 643 mm) for 95%, 97.5%, and 99%, respectively. The prediction results indicate that the high mountainous areas (Asir and Taif) are expected to have more rainfall in the future than the rest of the regions in SA. The use of non-traditional water resources is the solution to future challenges.
      PubDate: 2021-10-09
       
  • Lead-lag correlations between snow cover and meteorological factors at
           multi-time scales in the Tibetan Plateau under climate warming

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      Abstract: Snow in the earth-atmosphere system contains high impact, but its correlation with meteorological factors at multi-time components has not been fully addressed over the warming Tibetan Plateau (TP). Here, the correlations between snow cover and six meteorological factors were examined at inter-annual, annual, and intra-seasonal components (2003–2018) reconstructed by the ensemble empirical mode decomposition method. Firstly, inter-annual snow cover area with significant decreases in summer (− 0.076% per year) and autumn (− 0.318% per year) shows strong correlations with air temperature (r < − 0.70 in spring and summer), precipitation (r > 0.57 in summer and winter), and shortwave radiation (r < − 0.45 in summer and winter). Moreover, a maximum lead-lag correlation coefficient (MLLCC) was proposed to derive the lead-lag correlations at the two remaining components. In annual components representing the annual cycle of the original time series, shortwave radiation leads snow cover variations by 50 days, and that snow cover leads wind speed variations by 42 days due to the influence of snow on the atmospheric circulation. In the high-frequency intra-seasonal component associated with the event scale, precipitation, shortwave radiation, and air humidity lead snow cover variation by 2 to 6 days. Meanwhile, snow cover leads longwave radiation variations by 3 to 5 days due to the surface albedo being changed by snow. Specifically, intra-seasonal correlations are more significant in winter-spring due to larger snow cover variability. Additionally, with climate warming, the correlations of snow cover with temperature and radiations have been enhanced with increases of the MLLCCs by 0.05–0.29. However, its correlation with precipitation has been weakened with the decrease of MLLCCs by 0.09–0.22. The results of this study will help to deepen our understandings of hydroclimatic dynamics under climate warming in the TP.
      PubDate: 2021-10-08
       
  • Spatio-temporal changes of precipitation in the Hanjiang River Basin under
           climate change

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      Abstract: The Hanjiang River is the source of water for the Middle Route of the South-to-North Water Diversion Project. So a reasonable evaluation on water resources future changes in the Hanjiang River Basin (HRB) is essential to ensure its water safety. In this study, we firstly evaluated the China meteorological forcing dataset (CMFD), and then used it to measure the applicability of the eight global climate models (GCMs). Finally, the empirical orthogonal function (EOF) and clustering algorithms were used to analyze the spatial distribution status of precipitation in the HRB. The results showed that the BCC-CSM2-MR model has the best effect, followed by the IPSL-CM6A-LR model. In the SSP126, SSP245, SSP370, and SSP585 scenarios, total precipitation in the HRB all shows an increasing trend. However, the difference in the spatial distribution of precipitation intensified, for the upstream and downstream tended to be wetter, while the middle reaches had less precipitation. In the future climate scenarios, the areas with more precipitation (Class 1) increased significantly, while the areas with medium precipitation (Class 3) decreased significantly, which will enlarge the precipitation difference between regions in the HRB. The increase in the total amount of precipitation, coupled with the increase in the spatial heterogeneity of precipitation, will make the HRB more vulnerable to flooding disasters. Therefore, the water resources management measures in the HRB should be strengthened in the future.
      PubDate: 2021-10-08
       
  • Seasonal variations in the synoptic climatology of air pollution in
           Birmingham, UK

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      Abstract: A synoptic typing approach was undertaken to examine the seasonal relationship (winter versus summer) between air mass types and pollutant concentrations of O3, PM10, NOx, NO2 and CO in Birmingham, UK, from 2000 to 2015. Daily means of seven surface meteorological variables were entered into a P-mode principal component analysis. Three principal components explained 72.2% (72.9%) of the variance in winter (summer). Cluster analysis was used to group together days with similar PC scores and thus similar meteorological conditions. Six clusters provided the best air mass classification in both seasons. High pollutant concentrations were associated with anticyclonic types. In particular, tropical (polar) continental air mass type was most likely to produce extremely high concentrations in summer (winter). In winter, a sequence of Polar Continental (cool and humid) and Binary Mid-latitude Anticyclonic Maritime—Sub-Polar Cyclonic Maritime (cold and dry) induced severe pollution episodes in all pollutants. Whilst the mean duration of severe pollution episodes varied little between winter and summer (O3 was an exception, with severe episodes lasting 20% longer in summer), high pollutant extremes were more common in winter. This was due to more favourable meteorological conditions (e.g. temperature inversions) and increased anthropogenic emissions during the cold season.
      PubDate: 2021-10-08
       
  • Indoor temperature variability in the Sahel: a pilot study in Ouagadougou,
           Burkina Faso

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      Abstract: Very little research has documented the exposure of populations in Africa to extreme heat. We measured indoor air temperature and humidity hourly for 13 months in seven houses of contrasted architecture and construction materials all in the northern neighbourhoods of Ouagadougou, Burkina Faso. These measurements are compared to air temperatures recorded at the synoptic weather station of Ouagadougou airport and to land surface temperature estimates from Landsat satellite images at seven dates with clear-sky conditions. The results reveal huge temperature differences (exceeding 10 °C) between houses, especially in the afternoon hours of the warmest season. Indoor temperature is also much more variable than land surface (outdoor) temperature in the same locations, as estimated by satellite imagery. Houses with greater thermal inertia smooth the afternoon temperature peak, reducing heat exposure. Heat stress bioindicators reveal that danger thresholds, while rarely reached in some houses, are frequently exceeded in others year round except for the core of the cold winter season (December and January). In spring, the hottest season, the danger threshold is almost permanently exceeded in these dwellings, exposing their inhabitants to significant heat stress. This pilot study shows the primary role of housing in modulating indoor temperature, raising questions of public health and habitability of Sahelian regions in a warming world. This issue will be of increasing importance with ongoing climate change, hence the need for further, more detailed instrumented campaigns in African settlements. Highlights 1. Indoor temperatures vary tremendously between houses. 2. Contrasts between houses are greatest in the hottest hours of the warm season. 3. Thermal danger or extreme danger levels are often exceeded in some houses. 4. In some houses, heat stress may occur in all seasons except winter. 5. More instrumented campaigns for heat stress in African housing are needed.
      PubDate: 2021-10-07
       
  • Towards crop yield estimation at a finer spatial resolution using machine
           learning methods over agricultural regions

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      Abstract: Reliable yield estimation is crucial for food security and agricultural production especially in the intensively agricultural region. This study constructed a gridded yield estimation framework by driving machine learning models with remote sensing vegetation index and meteorological forcing. Among eight machine learning methods, support vector machine (SVM), k-nearest neighbor regression (KNN), and Gaussian process regression (GPR) models outperformed the others. Precipitation, temperature, and the fraction of photosynthetically active radiation are key factors for yield estimation. The yield estimation at county level and regional level were further conducted to explore the scale effect (estimation accuracy varies with spatial resolution). Different scales hold diverse spatial variability information. Finer scales that are more representative of spatial variability generally result in the better accuracy. This study demonstrates that a more accurate yield estimation can be achieved at a finer grid level, thus providing guidelines for agricultural planting structure.
      PubDate: 2021-10-06
       
  • Climate change in Brazil: future scenarios classified by Thornthwaite
           (1948)

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      Abstract: Climate Classification System (CCS) is an important tool for validating climate change models, subsidizing the characterization of new areas suitable or unfit for agricultural activity according to future climate change scenarios. This study aims to classify the climate of the Brazilian territory in the various climate change scenarios of the IPCC through the Thornthwaite system (1948). We used a 30-year historical series (1989–2019) of climatic data of average air temperature (°C) and rainfall (mm), obtained from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources platform (NASA/POWER). Potential evapotranspiration (ETP) was estimated by the method of Camargo (1971); the climatological water balance (CWB) was calculated by the method of Thornthwaite and Mather (1955), using 100 mm of soil water storage capacity. CWB extracts were combined for classification by Thornthwaite (1948). The scenarios used were based on the IPCC (2014) projections and the study of Pirttioja et al. (2015). The Brazilian territory had an average air temperature of 22.20 °C (± 3.20) °C and annual precipitation of 1987 mm (± 725) mm. The climatic classification of Thornthwaite presented 108 climatic classes for the current scenario with a more significant predominance of the classes ArAʹaʹ, B4rAʹaʹ, and B3rAʹaʹ representing 20.54%, 15.62%, and 9.46% of the Brazilian territory, respectively. The climate class ArAʹaʹ had 39.20% in the North and 14.97% in the Midwest. The South region has a predominance of 24.31% for the class ArBʹ3aʹ. In the Southeast and Northeast, the climate classes B2rBʹ3aʹ and DdBʹ2aʹ represented 14.80% and 15.26% of the regions, respectively. The S5 scenario was considered more favorable to establishing crops, with 48.04% of Brazil represented by the climate class ArAʹaʹ. Furthermore, the most catastrophic scenarios for crops were S3 and S4, promoting Brazil a predominance of classes B3rAʹaʹ in 18.02% and B1rAʹaʹ in 21.04%, respectively, favoring the occurrence of arid and dry climates in large part of the Brazilian territory.
      PubDate: 2021-10-05
       
  • Trends of freezing period and its main cause on the Qinghai-Tibetan
           Plateau from 1961 to 2018

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      Abstract: The ecosystems of Qinghai-Tibetan Plateau (QTP) are very sensitive to climate change because of their unique structure and function. However, little attention has been paid to variations in cold non-growing season. In this study, based on daily mean temperature from 63 meteorological stations throughout the QTP during the period 1961 − 2018, the spatial and temporal variations in the freezing period (FP) were investigated. The FP was defined as the period between the date of the first autumn freeze and the date of the first spring thaw in the second year. Understanding how the FP changes are imperative in predicting future climate change and decision-making for implementing ecological conservation on the plateau. The results showed that the start of freezing period (SFP) exhibited a pronounced increasing trend with a rate of 0.0704 days year−1 and the end of freezing period (EFP) showed an obviously decreasing trend with a rate of − 0.2537 days year−1 at the regional scale. The length of freezing period (LFP) presented a significant negative trend at a rate of − 0.3256 days year−1 for regional scale, which was mainly attributed to the earlier EFP. Spatially, later mean SFP, earlier mean EFP, and shorter mean LFP mainly occurred in the south of the QTP, covered the plateau temperate semi-arid (HIIC2) and plateau temperate humid/sub-humid (HIIAB1). For interannual trends, greater delayed SFP and greater advanced EFP were mainly observed in the south and east of the plateau. Furthermore, this study found that the variations in the SFP, EFP, and LFP were highly dependent on the elevation with EFP and LFP are positively correlated with elevation, while SFP is negatively correlated with elevation. At the regional scale, mean annual temperature was positively correlated with SFP and negatively correlated with EFP and LFP. Increasing temperature dominated interannual variation in FP on the plateau.
      PubDate: 2021-10-04
       
  • Impact of global warming on meteorological drought: a case study of the
           Songliao Plain, China

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      Abstract: Global warming has increased the prevalence and severity of natural disasters, such as the increased frequency and intensity of drought events. In this study, we employed the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) to analyze and predict the temporal and spatial variation of drought and its characteristics in the Songliao Plain under two global warming scenarios (1.5 and 2 °C). We used climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project. The results show that drought will become more frequent in the future; it is more serious at representative concentration pathway (RCP) 8.5 than RCP4.5 and under global warming of 1.5 °C than 2 °C. Geographically, the SPEI indicates that there are signs of drought in the northwest and northeast of Songliao Plain while the SPI indicates that drought decreases from north to south. In terms of drought characteristics, the drought barycenter expressed by the SPEI moves to the northeast, while the drought barycenter expressed by the SPI moves to the northwest. The SPI relies on a single meteorological factor, making the SPI mutation test more stable. Finally, compared to the historical period (1976–2005), the frequency and duration of drought have increased, and it is more reliable to use the SPEI to monitor drought in this area.
      PubDate: 2021-10-02
       
  • A study on copula-based bivariate and trivariate drought assessment in
           Godavari River basin and the teleconnection of drought with large-scale
           climate indices

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      Abstract: The single variable-dependent drought cannot adequately define the onset and withdrawal characteristics of the droughts. A Multivariate Standardised Drought Index (MSDI) is developed in the present study, based on precipitation and soil moisture using bivariate copula function. Reconnaissance Trivariate Drought Index (RTDI) is also developed combining precipitation, soil moisture and evapotranspiration. MSDI and RTDI represent meteorological and agricultural droughts by linking the climate status in an effective way. The best fitted copulas obtained for bivariate and trivariate analyses are Frank and Student’s t copulas respectively. The two drought indices are developed and tested to study the onset and withdrawal characteristics of drought and their trends. Cross-wavelet analysis (CWA) is performed to identify the substantial effect of large-scale climate anomalies on the derived drought indices. The large-scale climate factors like sea surface temperature (SST), Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD) and Indian summer monsoon rainfall (ISMR) are considered in this study. ENSO, IOD and ISMR showed significant influences on the drought variability. The 3-month MSDI is significantly influenced by ISMR while SST showed a significant teleconnection with RTDI-3. The SST showed a strong influence on both 6-month MSDI and 6-month RTDI. This study is robust and reliable for future drought assessment and will provide a great platform to develop warning criteria on onset and termination of droughts.
      PubDate: 2021-10-02
       
  • Correction to: Long‑term climatic water availability trends and
           variability across the African continent

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      PubDate: 2021-10-01
       
  • Convective available potential energy (CAPE) in Pakistan and its
           association with precipitation and temperature

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      Abstract: This study presents analyses of convective available potential energy (CAPE) and its association with spatial distribution of precipitation and temperature over sparsely located 45 meteorological stations of Pakistan for the period 1987–2016 on both seasonal and annual time scales. The analyses are performed using various data products viz. monthly precipitation of Pakistan Meteorological Department (PMD), monthly mean gridded temperature of Climate Research Unit (CRU), and CAPE of ERA-Interim reanalysis datasets. Seasonal and annual climatology of CAPE, precipitation, and temperature are presented over each PMD station in the form of thematic maps. Our results show that the stations located in the areas of relatively high values of CAPE also receive relatively high temperatures and precipitation amounts generally. Spatial distribution of correlation coefficients between CAPE and both precipitation and temperature, and trends of the three parameters at the stations are also presented on both the time scales. Based on identified association of CAPE with precipitation and temperature, and trends of the parameters, 25(7), 10(7), 17(10), 22(4), and 16(10) station have been identified that are likely to receive increased(decreased) amount of precipitation in winter, pre-monsoon, monsoon, post-monsoon and annually, respectively, in future. Clusters of these stations identify the related regions also.
      PubDate: 2021-10-01
       
  • Multistage spatiotemporal variability of temperature extremes over South
           China from 1961 to 2018

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      Abstract: The variability of air temperature extremes exerts a great influence on agricultural production and the global hydrologic cycle. It has been the focus of attention for the past several decades. Using observed surface air temperature from 192 meteorological stations in South China maintained by the China Meteorological Administration, this study computed and analyzed 10 extreme temperature indices and the mean temperature at multiple spatiotemporal scales for the period from 1961 to 2018. These indices were analyzed with particular reference to the growing season of rice. Results showed that the variation trends of all annual indices exhibited different north–south patterns across decades, and the most recent 20-year period experienced greater warming than previous periods. The regional averaged rates of the annual mean maximum temperature, the annual mean minimum temperature, summer days, and tropical nights were 0.163 °C decade−1, 0.197 °C decade−1, 1.2 days decade−1, and 5.4 days decade−1, respectively. Except for the month of April, the southern region mostly experienced stronger warming than the northern region, especially in summer and autumn. Nighttime warming was usually greater than daytime warming, especially in June and October. Most temperature indices showed very weak correlations with large-scale atmospheric oscillations.
      PubDate: 2021-10-01
       
  • Intercomparison and uncertainty assessment of methods for estimating
           evapotranspiration using a high-resolution gridded weather dataset over
           Brazil

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      Abstract: All methods for estimating evapotranspiration (ETo) require accurate and complete meteorological datasets. However, the common lack of such datasets in Brazil, as well as the definition of the method that best represents the spatiotemporal pattern of ETo, are the main challenges to assess and mitigate the effects of climate variability (natural or due to anthropogenic climate change) in the Brazilian agricultural production systems. In this sense, this work aims to assess the spatiotemporal pattern of ETo, identify, and select among twenty-nine methods the one that presents the best performance in estimating ETo for different regions of Brazil using a high-resolution gridded weather dataset (GWD). In this study, performance is evaluated by comparing the ETo results obtained through the different methods to that estimated by the Penman–Monteith method. The weather variables used were near surface air temperature (maximum and minimum), relative humidity, wind speed at 2 m, global solar radiation, ETo, and sea level pressure in a daily basis from 1980 to 2017. Through principal component analysis (PCA), the behavior of ETo was mainly influenced by the global solar radiation, maximum air temperature, and relative humidity. For this reason, the performance of the methods varied across the Brazilian regions and seasons. The Turc and Abtew methods showed the best performance in estimating daily ETo, with lower RMSE (~ 0.5 mm day−1) and MAPE (~ 12%) and higher c-index values (~ 0.75), with slight advantage of Turc method, for all Brazilian regions and seasons. Also, the ETo estimation by Turc and Abtew using the GWD dataset showed a good agreement with Penman–Monteith method. Finally, the Hargreaves, Penman Original, and Stephens Stewart methods stood out for the Brazilian Northeast region (mean RMSE of 0.7 mm day−1, mean MAPE of 14%, mean c-index of 0.7), in areas that presents predominantly arid and semiarid climate conditions.
      PubDate: 2021-10-01
       
 
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