Theoretical and Applied Climatology最新文献

El Niño-Southern Oscillation (ENSO) tropical cyclones (TCs) are important moisture sources in semiarid, mountainous Northwestern Mexico. Studies conducted in this region have not expressed differences between coastal and mountainous regions under different ENSO scenarios, instead, changes have been explored in the entire region as a whole. Attempting to fill this gap, the present study conducted an analysis of observed changes in rainfall contribution of landfalling tropical cyclones under five scenarios: (1) El Niño, (2) La Niña, (3) El Niño to La Niña, (4) La Niña to El Niño, and (5) Neutral on mountainous, foothill and coastal regions. In addition, the changes observed were explored under five scenarios in monthly precipitation peak and seasonal cumulative precipitation, which are important characteristics during the North American Monsoon (NAM). The results indicate that most changes occur in the coastal region during La Niña, El Niño to La Niña and Neutral scenarios, where more than half of the stations recorded average precipitation above their regional climatology. Thus, six TCs made landfall with an average of 73% of stations that recorded accumulations above their regional climatology (i.e. NAM precipitation) mainly affecting the southern foothill region. Although the observed changes do not show a well-defined seasonal pattern distinguishing the three regions, changes may be identified and explained by the latitudinal gradient, relief and soil moisture characteristics strongly influenced by local factors. Unfortunately, these results make it difficult to forecast the precipitation response under the different scenarios.

Drought is caused by imbalances in the hydrological cycle's variables, especially lack of rainfall, which is frequently brought on by climate change and can occur anywhere on the Earth. This study aims to conduct a detailed seasonal drought analysis through the seasonal precipitation statistics, groundwater level, and soil moisture in Bankura District during 1991–2022. The study utilized the standardized precipitation index, Standardized water-level Index, and Standardized soil moisture index to assess the meteorological drought, hydrological drought, and agricultural drought at multiple time scales. The three drought indices have been calculated by the ‘Standardized Precipitation-Evapotranspiration Index’ package using ‘R’ programming, which make it possible to compare the drought situations in various climatic zones. The three months standardized precipitation index was used in the seasonal analysis of different types of drought. The modified Mann–Kendall trend analysis was used to acquire information on the course of the drought and rains. Correlation analysis was also done to evaluate the dependency of agricultural drought upon the meteorological drought and hydrological drought. Seasonal droughts in meteorological drought, hydrological drought, and agricultural drought have been compared to the production of the four major seasonal crops. The trend of rainfall showed -0.184, -1.149, -1.263 and -3.598 mm decreases in pre-monsoon, winter, post-monsoon and monsoon season respectively. The occurrence of drought with negative standardized precipitation index, standardized water-level index, and standardized soil moisture index values frequently depicted dry events in the study area. The results show that the drought harms the productivity of food grains, with production losses of 122.77 thousand tonnes and yield rate losses of 292.37 kg per hectare from the average, respectively. This study also considered non-structural and structural efforts from the governmental, stakeholder, and research communities to mitigate the seasonal drought, frame drought-resilient agriculture, and promote sustainability.

Surface energy partitioning directly connects to the urban heat island effect, which consequently changes regional climate, the health of the urban dwellers, and anthropogenic energy use. In order to quantify land-atmosphere fluxes from urban areas and the impact of the level of intensity of development, we use seven site-years of land-atmosphere flux data from three locations averaged to seasonal timescales through binning by temperature. Additionally, all three of our study sites include urban rivers, allowing us to examine urban areas with high and low amounts of potential evapotranspiration. As expected, the urban river decreases the Bowen Ratio of observed fluxes, primarily through lowering sensible heat fluxes. Latent heat fluxes are positively correlated with urban density with coming from the river areas and negatively correlated with latent and sensible heat fluxes when coming from the urban river. We conclude that effective urban redevelopment guidelines can adopt this knowledge to decrease the urban heat island effect and reach sustainability targets to counteract increased temperatures from climate change.

Integrated Precipitable Water Vapor (IPWV) wields significant influence over atmospheric processes, the climate system, and the hydrological cycle. Spatial and temporal variability characterizes water vapor distribution in the atmosphere, with equatorial regions registering elevated water vapor percentages. There are various types of instruments and methods to assess the quantity of moisture in the air. Global Navigation Satellite System (GNSS) and radiosonde techniques have been widely used to estimate IPWV in the atmosphere. European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-5) is the latest reanalysis IPWV dataset. This study aims to assess the congruence of ERA-5-derived IPWV with GNSS-derived IPWV and study the spatial and temporal variability of IPWV over Indian region. The IPWV data from 16 monitoring stations of GNSS Atmosphere Water Vapor Watch Network of the India Meteorological Department (IMD) have been compared with ERA-5 data. The IPWV data from GNSS and ERA-5 are in excellent agreement as corroborated by correlation coefficients spanning 0.97 to 1.00 and the Root Mean Square Error (RMSE) values varying between 1.5 mm and 5.6 mm. IPWV values exhibit prominent seasonal variations, with minimum values during the winter months and peak appears between June and September, aligning with warm and moist monsoon season of India. The ERA5 data from 1981 to 2020 were used to study variability and trend over Indian region. Strong positive correlations are observed between rainfall and IPWV. The results indicated IPWV trends are moistening especially over Indian landmass, the Indian Ocean, Arabian Sea and Bay of Bengal during all the seasons except winter.

This study investigates the spatiotemporal variability of drought patterns in Pakistan on an annual timescale over 50 years (1971 – 2020) using six distinct drought indices [viz., Standardized Precipitation Index (SPI), Agricultural SPI, Reconnaissance Drought Index (RDI), Effective RDI, Deciles Index (DI), and Percentage Departure (PD)]. Empirical Orthogonal Function (EOF) analyses are employed on the SPI drought index to evaluate interannual variations in drought and their correlation with large-scale ocean–atmosphere circulation patterns. The magnitude of the trends is measured using the non-parametric Sen’s slope estimator, while their statistical significance is evaluated through the Mann–Kendall test. To further explore potential shifts in the correlations between the annual SPI and various climate indices, Rodionov’s regime shift detection test is applied. Our findings revealed six drought years: 1971, 2000, 2001, 2002, 2015, and 2018. The most intense and prolonged episode of drought, reaching an extreme category, occurred from 2000 to 2002, affecting over 60% of Pakistan’s total area. The leading EOF mode of the annual SPI demonstrates a robust relationship with the Pacific Decadal Oscillation (PDO). The second mode characterizes a significant Tropical Southern Atlantic (TSA) pattern, suggesting some level of predictability in drought occurrences across Pakistan. Moreover, regime shift analysis reveals two significant shifts: one in 2006 in the correlation between SPI and PDO, as well as Niño 3.4, and another in 2013 between SPI and TSA. This study can provide valuable insights for policymakers to develop climate-resilient agricultural and water resource management strategies, fostering sustainable development in drought-prone areas of the country.

Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional climatology and environmental change (e.g., modelling of droughts and snow/ice covers, evaluation of clouds in numerical models, mapping of solar energy potentials). We present a new gridded dataset of relative (and derived absolute) sunshine duration for Austria at a grid spacing of 1 km, extending back until 1961 at daily time resolution. Challenges in the dataset construction were consistency issues in the available station data, the scarcity of long time series, and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method, which combines station data with satellite data. The methodology merges the data sources non-contemporaneously, using statistical patterns distilled over a short period, which allowed involving satellite data even for the early part of the study period. The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the analyses explain 47% of the spatial variance in daily sunshine duration at the stations. Evaluation revealed a slight systematic underestimation (− 1.5%) and a mean absolute error of 9.2%. The average error is larger during winter, at high altitudes, and around the 1990s. The dataset exhibits a conditional bias, which can lead to considerable systematic errors (up to 15%) when calculating sunshine-related climate indices.

The sustainability of irrigated agricultural systems depends on the water use efficiency based on management. The efficiency is reach first by an adequate estimation of crop water demand. However, the lack of weather data is a limitation to plan and estimation irrigation demand in Brazil. This way, the aim of this study was to investigate NASAPOWER gridded system as alternative source of weather data in Brazil. First, we tested how reliable are the meteorological variables between NASAPOWER and ground weather stations. Further, we calculated the maximum number of consecutive dry days within the crop cycle, and the irrigation demand through the soil-crop water balance for nine crops. The performance was investigated through coefficients of determination (r²), agreement (d) and normalized root mean square error (nRMSE). In general, air temperature and incoming solar radiation presented the best statistical metrics (r² ~ 0.53–0.83; d ~ 0.84–0.94; and nRMSE ~ 8.61–23.5%), whereas the wind speed had the worst (r² ~ 0.09; d ~ 0.53; and nRMSE ~ 93%). NASAPOWER figured as a valuable tool for determine the number of dry days and number of irrigation events during the crop cycle. Irrigation demand showed a good relation between NASAPOWER and ground weather station (r² = 0.79 and d = 0.94), but with nRMSE of 53%, due to a higher deviation when irrigation demand is above 200 mm cycle^− 1. NASAPOWER showed potential as source of meteorological information for irrigation management for different cropping systems, where local adjustment could improve performances for crops with long cycle.

A month-long numerical simulation investigates summertime sea breezes in the North China Plain (NCP), occurring predominantly from late afternoon to night-time and penetrating over 100 km inland. The key factor driving this phenomenon is identified as a persistent lee-side cyclone in the western NCP, formed by dynamic interactions between upper-air north-westerly winds and mountain barriers upstream. Throughout a summer month, the lee-side cyclone varies in strength diurnally, significantly influencing sea breeze development. A case study of high-resolution simulation provides detailed insights into the wind field and the evolving lee-side cyclone structure. In the evening peak, three sectors of air flows—westerly, southerly, and south-easterly—encompass the cyclone core, demarcated by fronts with sharp gradients in temperature, moisture, and wind speed and direction. As the south-easterly sea breeze intensifies, it swiftly advances inland along the mountainside, forming an arch-like intrusion path. With the weakening and south-westward movement of the lee-side cyclone, the sea breeze transforms into an inertial current, guided further south-westward. The vertical structure of the sea breeze is revealed, including the depth of its moisture-layer and its uplifting effect on the warmer inland air mass at the cyclone core. The interaction between the lee-side cyclone and sea breeze facilitates the transport of substantial water vapor from the Bohai Sea coastline to the interior of the NCP. These findings provide new insights into the summertime sea breeze mechanism in the NCP, with implications for local weather patterns, water vapor budget, and air pollutant transport.

Sodium adsorption rate (SAR), which significantly affects soil and plant health, is determined according to the concentration of sodium ions, calcium, and magnesium in irrigation water. Accurate estimation of SAR values is vital for agricultural production and irrigation. In this study, hybrid swarm intelligence-based neural networks are used to model sodium adsorption ratio in irrigation water quality parameters in a semi-arid environment at Sidi M’Hamed Ben Aouda (SMBA) dam, Algeria. For this, the nature-inspired optimization techniques of particle swarm optimization (PSO), genetic algorithm (GA), Jaya algorithm (JA), artificial bee colony (ABC), and firefly algorithm (FFA) and the signal processing technique of variational mode decomposition (VMD) have been combined with artificial neural networks (ANN). Correlation matrices were used to select the data entry structure in the established models. Water quality parameters with a statistically significant and medium to high relationship with SAR values were presented as input to the model. The overall performance was measured using various statistical metrics, scatter diagrams, Taylor diagrams, correlograms, boxplots, and line plots. In addition, the effect of input parameters on model estimation was evaluated according to Sobol sensitivity analysis. As a result, the GA-ANN algorithm demonstrated superior performance (MSE = 0.073, MAE = 0.193, MAPE = 0.048, MBE=-0.16, R² = 0.934, WI = 0.968, and KGE = 0.866) based on the statistical indicators, indicating better results compared to other models. The second-best model, ABC-ANN (MSE = 0.084, MAE = 0.233, MAPE = 0.066, MBE=-0.135, R² = 0.897, WI = 0.965, and KGE = 0.920) was also selected. The weakest prediction outputs were obtained from the VMD-ANN model. The accurate and reliable estimation of SAR in irrigation water has the potential to facilitate improvements in agricultural irrigation management and agricultural production efficiency for farmers, agricultural practitioners, and policymakers.

Studies have explored concurrent hot extremes and dry events across the world, however, the modulation of average temperature regimes during droughts is lacking. This study explores the differential intensification rates in average temperatures in the historical past and projected future for different climatologies (dry, wet, and average) over the Indian subcontinent, and the intensification rates are linked with established atmospheric feedback mechanisms. Thereafter, future differential shifts in temperatures associated with different climatologies were studied under climate change for the 2 °C and 3 °C warming worlds using CMIP6 simulations. Results show that temperature intensification rates are much more pronounced under dry/wet climatology than average climatology. Dry climatology temperatures (Td) exhibit extensive cooling trends in northern India while warming trends are reported in southern India. Wet climatology temperatures (Tw) show extensive warming trends in northern India. Further, analysis of atmospheric moisture and aridity metrics such as vapor pressure deficit (VPD) and relative humidity (RH) show a stronger linkage with temperatures during the dry/wet climatology compared to the long-term average. Multi-model shifts under climate change project cooling and warming Td shifts under 2 °C and 3 °C levels, respectively with greater pronounced temperature shifts in northern regions. The results of this study have implications for water resource management, drought risk reductions, and mitigation of agricultural crop losses.