Theoretical and Applied Climatology最新文献

This study investigates the variability and forecasting ability of time-trend in mean annual surface air temperatures in Greece. Using Gaussian time-trend models, we first investigate some basic statistical characteristics associated with time-trends, such the mean and variance. This can reveal whether temperatures’ mean and volatility changes are associated with time. To do so, we have used mean measures of the minimum and maximum air temperatures observed at several meteorological stations of the Hellenic Meteorological Service located in Greece for the 1960-2010 period. As a second experiment, we investigate whether temperature trends are forecastable or not using various Gaussian time-trend and no-time-trend models. The results are highly significant since they reveal the seasons, the periods and the type of models for which the inter-annual trends out-perform the no-trend ones. Moreover, they also show the statistical characteristics, such as the mean and variability of the time-trend under various seasons and sub-periods.

El Niño Southern Oscillations (ENSO), being the largest global air-sea interaction phenomena, has a profound effect on global monsoon. In this study, for the first time we have examined the ENSO – Monsoon relation through nonlinear kinetic energy (KE) exchanges outlook across scales. Indian summer monsoon (ISM) and Western North Pacific Summer Monsoon (WNPSM) are considered to be the two major components of global monsoon on which ENSO has a huge impact. The ENSO – Monsoon nonlinearity can be explored by unraveling the unstable (stable) relation between ENSO and Monsoon during El Niño (La Niña) period through the mechanism of scale interactions among El Niño / La Niña, dominant high (HFO) and low frequency oscillation (LFO) considering ENSO itself as a LFO of time period 12–18 months. It is found from the analysis that more (less) KE is transferred to synoptic scale and 10-20 day LFO from El Niño 1997-98 compared to that from 2015 - 16 episode over ISM (WNPSM) region. Moreover, the opposite energy exchange between El Niño and 30-60 day wave is observed over these regions, attributing to contrasting rainfall variation over ISM and WNPSM zone. Sharp (comparatively less) variability in energy exchange among El Niño (La Niña), HFO and LFO over ISM and WNPSM regions make El Niño – Monsoon relation highly unstable whereas La Niña – Monsoon relation a stable one. Unveiling El Niño-ISM, El Niño-WNPSM, La Niña-ISM and La Niña-WNPSM relations in terms of inter-scale KE exchanges for the two El Niño and two La Niña events may lead to enhance the understanding of ENSO-monsoon nonlinearity. This work will make an avenue for further research in the space of nonlinearity between the two large scale systems that may be expected to be more complex due to the global warming and climate change as a futuristic study.

This study has been undertaken to predict the Surface Latent Heat Flux (SLHF) over the North Indian Ocean (NIO) in the future period (2020–2099) under different emission scenarios. For this 12 Coupled Model Inter-comparison Project 6 (CMIP6), Global Climate Models (GCMs) SLHF and ERA5 SLHF data have been used. It is observed that the SLHF is going to increase in the far future (2060–2099) under all emission scenarios probably due to higher warming. In the near future (2020–2059) particularly in the first half (2020–2039) a clear fall in the SLHF is noticed. It is also observed that the SLHF rise under the Shared Socioeconomic Pathways 5 (SSP5) is more than that under the SSP2 in the far future due to higher warming. Seasonal variation of the SLHF depicts that under SSP5 in the far future, the rise in the SLHF is the maximum in MAM. The rise of the SLHF in the far future can be attributed to the rise in the SST over the NIO but the reason for the fall of the SLHF in the first half of the near future is not clear.

Droughts and climate change are causing severe and persistent dust storms in the arid and hot regions. The potential existence of significant relationships between the Oceanic Niño Index (ONI), Indian Ocean Dipole (IOD), and the MERRA-2 dust column mass density data are thus investigated in the State of Kuwait during 1990–2021 as a study case in the hot and arid regions. The findings show that the correlation starts from ONI-DJF (December–January-February) with the Pearson test value of -0.36 and increasing to a value of -0.55 on ONI-AMJ (April-May-June), the same results were obtained when applying the Spearman correlation test. The correlation exists between the July dust column mass density and IOD-January with a value of -0.484 and − 0.544 over Pearson and ' ‘Spearman’s test, respectively, all estimated p-values are lower than 0.05. The results of the One-Way ANOVA show that the output is pretty straightforward and statistically significant at p > 0.05, which was confirmed by the nonparametric Kruskal–Walli’s test and Mann-Whitney Test. This considerable correlation means that active dust storms are linked with La Niña, while light dust storms occur during the El Niño event. A positive correlation occurs between precipitation and ONI - SON (September-October-November), while a negative correlation is found between precipitation and IOD-July. Our preliminary findings in this paper indicate that an appearance of -0.7°C in ONI-DJF could serve as a definitive marker of the La Nina phase and an indicator for at-risk communities to prepare for the expected activity dust storm.

Punjab state has set the standard for agricultural growth and was the initiator of the Green Revolution in India, producing the highest quality wheat, rice, cotton and other crops. Although Punjab’s land is fertile but over time, but ecological resources, i.e. climate, soil and groundwater have got altered due to which farmers are still unaware to decide which crops are best to grow in their areas for the highest yields and highest financial returns. Agro-Eco-Resource zonation (AERZ) is a classification technique that uses meteorological, soil and groundwater data of the targeted region for proper zonation with higher priority towards crop suitability factors. In the present study 8 major factors (minimum temperature, maximum temperature, rainfall, soil organic carbon, soil texture, soil pH, groundwater depth and groundwater level fluctuations) were used for creating zones for the entire state of Punjab. Analytical Hierarchy Process (AHP) was used for assigning weightage to each factor and the inverse distance weighting interpolation was used to create the thematic maps after importing the data into a Geographic Information Systems (GIS) using attribute tables. These maps were then superimposed so that it would be possible for zoning by using all the stated factors. Based on the degree of suitability for agriculture from highest to lowest, AERZ were identified on a final map. The AERZ map were then validated by comparing them with the rice and wheat productivity data maps. The state was classified into 4 major classes, wherein the zone 4 had conditions most suitable for agricultural crops while zone 1 had constraints and so was least suitable for cultivating crops. The geographical area under zone 3 was nearly 50% followed by zone 4 (30%), zone 2 (19%) and zone 1 (0.7%). The roadmap for sustaining the agricultural productivity in the state revealed that cultivation of resource intensive crops (Rice/ Basmati rice, Wheat, Maize and Vegetables) should be curtailed to only 30% (zone 4) of the total geographical area of the state. Further, the agro-eco-resource constrained ~ 1% (zone 1) of the land area may preferably be kept fallow or used for forest cover. Therefore, AERZ using AHP and GIS techniques is a useful tool for agricultural planning and management. It helps in identifying the best crops that can be grown in a particular region, which in turn helps in improving the efficiency and productivity of agricultural production.

We analyzed long-term soil CO₂ concentrations, soil temperature and moisture, with simultaneous canopy microclimate measurements, in a subtropical 20 years old oak forest in Huainan, China, in the East Asian monsoon region. Daily soil CO₂ effluxes were quantified using the gradient method. We investigated the relationship between soil CO₂ concentrations and efflux, and both soil temperature and water content, and the effects of phenological stages and seasons. explored how abiotic factors (soil temperature and moisture) along with LAI influence soil CO₂ efflux. Subsurface soil CO₂ concentration and efflux showed clear seasonal variations, with an increase in spring to a small peak, reaching a maximum in summer, then a decrease in autumn but with a sharp second peak when precipitation season began, and finally a decrease to a minimum in winter. In this forest ecosystem, the calculated soil subsurface CO₂ efflux at 0.02 m averaged 1.40 µmol m⁻² s⁻¹ and ranged from 0.24 to 4.11 µmol m⁻² s⁻¹ over a whole year from September 1, 2018 to August 31, 2019. Seasonal fluctuations in soil CO₂ concentrations and efflux were primarily influenced by soil temperature, with soil moisture exerting secondary effects throughout the entire study period and in most cases. However, during warm, dry periods like summer or maturity stages, soil water content emerged as the primary factor governing CO₂ efflux. The combined influences of soil temperature and water content were different in different phenological stages, and highly regulated by drought and rains. For instance, in the phenological mature stage, soil CO₂ concentration was mainly affected by soil water content. In the mature stage, soil CO₂ efflux correlated more closely with soil water content than with soil temperature following a spring drought episode.

By using ERA5 reanalysis data, this paper combined existing research definitions of gale processes to analyze the variation characteristics of gale processes occurring in the South China Sea region, and to study the response of the gale process to East Pacific sea surface temperature. The results indicate that the gale process shows the significant annual variation and the most frequent in winter and least frequent in summer. While there is no significant trend in the frequency of gale processes in each season. There is a significant negative correlation between the frequency of gale process and the East Pacific sea surface temperature in spring, autumn, and winter. When the sea surface temperature in the eastern Pacific rises abnormally, the frequency of gale processes in the South China Sea decreases. When abnormally high, the frequency of gale process in the South China Sea increases. In summer, this relationship is the opposite. When sea surface temperature is abnormal, the anomalous anticyclone triggered in the western Pacific, and has a weakening effect on the prevailing winds in the South China Sea, leading to a reduction in high wind speed and gale events, thereby reducing the frequency of gale process.

The management of water resources is no longer an option but a necessity especially in countries with a high rate of population growth like Pakistan. Pakistan has a high-water scarcity, an extremely varying topography, and is also one of the countries encountering the problem of climate change. Therefore, prudent utilization of available water resources is a matter in the plains of Pakistan as in its high-altitude regions. Instead of wasting water which is a valuable resource, rainwater harvesting is a way of saving rainwater as an alternative source of fresh water for better use. The aim of this study was to search for potential rainwater-harvesting areas in a high-altitude Gilgit River basin. This research employed the SCS-CN technique in estimating surface runoff in the GIS environment. The geospatial study used four input parameters: slope of the land, land covers, surface runoff and drainage density. The research concluded that some lands, especially those that are deserted in dry area and agricultural lands, can serve as the sites of rainwater harvesting, with only 4% of area that was highly suitable for the purpose. Nevertheless, 37% of the investigated area was shown as totally unviable for rainwater collection installations because of steep slopes and substantial volume of snow. Simultaneously, the rainwater storage design solutions suggested were check dam, percolation tank, and agricultural ponds with the area of 3.5%, 1%, and 0.75% of the given land area, respectively, for rainwater harvesting. The locations of potential catchment and planned rainwater harvesting structures would assist the local authorities and the policy makers to provide plans and design on the construction of water storages and also the optimization of water use.

The near-term performance efficiency in predicting the dry and wet years in the West African monsoon (WAM) season (May to October) has been studied from 1979 to 2050 using the CRU observational rainfall, NCEP RII atmospheric circulation fields, and CORDEX-Africa outputs in the historical and RCP 8.5 experiments. The dry and wet years from the 6-month SPI at the Western Sudano Sahel (WSS), Eastern Sudano Sahel (ESS), and Guinea Coast (GC) rainfall regions, respectively, have shown consistency in the associated features during such WAM season extremes. The ensemble mean of the historical outputs (1979 to 2005) shows varying simulations of the WAM season; non-significant correlation in rainfall in GC and its overestimation at the three regions, the underestimating (overestimating) of Moisture Flux Convergence (MFC) in the Sahel (GC), and the “non-reasonable” performance by the Kling-Gupta efficiency in simulating the zonal moisture flux in GC and meridional moisture flux in ESS. The bias-correction of the RCP 8.5 outputs has shown improved performance efficiency of the models simulations from 2006 to 2021, however, with the attendant limitations in the technique. The bias-corrected rainfall showed underestimation at all regions although indicating negative significant correlation at the GC (r = -0.33, at 99.9% Confidence level from t-test) whereas the MFC has shown reasonable performance in the GC (KGE = -0.39). However, the ensemble mean of the models presents greater efficiency in projecting the WAM dry and wet years although there are yet huge uncertainties in the projections indicated by the MBE values. The 6-month SPI projections from the improved RCP 8.5 simulation present 2048 to be dry and 2035, 2042 and 2047 to be wet years during the WAM from 2022 to 2050. Noteworthy is the impact of MFC on rainfall being consistent in both the historical and the bias-corrected models’ outputs, having a greater impact by 2050.

In the rapidly changing climate of arid desert regions, evaluating the comprehensive characteristics of humidity levels is crucial for agricultural, urban, and infrastructural planning, as well as for minimizing potential public health impacts. We investigated variability and trends of humidity levels in major Saudi Arabian cities during 1982–2022, focusing on the influence of meteorological factors such as average, maximum and minimum temperature, rainfall, and windspeed. Employing the Probability Density Function and descriptive statistics, variability of climatic factors was analyzed. The Mann–Kendall Test (MKT) and Innovative Trend Analysis (ITA) were employed to identify monthly and annual trends. The magnitude and changing patterns were determined by calculating Sen’s Slope and ITA slope. Findings of the MKT and ITA showed similar trends in humidity levels across all the cities. ITA result revealed that humidity in Riyadh and Taif decreased at a rate of 0.012% and 0.016% per year, respectively, while increased in Jeddah, Makkah, and Madinah at a 0.05 confidence level. The influence of climatic factors on humidity was assessed using Pearson’s correlation coefficients, multiple regression model, and wavelet transform coherence (WTC) for each city, pinpointing temperature as the key driver of humidity variability. The dominance of temperature features was corroborated by strong power spectrums in the WTC across various time periods and scales. The in-depth analysis of humidity dynamics in this study provides critical insights for the development of climate-resilient infrastructure and formulation of public health strategies in Saudi Arabian cities.