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Climate change is a major concern, as it profoundly affects many facets of our lives. It has brought about several issues, including declining water supply, reduced agricultural yields, increased drought occurrences, and increased heat waves. Amidst these challenges, the influence of short-term drought events on plant growth and irrigation schedule emerges as a critical concern. However, despite these evident consequences, a nuanced understanding of the intricate relationship between the severity and duration of short-period drought/deficit events still needs to be explored. This paper analyses fortnightly water deficit periods over different regions of India, which would be more relevant to re-scheduling the irrigation events than monthly or longer duration in preventing crops from reaching the permanent wilting point. Hence, this work considers analyzing 15-day Standardized Precipitation Index (SPI) trends and drought characteristics using conventional methods and Innovative Trend Analysis (ITA) techniques. The analysis uses gridded rainfall data from the India Meteorological Department (IMD), which has a spatial resolution of 0.250-E and 0.250-N. The data spans the period from 1970 to 2021. The ITA and Mann Kendall (MK) displayed nearly identical areas of increasing and decreasing trends, but ITA effectively identified significant trends. While MK and Modified Mann Kendall (MMK) could only indicate significant trends for 12.94%, 9.57%, and 9.9% of grid points for SPI, drought severity, and duration, respectively, ITA was able to identify significant trends at 44.31%, 10.9%, and 10.1% on an annual scale. The ITA method effectively identified the significant trends and magnitudes of fortnightly SPI and drought characteristics. The Tropical monsoon (Am), Tropical savannah (Aw), Arid desert hot (BWh), Arid steppe hot (BSh), and Temperate dry winter warm summer (Cwb) climatic zones have shown a significant increase in annual drought severity. Similarly, a significant increase in monsoon drought severity is observed across several states, including Gujarat and Mizoram, impacting diverse geographic extents. The present study can help policymakers and water resource managers decide on water allocation, irrigation, and crop management practices. [ABSTRACT FROM AUTHOR]

The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

Satellite-based rainfall estimates are a good and cost-effective alternative to low-density rain gauges because they provide information for these areas. In this study, the accuracy of seven satellite-based precipitation datasets SPDs (TRMM-3B42v7, CHIRPSv2, RFEv2, ARC2, PERSIANN-CDR, GPCPv3.1, and TAMSATv3.1) was evaluated against ground-based observations. The performance of these datasets was evaluated at daily, dekadal, and monthly time scales from 2001 to 2014 using continuous and categorical statistical indicators. The results showed that CHIRPS and TAMSAT slightly underestimate precipitation in the Sahelian and Sudano–Sahelian zones by 2.1 to 8.3%, while the other datasets overestimate by between 3.7 and 13.2%. In the Sudanian zone, they all underestimated rainfall by 1.8 to 9.4%, except for GPCP and PERSIANN-CDR, which slightly overestimated rainfall in the range of 1.7 to 4.9%. The best performance was attributed to TAMSAT on the daily scale, while GPCP showed the weakest performance. CHIRPS dataset demonstrated the most accurate estimate of ground precipitation across all zones on the dekadal time scale, whereas TAMSAT showed unsatisfactory performance. On the monthly scale, CHIRPS again exhibits strong performance in the Sahelian and Sudano–Sahelian zones, ahead of TRMM-3B42. In the southernmost area, PERSIANN-CDR consistently exhibits good agreement with ground rainfall. In contrast to the results for the Sahelian and Sudano–Sahelian zones, larger errors were found for all SPDs within the Sudanian zone. The results suggest that the TAMSAT and CHIRPS datasets appear to be a good alternative source of information to rain gauge data. Therefore, these datasets could be valuable for different stakeholders such as meteorologists, hydrologists, and agronomists in Burkina Faso and support operational applications. [ABSTRACT FROM AUTHOR]

Gridded rainfall datasets based on various data sources and techniques have emerged to help describe the spatiotemporal features of rainfall patterns over large areas and have gained popularity in many regional/global climatic analyses. This study explored future variations of rainfall characteristics over peninsula Malaysia and Singapore region based on rainfall indices of PRCPTOT, Rx1day, Rx5day, R95pTOT, R1mm, and R20mm, under 9 CORDEX-SEA RCM datasets with RCP8.5 emission scenario. A monthly quantile delta mapping method (MQDM) was adopted for bias-correction of the RCM modelled data. It was indicated that all the studied rainfall indices have long-term variations both temporally and spatially. Generally, the further the future, the higher the variability and uncertainty of indices. For the study region, the relative increments of the medians from RCM models averaged over all climatic zones in the far future are 40.3%, 25.9%, and 4.7% for Rx1day, Rx5day and R95pTOT, respectively. The annual rainfall amount (PRCPTOT) in the long run would likely increase mainly in the northeast coastal zone and drop in most of other areas over the peninsula, with the median being -5.9% averaged over all zones. The frequency of wet days (R1mm) would generally drop over the whole peninsula, with the median averaged over all zones being -6.8% in the far future. The frequency of heavy rains (R20mm) would overall decrease (by -3.4% in average in the far future) but might still notably increase in the northeast zone (NE) at both annual and southwest monsoon. The extreme conditions implied from individual RCM models could be more alarming. The study result could be useful in revealing the essential spatiotemporal variations of rainfall over the peninsula from short- to long-term futures and supporting large-scale flood risk assessment and adaptation planning. [ABSTRACT FROM AUTHOR]

Climate modes like ENSO (El Nino Southern Oscillation) and IOD (Indian Ocean Dipole) produce an impact on the monsoon rainfall over India. Monsoon rainfall is extremely important for the agriculture of our country. The impact of these climate modes on monsoon rainfall thus in turn affects the rain-fed crops (kharif). In this study, four kharif season crops namely rice (Oryza sativa), maize (Zea mays), pulses and sugarcane (Saccharum officinarum) are chosen over four arid/semi-arid agro-climatic zones of western India to study the effect of the climate modes on selected crops. The detailed analysis has been carried out to show the impact of rainfall in the El Nino/La Nina (phases of ENSO) and IOD years on the crop productions over the mentioned zones viz. (Central plateau and hills region; Western plateau and hills region; Gujarat hills and plains region; Western dry region) from 1966–2011. The results show that rice productions which require hot and humid conditions have been largely affected during drought years associated with El Nino which results in poor rainfall over all the zones. The production of pulses which does not require excess humid conditions shows marginal improvement during the neutral years or non-El Nino/non-La Nina years. Maize production seems to be better in La Nina years and worst in the El Nino years as La Nina years are responsible for good rainfall in all zones. El Nino years provide a minor impact on sugarcane productions in different zones. La Nina years are well suited for sugarcane production in all zones of our study as sugarcane requires a good amount of moisture. Positive IOD years are associated with poor crop productions as compared to negative IOD years mostly in all zones as most of the positive IOD years happen to be El Nino years. El Nino-rainfall relation being dominant than positive IOD-rainfall relation is, therefore, responsible for negative rainfall anomalies over the selected zones. [ABSTRACT FROM AUTHOR]

The most practical methods to predict climate change are global climate models (GCMs). This research set out to evaluate the ability of 19 GCMs from the Coupled Model Intercomparison Project 6 (CMIP6) to reproduce the historical precipitation, maximum, and minimum temperature (Pr, Tmx, and Tmn) of climate prediction center data for the Amu Darya river basin (ADRB), as well as to project the climate of the basin using the chosen GCMs. The Kling-Gupta efficiency (KGE) metric was used to assess the effectiveness of GCMs to simulate the annual geographic variability of Pr, Tmx, and Tmn. A multi-criteria decision-making approach (MCDMA) was used to integrate the KGE values to rank GCMs. The findings showed that AWI-CM-1–1-MR, CMCC-ESM2, INM-CM4-8, and MPI-ESM1-2-LR best replicate observed Pr, Tmx, and Tmn in ADRB. Projection of climate employing the selected GCMs indicated an increase in precipitation (9.9–12.4%), Tmx (1.3–4.9 °C), and Tmn (1.3–5.5 °C) in the basin for all the shared socioeconomic pathways (SSPs), particularly for the far future (2060–2099). A significant variation can be seen in Tmx and Tmn over the different climatic zone. However, the intercomparison of selected GCM projected revealed high uncertainty in the projected climate. The projection uncertainty is noticed highest for Tmx. The uncertainty is also noticed higher in the far future and higher SSPs compared to the near future and lower SSPs. [ABSTRACT FROM AUTHOR]

Sufficient site-level observations are needed to resolve the discrepancies between in situ and space-based observations for reliable climate mitigation and adaptation strategies. Resolving such discrepancies from climate-sensitive and understudied Himalaya is the priority for the Indian sub-continent. This study investigates characteristics and dynamics of equivalent black carbon aerosol (eBC) including sources over a glaciated valley at the transitional climate zone between central and western Himalaya. Thermo-topographic factors influencing valley-scale dynamics of eBC were observed by coupled measurements on instantaneous eBC (Aethalometer) and meteorological state parameters at a high altitude (4000 m asl) in the Indian Himalaya covering an annual cycle (October 2015 to August 2016). Results indicate a very large variation (38–5638 ng m−3) in mean daily BC concentration (308 ± 37 ng m−3). Seasonally, the eBC concentration was found highest during the pre-monsoon (1276 ± 115 ng m−3) and lowest during the monsoon season (308 ± 37 ng m−3). In contrast, the magnitude was comparable during winter and post-monsoon seasons (400–500 ng m−3). BC-induced mean annual radiative forcing at the atmosphere was + 10.1 ± 3.0 W m−2. These results indicate a substantial eBC burden over the region even at this high tropospheric altitude in this pristine glacier environment. On the other hand, the diurnal-scale variability in eBC concentration is primarily governed by high-altitude meteorological processes. Further, source apportionment analyses underscore the seasonal scale influence of monsoon and westerly circulation systems. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess spatial and temporal changes in extreme precipitation events from 1983 to 2017 over different climatic regions of Nigeria. Ground observations of daily precipitation in 17 meteorological stations scattered over the country were methodically analysed. Twelve widely used indices for the assessment of intensity (PRCPTOT, R × 5Days, SDII, R95p, R99p and R95pIndex) and frequency (CWD, CDD, R1mm, R10mm, R20mm and R25mm) of extreme precipitation were adopted. Linear trends were calculated using a least-square-fit approach, while significant trends were identified using the Mann–Kendall non-parametric test. Results revealed general significant (at p < 0.05) downward annual trends in precipitation intensity indices [i.e. total wet day precipitation, (PRCPTOT), daily precipitation (SDII), 5-day maximum precipitation, (R × 5Days), very wet (R95p) and extremely wet days (R99p)], particularly in the Savannah and Sahel regions. The frequency indices [i.e. consecutive dry days (CDD), wet days (R1mm), heavy (R10mm), very heavy (R20mm) and extremely heavy precipitation (R25mm)] increased significantly in the Sahel but decreased in other zones. We established that R1mm decreases with decreasing SDII, while CWD declines with reducing SDII and R95p in the entire country. The study further revealed that Sahel and Savannah zones are highly vulnerable to severe droughts with potential negative implications on food security and water resources. It concluded that recent changes in climate had significantly impacted the extreme precipitation events within and across different climatic regions of Nigeria. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]