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In Ethiopia, large-scale agricultural investment (LAI) is promoted to foster adaptation to climate change among smallholder farmers by improving farming conditions and productivity. However, little has been known about this condition so far. Therefore, the aim of this paper is to study the implications of LAI for the capacity for climate change adaptation among smallholder farmers, based on a study conducted in Merti district in the Central Rift Valley of Ethiopia. An asset-based approach was used to measure the adaptive capacity of smallholders, via data collected from a household survey, undertaken among smallholder households who had, or had not, benefited, from LAI, and supported by key informant interviews and focus group discussions. Descriptive and inferential statistics and qualitative thematic analysis methods were used to analyze the data. The study found that the adaptive capacity of smallholder households was improved under the influence of LAI. LAI motivated and prompted farmers to use technologies that fostered climate change adaption, including the use of irrigation, improved crop varieties, chemical fertilizer, and engagement in alternative sources of income, which improved their food security status. But this technology and knowledge transfer mainly occurred through observation (learning by doing), not by capacity-building. However, both group of smallholders (within and without contact with the LAI) received similar benefits through government rural extension services. Therefore, the paper concludes that scaling up these technologies and alternative income sources could enhance the adaptive capacity of smallholders, but that LAIs should contribute actual technical and financial support as part of benefit-sharing or corporate responsibility. [ABSTRACT FROM AUTHOR]

Thermal comfort is usually calculated using discrete point measurements. This procedure is not suitable to study thermal comfort for inhabited areas with rugged terrains where climate gradient is high. The wide availability of remote sensing data and GIS tools has revolutionized data management, processing, and visualization. The present paper implemented digital elevation data, GIS tools, and a computational algorithm to generate spatially continuous maps of climatological elements which were employed to derive thermal comfort levels across Jordan. Results show detailed information of the spatial distribution of the degree of thermal comfort in winter and summer across the country which cannot be resolved using discrete point measurements. It is shown that most mountainous areas in the country, where most urban centers are situated, experience "slightly warm" to "warm" indoor apparent temperatures in summer in 50% of the diurnal cycle. The Jordan Valley and the desert experience high indoor apparent temperatures in summer with warm and hot conditions for the most part of the diurnal cycle. Cold conditions prevail over most parts of the country in winter, with the heating degree days ranging from 2200 in the southern mountains to values close to zero near the Dead Sea area. Mountainous and desert areas experience cold or very cold conditions in more than 75% of the diurnal cycle. The presented procedure demonstrated that the very low levels of ambient vapor pressure is an important atmospheric forcing contributing to the widespread cold conditions prevailing over the desert areas in winter. The efficiency of direct evaporative cooling systems to achieve thermal comfort in the various parts of the country is investigated. The procedure presented can be used over regional scales with different levels of spatial resolutions for a wide range of climatological studies. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. The main aim of this paper was to determine changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 and to identify changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI). Cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020. While a decrease in the number of months with SPEI-1 below − 1 was observed in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. This is an important finding for the agriculture or forestry because it affects work management and planning. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. The increase in drought intensity was not spatially consistent over the cluster area, mostly reaching a slight decline of about − 0.1 to − 0.3, which means slight intensification of drought periods in 1991–2020. [ABSTRACT FROM AUTHOR]

The frequency of extreme events is increasing on a global scale due to various factors, accounting for natural disasters such as landslides, floods, and droughts. The Heilongjiang province, which is agriculturally important for China, has suffered from several extreme rainfall events in the past as a high-latitude plain region. Analysis of historical rainfall characteristics of the Heilongjiang region helps to understand and predict the behavior of extreme rainfall events and provides a reference for reducing agricultural economic losses. Precipitation data from 1974 to 2017 is selected to estimate whether rainfall series in Heilongjiang obey the Gumbel-Logistic Model. We compared the Gumbel-Logistic Model based on the data length of 5 years, 10 years, and 20 years with the original 44-year data to determine the appropriate data partition length and explore the reasons for errors. Combining the empirical frequency and KS test, this paper concludes that the proposed model is appropriate for the representation of the joint distribution of monthly extreme rainfall and annual rainfall. The proposed model improves the rainfall assessment of these two positively correlated variables in Heilongjiang. Univariate and bivariate return periods and correlation coefficients are derived as the basis for assessing the risk of extreme rainfall. The proposed model obtained from the 5-year data length has the largest error, while the distribution curve is progressively smoother as the data length increases. [ABSTRACT FROM AUTHOR]

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series. [ABSTRACT FROM AUTHOR]

This paper analyzes the correlation between precipitation and deforestation and trends in the Brazilian Amazon Biome from 1985 to 2021 using rain gauge measurements. A total of 187 stations were selected after filtering time series with less than 10% of missing data. Deforestation data was acquired using the MapBiomas dataset. Results show that deforestation rates vary across different locations and may be influenced by various factors. The analysis also found significant negative correlations between precipitation and deforestation, with a range of − 0.49 to − 0.33, as well as significant positive correlations, with a range of 0.34 to 0.57. These findings underscore the importance of continued monitoring of precipitation patterns in the region and the urgent need for measures to mitigate deforestation, which is critical for the hydrological cycle of the region. The highest deforestation rates and values were found between latitude 0 and − 12.5, where the arc of deforestation lies. Additionally, this study found a significant negative correlation between precipitation patterns and deforestation, indicating that deforestation led to a reduction in rainfall in the region. [ABSTRACT FROM AUTHOR]

As a percentage of the total global energy supply, wind energy facilities could provide 10% of the total global energy supply by 2050 as reported in IEA World Energy Outlook (2022). Considering this, a just transition to renewable and sustainable energy in South Africa is a genuine possibility if steps are taken immediately to achieve this. The Eastern Cape Province exhibits a strong wind resource which can be exploited towards expediting such a just energy transition. No research and related modelling have, to date, been undertaken in quantifying and relating the detailed P50 energy yield analyses of representative wind energy facilities in temporal and spatial dimensions to the occurrence of specific synoptic types in South Africa. To quantify this energy meteorology climatology for a suitably sized geospatial area in the Eastern Cape Province of South Africa (spatial focus area, latitude −30 to −35, longitude 20 to 30), the approach of using self-organising maps is proposed. These maps are used to identify the most common synoptic circulation types occurring in the Eastern Cape and can subsequently be mapped onto an equivalent time resolution wind energy production timeseries calculated based on probable wind energy facility sites. This paper describes comprehensive methodologies used to model the wind energy facilities, calculate with high confidence the P50 energy production, and then identify the predominant synoptic weather types responsible for the wind energy production in this spatial focus area. After quantifying the energy production, running a self-organising map software generates a purposely selected 35 node map that characterises archetypal synoptic patterns over the 10-year period. The synoptic types can be ranked by the highest energy production. It is shown that in this spatial area, monthly wind energy production is higher during the winter months. When the well-established high-pressure cells move northward, synoptic types associated with higher energy production are frequent and include tropical and temperate disturbances across South Africa, patterns resembling a ridging anticyclone off the west coast of South Africa and low-pressure cells occurring to the north and south. Low energy producing patterns show characteristics of the high-pressure cells moving southwards producing fine weather and mildly disturbed conditions. The purpose of this methodology is that it provides the foundation required to derive long-term frequency changes of these synoptic weather systems using global climate model ensembles and thus changes in wind energy production. [ABSTRACT FROM AUTHOR]

Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. [ABSTRACT FROM AUTHOR]

Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future. [ABSTRACT FROM AUTHOR]

The present study majorly focused on the degree of rainfall alteration (DIRA) using an indicator-based novel approach. Most of the previous works majorly focused on only magnitude parameters for measuring monthly rainfall change; however, alteration may also come from other relevant aspects like frequency, timing, rate of change, and duration of rainfall. The present paper considered 33 indicators to assess alterations in rainfall, encompassing various such aspects. Subsequently, it computed the degrees of rainfall alteration categorized as low, moderate, and high range of variability (RVA). This computation was based on the gridded rainfall data endorsed by the Indian Meteorological Department (IMD). Getis-ord was applied to compute the significance of rainfall patterns. The study revealed that middle RVA-based DIRA is low (< 0.33) in the case of magnitude indicators across IMD-defined meteorological sub-divisions, but moderate DIRA (0.33–0.67) was detected in some parts in the case of rate of change, frequency, duration, and timing indicators. This change was detected rather high (moderate to high) over wider parts of India (six and sixteen sub-divisions) in the case of low and high DIRA. In monsoon and post-monsoon seasons, north-eastern hilly hot spots (95–99% level) were weakened, while it was found enhanced in north-western India. A new rainfall hot spot (95%) was identified in Odisha, Telangana, and Vidarbha transition in 1991–2021. Taking into account the notable success achieved through the IRA approach, the study strongly recommends its application across various related domains. [ABSTRACT FROM AUTHOR]

Drought concepts, definition, and classification processes have been in the field of research for decades, and especially the effects of global warming and the resulting climate change have accelerated these researches. The most used procedure for drought categorization has been in the literature under the name of Standardized Precipitation Index (SPI) with the recommendation of the World Meteorological Organization, and many modified versions of it have appeared in the open literature. Although conceptually the SPI is based on the probabilistic standardization index (PSI), some publications use the statistical standardization procedure (SSP) incorrectly. This paper lays out and explains the difference between the two types of standardization. In proper SPI applications, it is necessary to convert a given hydro-meteorological time series probability distribution function (PDF) to a standard (Gaussian) PDF with zero mean and unit standard deviation. SSP cannot accomplish this task, because the original PDF type remains the same after shifting and scaling applications. The comparison of PSI vs. SSP is provided along with their pros and cons. Accurate application of PSI is presented for annual Danube discharge, New Jersey temperature, and Istanbul/Florya precipitation records. [ABSTRACT FROM AUTHOR]

Meteorological data are essential in precision agriculture in the Canadian Prairies and are often associated with spatiotemporal discontinuity and scarcity. Reanalysis data products aim to address this challenge and have recently gained popularity. The European Centre for Medium-Range Weather Forecasts' ERA5, and its high-resolution land component, ERA5-Land, are two reanalysis datasets that provide hourly estimates of many climate variables globally. This paper focuses on evaluating the performance of ERA5 and ERA5-Land over the Canadian prairies, utilizing data from 109 weather stations situated in southern Manitoba, Canada. Various variables are investigated at daily, monthly, and annual aggregation periods, including air temperature, ground temperature, soil water content, wind speed, precipitation, and evaporation. The datasets are evaluated regarding seasonal bias and spatial distribution of errors over the study area. Regression parameters are also presented to address the biases. Among the investigated variables, air temperature and wind speed exhibit the lowest errors. The evaluation further reveals an overall tendency to overpredict ground temperatures and precipitation while underpredicting evaporation. Based on these findings, the ERA5 and ERA5-Land datasets hold significant potential in applications such as climate-smart agriculture, energy demand analysis, assessing renewable energy resources, and facilitating sustainable urban development. [ABSTRACT FROM AUTHOR]

Atmospheric gravity waves are essential meso-small-scale oscillations that facilitate material exchange within the atmosphere. These waves can significantly affect the ozone layer in the Upper Troposphere and Lower Stratosphere (UTLS) on the Qinghai-Tibetan Plateau. However, the influence of gravity wave dynamic processes on upper stratospheric ozone has rarely been studied. This paper identifies the gravity waves on the Tibetan Plateau based on ECMWF Reanalysis 5 (ERA5) data, analyzes the response of the upper stratospheric ozone to the event, and simulates the dynamic propagation mechanism of gravity waves by the Weather Research and Forecasting (WRF) model. This analysis reveals that between 15:00 and 21:00 UTC on July 29, 2015, gravity waves propagated from the surface of Tibetan Plateau (450 hPa) up to the upper stratosphere (20 − 3 hPa) in an arc-shaped structure and tilted to the east with height. The gravity wave signals started to weaken at 21:00 UTC on the same day. Influenced by the easterly rapids, gravity waves partially broke near 3 hPa at 02:30 UTC on July 30, but gravity wave signals were still present, and gravity waves completely broke and released energy at 04:00 UTC. During this process, ozone in the 20–3 hPa region (upper stratosphere) on the Qinghai-Tibetan Plateau responds well to gravity waves, and the ozone mixing ratio began to drop in ozone concentration 30 min after the partial breakup (03:00 UTC). The ozone dropped drastically by about 0.014 ppmv from 04:00 to 05:00 UTC. The WRF simulation results agree well with ERA5 and accurately capture the intricate characteristics of gravity waves. Furthermore, the breakup of gravity waves caused a total drop in ozone of 0.024 ppmv. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

Abnormal changes in air temperature cause natural disasters such as droughts, hailstorms, and storms, thereby affecting the normal lives of human beings. Consequently, timely and accurate air temperature prediction is essential for human production and livelihood. Traditional air temperature prediction methods are less accurate and less consider the spatial relationship between air temperature in different regions. In this paper, we propose a new deep learning model, convolutional long short-term memory based on channel attention and spatial autocorrelation (ConvLSTM-CASA), which focuses on the spatial correlation between ambient air temperatures and can effectively capture the interaction of air temperatures in different regions. The results show that the ConvLSTM-CASA model has an average R2 of 0.954 and MSE of 5.245 for pentad-mean temperature prediction over the Yangtze River basin. Compared with baseline forecasting models, the MSE accuracy by the ConvLSTM-CASA model improved by 72.45%, 48.95%, 48.97%, 47.79%, and 22.63% over the decision tree regression (DTR), multiple linear regression (MLR), random forests (RF), long short-term memory (LSTM), and ConvLSTM models, respectively. The ConvLSTM-CASA model is expected to outperform the ConvLSTM model over 90% of the area, suggesting robust prediction skill improvement over space. The ConvLSTM-CASA model provides new insights for data-driven pentad-mean air temperature prediction by including elaborate channel and spatial feature modeling, which aid individuals in comprehending the intricate patterns of air temperature fluctuations. [ABSTRACT FROM AUTHOR]

This paper presents a validation of outputs from some GCMs of the CMIP6 project when used to assess climate projection and hydrological flows at a catchment scale for the case study area of the Lombardy region (Northern Italy). The modeling chain consists of (i) a choice of climatic scenarios from 10 GCMs of the CMIP6, (ii) the application of a stochastic downscaling procedure to make projections usable at the local scale, and (iii) the use of a semi-distributed physically based hydrological model Poli-Hydro for the generation of hydrological scenarios. Data on observed precipitation and temperature were collected from automatic weather stations, and the hydrological budget of four target catchments within the study area was assessed using Poli-Hydro. An ex-post (back-casting) analysis was performed upon the control data series from the GCMs by comparing statistics of relevant climate variables and model-simulated discharges against observed counterparts during the historical period 2002–2014. Then, during 2015–2021, the goodness of projections was assessed using confidence intervals. Our results show that the accuracy of GCMs in representing regional climate is not always reflected in a credible evaluation of local hydrology. The validation of climate patterns provides somewhat poor results; thus, the interaction among climate and hydrology needs to be explored carefully to warrant the credibility of hydrological scenarios. Overall, the spatial and temporal consistency of GCM projections, as explored here climatically and hydrologically, provides a clue about their dependability for basin scale management. [ABSTRACT FROM AUTHOR]

There are several standardized meteorological, hydrological, agricultural, and environmental indices for drought classification into a set of dry categories all of which are derived from the basic concept of standardized precipitation index (SPI). Almost all these indices are based on crisp (bivalent) logic where boundary limits between neighboring categories are numbers without transboundary inclusiveness. The SPI procedure depends on the standard normal (Gaussian) probability distribution function (PDF) with zero mean and standard deviation equal to one. This paper proposed fuzzification of the SPI limits among categories for the inclusion of more than one category with different membership degrees. This method is referred to as the fuzzy SPI (FSPI) procedure, which provides drought tracing possibility and categorization. At the end for a numerical value, one can defuzzify the fuzzy result through defuzzification methods. FSPI provides preliminary warning system in terms of two or more fuzzy rule propositions and categories. Thus, one can know the logical alternatives of the drought behavior of a given hydro-meteorology variable. The application of the FSPI is presented for New Jersey State wise and Istanbul annual precipitation records. [ABSTRACT FROM AUTHOR]

The present paper aims to model statistical structure of heavy-tailed precipitation data. In particular for agricultural purposes in Canadian Prairies, this is essential in many aspects such as assessing and managing risks resulting from the occurrence of unexpected precipitation events. Daily (or weekly) precipitation time series often contain many zeros (on dry days) and also exhibit important characteristics such as heavy-tailedness and volatility clustering. These features make it challenging to develop an effective model from both theoretical and practical viewpoints. In this paper, we propose a dynamic mixture model constructed based on a generalized Gaussian crack distribution with a GARCH specification to take into account the full range of precipitation measurements with a sufficient flexibility to fit both thin- and heavy-tailed data, and stochastic volatility. The method of maximum likelihood estimation with the profile log-likelihood algorithm is illustrated with some simulation studies. The model fitting results on a historical dataset from twelve stations in Canadian Prairies show the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Two algorithms have been proposed in this paper. One is another scanning t test of trend change points in regression slope coefficients in two phases, along with a coherency analysis of trend changes between two time series. It is different from the previously published scanning Fmax test of trend changes. The second is a fuzzy weighted moving average (FWMA). Then, the algorithms were applied to two series of monthly temperatures over global land and ocean surfaces for 1850–2021. The applied results show that significant changes in segment trends appeared in two gradations on the interdecadal and intradecadal scales. All subsample regression models were found to fit well with those data. Global warming started in April 1976 with a good coherency of warming trends between land and sea. The global warming "hiatus" mainly occurred in SST cooling from November 2001 to April 2008, but was not evinced over land on interdecadal scales. The "land/sea warming contrast" was detected only in their anomaly series, but disappeared in their standardized differences. We might refer to the anomalies in distribution N(0,s) as "perceptual" indicators and refer to the standardized differences in N(0,1) as "net" indexes. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

In the midsummer of 2022, a rare regional high-temperature event occurred in the Yangtze River Basin. Chongqing, as the center of this event, also experienced its strongest high temperature since 1961. In this paper, the characteristics of this event in Chongqing are reviewed in detail and compared with those of 2006. We have confirmed the following conclusions with detailed data: (1) In the midsummer of 2022, the number of days that the daily maximum temperature is exceeding 35 ℃ in Chongqing was the most since 1961, with the number of days that the daily maximum temperature above 40 ℃ increasing significantly compared with 2006. (2) The impact range of this event in Chongqing was quite extensive, especially the range with the daily maximum temperature exceeding 40 ℃ was the widest since 1961. (3) The high-temperature event was more extreme in 2022 than that in 2006. The maximum of daily maximum temperature was 45 ℃, and this is the first time that China has recorded a high temperature of 45 ℃ outside Turpan. There were 5 stations with daily maximum temperature exceeding 44 ℃, accounting for 1/3 of the number of stations with such temperature (15 stations) in China. (4) In the midsummer of 2022, Chongqing was dominated by the compound high-temperature day. Compared with 2006, the daytime high-temperature day decreased significantly, while the compound one increased obviously. [ABSTRACT FROM AUTHOR]

The Antarctic Peninsula (AP) is a climate change hotspot, in which any alterations to the atmospheric circulation can lead to significant impacts on the cryosphere. Even though substantial evidence has been found on the effects of human-related greenhouse gas emissions on the climate of the AP, less studies have analyzed the effect of variations in stratospheric ozone concentrations on the regional climate, and that is the focus of this paper. Results show significant links between the concentration of stratospheric ozone and the regional circulation, as well as with variations on the frequency and intensity of cold air snaps east of the AP. Significant relationships are found between changes in ozone concentrations and minimum/maximum temperature distributions over AP weather stations over most of the seasons. A strong link is also found with the location, depth and intensity of the Amundsen Sea Low west of the AP, which is in most cases responsible for the temperature anomalies identified in this study. [ABSTRACT FROM AUTHOR]

In light of the rapidly growing agricultural sector, spotting signs of climate change is crucial. Even a slight change in climate behaviour can hinder crop development. As a result, it is necessary to investigate these climatic signals at a localised regional scale. The current methods mainly emphasise on outliers when identifying abnormalities in geographical and temporal data. This disregards the possibility of negative impacts brought on by climatic changes at each location. This paper introduces a novel mathematical approach, named the probabilistic convolution neighborhood technique (PCNT), that finds the temporal and spatial coherence at each point. Its ability to examine distribution at all grid points makes it easy to identify even a slight change in climatic variables. This new approach has been applied to study the variations in parameters, namely humidity, precipitation, mean, and maximum temperatures, that are collected from the Indian Monsoon Data Assimilation and Analysis (IMDAA) for the time period 1980 to 2020. The datasets are daily single-level datasets from which annual and seasonal (Kharif, Rabi, and Zaid) data are obtained. Spatio-temporal properties, along with inter-vicennial and inter-decadal variations of climatic variables with regions having low, high, and normal probability distributions, are studied. The findings showed a pattern in the temporal features between Kharif and Rabi. The mean temperature for the annual and seasonal data showed decreasing trends for tail diagnostics. The study brings out an understanding in the probable change in various metrics, which are crucial in the context of crop growth and agricultural practice. [ABSTRACT FROM AUTHOR]

Understanding the future patterns of precipitation behaviour in unique geographical areas, largely determined by their orography and local scale, can help lay the foundations for a new precipitation model for the design of the city's main urban drainage infrastructures (intensity-duration-frequency curves, mathematical functions that relate precipitation intensity to duration and frequency of occurrence, hereafter IDF, for the short-, medium- and long-term future). This will definitely contribute to the improvement of the city's resilience to the effects of climate change. In this paper, the projections of a subset of climate change models from both the sixth phase of the Coupled Model Intercomparison Project (CMIP6; with a total of 5 simulations) and Euro-CORDEX (for a set of 51 simulations) have been adjusted to the municipality of Alicante (in the southeast of Spain), using the Climadjust tool (climadjust.com). These projections contain different climatic variables. The rainfall variable has been used to derive a new framework of boundary conditions to help design more resilient infrastructure for torrential rainfall events and urban flooding. The projections corresponding to three climate change scenarios (CMIP6: SSP1-2.6, SSP2-4.5, SSP5-8.5; and Euro-CORDEX: RCP2.6, RCP4.5, RCP8.5) are considered with daily resolution and, by applying statistical techniques of temporal disaggregation (by means of a cascade model), hourly (and sub-hourly, reaching 30-min resolution) disaggregation. The results at hourly and 30-min resolutions are used to construct IDF curves of future climate, grouped into short-term (years 2015 to 2040), medium-term (years 2041 to 2070) and long-term (years 2071 to 2100) sub-scenarios. The selected future climate IDFs for an adverse climate change scenario (SSP2-4.5 and SSP5-8.5) show increases in rainfall intensities, higher the shorter the rainfall duration, for return periods greater than or equal to 25 years, whereas for return periods under 25 years the current IDFs can be representative of future scenarios. Current calculations and future projection of the torrentiality index for severe climate change scenarios, as well as the climate change factors, show an increase in the frequency and magnitude of the heaviest rainfall. This fact corroborates the hypotheses of greater general torrentiality in future rainfall in this specific area of the Spanish Mediterranean coast. [ABSTRACT FROM AUTHOR]

The El Niño Southern Oscillation (ENSO) is a major influence on interannual variability of rainfall in stations in the tropical southwest Pacific. Predictions of seasonal rainfall, especially a season or two ahead, are of great value to these countries. This paper therefore examines the correlations over ~ 60 years between seasonal rainfall and 8 ENSO indicators at 16 island stations, allowing for lead times. The results show the influence on rainfall of the position and movement of the South Pacific Convergence Zone (SPCZ) during ENSO events, and that the Southern Oscillation Index (SOI), the sea surface temperature anomaly in the central Pacific (Niño 3.4), and the warm water volume in the eastern Pacific (WWV1) have longer lead times compared to most other ENSO indicators. These indicators can therefore be used with confidence in SCOPIC, a widely used statistical tool for prediction of seasonal rainfall. (As global climate models generally have systematic errors in their depiction of the SPCZ, they cannot yet be used directly to reliably predict seasonal rainfall in this region.) For several sites near the SPCZ, we find that a moderately good forecast of rainfall in both spring and summer can be made from indicators measured in June–July (i.e., 3–6 months in advance.) [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) changes have some implications on land–atmosphere interactions, surface energy budget, and hydrological cycle. The understanding of LULC changes and their interactions with the environment provide better management of agriculture, forests, and water resources. LULC changes are frequently observed in developing countries such as Türkiye and much research has been conducted in this field. However, few studies investigated the countrywide LULC changes and their interactions with energy fluxes and precipitation. In this paper, LULC changes in Türkiye over the last two decades have been evaluated by utilizing an Enhanced Vegetation Index (EVI) derived from the Terra satellite. The latent and sensible heat fluxes were obtained from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) reanalysis datasets. Precipitation data were acquired from the Tropical Rainfall Measuring Mission (TRMM) satellite. The results showed that EVI has an increasing trend over Türkiye between 2000 and 2021, while climate change–induced desertification, droughts, and forest fires have threatened the vegetative cover in some portions of the country. The latent heat flux has a rising trend due to increasing vegetative cover and irrigated areas. A slight reduction was observed in sensible heat flux, while a sharp increase was witnessed in specific humidity. EVI represented a high positive correlation (R = 0.84) with sensible heat flux and a moderate positive correlation (R = 0.69) with latent heat flux. No significant relation was determined between EVI and precipitation on a monthly basis. [ABSTRACT FROM AUTHOR]

This paper studies the spatiotemporal variability of the IRt index (defined as the difference between the percentage of precipitation corresponding to warm days and cold days in year t) calculated at 35 stations in the Iberian Peninsula during 1951–2019. Spatial variability is estimated using cluster analysis (Euclidean distance, Ward's method), which allows regionalization of the data set for each year's season. For each cluster, the < IR > index is defined as the average of the indexes corresponding to each cluster element. The temporal evolution of the < IR > indices is analyzed by studying the appearance of trends using the Mann–Kendall test and the possible existence of various subperiods using the cumulative sum of deviations and the t-test for the difference between the means. The role of EA, EA/WR, NAO, SCAN and WeMO teleconnection patterns in modulating the relationship between temperature and precipitation is analyzed by applying multiple regression analysis. The results show a trend towards positive (or less negative) values in the relationship between temperatures and precipitation (of varying magnitude depending on the region and season) and the contribution of atmospheric dynamics to these changes. [ABSTRACT FROM AUTHOR]

Agricultural sectors worldwide are under direct threat from climate change conditions. In Iran, agricultural production has decreased due to droughts originating in an increase in annual maximum temperatures—with the corresponding increase in crop respiration and evapotranspiration—and a decrease in accumulated precipitation. Based on a static computable general equilibrium approach, this paper reports implication for the Iranian economy from the effects of climate change on agriculture––as modeled through three scenarios relying on assumptions about the magnitude of continued reduction in total agricultural production. Reductions of 6%, 12%, and 18% in total agricultural production reasonably cover the range of impacts that climate change is expected to impose on the Iranian agricultural sector––under the assumption that no behavioral adaptations or policy interventions are in place. Our simulations suggest that effects on the Iranian economy imply a reduction in GDP ranging between 3.7 and 6.3%. In addition, 5–17% of labor moves away from the agriculture sector––this labor relocation occurs due to declining agriculture incomes. Findings illustrate that climate change will reduce households' consumption and income in all economic sectors, particularly among rural households. We suggest that policies in Iran should focus on improving cultivation methods to save water resources and alleviate the expected effects of climate change. The current study's outcomes are helpful for policymakers, especially in countries with water scarcity. [ABSTRACT FROM AUTHOR]

To clarify the current situation, hotspots, and development trends in the field of rice and climate change topic research, a massive literature dataset were analyzed from the Web of Science database by bibliometric method. The research theme was chosen given the continuous increase of studies related to climatic changes and their consequences to rice. Based on the Web of Science core database, this study analyzed 4170 papers in the field of rice and climate change topic research from 1990 to July 2022, which include 86 highly cited papers and 3 hot papers. Papers were mainly written in English (4157, 99.688%), from 16,363 authors, 4017 organizations, and 129 countries/territories, published in 841 journals and seven book series. The top five Journals are Science of the Total Environment (136, 3.261%), Sustainability (89, 2.134%), Agronomy Basel (81, 1.942%), Agricultural and Forest Meteorology (77, 1.847%), and Climatic Change (74, 1.775%), each published more than 74 papers. Top five countries and regions of People's Republic of China, the USA, India, Australia, and Japan were the major article contributors, each published more than 360 papers. Top five organizations of Chinese Acad Sci, Nanjing Agr Univ, Univ Chinese Acad Sci, Chinese Acad Agr Sci, and Int Rice Res Inst (IRRI) were popular based on contribution of articles more than 133 papers each. Among the all authors, top five authors were Tao Fulu, Pan Genxing, Zhang Zhao, Hasegawa Toshihiro, and Iizumi Toshichika, each published more than thirty papers. All keywords were separated into eight clusters for different research topics. Visualizations offer exploratory information on the current state in a scientific field or discipline as well as indicate possible developments in the future. The results will help researchers clarify the current situation in rice and climate change adaptation science but also provide guidance for future research. This work is also useful for student identifying graduate schools and researchers selecting journals. [ABSTRACT FROM AUTHOR]

The utilization of regional climate methods (RCMs) to predict future climate is an important study under the changing environment. The primary objective of the paper is to correct the temperature and precipitation simulations for the period of 1980–2005 and 2026–2098 in the Wei River Basin (WRB), to evaluate the performance of RCMs for the period of 1980–2005, and further, to analyze the future changes of projected temperature and precipitation during 2026–2098. In this paper, the linear scaling method was used to correct the temperature simulations. Quantile mapping, local intensity scaling method, and hybrid method were used to correct the precipitation simulations. The future changes of projected temperature and precipitation for the near term (2026–2050), mid-term (2051–2075), and far term (2076–2098), relative to the period of 1980–2005, were investigated under RCP 2.6 and RCP 8.5. Results indicate that (1) the temperature biases were either warm or cold in the spatial scale, and the precipitation wet biases were detected. After correction, HadGEM2-ES driven by RegCM4-4 had the best temperature reproducibility, and NCC-NorESM1-M driven by RegCM4-4 had the best precipitation reproducibility. (2) Under RCP 2.6, the projected annual, winter, and spring temperature showed decreasing trends. The temperature was higher than that for the period of 1980–2005 except for the spring temperature decreases in the Beiluo River Basin. Under RCP 8.5, the temperature showed significantly increasing trends. The temperature for the near term was similar to that of the period of 1980–2005, while the temperature increased significantly for the mid-term and far term. (3) Under RCP 2.6, the precipitation had decreasing trends. Under RCP 8.5, precipitation trends were also spatially distributed. The relative deviation of winter precipitation was the largest. Relative to the period of 1980–2005, the light- and moderate-rain days showed little change for the period of 2026–2098, while the extreme-rain days showed significantly increasing trends. [ABSTRACT FROM AUTHOR]

In many media, especially in prime-time television shows, we often hear that the temperature is or will be higher than average or above average. It is seldom said which average it is, namely which period this comparison refers to. It is also rare to hear how much this above-average temperature is higher than one average. The question is what the climate average or climate normal is? The World Meteorological Organization (WMO) during the twentieth century defined a period of 30 years as the standard reference for calculating climate normal. The 30-year period, known as the climatological standard normal, serves as a benchmark against which current observations can be compared to the previous one. The last climatological standard normal period 1981–2010 is still in use while a new one is in preparation for a 30-year period that will cover the period from 1991 to 2020. It is expected that this change will be in use from the beginning of 2022. In this paper, based on the temperature change data obtained at the meteorological stations Split Marjan and Zagreb Grič, the differences in the conclusions about the last air temperature changes in the period of the last 10 years (2011–2020) are studied. The following four different 30-year climate normal periods are analysed: (1) 1961–1990; (2) 1971–2000; (3) 1981–2010; (4) 1991–2020. The analyses performed in this paper indicate the necessity of constant changing of the 30-year climate normal period used to estimate variations in recent air temperatures. Given the sudden rise in air temperature over the last 30 years, the period from 1991 to 2020 should be currently used for comparison and assessment of the most recent temperatures and other climatic parameters. [ABSTRACT FROM AUTHOR]

The Western Ghats (WG) is one of the important biodiversity hotspots and rainfall intense regions in south Asia. In this paper, the long-term trend analysis is carried out during southwest monsoon rainfall over different latitudinal regions of the WG. Further, the gridded daily rainfall data (0.25 × 0.25 spatial grid resolution) is classified into six intensity bins: dry, low, moderate, heavy, very heavy, and extreme, from 1951 to 2015. A detailed trend and variability analysis of rainfall and its frequency in different intensity bins along with their relative contribution were carried out. The results of different rainfall intensity bins depict that the moderate and low intensity bins contribute a maximum share in seasonal rainfall with 45% and 20%, respectively, from the 30% and 50% rainy days in all the regions. Heavy and very heavy intensity bins together contribute about 30% of the seasonal rainfall from about 9% rainy days. Extreme intensity bin recorded an alarming share of 6% with only 1% rainy day occurrences. In general, the relative contribution shows an ascending pattern of moderate, low, very heavy, heavy, extreme, and dry intensity bins. A decreasing trend in rainfall was observed in heavy and very heavy intensity bins, while other intensity bins illustrated mixed trends in different study regions. Extreme rainfall events marked a major shift upward in the WG region during the study period. Overall, our result portrays a negative trend in rainfall during the southwest monsoon season over all the study regions during the study period. [ABSTRACT FROM AUTHOR]

In the past, long-term recordings of solar radiation energy were not commonly conducted. However, several observations using the Arago-Davy actinometer were made in different parts of the world during the nineteenth century. In this paper, we propose a method to convert actinometric degree data into information on global solar irradiance on a horizontal surface. We utilized hourly actinometric degree data from the Montsouris Observatory in Paris recorded between 1873 and 1877. Three models were tested for estimating solar global irradiance at ground level from actinometric degrees. Despite the quality of the solar irradiance data provided by the Twentieth Century Reanalysis Project version 3 (20CRv3) was subjected to criticisms, these data are used here as a reference, taking into account the lack of similar data for the nineteenth century. One of the main challenges in this study is the fact that the solar irradiance incident on the Arago-Davy actinometer is not global solar irradiance on horizontal surface, G H , a quantity which is usually measured and recorded. To address this issue, a proxy value of G H , named G H + , is defined by using the first sub-model of the Ferrel-Pouillet model (named FP1). The analysis showed that G H + is suitable for estimating both hourly and daily averaged values of the global solar irradiance G H . However, the model FP1 requires two input parameters, namely the actinometric degree D and the bright-bulb thermometer temperature t bright - bulb , while most long-term observations on the globe have data only for D . This makes the FP1 model less useful in these cases. To address this, a method was proposed that relies only on D as an input parameter. This method provides hourly and daily averaged irradiance data that are similar to those obtained using the FP1 model with two input parameters. The accuracy of this method is expected to remain the same in colder climates but decrease in warmer climates. The proposed method also provides credible lower and upper bounds for the interval of variation of monthly averaged irradiance. Additionally, at the level of monthly averaged solar irradiance data, the proposed method and the 20CRv3 project seem to support each other. [ABSTRACT FROM AUTHOR]

Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error (RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901. The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland; however, E-OBS is significantly warmer than CarpatClim over the mountains. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. The evaluation method can be applied to other datasets, different time periods and areas. It is also suitable to find dataset errors, which is also exemplified in this paper. [ABSTRACT FROM AUTHOR]

The great Kolkata Cyclone of 5 October 1864 is still felt in the hearts of people in Bengal after 158 years. Almost every year, the inhabitants of Bengal are frequently threatened by various types of cyclones. In the last three centuries, from 1737 to 2021, the people of Bengal have witnessed more than one hundred fifty cyclones, tornadoes, and typhoons. Cyclones that occurred in the Bay of Bengal in October of 1737, 1864, 1874, 1876, and 1942 were severe and caused great loss of life and property. But the Kolkata Cyclone on the 5 October 1864 was one of the deadliest cyclones of Bengal. It was also one of the most destructive cyclones in the world. The devastation of the cyclone was terrible. It appeared that the total loss of life by the cyclone in Bengal actually reported by competent authorities was not more than sixty thousand. The loss of life in the Bengal had been partially ascertained. This paper is a case study of a natural calamity that swept over the Bengal on 5 October 1864 with reference to broader questions relating to climate and human settlement. The most striking part of the description is the human casualties. Finally, it shows how the effects of this natural calamity devastated thousands of lives in then-Bengal. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to analyze the rainfall regimes of Pernambuco, a northeastern Brazilian state, based on the seasonality indices (the individual, SI i , and the general, SI ¯ ) and the replicability index, RI . These indices were derived from monthly rainfall data, recorded between 1953 and 2012, at 125 weather stations. The modified Mann–Kendall test and Sen's slope estimator were applied to investigate trends in the SI i time series. A regression analysis of SI i ¯ , SI ¯ , and RI with the geographical longitude produced a statistically significant linear correlation. The calculated SI ¯ index indicates that the mean rainfall regime of the coastal area (Zona da Mata) and the central area (Agreste) of the state can be classified as rather seasonal with a short drier season. The western part (Sertão), in turn, exhibited mean rainfall regime between seasonal and markedly seasonal with a long drier season. Concerning the replicability index, results suggest that Agreste is the region with the least replicable rainfall regime in the state. The analysis of the two 30-year sub-periods shows significant changes in the mean values of SI i indicating the shift in rainfall regime toward extreme seasonality with prolonged dry season in the Sertão region and toward more regular with shorter dry periods in Zona da Mata and Agreste region. [ABSTRACT FROM AUTHOR]

This paper continues the work of Kang and Larsson Theoretical and Applied Climatology 116:537–548 (2014) What is the link between temperature and carbon dioxide levels? A Granger causality analysis based on ice core data. Theoretical and Applied Climatology 116:537-548, by adding methane to the study of causality between temperature and carbon dioxide. The data used goes 800,000 years back in time which is possible due to it being extracted from ice cores located at Dome C in Antarctica. First we use linear interpolation to make the three data sets equidistant to be able to employ statistical methods. We adjust for a deterministic trend and find the best model fit to be an autoregressive model with lag two and a piecewise quadratic trend. By employing multivariate Granger causality tests we find strong evidence that temperature, carbon dioxide and methane all Granger cause each other in both directions i.e. carbon dioxide Granger causes temperature and temperature Granger causes carbon dioxide, etc. This is in accordance with the findings of Kang and Larsson, with the extension that we can add methane to establish a trivariate feedback system between temperature, carbon dioxide and methane. [ABSTRACT FROM AUTHOR]

Photosynthetically active radiation (PAR) (expressed in photosynthetic photon flux density, Qp) is an important parameter in different biological and ecological processes. Despite its importance, there is no worldwide network for its measurement. This paper analyzes the behavior of PAR in the period of 1 year, from April 2020 to March 2021, using the information collected from a meteorological station located in the city of Chihuahua, Mexico. The annual mean value of Qp at noon was 1820.1 μmol m−2 s−1; the annual mean value of Qp/Rs at noon was 1.96 μmol J−1; the annual mean daily of Qp was 42.9 mol m−2 day−1. The information collected throughout the year at the meteorological station was used to develop an empirical model to estimate Qp in Chihuahua City and its zone of influence. The developed model provided satisfactory estimations of Qp by having an MBE of 14.7 μmol m−2 s−1, RMSE of 22.8 μmol m−2 s−1, and R2 > 0.99. [ABSTRACT FROM AUTHOR]

The aerosol optical depth (AOD) is an indicator of particles suspended in the atmosphere, which measures the attenuation of transmitted spectral radiant energy through aerosols. It has been proven to be an essential factor in the estimation of air pollutants concentration and surface solar radiation. However, due to the sparse distribution of ground-based measurements and the high missing rate of satellite retrieval products, the reanalysis data with high spatial–temporal continuity and large spatial range is an excellent supplementary data source. However, compared with satellite monitoring and ground-based measurements, the reliability of the reanalysis data has been questioned. Therefore, in this paper, we evaluate the accuracy of the Monitoring Atmospheric Composition and Climate (MACC) AOD values at the wavelength of 550 nm (AOD550) using the ground-based observations data from 19 AERONET stations in the Chinese mainland for the period 2003–2007. In addition, a data fusion correction model based on the random forest regression is constructed and analyzed. The correction results demonstrate that the correction model, which captures the non-linear relationship, has a satisfactory correction effect overall, with R from 0.771 to 0.978, MAE from 0.225 to 0.047, and RMSE from 0.400 to 0.120. Furthermore, with the aid of model-agnostic machine learning explanation algorithms, we weigh the relative importance of the independent variables and visualize the complicated relationship between the model errors and the important input features. In general, the availability of MACC AOD was improved, and the correction model also provided a reference for the correction of other reanalysis data. [ABSTRACT FROM AUTHOR]

Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Satellite-based remote sensing rainfall products have the advantage of large-scale synchronous coverage, but the reliability of their inversion still needs to be improved. Correcting the bias of satellite-based precipitation estimates (SPEs) is a major challenge in applications such as environmental modeling, hydrology, and water resource management. In this paper, a new bias correction method—a Gaussian process regression (GPR) model method based on rain intensity classification—is proposed to improve the accuracy of a satellite precipitation product—the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run (FR) product—at the daily scale in the Guangdong-Hong Kong-Macao Greater Bay Area. By comparing the effects of the proposed method with those of other existing classical correction methods, namely, quantile mapping (QM), the support vector machine (SVM) approach and direct GPR, it is found that all four methods improve the accuracy of the FR product to varying degrees. GPR based on rain intensity classification has the best effect of FR product improvement, with the CORR increasing from 0.55 to 0.59, the RMSE ranging from 14.37 to 12.79 mm/day, and the BIAS ranging from − 0.14 to 0.03 during the validation period. GPR without rain intensity classification also yields good results, with the QM and SVM methods being the least effective. [ABSTRACT FROM AUTHOR]

This paper deals with precipitation events, particularly the problem of recognising events individually, of distinguishing one local meteorological/precipitation event from another. The final aim was to arrive at a more appropriate conceptualization of "distance" between individual annual events. A criterion for identifying annual precipitation events, inside a 5 min-timestep series of pluviometric data, was proposed. Using this numerical criterion, a series of annual distances between events were obtained and compared with those obtained by other two known and applied statistical methods. This comparison would be important for linking the so-called statistical methods to the real data of the series, analysed through a numerical process. This would make it possible to provide a kind of "measure" for the understanding of real data via a statistical model. Results showed that distances between events, obtained through numerical analyses, are almost always different from those obtained by statistical methods. The supposed Poisson-process, generally matched to, and widely applied in the field of rainfall data, showed some contradiction with the analysis conducted in this paper. The so-called Lag method shows a numerical method type distribution, but produces some important internal deviations from numerical results. It seems that the method proposed in this paper can reproduce the real distances between events more realistically compared to the statistical methods considered here. Exponential distribution does not seem highly performing, at least in the case of the annual analysis, with series of a 5-min time-step resolution, obtained from rain gauges installed in the Veneto Region of Italy. It is believed that the numerical criterion presented in this paper can be considered adequate for determining the distances between events, within an annual time-window. [ABSTRACT FROM AUTHOR]

Selecting the appropriate climate models and determining their strengths and weaknesses are the key steps in examining climate change impacts on water resources. In this paper, the efficiency of monthly, seasonal, and annual precipitation estimations by 21 Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) was evaluated against the Observational Precipitation (OP) of 50 synoptic stations located in eight Precipitation Zones (PZs) of Iran. First, the average of GCMs precipitation for the period 1976–2005 was evaluated vs the OP data at both PZ and national levels. Then the efficiency of the GCMs and their Ensemble Average (EA) in estimating monthly, seasonal, and annual precipitation were determined using NSE, NRMSE, KGE, and PBIAS statistics, and subsequently, the GCMs were ranked according to their statistics. The results show that the time variability of the GCMs' average annual precipitation agrees with that of the OP, but it underestimates the extreme values. The agreement of the models' estimations with observation is generally lowest in summer and the highest in winter and spring. Most of the GCMs show a lower ability in precipitation estimation in dry seasons than in wet seasons. EA of the models for all individual months shows the highest errors in the coastal areas of the Caspian Sea, especially for the spring and winter seasons. The examination of the GCMs' efficiency at the national level shows that the EA and GFDL-ESM2G models have the least errors at monthly and annual time scales, respectively. [ABSTRACT FROM AUTHOR]

Rainfall onset dates (ROD), rainfall cessation dates (RCD) and length of rainy season (LRS) are crucial for crop production and food security in Eastern Africa yet scantily documented. This paper seeks to investigate the spatial patterns of these parameters. Data used are Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0) and National Oceanic and Atmospheric Administration (NOAA) gridded temperature. Threshold of 1 mm for rainy day, 20 mm over 5 days with at least 3 rain days and dry spell not exceeding 7 days in the next 21 days were used to determine RODs. The Potential Evapotranspiration (PET) and Water Balance (WB) criteria were computed to determine RCDs. The differences between ROD and RCD were used in calculating LRS. The results showed early rainfall cessation over more than 30 counties in Kenya lead to shortened rainy season by 10–20 days during MAM season. Similarly, 20–40 days early onset dates are observed in most counties in upper Nile, Unity, and Jonglei states in South Sudan, while 20–40 days delayed rainfall onset was observed in Khartoum and southern parts of Nile state western Darfur, eastern and Aljazeera states in Sudan, most parts of Ethiopia and Eritrea districts during JJA season. Highlands of western and Nyanza region in Kenya, most parts of Uganda observed rainfall onset by March and no sign of cessation before November. Early cessation over northern Uganda districts was behind shortened LRS, furthermore, the early RODs over western and southwestern Uganda districts increased LRS. Prolonged dry conditions over northern Sudan, southeastern parts of South Sudan, northern Kenya, central Somalia, northern Darfour, Kordofan and northern parts of Sudan exacerbated by significant delayed onset and early cessation of rainfall. These findings are important for rain-fed agricultural planning and food security in the IGAD region of Eastern Africa. [ABSTRACT FROM AUTHOR]

General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented. [ABSTRACT FROM AUTHOR]

For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (αt) and volume-based (αv) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of αt, αv, and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, αt had the highest variations, while αv had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM). [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze the existence of cold-air pools on anticyclonic nights in a narrow urban valley where three hospital units and a road with intense traffic are located and where the slopes associated with proximity to a medium-size mountain introduce different factors from that observed in other valleys where this phenomenon forms on cold, calm nights. On these nights, it is observed that cold-air pools develop in the valley just after sunset, in opposition to the ridge that dominates the valley (thermal belts), and it is found that their formation, development, and disappearance after the first hours of the day is a daily phenomenon. Using fixed data loggers and portable data loggers on the surface and at altitude coupled to a UAV (drone), it was possible to calculate the variation of the air temperature (as well as to locate the inversion layer) in different sectors defined along the valley. The finding of temperature oscillation along the longitudinal profile and at altitude, reflects a double source of feeding: in-situ irradiation, and the air draining down the slopes of the massif. [ABSTRACT FROM AUTHOR]

Probability distributions of wind data in a short time scale, such as hourly, are critically important for the risk assessment in relation to wind energy, optimal route scheduling for commercial airlines and unmanned aerial vehicles. It is known that the probability distributions of wind direction, wind speed, and wind gust have a large variety—ranging from Weibull to Lognormal distributions. A systematic method is needed to determine which distribution best fits a given wind dataset in a very efficient manner. This paper attempts to provide such a method. It is the best group fitting (BGF) method that efficiently fits the hourly wind data to a large number of distributions simultaneously, in contrast to the traditional one-by-one data fitting. The BGF method utilizes a Python package named Fitter which uses Scipy package, tries 80 distributions simultaneously, and checks what is the most probable distribution. We then apply the BGF method for the hourly data of u-wind, v-wind, wind speed, and wind gust in four geologically different locations, including the Alta Wind Energy Center, the Rocky Mountain region, the Colorado Plateau, and the San Diego urban district. The performance of the commonly used distributions and a wider selection of distributions are ranked for these locations at different times of a day. Our datasets and results show that, in addition to the traditional distributions such as Weilbull distribution and Lognormal distribution, the Burr distribution class stands out among the wind distributions and even outperforms the most used distributions in the literature. Our fitting results to the operational wind data have demonstrated that it is feasible to use BGF to make an efficient group fitting, which may imply that BGF can be used to search for the best probability distribution from different hourly weather datasets. [ABSTRACT FROM AUTHOR]

Persistent droughts are known to induce stress on agricultural crops and forest ecosystems. Severe long-lasting droughts lead to food insecurity. With rising air temperatures and declining precipitation totals, there is often a presumption that droughts are becoming more severe. To understand the complexity of interrelated physical processes on various temporal scales, it is important to analyse trends and mutual relationships between drought-related climatic variables. In this paper, we analyse monthly time series of near-surface air temperature, precipitation totals, potential evapotranspiration, climatic water balance, and two meteorological drought indices: the standardised precipitation index (SPI) and the standardised precipitation-evapotranspiration index (SPEI), at a total of 71 locations located within the western Carpathian Mountains (Slovakia) and the adjacent northern part of the Pannonian Plain. The analysed data cover the time period 1981–2019. Quantile regression was used to determine the slopes of temporal trends in the investigated climate variables. The mutual relationships between precipitation and air temperature were analysed on a seasonal basis. Negative coupling (~ − 5.1%/°C) between precipitation and air temperature was observed in the spring season. Positive coupling between precipitation and air temperature was observed in the remaining part of the year. Spatio-temporal analyses of SPEI revealed intensification of meteorological drought in the spring season that continues into summer at most of the analysed locations. The negative temporal trends in the SPEI suggest that the severity of meteorological drought has increased over the investigated period. [ABSTRACT FROM AUTHOR]

The paper aims to forecast the Philippine storm frequencies using nonlinear Poisson autoregressive model with exogenous variables. The nonlinearity is defined by its kernel which is an artificial neural network (ANN) with one hidden layer and two output neurons, and is trained to simultaneously forecast two semesters ahead for a given input. Furthermore, the covariates studied were the Average Sea Surface Temperatures in the NINO3.4 region (5∘− 5∘S,170∘− 120∘W) and the Average Sea Surface Temperatures in the eastern pole (0∘− 10∘S,90∘− 110∘E) of the Dipole Mode Index. The data, taken from the Japan Meteorological Agency's Regional Specialized Meteorological Center with time points running from 1950 to October 2021, is modeled at a semester-level granularity. The estimation is done using maximum likelihood estimation by minimizing the negative log-likelihood function. Furthermore, Bayesian hyper-parameter optimization was used to tune the model across different activation functions, number of hidden neurons, training optimizers, and train and validation splits. Lastly, the best model from the hyper-parameter optimization was then compared to univariate Poisson autoregressive model (and its Bayesian counterpart) and Negative Binomial Autoregressive model. The proposed model captures well the characteristics of the data both in terms of the point forecast and the associated uncertainties. [ABSTRACT FROM AUTHOR]