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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]

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 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]

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]

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]

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]

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 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]

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]

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]

Global warming can lead to a more frequent occurrence of hot days and heat waves and fewer cold days and cold waves. In this paper, the daily mean temperature (TM) was divided into 7 range categories (TM < − 15 °C, − 15 °C ≤ TM < − 5 °C, ... TM ≥ 35 °C). Then, the temperature days (TDs) were obtained and expressed as TD1, TD2, ..., TD6, and TD7, which correspond well to climate zones. The changes in future TDs are obvious, especially for TD1 and TD6. Under the Representative Concentration Pathway (RCP) 8.5 scenario, TD1 will decrease the most in the polar climate zone, from 162 to 102, while TD6 in warm tropical countries and regions, such as Brazil, Nigeria, and Congo, will increase by more than 70 days, reaching at least 300 days per year after 2066. By summarizing the anomalies of TD1, TD2, TD6, and TD7 in combination into 12 templates, the changes in TD1 and TD2 were determined to be more pronounced than those in TD6 and TD7. Although carbon dioxide emissions will remain basically stable from 2066 to 2095 under the RCP4.5 scenario, both TD1 and TD2 will decrease in central Antarctica, Eastern Europe, and northern Russia, and the melting of ice and snow will be irreversible. Countries such as Saudi Arabia and Australia will still face a continuous increase in both TD6 and TD7. Temperature range changes also affect the humidity of the climate: in the projected future, humid climate areas will decrease, while arid climate areas will increase. The boreal drylands in the middle and high latitudes will be replaced by temperate drylands. [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

Climate change is one of the most frequent topics in climatic literature over the last three decades. One of the significant concerns with a potential change in climate is that extreme events will occur with a higher frequency. This paper focuses on spatial distribution and changes that occurred in extreme precipitation indices in Romania over a 53-yr period: 1961–2013. Gridded daily precipitation data have been used at a spatial resolution of 0.1° × 0.1° (about 11 km × 11 km). A set of 14 indices established by the Expert Team for Sector-Specific Indices for agriculture and water resources sectors has been calculated. They are both frequency and intensity indices: four are fixed threshold indices (R10, R20, CDD, and CWD), four are station-related thresholds (R95p, R99p, R95pTOT, R99pTOT), and six indices were detected without using a threshold (Rx1day, Rx3days, PRECPTOT, SDII, SPI, SPEI). The study's main finding is that most of the indices registered increasing trends but not statistically significant at the country level. The only exceptions are the drought-related indices (CDD, SPI, and SPEI), for which we found a dominant decreasing trend. In the northern half of the country, increasing trends were prevailing, and in the southern one, those decreasing registered a broader spatial coverage. SPI and SPEI recorded mainly significant changes: SPI trends are almost equally divided between increasing and decreasing. For SPEI, more than 70% of the country was characterized by a significant decrease. [ABSTRACT FROM AUTHOR]

Climate change may cause adverse impact on agricultural production that could jeopardize food availability and security. In this paper, we investigate how changes in mean values and variability of weather variables may affect rice yield using panel data for 30 districts of Tamil Nadu State from 1971 to 2018. We estimate a fixed-effects regression model with panel-corrected standard errors. Results show that rainfall and temperature have a statistically significant impact on rice yield. Furthermore, weather variability, measured as standard deviations of temperature and rainfall, has a negative effect on rice yields. We use the Coupled Model Inter-comparison Project-5 outputs and dynamically downscaled the weather outputs using the regional climate model. The results of the climate model showed that the rise in temperature and disruptions in rainfall patterns including both excessive and deficient rainfall events in Tamil Nadu will continue in the future under different climate scenarios. Projected changes in the weather variables are likely to decrease rice yield in Tamil Nadu from 0.7 to 6.3% under the low emission scenario and 4.1 to 20.1% in the high emission scenario during 2022–2050 (relative to 1971–2018). These projections have implications for the planning and targeting of climate adaptation technologies such as drought-tolerant and flood-tolerant varieties to lessen the adverse impact of weather variability due to climate change in the future. [ABSTRACT FROM AUTHOR]

An increase in energy demand and consumption is one of the significant challenges of the world community. Global climate change and temperature rise can significantly affect energy demand, especially in the building sector. Green passive design strategies (GPDS) such as green roof and green wall are considered a passive energy-saving technology which can deal with further climate change in near future. This paper compares the energy demand and CO2 emissions of a building with different structural scenarios during the current (2000–2019) and future climatic conditions (the 2050s) in two hot-dry (Kerman) and hot-humid (BandarAbbas) climate samples in Iran. The base case, green roof, and green wall modeling of the selected building have been developed by DesignBuilder software. Results revealed that 61% of the annual energy consumption of Kerman is related to the heating sector, while it will be changed to 47% under the effect of climate change and based on RCP2.6. However, 99% of the annual energy consumption of BandarAbbas belongs to cooling demand and it will not change by 2050s. Also, the maximum heating and cooling energy demand were calculated for the base building. Based on the results, green wall has more efficiency in optimizing total energy consumption compared to green roof in both climate types. On the other hand, GPDS are more efficient to optimize heating energy demand in comparison with cooling energy demand. Furthermore, the green wall strategy has better performance in reducing CO2 emissions as well. Accordingly, CO2 emissions reduce in Kerman by 2.73% and 2.93% by the implementation of the green wall during the observation period and 2050s, respectively. Meanwhile, this strategy can reduce CO2 emissions by only 1% per year in BandarAbbas during all studied periods. [ABSTRACT FROM AUTHOR]

The paper analyses time series of thermal growing season (GS) start (GSS) and end dates (GSE) and length (GSL) in three cities representing urbanised areas of Central Europe (Kraków, Prague, Vienna) in the period 1792–2020. The classification of dates of growing season start and end, as well as length of the designated growing seasons, was conducted from climate data. An attempt was made to identify the dominant patterns of GS course, considering its start date, length, and end date collectively. In the period 1972–2020 in Central Europe, the growing season was prolonged, although the changes in particular stations selected for analysis occurred unevenly and simultaneously resulted from different causes. Three subperiods can be designated, differing in the intensity of changes in the start and end dates, as well as growing season length. The intensification of the rate of the occurring changes was recorded in all stations at the end of the twentieth and in the twenty-first century. There is a trend of decreasing frequency of short and abnormally short periods and more and more frequent occurrence of long and abnormally long seasons in the analysed multiannual period. Regardless of the differences between the stations in the designated GS types, the shortest of them were observed simultaneously at all three analysed stations in the period 1830–1860 and at the beginning of the twentieth century. The opposite type, representing the longest GS, is most abundant since the 1990s in Central Europe. [ABSTRACT FROM AUTHOR]

The traditional approach to characterize extreme rainfall events is based on using past observed data to fit a probability distribution, which is used to associate a certain probability of occurrence to a rainfall magnitude, under a stationary assumption. Observed series, however, may exhibit trends and abrupt changes, resulting from the natural hydro climatic variability and the climate change context, which are not usually considered in the stationary modelling procedure and which may affect the characterization of extreme events. In this paper, it was evaluated the presence of these temporal changes in rainfall series and their impact to estimate design quantiles, using rainfall data of 317 stations, located in São Paulo State, Brazil. The results showed that extreme rainfall events are changing in the state. Low-return period events (up to 10 years) are intensifying. Moderate- and high-return period events, on the other hand, did not present a clear spatial pattern. Trends and abrupt changes may significantly influence the estimation of design quantiles. The presence of the former in rainfall series exhibited more influence on low-return period events, showing, however, no clear relationship with high-return period design quantiles. The presence of the latter seems to influence the characterization process, affecting estimated quantiles of all evaluated return periods. Such findings highlight the need to consider the presence of trends and abrupt changes and their influence on design quantiles for a more reliable characterization. [ABSTRACT FROM AUTHOR]

Many regions around the world are facing climate changes, with substantial increase in air temperature over the past decades, which is mainly related to continental and global warming forced by the higher greenhouse gas (GHG) emissions. The objectives of this study were to use the Köppen climate classification to detect local climate change based on a historical series of 100 years and to assess if such change is related to those that are occurring in other spatial scales as a likely consequence of increasing GHG. This paper brings a content full of innovative results. The study area presented an average annual air temperature increase by 0.9 °C between 1917 and 2016, rising from 21.4 °C for the first climatological normal (1917–1946) to 22.3 °C for the last one (1987–2016). Furthermore, in the summer months, the temperature rose from 24.5 to 25.3 °C, and in the winter months, such increase was from 17.1 (1917–1946) to 18.3 °C (1987–2016). Our findings showed the subtropical conditions (Cfa in Köppen's classification) in the study area persisted from the beginning of the analysis (1917–1946) until the climatological normal of 1979–2008, with a clear tendency of tropicalization after that with a change in the climate type of Piracicaba from subtropical to tropical, which can now be classified as tropical with dry winter (Aw climate type). The local average air temperature showed concordances with the long-term air temperature anomalies from regional, continental, and global scales, indicating that all of them may be linked with increasing GHG emissions, since well-defined long-term linear relationships (r2 = 0.99) were observed between continental and global average air temperature anomalies and atmospheric CO2 concentration observed at the NOAA Lab in Mauna Loa in the last 59 years. While the local and regional forcing effects remain to be fully unraveled, our study provided a valid and strong scientific sound evidence that climate change occurred in Piracicaba, southeastern Brazil, in the last 100 years. [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]

The rising temperatures and decreasing rainfall are expected to have negative effects on ecosystem services by causing significant shrinkage or shift of forest distribution particularly in Mediterranean basin. In this study, it was aimed to determine the distribution of oriental beech (Fagus orientalis Lipsky.) by modelling the current and future potential locations of the habitats. With Maximum Entropy (MaxEnt) approach, we predicted its distribution under current and future conditions (RCP 4.5 and RCP 8.5) in Turkey. Modelling was performed by using eight bioclimatic variables that show significant relationship to the current distribution of oriental beech and were widely used in the literature. The fitted model had high quality (93.5% AUC) and is biological meaningful. The prediction of warmer condition in future showed that the populations on the southern slopes of the North Anatolian Mountains were expected to shrink and that there would be reduction in the populations found in the main distribution area in the Istranca Mountains and the Northern Anatolia, especially in populations in the transition zone of Central Anatolia. Also, oriental beech would lose its isolated-marginal populations in the southeast of Turkey. The results highlight the importance of a genetic conservation programme for beech population in Turkey. Otherwise, the genetic pools seem to extinct under climate change. Furthermore, the paper is intended to provide a starting point for a monitoring of oriental beech at the edge of its distribution, to observe its climatic migration. [ABSTRACT FROM AUTHOR]

The variability of occurrence of snow cover and the impact of atmospheric circulation on the snow cover occurrence in the period 1966/1967–2019/2020 in Poznań (Poland) have been examined. The implementation of the primary study objective covers the comprehensive analysis of the winter snow and thermal conditions using various indicators. This paper is based on daily data from the years 1966–2020 concerning the winter period. Winters in Poznań are highly variable and differentiated, considering the duration of particular seasons, number of days with snow cover, mean snow cover thickness, winter snowiness coefficient, or winter severity index. Negative trends concerning days with snow cover total snow cover depth winter snowiness coefficient and winter severity index in Poznań prove statistically significant. A higher probability of occurrence of snow cover was determined during cyclonic than anticyclonic circulation. The westerly and northerly types especially favoured the occurrence of days with snow cover. The increase of snow cover was associated with the northerly inflow mainly. Westerly types of circulation caused the decrease of snow cover predominantly. [ABSTRACT FROM AUTHOR]

Understanding past changes in the characteristics of climate extremes forms an essential part of viable countermeasures to cope with climate-induced risks in a rapidly warming world. Thus, this paper endeavored to explore possible non-monotonic trend components in heavy rainfall events over the Central Highlands (Vietnam) by employing Şen's innovative trend analysis method in conjunction with the well-defined extreme rainfall indices. The outcomes show less spatially coherent trends in the intensity, frequency, and duration of extreme rainfall events across the study area, and most analyzed indices exhibited non-monotonic trend forms. The overall trends in the intensity and frequency, represented by maximum 1-day, 5-day precipitation amount (Rx1day, Rx5day), very and extremely wet days (R95p and R99p), and the number of very and extremely heavy precipitation days (R20mm and R50mm), were characterized by significant increases at three or four sites. Concerning dry and wet extremes, observed increases in consecutive dry days (CDD) and decreases in consecutive wet days (CWD) were identified significantly simultaneously at three sites. There is high domination of significant increases in high-value subgroups of these indices. Several sites also exposed significantly increasing trend behaviors in these indices within all low-, medium-, and high-value subgroups, thereby implying the intensity and frequency have intensified over recent decades. This study also indicated potential linkages between annual peaks of several extreme rainfall indices, characterizing intensity, frequency, and wet duration, usually coincided with negative Indian Ocean Dipole and La Niña events, while dry extremes were usually found during El Niño events based on correlation analysis. [ABSTRACT FROM AUTHOR]

The existing Laspeyres index, Paasche's index, and Fisher's index under classical statistics cannot be applied when observations are indeterminate, imprecise, and uncertain. In this paper, the Laspeyres index, Paasche's index, and Fisher's index are introduced to deal with the data having neutrosophy. We present these indexes when indeterminacy is present in the data. To see the aggregate change in the production of rice crops due to climate change under an uncertain environment, data is collected from nine districts of Punjab, Pakistan. From the data and comparative studies, it is concluded that the proposed indexes are more informative, flexible, and suitable to be applied under indeterminate conditions. [ABSTRACT FROM AUTHOR]

Research on heat waves (periods of excessively hot weather, which may be accompanied by high humidity) is a newly emerging research topic within the field of climate change research with high relevance for the whole of society. In this study, we analyzed the rapidly growing scientific literature dealing with heat waves. No summarizing overview has been published on this literature hitherto. We developed a suitable search query to retrieve the relevant literature covered by the Web of Science (WoS) as complete as possible and to exclude irrelevant literature (n = 8,011 papers). The time evolution of the publications shows that research dealing with heat waves is a highly dynamic research topic, doubling within about 5 years. An analysis of the thematic content reveals the most severe heat wave events within the recent decades (1995 and 2003), the cities and countries/regions affected (USA, Europe, and Australia), and the ecological and medical impacts (drought, urban heat islands, excess hospital admissions, and mortality). An alarming finding is that the limit for survivability may be reached at the end of the twenty-first century in many regions of the world due to the fatal combination of rising temperatures and humidity levels measured as "wet-bulb temperature" (WBT). Risk estimation and future strategies for adaptation to hot weather are major political issues. We identified 104 citation classics, which include fundamental early works of research on heat waves and more recent works (which are characterized by a relatively strong connection to climate change). [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the improved weather generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulation run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of general circulation model (GCM) scenarios to assess regional climate change impacts, especially hydrological effects. [ABSTRACT FROM AUTHOR]

Air temperature is the main climatic variable for climate change and global warming assessment. In different regions, different climatic features and forcing factors affect air temperature behaviour and development differently. In this paper, time series of monthly and annual absolute minimum, mean and absolute maximum air temperatures measured in the period Jan. 1949–Dec. 2018 at two meteorological stations, Zagreb Grič and Lastovo, were analysed. The stations are situated in distinct climatological areas in geographically and climatologically completely opposite parts of Croatia. The climate of Zagreb Grič station is mild continental. Station Lastovo is located on the small isolated Adriatic Sea island, which has a hot Mediterranean climate. The distance by air between the two analysed meteorological stations is 340 km. In time series of the absolute annual minimum air temperatures measured at both analysed stations, an increasing trend is not statistically significant, while in the time series of mean annual and absolute annual maximum air temperatures, the trends are statistically significant at the level p<0.01. The statistically significant trend of air temperature increase at both stations appeared in the warm season of the year generally from May to August. The day-to-day (DTD) method established a decrease in night-time to night-time air temperature variability, and an increase in the day-time to day-time temperature variability at Lastovo. In Zagreb Grič, the situation is the opposite but the process is very slow. [ABSTRACT FROM AUTHOR]

Global climate change is expected to have a major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control, and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various global climate models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, generalized extreme value (GEV) distribution, and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian model averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period, and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate. [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE) is an important link between carbon and water cycles, which is critical for the forests under future global climatic changes. WUENDVI was calculated by accumulated normalized difference vegetation index (NDVI) and actual evapotranspiration (ET) and could reflect the impacts of climatic changes and human activities on WUE. The three forestry regions of China are the northeast, southwest, and southeast areas. Among them, the northeast forest region is a natural forest region with forest stock accounting for more than 1/4 of China, and the southwest mountainous forest region is another natural forest region in China, with forest stock accounting for more than 1/3 of China, while the southeast forest area is mainly plantations. In 2018, the forest areas over three forestry regions of China were around 1,725,988 km2. This paper evaluated the changes in forest WUE and their relationships with climatic change and human activities over three forestry regions of China during 1961–2019. The main findings of this study were summarized as follows: (1) the spatial changes of WUE were gentle in the artificial forest region but fluctuant sharply in the natural forest regions. In the southwest forest region, the WUE increased with elevation, while it showed the opposite trends in the artificial forest region. Overall, the annual mean forest WUE increased in almost all regions of the study areas during 1961-2019; (2) in the northeast and southeast forest regions, the WUE presented a negative relationship with the temperature. In the southwest forest region, the WUE was positively correlated with the temperature and its increase rate slowed down significantly when the temperature increases by more than 1 °C. The WUE was negatively correlated with precipitation in the three regions and was more sensitive to the decrease of precipitation. The sensitivity of WUE to precipitation reduction was highest in the artificial forest region and lowest in the northeast forest region; (3) the forest WUE and WUENDVI were lowest in the artificial forest region but highest in the northeast forest region, while the net increase in forests area during 1980–2018 was largest in the artificial forest region (155,975 km2) but lowest in the northeast forest region (78,766 km2). In general, human activities had the greatest impact on the forest WUE in the northeast forest region. Human activities and climatic change had quite complex and interactive effects on forest WUE. Therefore, more attention should be paid to the joint influences of climate change and human activities on WUE. [ABSTRACT FROM AUTHOR]

Changes in amount and frequency of runoff are caused by interaction of the climatic and anthropogenic factors in human-nature system. Those part of the runoff variations caused by anthropogenic loadings are more manageable compared with climate effects occurring over a long period. In this paper, we propose a framework to decompose the effect of anthropogenic and climatic time series on streamflow. To fulfill this task, anthropogenic time series attribution (ATSA) method is introduced. ATSA employs the output of climate elasticity (CE) method to extract human-affected time series and naturalizes the annual streamflow. In other words, ATSA makes it possible to exploit CE output in time series analysis. Furthermore, temporal downscaling process performed by a hybrid discrete wavelet transform and artificial neural network (DWT-ANN) enhances the temporal resolution of ATSA. We applied ATSA framework on Urmia Lake (northwest of Iran) inflow over the 1972–2010 period. CE application indicated that the contribution of climatic and anthropogenic factors in reduction of the mean inflow to the lake is about 60 and 40%, respectively. ATSA also revealed how human exploitation affected the time series of the lake inflow. Roughly, 14,000 MCM of the lake inflow reduction was attributed to the anthropogenic impacts between 1995 and 2010 such that the removal of anthropogenic loading could have led to rising water level up to 1275.5 meters above the sea level (i.e., 1.4 m above the ecological level) in 2010. Furthermore, the effect of de-trended inflow time series was investigated via DWT-ANN. Trend-free pre-whitening Mann-Kendall (TFPW-MK) test indicated significant increase in temperature, potential evapotranspiration, population, reservoir capacity, irrigated area, and the number of wells, over 39 years of the study period. [ABSTRACT FROM AUTHOR]

The Indian Institute of Tropical Meteorology (IITM) has generated seasonal and extended range hindcast products for 1981–2008 and 2003–2016, respectively, using the IITM-Climate Forecast System (IITM-CFS) coupled model at various resolutions and configurations. Notably, our observational analysis suggests that for the 1981–2008 period, the tropical Indo-Pacific drivers, namely, the canonical El Niño-Southern Oscillation (ENSO), ENSO Modoki, and Indian Ocean Dipole (IOD). are significantly associated with the observed Kharif rice production (KRP) of various rice-growing Indian states. In this paper, using the available hindcasts, we evaluate whether these state-of-the-art retrospective forecasts capture the relationship of the KRP of multiple states with the local rainfall as well as the tropical Indo-Pacific drivers, namely, the canonical ENSO, ENSO Modoki, and the IOD. Using techniques of anomaly correlation, partial correlation, and pattern correlation, we surmise that the IITM-CFS successfully simulate the observed association of the tropical Indo-Pacific drivers with the local rainfall of many states during the summer monsoon. Significantly, the observed relationship of the local KRP with various climate drivers is predicted well for several Indian states such as United Andhra Pradesh, Karnataka, Odisha, and Bihar. The basis seems to be the model's ability to capture the teleconnections from the tropical Indo-Pacific drivers such as the IOD, canonical and Modoki ENSOs to the local climate, and consequently, the Kharif rice production. [ABSTRACT FROM AUTHOR]

The climate change impact appears as a decreasing or increasing monotonic trend in hydro-meteorology time series records due to greenhouse gas (GHG) emissions causing to global warming and climate change impacts. On the other hand, there may be abrupt changes in the form of jumps in these series due to natural and engineering activities. Although trend identification methods are rather common in the literature, jump determination conventional methodologies are rather rare and their applications present some restrictive asumptions like the serial independence and normal (Gaussian) probability distribution function (PDF). The methodology presented in this paper is away from each of such assumptions and it depicts the minimum number of upcrossing along horizontal truncation levels within the time series variation domain. The applications of the methodology are given for annual Danube River discharge records, Romania; New Jersey rainfall and temperature records, USA; monthly rainfall records and Van Lake level fluctuations, Turkey. [ABSTRACT FROM AUTHOR]

Current rainfall Intensity-Duration-Frequency (IDFs) curves generally were developed based on stationary extreme value assumption. However, climate change is expected to alter extreme rainfalls and invalidate the stationary assumption. So, it is crucial to develop future rainfall IDFs taking into account the impacts of climate change. Difficulty in preparing reliable rainfall time series with fine time step and capturing extremes for future climate scenarios has led to limited studies aimed at investigating the change of IDF curves due to climate change. In this paper, a robust stochastic rainfall model (NSRP) is employed to produce future rainfall IDFs for five stations across Karkheh and Karun basins in Iran. NSRP can generate long-term realistic rainfall series containing extremes. Moreover, it applies changes in different rainfall statistical characteristics, projected by GCMs, in the downscaling procedure. For each station, the model was calibrated using observed rainfalls series. Then, 3000-year daily rainfall series were generated, and historical IDFs were developed. Consequently, NSRP parameters were perturbed based on GCM projections. Then, 3000-year future rainfall series were generated, and future IDFs were developed. The climate change uncertainties were represented by employing two GCM (CanESM2 and HadGem2) and three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The results showed NSRP is able to reproduce observed extreme rainfalls in a wide range of time scales. Also, climate change will lead to a considerable increase in future extreme rainfall intensity in the study basins. As averages of all considered scenarios, rainfall intensities will increase between 22 and 206% in the future. [ABSTRACT FROM AUTHOR]

Trend identification analyses in any hydrometeorological data are necessary and critical for predictions and planning in many disciplines such as atmospheric, environmental, and oceanographic sciences; water engineering; global warming; and climate change applications. In the literature, many researches have employed trend analyses by Mann-Kendall (MK) and innovative trend analysis (ITA) methods. Especially in recent years, the ITA method is preferred in trend identification methodological applications, because it can present trend graphs with visual inspection, verbal inferences, and objective quantitative calculations. In this paper, the trends of sea surface temperature (SST) data are identified by the MK and ITA methods on double logarithmic graphs, which provide fractal geometrical appearances with power law features. The SST data trends are evaluated at 22 coastal area stations of Turkey with monthly records from 1969 to 2014. According to the MK trend test, only 6 of the 22 stations had a significant upward trend at 5% significant level and 4 of these stations are in the Mediterranean Sea coastal area. At 10% significance level increasing trend numbers become 14 stations. According to the MK test results, the data records have been grouped into two parts as warming and cooling periods considering the physical conditions of the SST data. On the other hand, the results of ITA application provide average behavioral form according to the mathematical power law equations. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are used to dynamically downscale global climate models (GCMs) to provide high-resolution projections of future climate change to better inform policy and decision making at the regional scale. However, biases from GCMs are transferred to RCMs and this can limit the usefulness of the regional climate projections. This paper investigates the influence of bias correcting 4 GCMs from the Coupled Model Intercomparison Project 3, for regional climate simulations over the CORDEX-Australasia domain using Weather Research and Forecasting. The GCM outputs are bias corrected against ERA-Interim reanalysis as a surrogate truth. Results show that over decadal time scales bias correction removes large systematic precipitation and temperature biases. However, bias correction also introduced biases where there were none, introduced biases of the opposite sign, or enhanced existing biases in other regions in some instances. The dynamical mechanisms driving the changes in the biases are explored. [ABSTRACT FROM AUTHOR]

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs), linking large-scale GCM data with small-scale local climatic data has found great interest. In this paper, in order to downscale minimum and maximum temperatures and precipitation predictands, the performance of three statistical downscaling techniques including Long Ashton Research Station-Weather Generator (LARS-WG), statistical downscaling model (SDSM), and artificial neural network (ANN) was compared based on Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5) in northwest Iran. For this purpose, a nonparametric test named Mann-Whitney test, Spearman correlation coefficient, and the root mean square error (RMSE) were utilized to assess the efficiency of downscaling models. To scrutinize the climate change impacts, periods of 1961–1990 and 1991–2005 were considered as the baseline and verification periods, respectively. The findings revealed the superior performance of the ANN model for minimum and maximum temperatures, while for precipitation predictand, the SDSM represented the best performance among the models. Simulation results for future temperature indicated an ascending trend as 0.1–1.3 °C, 0.3–1.7 °C, and 0.5–2.1 °C for LARS-WG, SDSM, and ANN techniques, respectively. On the other hand, simulation outputs for the precipitation indicated a descending trend of 10–30% in future precipitation of the region according to downscaling models under Representative Concentration Pathway 8.5 (RCP8.5) pessimistic scenario of Hadley Center Coupled Model version 3 (HadCM3) GCM model. [ABSTRACT FROM AUTHOR]

This paper describes changes of near-surface air temperature in southern South America from the Last Glacial Maximum (~ 21,000 years before present) to the end of the twenty-first century based on PMIP3-CMIP5 model simulations. The study shows modeled time evolution of temperature highlighting how different the projected future warming will be with respect to paleoclimatic changes documented in the region. In this context, model simulations suggest that the projected twenty-first century warming will be markedly higher than the change of temperature registered between the mid-Holocene (~ 6000 years before present) and the present. Even more, the regional warming projected by the following 60–80 years might be almost similar to or even higher than the pronounced increment of temperature developed between the Last Glacial Maximum and the present. This past/future comparison provides a new picture of the unusual warming expected for the following decades in southern South America in the context of climate changes developed through the last ca. 21,000 years. [ABSTRACT FROM AUTHOR]

In this paper, 16 cities representing four major architectural climate zones of China were selected, and the heating and cooling loads of office building during the period of 1961–2017 were simulated by using TRNSYS software. Daily extreme heating and cooling loads were determined by percentile methods, and the climate change impact on extreme loads was analyzed. The results showed that the days of extreme heating load of office building significantly decreased in almost all the selected cities, whereas there were no significant changes in the days of extreme cooling load. The daily extreme heating load was dominantly affected by the temperature (R2 = 0.43–0.79), but the daily extreme cooling load was related to multiple climate factors, including temperature, relative humidity, and solar radiation. Single temperature can only explain 3–37% of the changes of daily extreme cooling load, and the combination of multiple climate factors can explain 58–85%. Importantly, the effect of temperature on extreme cooling load showed decrease from the cold to hot climate zones, but the impacts of relative humidity and solar radiation gradually increased. These results suggest that the maximum design capacity of heating systems can properly decrease, and the design capacity of cooling systems may not largely increase with the climate warming. In addition, the combined effect of multiple climate factors rather than the single temperature, as well as the different responses of extreme cooling energy consumption to climate change in different climate zones, should be considered when making measures for improving operating efficiency of air-conditioning system. [ABSTRACT FROM AUTHOR]

Characterizing the response of temperature variables to agricultural irrigation is expected to be an important challenge for understanding the full impact of water management on regional climate change. In this paper, the trend analysis and abrupt change test were applied to detect the global warming effect. Then, the quantitative irrigation-induced cooling effects on temperature variables between April and August from 1970 to 2010 in the Lhasa River basin were estimated using historical time series of gridded meteorological records and a map of the area equipped for irrigation. Trends in the maximum temperature (Tmax) were statistically positive, and a significant increasing trend for the minimum temperature (Tmin) was detected at the 0.01 and 0.05 confidence levels. No abrupt changing point of warming was detected in the time series for Tmax. The abrupt changes in Tmin in the irrigation concentration period took place in 1995, 5 years later than the corresponding change in April. Affected by global warming, the increase in temperature was the largest in July and August, when the irrigation-induced cooling effect was also the most significant. The irrigation-induced cooling effect for Tmax and Tmin in April–August (except for June) ranged from − 0.017 to − 0.009 °C/decade and from − 0.011 to − 0.001 °C/decade, respectively, and the cooling effect for diurnal temperature range (DTR) ranged from − 0.011 to 0 °C/decade. The cooling effect on temperature reached above 0.01 °C in July and August, but for the growing seasons, the effect was weak, only 0.001 °C. The Tmax and Tmin trends during the whole growing seasons decreased by both 0.002 °C/decade, respectively, with a 10% increase in irrigation land proportion. Even in July and August, the trends were expected to decrease by about 0.005 °C/decade with a 10% increase in irrigation land proportion. The irrigation-induced cooling effect could partially slow global warming. [ABSTRACT FROM AUTHOR]

This paper demonstrates a climate change impact study on the hydrological process of a data-scarce Greek watershed. The Soil and Water Assessment Tool (SWAT) and, particularly, the ArcSWAT interface was used for the watershed simulation. The ERA-Interim reanalysis climate data regarding the period from 1981 to 2000 were used for the historical simulation of the watershed. The ArcSWAT simulated data were evaluated against the observed discharge data for the periods with the available data. The statistical evaluation confirmed the ArcSWAT model's capability in simulating the hydrological process of the research area. The climate change consequences on the hydrological components of the research area until the end of the twenty-first century were estimated by driving the ArcSWAT model with the Regional Climate Model Version 4 (RegCM4) forcing data under the extreme RCP 8.5 scenario, namely the simulations of the MPI and HadGEM2 general circulation models (GCMs), resulted from the spatio-temporal kriging approach. Based on the results, the increase in the minimum and the maximum temperature contributed to an increase in the actual evapotranspiration and the surface runoff. In contrast, the temperature increase caused a reduction in the infiltration. An increase (reduction) in the precipitation led to an increase (reduction) in the hydrological components. The climate change impact analysis of the Greek watershed showed that not only the precipitation changes but the temperature changes as well directly influence the water balance components of the research area and particularly the infiltration. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) typically have unique advantages to the climate change impacts assessment. However, each of them has the weaknesses, strengths, and scope of their own. This paper aims to provide a comprehensive comparison of performance of the SDSM and IWG. It is important to know for what application range each WG can be used. Also, a method that strengthens the performance of one WG in a particular case can be used to modify another WG where it is weak. The performance of the models was investigated and compared directly and indirectly. The results showed that each of the two models reproduces many of the climate variables characteristics appropriately. Although WGs typically do not have appropriate performance in the reproduction of low frequency variability and extreme values, IWG well reproduces extreme rainfall values and low frequency variability of temperature. But this model relatively underestimates low frequency variability of rainfall. By contrast, the SDSM underestimates extreme values. However, it well reproduces low frequency variability of rainfall, because SDSM models the local variables depending on the large-scale atmospheric variables in which low frequency variability naturally exist. As a result, in order to correct the low frequency variability of rainfall in the IWG model, it is suggested that the rainfall statistics to be modeled depend on large-scale variables, and based on which, the parameters of the IWG be changed over the years. By correcting the low frequency variability of rainfall of IWG model, an appropriate WG would be obtained to climate change impacts assessment. [ABSTRACT FROM AUTHOR]

This paper investigates the long-term monthly, seasonal, and annual trends of climatic variables, snow cover extent, and discharge in Langtang Basin, Central Himalayas. The Mann-Kendall test with variance corrections is used to detect the trend, and Sen's slope estimator is calculated to quantify the trend. The innovative trend analysis, a graphical method for trend detection, is applied, and the results are compared. The snow cover extent for the study area was derived from moderate resolution imaging spectroradiometer (MODIS) satellite images. The result suggests that there is a significant increasing trend for mean temperature and minimum temperature, while no remarkable trend is identified for maximum temperature. Similarly, precipitation shows a rising trend for all seasons except winter. Snow cover is a major source of fresh water for the region and is depleting at an alarming rate. River discharge, augmented by snowmelt and high rainfall, is upsurging. In summary, this study portrays the links of climatic variables with snow cover and river discharge. The outcome of this study will be useful for understanding the climate change status in the Central Himalayas and sustainably managing the watershed. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed the influence of future climate change on wind energy potential in the Republic of Serbia. We used climate projections from two regional climate models: the coupled regional climate model Eta Belgrade University-Princeton Ocean Model (EBU-POM) and the Nonhydrostatic Multiscale Model on the B grid (NMMB). First, the daily wind speed at 10 m above ground that was simulated with both models was verified against observed data at 13 meteorological stations in the reference period, 1971–2000. Then, the wind speed at 10 m above ground was bias corrected and extrapolated to 70 m above ground, and the wind energy density was calculated at the same height. Next, the wind speed and wind density energy in a future period, 2071–2100, were calculated and compared with the reference period. Based on the analyzed results, the annual wind speed from the EBU-POM model underestimates the observed values at all stations, while the annual wind speed from NMMB model overestimates the observed values at all stations. In comparison with the reference period, the annual wind speed in the future will decrease in Serbia. These changes are small but statistically significant. On the other hand, analyses for different seasons revealed that seasonal changes in wind speed will be in the range of ± 5% relative to the reference period. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental questions on climate change and variability: to what extent has climate changed and/or varied over years in two districts of different agro-ecological regions or zones and how do any changes differ between the zones or districts? Given the rural-rural migration pattern observed between the districts, understanding climate change risk to rural livelihoods cannot be overemphasised. To assess change and variability, we utilise rainfall data-records over a 36-year period from 1980 to 2016. Results show that there are wide variations and differences within and between the districts. Evidence suggests a general reduction in both annual rainfall and wet days. There is also ground to suggest that the rainy season duration is becoming shorter, given that rainfall onset is increasingly starting late, while cessation is increasingly coming early. Dry spells frequency and duration trends within rainy season show an increase over the period examined. We conclude that local climate in both areas has changed over the period investigated. However, while Livingstone seems to have experienced more droughts and unreliable rainfall, Kabwe experienced a bigger change in both rainfall and rainy season duration. We further conclude that migrants into Kabwe and other inhabitants are not any safer from climate change risk. [ABSTRACT FROM AUTHOR]

Satellite-based observations provide a unique data record to study the Earth system. Recent efforts of the space agencies to reprocess the archives of satellite observations aim to provide Essential Climate Variable (ECV) data records for manifold applications in climate sciences. Varying lengths of a data record or gaps in a data time series are likely to affect the analysis results obtained from long-term satellite data records. The present paper provides a systematic assessment of the impact of variations in the observational record of terrestrial ECVs for selected climate applications like trend detection and the analysis of relationships between different ECVs. As an example, the Sahelian drought and the subsequent recovery in precipitation and vegetation will be analyzed in detail using observations of precipitation, surface albedo, vegetation index, as well as ocean indices. The paper provides a different perspective on the robustness of long-term satellite observations than previous studies. It shows in particular that the long-term significant trends in precipitation and vegetation dynamics are rather sensitive to the investigation period chosen and that small data gaps can already have a considerable influence on the analysis results. It is therefore a plea for continuous climate observations from space. [ABSTRACT FROM AUTHOR]

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951-2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991-2000 compared with the earlier decade 1951-1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country. [ABSTRACT FROM AUTHOR]

Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability. [ABSTRACT FROM AUTHOR]

As a result of climatic change associated with global warming, aridity is an increasing problem in many parts of the world, including south-eastern and southern regions of Romania. This paper clarifies the concept of aridity, and discusses related concepts including indices of aridity, and their influence on some landscape and soil features including climatic water deficit (WD) and the depth to soil carbonates (DC). As used here, WD is calculated as the difference between precipitation sum (P) and the Penman-Monteith reference evapotranspiration sum (ETo-PM) over certain periods. Another three well-known aridity indices are also considered: De Martonne’s index (Iar-DM), Thornthwaite’s index (Iar-TH), the UNESCO (1979) P/ETo-PM ratio index (Iar-P/ETo-PM). WD is as high as −450 mm during the growing season in the most arid, south-eastern and southern regions of Romania, especially in the Dobrogea and Baragan areas. In other regions of Romania, including most of the plains and plateaus where agriculture is an important branch of the economy, WD reaches −100 to −300 mm during the growing season. The above aridity indices were spatially interpolated for specific periods by kriging, to generate relatively homogeneous areas. WD can also be seen as an aridity index which has the advantage of a more accurate quantification of the water supply needed for a reference crop, e.g. grass under standardised conditions, for various geographical regions. WD is significantly correlated with the other aridity indexes and with DC. This paper also examines the risk of aridity spreading, and suggests improvements to the water management system for agriculture in Romania. [ABSTRACT FROM AUTHOR]

Weather modification activities are performed predominantly by cloud seeding. Some operational projects have been performed for more than half a century and cover planetary scales. These activities lead to a large amount of deposited chemical materials (seeding agents) at the ground level during precipitation. These deposits depend on the amount of the seeding agent. In the future, increased amounts of seeding agent deposits could be a serious problem due to various negative effects on the human environment. Therefore, the main intent of this paper is to determine trends for the seeding agent amount over certain areas of Serbia. Four areas covered by the Hail Suppression Project in Serbia are considered: the target area in central Serbia and areas in western and central Serbia, which are well-known hailfall regions. The annual seeding agent amounts show a slow decreasing trend because fewer seedings were performed during the last decade of the last century, which was due to economic reasons. In contrast, the annual seeding agent amounts of the other analysed areas indicate an increasing trend induced by the transfer of rockets to these hailfall regions. The main difference among small areas is the mean agent amount and its maximum time position as a consequence of the high spatial and time variability of the hail. However, a sharp decreasing trend that is influenced by the implementation of new methodologies, seeding agents and delivery tools may also be a factor in the implementation of cloud seeding projects. The given method is not only strictly applicable locally and may be applied to any other cloud seeding scenario and seeding area. Dominantly increasing trends in the agent amount indicate that the importance of weather modifications in the future will be greater than ever and will have both positive and negative effects. [ABSTRACT FROM AUTHOR]