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

Air temperature holds significant importance in microclimate and environmental health studies, playing a crucial role in weather regulation. There is a need to develop a reliable model capable of accurately capturing air temperature variations. In this study, we focused on the Amazon-Cerrado transitional forest, constructing a robust predictive model for hourly temperature fluctuations. This forest, situated approximately 50 km northwest of Sinop, Mato Grosso, Brazil, is a transitional area, making it important to investigate its climatic behavior and ecosystems. We estimated air temperature using machine learning techniques such as Random Forest, Gradient Boosting, Multilayer Perceptron, and Support Vector Regressor, aiming to evaluate the most effective models based on relevant metrics. Performance assessments were conducted during both dry and rainy seasons to verify their adaptability. The top-performing Random Forest model demonstrated Willmott and Spearman indexes above 0.97. The air relative humidity, solar radiation, and volumetric soil water content were identified as the most important features, evaluated with Willmott and Spearman indexes above 0.95 in a model with such dimensionality reduction. These results underscore the efficacy of machine learning techniques in accurately estimating air temperature. [ABSTRACT FROM AUTHOR]

Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period. [ABSTRACT FROM AUTHOR]

According to Intergovernmental panel on Climate Change (IPCC) Climate change is the weather characteristics such as precipitation, air temperature, humidity, wind, sunshine, cloud cover, and atmospheric pressure at a specific location determined over a long period of at least 30 years. The main objective of this study was to analyse the climate trend and future projection in Guguf watershed of Southern Tigray, Ethiopia. 32 years (1987–2018) Meteorological data were collected from the Ethiopian Meteorological Institute. Download canESM2 (Canadian Second Generation Earth System Model). The Mann-Kendal trend test was used to test for the presence of trends using XLSTAT. The SDSM 4.2.9 decision support tool was used to downscale large scale predictors and project future climate change. The period from 1987 to 2018 was considered as a base period, whereas the period from 2019 to 2100 was considered as future periods. Historically, from 1987 to 2018, there was an overall increase in the mean annual minimum and maximum temperatures by 0.016 °C and 0.048 °C, respectively, with a little decrease in the average annual rainfall (up to 0.685 mm). The highest increment of maximum temperature recorded in October month up to + 2.7 °C in RCP8.5 scenarios. The precipitation increases up to a maximum of 49% (2073–2100) for the RCP4.5 scenario and 66% (2073–2100) for the RCP4.5 (representative concentration pathway 4.5) scenario in the Belg (February to May). Precipitation decreases in the Kiremt (June to September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5 scenarios. Future work needs to consider studying the effects of different climate change adaptation strategies. [ABSTRACT FROM AUTHOR]

Over the last 20 years, several record-breaking events linked to high air temperatures have enhanced the attention over extreme heat phenomena, especially during summer. Both 2022 and 2023 have been exceptional years in this regard. Heat waves, in particular, are a major natural hazard for the most fragile groups of the population. In order to address the points of impact, adaptation and future predictions, a quantitative analysis of all their specific aspects is necessary. These include the monitoring of their changes over time. This study consists of a comprehensive analysis at national level of several air temperature-extremes indices over Italy, including those specifically related to heat waves. The dataset is based upon a set of high quality daily maximum and minimum air temperature records, from more than 250 ground-based weather stations. The time frame considered ranges from 1991 to 2020, providing information about the present Italian climate. Two analyses are reported. One is targeted to find local features and is based on single stations data. The second one looks for a global signal related to heat extremes. The double level of the information (local and global) can be very useful in the comparison of results. The two methods are in agreement in describing an increasing presence of heat extremes, in terms of overall frequency, duration and intensity. Some areas appear as hot spots for the observed tendencies. [ABSTRACT FROM AUTHOR]

Over Northeast China, the area surrounding the Horqin sandy land (HSL) is an important plantation domain within the "Three north" Area of China. Compound heat waves and droughts can exert exceptionally negative impacts on ecosystems. However, less attention has been paid to the physical causes of the variability of the frequency compound droughts and heatwaves in this peculiar sandy region. In this study, salient climatic factors tied to the variability of the number of compound drought and heatwave day (CDHD) around HSL in late summer (July–August) are identified. Results show that the preceding wintertime El Niño–Southern Oscillation (ENSO)-associated SST mode can exert significant delayed impacts on the interannual CDHD fluctuations around HSL in the following late summer, serving as a precursory oceanic factor. Dynamical and thermodynamical diagnoses associated with the observational evidences and the Community Earth System Model version 2 (CESM2) tropical Pacific pacemaker experiment outputs reveal and confirm that the negative phase of ENSO (i.e. La Niña-associated sea surface temperature mode) in the prior winter can exert cross-season impacts on the enhanced CDHDs around HSL in the subsequent late summer through a critical synchronous atmospheric bridge of the western North Pacific cyclonic anomaly. This system may connect an anticyclonic anomaly prevailing over Northeast Asia via an anomalous meridional overturning circulation, which could act as a salient synchronous atmospheric factor. In this circumstance, favorable in situ meteorological and land surface conditions (e.g., increased 2-m maximum air temperature and suppressed rainfall/soil moisture deficiency) can be established, eventually sparking abnormal heat and drought conditions surrounding HSL and thus producing more late-summer CDHDs around HSL. [ABSTRACT FROM AUTHOR]

During the life cycle of a tropical cyclone from the homogeneous ocean-phase to the heterogeneous land-phase, estimating the catchment-scale evapotranspiration (ET) for agricultural water management is a challenging task. For assessing the catchment-scale ET and the causative weather variables associated with the land–atmosphere interactions during the Tropical Cyclone Phailin (TC Phailin), some popular parameterization scheme combinations in the Weather Research and Forecasting (WRF) model were selected. The suitability of different WRF parameterization scheme combinations (PSCs) were evaluated in the Brahmani River basin in eastern India to reproduce the observed weather variables of surface (2-m) air temperature, precipitation and atmospheric pressure at hourly and daily temporal resolutions during the pre-, at-, and post events of the TC Phailin. This study found that the 'Rapid Update Cycle' (RUC) LSM with 'Purdue Lin' microphysics and 'Arakawa convective' cumulus scheme performed the best. Overall, the PSCs could simulate the surface air temperature better than the precipitation during the short timeframe of the extreme event, whereas the overall regional pressure simulation showed a constant bias. The results reveal that WRF-LSM model that accounts for both local (clothesline) and global (oasis) advection effects could better simulate the ET flux compared to the corresponding MOD16A2 remote sensing product and the Food and Agricultural Organization (FAO)-56 Penmen-Monteith (PM) equation, especially during cloudy days. The local feedback of the TC Phailin over the land-surface ET flux and its climatic and land-surface drivers (soil moisture) during the pre-, at-, post-cyclone events reveal that: (a) the negative Bowen Ratio estimates during the heavy rainfall resulted in a reduced ET flux, (b) the negative sensible heat flux during this period facilitates for flow of heat from surface to atmosphere, cooling the soil of the river basin. Overall, this study aids in a better understanding of the moisture flux and energy transfer dynamics between the land–atmosphere system during the onset of a cyclone. [ABSTRACT FROM AUTHOR]

The analysis of air temperature data from May to September for the period 1966–2022 has revealed the occurrence of extreme phenomena, such as tropical nights and hot days, in the Lake Baikal area. The occurrence, multiyear and intra–annual variability of these extreme phenomena was investigated for the first time. The analysis identified two groups of stations, which are differ in the number and distribution of tropical nights and hot days. The first group, which includes inland and coastal stations, has higher values of the frequency of occurrence of tropical nights (0.21–1.68 days yr –1) and hot days (0.74–8.05 days yr –1). The second group, consisting of coastal, highland and island stations, has predominantly tropical nights (0.19–0.26 days yr –1). After 1990, these extreme events became more frequent. Tropical nights occur between June and September, while hot days are common from May to August, with the highest frequency of both in July. In this month, the proportion of tropical nights and hot days is identical and is 57% of their total number. July is the hottest month in the region. The increase in the frequency of tropical nights and hot days in the Baikal region is mainly due to global warming and changes in global circulation. The occurrence and variability of these extreme phenomena also depend on various factors related to the peculiarities of regional climate warming and local circulation of air masses, as well as on environmental conditions surrounding each station. [ABSTRACT FROM AUTHOR]

The escalating frequency and intensity of extreme climate events pose significant challenges in the context of the climate crisis, leading to substantial human and economic losses. This study aims to assess trends and disruptions in extreme climate indicators, focusing on precipitation and air temperature, within the watersheds of eastern Paraná state. Data from 1976 to 2015 were used, ensuring record failure rates below 5%. Trend analysis employed the extreme climate indicators developed by the Canadian Meteorological Service and the Mann-Kendall test, while discontinuities in the historical series were identified using normality tests (Pettitt, SNHT, and Buishand). Results revealed increasing trends in annual precipitation totals (PRCPTOT), the annual total of rainfall exceeding 95% of percentiles (R95p), and, notably, the index of daily rainfall concentration (SDII). The Upper Ribeira watershed displayed the highest frequency of trends associated with SDII. Moreover, widespread upward trends in air temperature were observed, particularly in Curitiba, where the average annual minimum temperature rose by 1.8 °C. Notably, many discontinuities in the historical climatic series were detected between the late 1980s and early 2000s. This research provides a comprehensive understanding of the modifications in extreme climate indicators in the study area, emphasizing significant trends of more heavy and concentrated rainfall and a robust increase in average air temperature. [ABSTRACT FROM AUTHOR]

Drylands, where the water supplies are scarce relative to the atmospheric moisture demands, occupy approximately half of the area of China. With global warming, the response of atmospheric water cycle is a crucial concern for sustainable development in China's drylands. This study investigates the characteristics of the water cycle in China's drylands during 1980–2019, as well as future projections estimated by the Community Earth System Model (CESM) during 2020–2100. The results show that the air temperature in China's drylands has increased significantly since 1980, especially after 2000, with greater warming in the warm season. Meanwhile, China generally tend to be drier from 1980 to 2019 as a whole, especially in the cold season. Both precipitation (P) and evapotranspiration (E) have increased, but the net effect is that drylands become drier (negative P-E) and wetlands become wetter (positive P-E), respectively. In the future, drylands are projected to become more humid due to increased precipitation, showing a pattern of "western wetter and eastern drier" during 2020–2100. These results could improve understanding of the water cycle and provide some guidance for adapting to climate change in drylands. [ABSTRACT FROM AUTHOR]

The air temperature lapse rate (TLR) is one of the essential parameters for glacio-hydrological studies. However, TLR estimations are limited in the glacierised regions of Himalaya due to a scarcity of long-term observations. Therefore, a dense in-situ monitoring network over a high Himalayan region is needed to estimate the TLR accurately. Here, in-situ air temperature data is obtained from Automatic Weather Stations (AWS) installed over the Chandra basin, western Himalaya, from October 2020 to September 2022. This data is used to estimate the TLR by regressing the air temperature with the corresponding elevations. We estimated the mean annual TLR of 3.8 ± 0.3 °C km-1 for the entire Chandra basin, significantly less than the standard environmental lapse rate (6.5 °C km-1). We found substantial seasonal variability in each TLR time series. The maximum TLR is 5.8 ± 0.2 °C km-1 during the summer, and the minimum is -1.6 ± 0.1 °C km-1 during winter, comparing all the meteorological stations. Further, we observe strong diurnal fluctuations of TLR, which has maximum and minimum values during 10:00 to 18:00 hrs and 20:00 to 09:00 hrs, respectively. The study highlights that the temporal variability of TLR is site-specific and strongly correlated with wind speed, relative humidity, and radiation fluxes. Furthermore, a temperature-index model is used to assess the implications of TLR by estimating glacier mass balance. This study highlights the importance of considering observed TLR to accurately model surface mass balance over the glacierised Himalayan region. [ABSTRACT FROM AUTHOR]

Frost on agricultural products in spring imposes heavy financial losses to agriculture particularly in northwest of Iran's orchards. Frost early warning is an effective way in preventing frost risk in apple orchards. This paper aims to validate frost early warning system in apple orchards of northwestern Iran by predicting flowering date of apple tree and its combination with WRF (Weather Research and Forecasting) model simulations of the 2-m temperature. To this end, stepwise multiple linear regression model was used for predicting flowering date; therefore thermal variables data including mean temperature, mean maximum temperature, mean minimum temperature, last frost date, heat wave duration, heat wave intensity, GDD and maximum standard temperature data larger than 1 (Z > 1) for the period of 2007–2016 (from March 1st until flowering date) in Kahriz agrometeorological station in northwest of Iran were calculated and added to the model. Also, to evaluate the accuracy of WRF model simulations, the 72-h simulations of the 2-m air temperature for internal computational grid in northwestern Iran (West Azerbaijan Province) in 17 synoptic stations were compared to minimum air temperature observed in the stations. Results revealed that the proposed stepwise regression method is a relatively precise model with a mean absolute error of 2.7 days between the predicted and observed flowering dates. Findings were also indicative of an acceptable accuracy of 72-h minimum air temperature simulations of WRF model in the study area. [ABSTRACT FROM AUTHOR]

In this study, high-resolution anthropogenic heat release (AHR) due to energy consumption is considered in Community Earth System Model version 1 (CESM1) to explore possible climatic effects and physical mechanisms due to AHR in eastern China (EC) in boreal summer. The results show that the heating effect of AHR on the lower atmosphere leads to an increase in the temperature difference between land and ocean, causing changes in atmospheric circulation and water vapor transport in EC in boreal summer. AHR affects the lower-troposphere stability and low clouds, impacting the surface energy balance. This will affect the surface temperature and further increase the occurrence of extreme high temperatures. The modeling results show that in the central part of the Beijing-Tianjin-Hebei and Inner Mongolia regions, the relative humidity increases by more than 1% due to AHR, leading to a decrease in low cloud cover. In the Middle-Lower Yangtze region, low clouds decrease by 2–3%, while the net shortwave radiation flux at the surface increases by approximately 5%. AHR leads to an increase of 0.19 K in the 2 m air temperature in the 25°N-35°N regions of EC on average in boreal summer, which will further impact the summer extreme high temperature in the Middle-Lower Yangtze Region. AHR acts as a non-negligible factor for the extreme high temperature in EC in boreal summer. [ABSTRACT FROM AUTHOR]

Scientific interest is increasingly drawn towards regional meteorological extremes, given their impacts on populations, infrastructure, and ecosystems. These extremes are shaped by complex interactions between internal climate variability and long-term trends. The aim of the present work is to evaluate changes in high-frequency variability and the influence of long-term trends on the frequency of occurrences of extremes, with a focus on surface temperatures over the period from 1962 to 2022 in Emilia-Romagna, a region of Northern Italy. Daily data of 2 m air temperatures averaged over the region are retrieved from ERACLITO, a high-resolution climate analysis. The distributions of daily temperature anomalies show a general broadening in 1992–2022 with respect to 1962–1991. This is true for maximum, minimum, and mean daily surface temperatures, especially during the summer and spring seasons. A significant warming trend of 0.37 °C/decade is detected in annual mean surface temperatures over the period considered. The study is completed with a comparison between the observed frequency of record-breaking annual temperature events, a hypothetical stationary climate distribution and a theoretical derivation that accounts for changes in trends and variability. During the last decade, the theoretical count of extreme events is 1.26, which yields a likelihood of 86% that this is owed to the trend rather than interannual variability. Idealized experiments demonstrate that the expected occurrences of record-breaking events in future decades depend on the warming rate rather than the warming level. Finally, an analysis performed at seasonal level shows that the majority (minority) of record events are occurring in the summer (spring) seasons. [ABSTRACT FROM AUTHOR]

Selin Co basin, a representative lake basin situated in the central region of the Tibetan Plateau (TP), is characterized by extensive areas of frozen ground and has undergone a remarkable expansion in its lake area. Evapotranspiration, as an important variable within the lake water cycle, as well as its influencing factors are still under debate at this basin. Actual evapotranspiration (ETa) plays a vital role in influencing land-atmosphere interaction processes and the hydrological cycle. By quantifying the amount of water that is actually transpired by vegetation and evaporated from soil surfaces, ETa provides important information about the mechanisms controlling evapotranspiration. In this study, the advection-aridity (AA) model was employed to estimate ETa in Selin Co basin. The estimated ETa and its influencing factors were investigated between 2001-2018, with an emphasis on examining both spatial and temporal trends. The results demonstrated that the multiyear mean ETa in the basin was 430.3 mm (excluding lakes) with an overall significantly decreasing trend (-5.8 mm/yr). In terms of its seasonal variation, ETa exhibited highest value during summer, while experiencing comparatively lower rates in autumn and spring. Furthermore, the spatial distribution of both the annual and seasonal ETa exhibited a similar decreasing trend from the southeast to the northwest of the basin. The sensitivity of several meteorological variables to modifications in ETa was also examined. Results indicated that specific humidity was the predominant variable, followed by net radiation, air temperature, precipitation, atmospheric pressure, and wind speed. In addition, the area covered by permafrost and seasonally frozen ground was approximately about 1.58 × 104 km2 and 4.22 × 104 km2 in the basin, and permafrost degradation in the northern region of the Selin Co basin resulted in increased average soil moisture content and underground ice melting, leading to a subsequent increase in ETa. [ABSTRACT FROM AUTHOR]

The intensity of Tropical Easterly Jet (TEJ) is profoundly associated with the Indian Summer monsoon rainfall (ISMR). Epochal analysis of the TEJ intensity has been carried out by dividing the period of 1976–2021 into two equal epochs, E1 (1976–1998) and E2 (1999–2021), with a breaking point corresponding to the 1998/99 climate shift. Further, the evolution of the relationship between ISMR and the intensity of TEJ across the two epochs has also been studied using Mann–Kendall (MK) trend analysis and Innovative trend analysis (ITA) techniques. Diabatic heating over the Tibetan anticyclonic and TEJ regions is also analyzed to study epochal variability. The relationship dynamics are further explored using multivariate correlation analysis of ISMR with the TEJ intensity, meridional temperature gradients (at 150 hPa and 200 hPa), air temperature anomaly index, and outgoing longwave radiation (OLR). The study indicates the reversal or stabilizing of the significant declining trend in the meridional upper-level tropospheric temperature gradient in E2, specifically at 150 and 200 hPa. OLR shows a declining trend of 0.12 Wm−2 yr−1 in E2 with a 90% confidence level, whereas with an insignificant decreasing trend of 0.06 Wm−2 yr−1in E1. An increment in diabatic heating (Q1) and a regime shift is observed in the Tibetan anticyclonic (TP) region in E2 compared to E1. TEJ intensity increased with a confidence level above 90% at 150 hPa (0.04 ms−1 yr−1) and 200 hPa (0.06 ms−1 yr−1.). Longitudinal oscillation in the position of the TEJ core is also observed; specifically, regime shift in the longitudinal position of the TEJ core is observed in E2 compared to E1. Multivariate correlation analysis of TEJ intensity at 150 and 200 hPa; meridional temperature gradient between TEJ and TP region at 150 and 200 hPa; OLR with ISMR indicate increased correlation of tropospheric temperature gradients at 150 and 200 hPa with OLR and ISMR. A marginal increase of 0.05 in the correlation coefficient between TEJ intensity and ISMR is also reported at 150 hPa. [ABSTRACT FROM AUTHOR]

Observations show that the warming trends of annual and seasonal surface air temperature (SAT) in China have been about 0.15 ~ 0.31 ℃/10a in China since the 1950s. The warming trend is higher in winter and spring than in summer and fall, and higher in the northern region than in the southern region. Additionally, the warming trend is higher in the western part of China than in the eastern part, except for spring. These variations have been well simulated by the recent CMIP6 models. Using a perfect model method, the all-forcing (ALL) + anthropogenic aerosol forcing only (AER) + natural forcing only (NAT) strategy has been applied to the detection and attribution of SAT trends in China since 1951. It is found that GHG (greenhouse gas forcing only) is detected robustly in all seasons and regions except winter in eastern China, contributing to a warming of 0.21 ~ 0.36 ℃/10a. This indicates that the greenhouse gas from human activities is the primary factor causing the warming in mainland China over the past 70 years. The contribution of the AER has only been detectable in the variations in summer of the whole mainland China and western China with neglectable effect since the late twentieth century. The NAT forcing can also be neglected. [ABSTRACT FROM AUTHOR]

Teleconnections like the El Niño/Southern Oscillation affect climate and weather conditions across the globe, including conditions that modulate tornado activity. Early studies of teleconnection/tornado activity relationships provided evidence of links between one teleconnection and tornado activity. Later attempts introduced multivariate approaches by analyzing bivariate distributions and integrating multiple teleconnections in statistical models to predict variability in tornado activity. However, little attention has been given to teleconnection interactions and the role of these interactions in modulating tornado activity. Here, we employ a data-driven, multiple logistic regression modelling approach to explore the interactions between the El Niño/Southern Oscillation, North Atlantic Oscillation, Artic Oscillation, and Pacific North American pattern and their ability to predict the odds of an active tornado period in the southeastern United States. We develop models at the annual, seasonal, and monthly scales and, in doing so, illustrate that the teleconnections and teleconnection interactions that best predict the odds of an active tornado period differ across timescales and that the relationships exhibit clear seasonality. We also show climate conditions associated with select interactions, like the ENSO-NAO interaction in spring, that help explain the elevated tornado activity, namely anomalously high near-surface air temperature and humidity steered by an anomalously strong subtropical high. [ABSTRACT FROM AUTHOR]

This study analyzed the occurrence of cold days, very cold days and cold spells in Poznań in the years 2008/09-2022/23. A cold day was defined as a day with Tmax < 0.0 °C and ≥-10.0 °C, whereas a very cold day was defined as a day with Tmax <-10.0 °C. In the next step, cold spells were determined, which are sequences of at least 5 days with Tmax < 0.0 °C. Circulation conditions were determined based on daily values of sea-level pressure, the height of the 500 hPa isobaric surface, as well as their anomalies and air temperature anomalies at the 850 hPa isobaric level. The conducted research showed a decrease in the number of cold and very cold days in Poznań in the years 2008/09-2022/23. A variation in the number of cold and very cold days and cold spells was noted across the city area, which is a consequence of the form of land use. The occurrence of cold spells in Poznań was associated with higher than average sea-level pressure. [ABSTRACT FROM AUTHOR]

A large part of Brazil is highly vulnerable to climate changes projected for the end of the 21st century. Analyzing these vulnerabilities is particularly important for agriculture, since the country is one of the largest agricultural commodity producers in the world. Changes in the reference evapotranspiration (ETo) can impact crops and make cultivation unfeasible. However, studies on ETo patterns under climate change scenarios for Brazil have been restricted to regional scales and use too few climate models or too simplified water balance models for their analysis. This can lead to uncertainties in assessing the impacts of climate change on ETo. Therefore, this study seeks to analyze ETo patterns in Brazil towards the end of the 21st century using two methods that are better at estimating regional ETo, i.e., the Turc and Abtew methods, under two radiative forcing scenarios (RCPs 4.5 and 8.5). Daily data on near-surface air temperature (mean and maximum), global solar radiation, and near-surface relative humidity from six General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to analyze the simulations and projections for climate change. The performance of climate simulations is heterogeneous among the GCMs, with overestimations (~ 2.5 mm day− 1) in some models, and underestimations (~ 1.5 mm day− 1) in others. In general, climate change projections indicate increases of up to 1 mm day− 1 in ETo, mainly in the North, Northeast, and Center-West regions of Brazil. Both estimation methods showed similar spatial patterns, however the Turc method projected lower intensity changes compared to the Abtew method. Highlights: The Turc method showed the best performance in estimating ETo, resulting in more reliable climate simulation and projections. There was divergence between climate models when simulating solar radiation and relative humidity. Climate models projected an increase in temperature (mean and maximum), and a reduction in relative humidity towards the end of the 21st century. The projected ETo showed similar patterns between the Turc and Abtew methods. Increases from 0.4 to 1 mm day− 1 are projected for ETo in the North, Northeast, and Center-West of Brazil, and from 0.2 to 0.4 mm day− 1 in the South of Brazil. [ABSTRACT FROM AUTHOR]

Extreme cold events can have several consequences in different sectors, including human health, infrastructure, agriculture, and the economy. The frost events that affect central and northeastern Argentina are associated with stationary Rossby waves triggered by tropical heat sources and convection, which result in intense polar air advection. However, the processes responsible for these cold events include complex interactions between these wave trains and mechanisms at different atmospheric scales, such as the Madden–Julian Oscillation (MJO), which can alter the temperature patterns. This study investigates the impacts of the MJO on air temperature and associated circulation in years with extreme occurrence frequency of cold events in central southern South America during the winter. The results showed that the MJO convection can enhance or weaken the temperature anomalies during winters with a maximum and minimum frequency of generalized frosts (GF) occurrence due to Rossby wave train propagation triggered in the tropical region. Moreover, the effect on the anomaly patterns in these events depends on the MJO phase. Our analysis shows that individual MJO phases can modulate the temperature anomalies even in an unfavorable basic state (as in winters with an extreme frequency of GF and emphasizes the importance of considering the MJO in the predictions of the temperature anomalies associated with GF over southeastern South America. [ABSTRACT FROM AUTHOR]

Evidence of climate change can be observed in multiple climate variables, including air temperature rises and precipitation pattern changes. To manage water resources and agriculture effectively, it's important to project climate variables' changes at the local level, as these changes can vary depending on the specific area. The baseline weather data trend was analyzed using the percentage change (PC) method and the Innovative Trend Analysis (ITA) technique at three cluster levels: cluster 1 (PC: 0–20%), cluster 2 (PC: 21–80%), and cluster 3 (PC: 81–100%). The precipitation (Prec.), maximum (Tmax), and minimum (Tmin) temperatures showed downward trends in 9, 4, and 6 stations out of 24 stations, respectively. The SDSM model performed best in predicting Prec., while the LARS-WG model was more effective in predicting Tmax, Tmin, and solar radiation (SR). The average monthly Prec. percentage change shows both rising and falling trends in different weather areas for all three time periods (2040, 2060, and 2090) and for both RCPs (RCP4.5 and RCP8.5). In contrast to precipitation, both Tmax and Tmin consistently showed an upward trend across all meteorological stations for both RCPs and three-time frames. Across the four distinct plain regions, the overall projection suggests a slight increase in precipitation. The study predicts the highest increase in precipitation to occur in June across all meteorological stations. Seasonally, the greatest increase in precipitation is projected during summer (JJA) by 5.10%, while the largest decrease is expected during winter (DJF) by 3.29%. Additionally, precipitation variability shows an increase from RCP4.5 to RCP8.5 and from near-term (2040) to long-term (2090), with the northern Jiangsu Plain exhibiting the highest variation. The biggest rise in Tmax/Tmin was observed at RCP 8.5, by 2.69/2.39 °C, and in the long term (2090), by 3.25/2.86 °C. This was compared to RCP 4.5 by 1.73/1.51 °C, in the near term (2040) by 1.24/1.08 °C, and in the mid-term (2060) by 2.14/1.90 °C. The highest increase in Tmax is expected compared to Tmin, leading to the highest diurnal temperature (DTR) at all three periods and both RCPs. Seasonally, the highest increase is projected in the autumn for both Tmax and Tmin. Similar to Tmax and Tmin, the longest time period (2090) exhibits the highest increase in solar radiation, followed by the midterm (2060) and then the short term (2040). Unlike Tmax and Tmin, the highest increase in SR is predicted during the summer season (JJA), while the lowest increase is projected during the winter season (DJF). The future projections highlight the expectation of a wettest and hottest summer, along with the driest and coldest winter. These findings provide valuable insights for water resource planners, agricultural managers, and policymakers, as these climate variables play a significant role in crop production and water allocation decisions. [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]

Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers. [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]

Relative soil moisture is of great significance to the growth and yield of sugarcane. In this study, we use the relative soil moisture from the China Meteorological Administration Land Data Assimilation System (CLDAS) to dynamically evaluate the water requirement of sugarcane and its growth adaptability at different growth stages. Based on the data of relative soil moisture, air temperature, precipitation, and soil temperature, a sugarcane yield model is established to analyze the projected change trends of sugarcane yield in China from 2020 to 2100 under three future scenarios. Analysis results show that sugarcane requires more water during the elongation stage but less water at the ripening stage. The relative soil moisture from the CLDAS can be used to calculate the proportion of the daily suitable area to the total planting area. The combining of relative soil moisture data and water requirement indicators can better characterize the water requirement during sugarcane growth. Suitable relative soil moisture during the tillering and elongation stages is the most critical factor that directly affects the sugarcane yield. From 2020 to 2100, sugarcane yield will increase first and then decrease sharply. The increase in emissions can lead to an apparent downward trend in sugarcane yield. Based on the CLDAS data and water requirement indicators, a new method for monitoring the sugarcane growth throughout the growth period is proposed in this study. In the SSP370 and SSP460 scenarios, the sugarcane yield showed a downward trend, and there were mutations in 2064 and 2052, respectively. After the mutation, the yield decline trend was more obvious. Under the SSP585 scenario model, the sugarcane production showed an upward trend from 2022 to 2033 and a downward trend after 2033, and a mutation occurred in 2051. After the mutation, the downward trend of sugarcane production was more obvious. [ABSTRACT FROM AUTHOR]

Over the last 50 years, global air temperature has increased substantially especially over continents, causing higher rates of evaporative demand that can lead to an increase in drought occurrence and intensity worldwide. The Standardized Precipitation Index (SPI) is a drought index that solely uses precipitation, while the Standardized Precipitation Evapotranspiration Index (SPEI) uses the difference between precipitation and potential evapotranspiration and, therefore, incorporates the impacts of warming more directly. Here, we explore trends in SPI and SPEI, the relationship between SPI and SPEI, and how this relationship varies across different climate types. We find that a majority of the terrestrial surface has positive trends in SPI while most has negative SPEI from 1971 to 2022, a period of consistent global warming. Although the two indices show positive correlation with each other across the globe, we find decreasing correlation across all aridity types from the humid to the hyper-arid regions. The difference of correlation across two recent 26-year periods indicates that humid regions have consistent correlations through time while arid regions experience substantial interdecadal variation. Using a spatially weighted multiple linear regression, we find that SPI and SPEI trends have opposing relationships with temperature trends; specifically, it has a direct relationship with SPI trends and an inverse relationship with SPEI trends. In contrast, both vapor-pressure deficit and incoming solar radiation have an inverse relationship with SPI and a stronger inverse relationship with SPEI. Ongoing increases in temperature and vapor-pressure deficit are likely to cause further decoupling of the two indices worldwide. [ABSTRACT FROM AUTHOR]

The west side of the Antarctic Peninsula (AP) has shown great variability since the middle of the last century characterized by warming mainly because of the oceanic and atmospheric effects such as the disintegration of floating ice and the strength of westerly winds. Here, we used two climatic databases (reanalysis from 1979 to 2020 and surface stations from 1947 to 2020) to investigate trends in extreme air temperatures and wind components in the oceanic region between 55° S and 70° S in the west (75° W) and in the east sector (45° W) and over the AP (60° W). Non-parametric statistical trend tests and extreme value approaches are used. A set of annual, monthly, and seasonal series are fitted. The extremal index is applied to measure the degree of independence of monthly excesses over a threshold considered extreme events. Increasing trends are verified in the annual and monthly temperature and wind series. The greatest trends are observed for seasonal series from reanalysis without change-point in summer and winter. Decreasing trends are observed for maximum temperature in summer and positive trends mainly for the westerly winds over the AP. But in winter, the maximum temperature shows an increasing trend also over the AP. Most of reanalysis seasonal minimum temperature and wind components as well as maximum and minimum temperatures from stations present increasing trends with change-point but tend to stability after the breakpoints. The generalized distribution (GEV) is used to fit temperatures and westerly wind between South America (SA) and north of the AP. The 100-year return levels exceed the maximum value of the maximum temperature in Esperanza and maximum westerly winds at several grid points. Pareto and Poison distributions are applied for the maximum temperatures from stations and the 100-year return levels are not exceeded. Our findings show significant positive trends for monthly wind components near the SA in the region of the westerly winds whose changes in position influence directly the SAM, which modifies the atmospheric patterns in the South Hemisphere (SH). A predominance of seasonal warming is identified, which may impact the climate with consequences not only locally but also in other regions. [ABSTRACT FROM AUTHOR]

Using monthly mean data from the European Centre for Medium-Range Weather Forecasts interim reanalysis dataset and precipitation data from the Global Precipitation Climatology Project, we investigate the connections among the atmospheric thermal condition over the Tibetan–Iranian Plateaus (TIPs), the intensity of the Asian westerly jet stream (AWJS), and climate over the Eurasian–African region during boreal summer. At the entrance of the AWJS, the tropospheric air temperature over the TIPs (TTIP) has a significant negative correlation with precipitation and a positive correlation with surface air temperature (SAT) over West Asia, Eastern Europe, and western Russia. Furthermore, the TTIP has a significant positive correlation with precipitation over Northeast Africa. In contrast, at the exit of the AWJS, the TTIP has a significant negative correlation with precipitation and a positive correlation with SAT over East Asia. The TTIP has a significant positive correlation with precipitation over Northeast Asia. This distribution of TTIP-related precipitation and SAT over the Eurasian–African region can be explained by the four-quadrant conceptual model of a straight jet streak. The enhanced temperature gradient from the mid–lower to higher latitudes over the Asian continent, associated with a high TTIP, results in the enhancement of the AWJS. Two anomalous opposite meridional circulations appear at the entrance and exit of the AWJS, respectively, and are responsible for the distribution of the TTIP-related precipitation and SAT over the Eurasian–African region. [ABSTRACT FROM AUTHOR]

Arid regions are characterized by the fragility of their ecosystems, which are very vulnerable to climate change. The increase in aridity in these regions makes them more exposed to drought with serious consequences. This contribution analyzes the trend of aridity and drought in the Algerian steppes, during the period 1951–2022. Robust statistical tests (Mann–Kendall and Sen) and the Reconnaissance Drought Index (RDI) are used. Monthly rainfall data and monthly average temperature as well as potential evapotranspiration (PET) data from the Climatic Research Unit (CRU), characterized by a spatial resolution of 0.5°, were used. The results showed an increase in annual aridity, which led to an expansion of drylands. The change was characterized by the transition of 9.20% of the steppe surface from the semi-arid class to the arid class. At the same time, the entire subhumid class (1.67%) made the transition to the semi-arid class. On the other hand, a significant tendency towards drought over most of the steppe territory (81%) was recorded. The analysis of the results revealed the influence of precipitations and air temperatures in this enlargement. These changes have contributed to the degradation of the environment and natural resources and to an ecological and socio-economic imbalance. [ABSTRACT FROM AUTHOR]

Annatto, a globally valued crop, has multifaceted applications ranging from food coloring to medicine. While adaptable, its productivity is influenced by climate, making it vulnerable to global warming. This study investigates the agroclimatic suitability for annatto cultivation in Brazil, considering current and projected climate scenarios, highlighting potential impacts on agriculture, the economy, and environmental sustainability. This study aims to carry out the annatto (Bixa orellana L.) agroclimatic zoning for Brazil in the current scenario and the SSP5-8.5 climate change scenario for CMIP6. Air temperature and daily precipitation data for the 1970–2000 and for 2021–2100 for the IPSL-CM6A-LR model the SSP1-2.6, SSP2-4.5, SSP3-2.6 and SSP5-8.5 in GeoTIFF format. This study was carried out for the entire Brazilian territory. Future climate variables were obtained by the WorldClim 2.1 platform for the IPSL-CM6A-LR model in the periods 2021–2100 and the SSP1-2.6, SSP2-4.5, SSP3-2.6 and SSP5-8.5 scenario. Thus, zoning was carried out using geographic information systems software (QGIS), automated with the Python language. The annatto climate zoning for the current scenario showed five classes. The most predominant class was restricted due to excess water (42.9%), followed by the suitable class (32.5%), standing out the central Brazilian region: Mato Grosso do Sul (91.6%), Goiás (80.8%), and Tocantins (92.6%). Climate change affected negatively annatto climate zoning in all scenarios. The suitability class for the SSP5-8.5 scenario decreased in all periods, with means of 27.9% (2021–2040), 20.2% (2041–2060), 12.1% (2061–2080), and 6.5% (2081–2100). Climate changes significantly impact annatto cultivation areas in Brazil, particularly by 2081–2100, with a notable reduction in suitable zones. Key producing regions, like Monte Castelo-SP and Dracena-SP, face unsuitability, threatening local economies. The study underscores the urgency to explore annatto's adaptive capacity against the direst IPCC scenario (SSP5-8.5). [ABSTRACT FROM AUTHOR]

Air temperature (AT) is an extremely useful metric for a wide range of climate change applications and studies. While its data are usually obtained from measurements at meteorological stations, this may be limited over large areas due to the unavailability of weather stations or lack of reliable data due to station-specific errors. With the availability of remote sensing data, including moderate resolution imaging spectroradiometer (MODIS), many of these constraints are overcome. In this study, two machine learning (ML) algorithms, including random forest (RF) and Cubist, and linear regression were employed to estimate AT using MODIS data over Morocco. The ML models were found to be quite well suited to predicting AT, recording RMSE values ranging from 2.40 to 2.76 °C. The Cubist and random forest models, with RMSE of 2.40 and 2.42 °C, respectively, and R2 of 0.82, were more accurate than the linear model. The linear model had the least predictive power, recording RMSE and R2 of 2.76 °C and 0.75, respectively. This study highlights the importance of station data availability, with model performance reduced at remote stations, particularly in the east and south of the country. Despite this, the use of ML techniques in conjunction with satellite data to predict AT in Morocco provides an interesting alternative, which could be a useful tool for climate scientists as well as policy makers. [ABSTRACT FROM AUTHOR]

Climate change and intensification of urban heat island are making the outdoor environment more uncomfortable during hot weather. Improving the outdoor thermal comfort of urban parks is crucial for residents' health due to their significance for daily activities. Taking Hot Spring Park in Fuzhou, China, as an example, based on COMfort FormulA (COMFA) model, this study examined the outdoor thermal comfort and revealed related influencing factors, through questionnaires, field measurement of meteorological and landscape variables. The results show that (1) air temperature and solar radiation are the most important variables affecting outdoor thermal comfort while wind speed and relative humidity are less influential. (2) The results allow extending the neutral thermal comfort range from 50 W/m2 (value of literature) to 80 W/m2 in hot summer and warm winter areas. However, people will be unbearable when the value of COMFA model reaches 177 W/m2 or higher. (3) Increasing the green coverage and canopy density of the park can significantly improve outdoor thermal comfort, and open water surface has almost no positive effect. This study extended the understanding of outdoor thermal comfort in hot summer and warm winter areas and can provide the basis for the design of urban parks and the improvement of outdoor thermal environment. [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]

The growth and yield of important agricultural crops are severely diminished under heat stress, as well as being eminently affected by subtle changes in wind. Thus, the main objective of this study is to develop a geostatistical interpolator according to the spatial distribution of mean air temperature and wind speed in the state of Pernambuco. Annual mean air temperature and wind speed data were used to evaluate the applicability of two geostatistical interpolators to different LULC in three periods (2010, 2015, and 2020). The datasets adopted include TerraClimate and the MapBiomas Project (Collection 7). This study detected that OK's R2 proved to be a measure sensitive to the variability of the data since it differed by more than 40% among the geostatistical methods, but OK was not affected by this effect. So, this highlighted the geostatistical power of the OK regardless of the presence of outliers, as well as revealing that COK can be successfully used as an alternative interpolator of the Tair using Tmin as a covariate, since it showed only 1% difference in the R2 and CCC compared to OK, and is also able to obtain better fit of the model. Another finding was the sensitivity of the R2 to inter-annual climate variability, where the years with above-average Ws and severe drought associated with El Niño produced higher coefficients (≥ 0.73). This manuscript made it possible to propose appropriate management techniques to mitigate the effects of climate change and identify strategic development regions with high farming potential. [ABSTRACT FROM AUTHOR]

The influence of topography and land cover on air temperature space-time variability is examined in an urban environment with contrasted topography through simple and multiple linear regression (SLR and MLR) models, ran for each hour of the period 2014–2021, to explain spatial patterns of air temperature measured by a dense network. The SLR models reveal a complementary influence of topography and land cover, with the largest influence during daytime and nighttime, respectively. The MLR significantly improves upon the SLR models despite persistent intensity errors at night and spatial errors in the early morning. Topography influences air temperatures all year round, with temperature decreasing with height during the day and frequent thermal inversions at night (up to 30% of the time). Impervious surfaces are more influential in summer and early fall, especially during the late afternoon for the fraction covered by buildings and during the early night for the distance from the city centre. They contribute to increase air temperature close to the city centre and where the fraction covered by buildings is large. By contrast, vegetation contributes to cool air temperature during the night, especially in spring and early summer for field crops, summer and early fall for forests, and late fall and winter for low vegetation. Our framework proves to be a low-cost and efficient way to assess how strongly and how recurrently the static surface conditions influence air temperature along the annual and diurnal cycles. It is easily transposable to other areas and study fields. [ABSTRACT FROM AUTHOR]

The Eastern Mediterranean region is a major climate change hotspot. The island of Cyprus is likely to face increases in the frequency and intensity of hotter weather conditions and heatwaves in the near future. Studies conducted on the long-term temperature changes in Cyprus are very limited. Here, we present an updated and most detailed assessment of the maximum, minimum, mean and diurnal temperature series in North Cyprus for the period 1975–2021. Data obtained from the meterological stations of North Cyprus have been analysed using Mann–Kendal (MK) test and Theil–Sen slope estimator. Overall the mean minimum temperature trend (Tmin) showed the highest warming rate 0.61 (0.24 ≤ Tmin ≤ 0.99)°C decade−1 followed by the mean temperature trend (Tmean) 0.38 (0.29 ≤ Tmean ≤ 0.50) °C decade−1 and the mean maximum temperature trend (Tmax) 0.28 (0 ≤ Tmax ≤ 0.50) °C decade−1. The magnitude of the warming trend observed in the overall mean minimum temperature of North Cyprus 0.61 °C decade−1, is one of the fastest warming trends reported in the literature. A negative association was detected between the direction of prevailing winds of North Cyprus and the magnitude of increase in the mean temperature trends of the locations with coastal Mediterranean climate, which has pointed out the importance of prevailing winds regarding their cooling effect in coastal areas. The diurnal temperature range trend of North Cyprus indicates an apparent decrease (− 0.33 °C decade−1). The warming impact of urban heat island effect was detected in temperature trends of Nicosia in the Mesaoria plain. The information provided here is invaluable to consider in any climate assessment and adaptation plan in Cyprus. If the current warming trend persists into the future, it will devastatingly impact all sectors and natural systems in the region. [ABSTRACT FROM AUTHOR]

Currently, changes in rainfall regimes have intensified biomass burning in central Brazil. These fires, resulting mainly from anthropic action, emit large amounts of gases and aerosols into the atmosphere. The emission of these constituents in the dry season can drastically alter the functioning of ecosystems. In this work, it was verified that the high concentrations of Black Carbon (BC) have a direct relationship with the air temperature and influence the carbon capture process. This study was the result of a long-term campaign carried out in the northern region of the Brazilian Pantanal, between 2017 and 2019. Measurements of CO2 fluxes were evaluated together with measurements of temperature, radiation and BC concentrations. The results showed that in some episodes, the increase in BC concentration inhibits photosynthetically active radiation and increases air temperature. As a consequence of the increase in BC concentration, a reduction in carbon capture was observed. In a specific episode in October 2017, this work estimated a deficit of 1.65 kg.m−2 in CO2 capture due to the emission of BC in the Pantanal. [ABSTRACT FROM AUTHOR]

The annual, seasonal, and monthly trends of air temperatures were analyzed for thirteen urban and five rural meteorological stations in Kazakhstan for the 1963–2020 period. The non-parametric Mann–Kendall (M–K) rank correlation and Sen's slope estimator methods and the parametric least-squares linear regression (LSLR) were used to determine whether there were positive or negative statistically significant trends in mean, average maximum, and minimum air temperature time series along with diurnal temperature ranges (DTRs), and temperature differences between five large and small cities. In addition, Kazakhstan's annual and seasonal air temperature series were analyzed in terms of autocorrelation (serial correlation) coefficients. Coefficients of variations indicated that mean annual temperature variability is the highest in northern cities. Results of the M–K trend test indicated that the highest and lowest increases in the mean air temperatures were observed in spring and autumn, respectively. The magnitudes of the significant increasing trends in annual air temperature ranged between 0.23 °C/decade at Karagandy and 0.54 °C/decade at Kyzylorda. Annual and seasonal diurnal temperature ranges (DTRs) reveal insignificantly decreasing and increasing trends at most of the stations characterized by urbanization. According to the results of both M–K and LSLR tests, annual and winter air temperature differences of some station pairs tend to significantly increase, which may mean that the differences in the calculated temperature range between large and small cities might have widened significantly. However, significance test for the calculated autocorrelation coefficients of the annual and seasonal air temperature data showed that most of the series clearly appear as a low-frequency variability on the significantly increased long-term averages. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is a key variable for understanding the water cycle and is essential for managing water resources in arid and semi-arid regions. Employing the high-resolution CRU TS version 4.05 gridded datasets and utilizing a substantial network of 48 meteorological gauging stations, this analysis spans a temporal scope from 1901 to 2018, offering valuable insights into ET0 trends. The results of the Mann-Kendall test show that the majority of stations exhibit a significant upward trend in ET0, with the strongest trends observed in June (Z = 5.58), July (Z = 6.21), and August (Z = 4.42). The magnitude of the trend is found to be faster in the eastern region, with the maximum values observed in May and July. The upward trend in ET0 is significant throughout all seasons and is most notable in summer at southern stations, while in winter, the trend is more prominent at northeastern stations. Annual ET0 trends are highest in the northeastern parts of the South, like Biskra (Z = 8.65), El Oued, and M'sila (Z = 8.54). However, the lowest was observed in Adrar, Tamanrasset, and Ghardaia provinces. Factors such as changes in air temperature, net radiation, wind speed, relative humidity, and land use may influence ET0 levels. These findings provide insights into the changes in water requirements for plants and soil surface evaporation over time, which can have implications for water resource management in Algeria. [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]

This work examines the topoclimatic characteristics of Zahlé, specifically thermal breezes and the Urban Heat Island (UHI) phenomenon. Three-hourly data from 1994 to 2021 from conventional weather stations shows a frequency of breezes in the summer season of 74%. In Rayak, mountain and valley breezes have been noticed alternating between day and night, channeled by the topography of the Bekaa Valley. In Houch El Oumaraa, valley-sea breezes dominate during the day and mountain breezes occur at night. These breezes affect the distribution of air temperature and humidity, creating an average thermal contrast of 4.3°C between the city and its surrounding countryside at night. Data from weather stations implanted in 2022 and car surveys at a fine scale (half hourly data) level has confirmed the previous findings: During nighttime, the mountain breeze dominates and brings cool and moist air from the mountains into the valley at a low speed (<1 m/s). During the daytime, a valley breeze dominates and can reach speeds of up to 4 m/s. Additionally, a sea breeze with faster speeds is detected. The car surveys indicate a temperature variation of 9 °C between the valley and the surrounding urban areas during early morning. [ABSTRACT FROM AUTHOR]

Meteorological disasters have increasingly become one of the important interferences limiting vegetation growth and productivity recent years, especially in ecologically fragile regions. However, it is unknown how much the impact of meteorological disaster on the vegetation in ecologically fragile area under the changing climate. This study aims to identify the spatial and temporal distribution of four meteorological disasters (drought, rainstorm, high temperature, and low temperature) as well as net primary productivity (NPP) reduction rate in Guangxi province, China, which has large ecologically fragile Karst areas. We also aim to quantify the relative impact of each disaster on NPP loss. During the last 20 years (2000–2019), there was a decreasing trend for drought indicator (drought days), but an increasing trend for rainstorm (cumulated rainfall for daily rainfall higher than 50 mm and less than 99.9 mm), high temperature (daily maximum air temperature higher than 35°C and less than 37°C days), and low temperature (cumulated thermal time for daily minimum temperature less than 0°C) indictors. In the northeast area of study region, all the intensity of rainstorm, high- and low-temperature disasters showed an increasing trend. Under the more intense and frequent meteorological disasters, 87% study sites (60 stations/69 stations in total) showed an increasing trend in terms of NPP reduction rate. Results on grey correlation between each meteorological disaster and NPP reduction rate showed that high-temperature disaster had the greatest impact (27%) on vegetation growth, followed by drought (25%) and rainstorm (25%) disaster, and then low-temperature (23%) disaster. Our results would be helpful for meteorological disasters warning, vegetation protecting as well as policies making for governments. [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]

We used monthly climatological datasets from the NOAA US Climate Divisional Database to detect long-term trends (1951–2021) in the nine climate divisions of Oklahoma, USA. We applied Hargreaves-Samani method to calculate reference evapotranspiration (ETo) and used 12-month standardized precipitation index to characterize meteorological droughts. Modified Mann-Kendall and Sen's slope non-parametric trend tests were performed to identify significant (p < 0.05) positive and negative trends in maximum, average, and minimum air temperature (Tmax, Tavg, Tmin, respectively), precipitation (P), and ETo on annual and seasonal time scales. Innovative trend analysis and least square regression were used to further support the results. Statistically significant positive trends were observed in annual Tmin in all climate divisions. Statistically significant increasing trends were also observed in Tmin and Tavg on a seasonal scale across different climate divisions whereas significant decreasing trends were observed in summer Tmax. Winter P showed statistically significant increasing trends across Oklahoma. ETo only showed significant decreasing trends in the South Central climate division on an annual basis, and in central and eastern parts of the state during summers. The rate of change in temperature ranged from −0.010 to 0.020 °C/year. The rate of change in P ranged from −0.16 to 3.16 mm/year. while ETo ranged from −0.44 to 0.42 mm/year. These trends have critical implications for agricultural management to cope with potential long-term climate impacts on agricultural production, pests and invasive species, water resources, and soil moisture. [ABSTRACT FROM AUTHOR]

The variation of surface air temperature is one of the most direct manifestations of global warming. Current research on this variable mainly focuses on climatology and extreme temperatures; however, the daily to monthly variation of temperature across extratropical regions is significantly affected by the annual cycle. Climate models play a key role in simulating the historical climate and assessing climate variability. This study investigated the temperature annual cycle and its variation in China from 1961 to 2014, using the observations collected at meteorological stations and the outputs of CMIP6 models. Regional differences and model discrepancies were evaluated to reveal the capabilities of CMIP6 models. The phase-max, phase-min, and amplitude were used to represent the annual cycle, after Fourier transformation was applied to daily temperature to filter high-frequency noise. The climatology, interannual variability, and long-term trends of the three above-mentioned parameters simulated in CMIP6 models were compared with those obtained from observations over the whole study area and in selected regions. The results showed that the models could reasonably describe the climatological temperature annual cycle from 1961 to 2014, although the values were underestimated relative to meteorological observations. Station observation and CMIP6 models also showed a similar spatial distribution of the phase-max, phase-min, and amplitude values, with an obvious north–south gradient. Station observations exhibited a large interannual variability, an advance in phase-max and phase-min, and a decrease in amplitude, which were poorly revealed by the CMIP6 models. [ABSTRACT FROM AUTHOR]

The Baltics is one of the European regions facing the most rapidly increasing air temperatures – a clear signal of climate change. This article comprises an analysis of the spatial distribution and variability of air temperature in Latvia, in the Baltics, comparing the most recent climate normal (1991–2020) with the previous two climate normals (1971–2000; 1981–2010), as well as with a reference period (1961–1990). Compared to the reference period, the annual mean temperature in the last climate normal was 1.2 °C higher across Latvia, corresponding to a warming rate of +0.4 °C decade−1, which is greater than the European average (between 0.17 and 0.22 °C decade−1). Compared to the reference period, a persistent rise of winter (DJF) temperatures by around 2 °C and a rapid increase in the easternmost locations (distant from the sea) were found. Summer (JJA) and spring (MAM) temperatures rose by around 1 °C. The autumn (SON) temperature increase was less pronounced and was only evident during the last two climate normals. Since the reference period, the minimal mean daily air temperatures (5th quantile) in winter, autumn and, especially, spring months have increased at a rate almost double that of the average (50th quantile) temperature (0.72 to 0.39 °C decade−1, respectively). We note that May or June and October experienced little to no average temperature increase, reported elsewhere in the Baltic region. We have confirmed the presence of two seasons in the spatial pattern of air temperature: one from April to July dominated by an N–S gradient and the other from August to March dominated by a W–E gradient. Furthermore, the possible mechanisms and implications of the observed seasonal pattern of the temperature increase are discussed, particularly considering land–atmosphere water and energy flux feedback. [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]

With the increased occurrence of hot spells in recent years, there is growing interest in quantifying the recurrence of extreme temperature events. However, pronounced temperature anomalies occur all year round, and a reliable classification in terms of the time of occurrence in the year is needed. In this study, we present a novel approach to classifying daily air temperatures that take into account the seasonal cycle and climate change. We model the distribution of the daily Swiss temperatures using the skewed generalized error distribution with four time-varying parameters, thereby accounting for non-Gaussianity in daily air temperature, while the climatic trend is modeled linearly with smoothed northern hemisphere temperature as an explanatory variable. The daily observations are then transformed into a standard normal distribution. The resultant standardized temperature anomalies are comparable within a year and between years and are used for quantile-based empirical classification. The approach is suitable to classify historical and current extreme temperatures with respect to the temperature range expected at the time of the event. For example, a heat wave occurring at the end of June is classified as less likely to occur than a heat wave of similar intensity occurring in mid-July, as is shown for the two 7-day heat waves that struck Switzerland in the summer of 2019. Furthermore, climate change has increased the probability of hot events and decreased the probability of cold events in recent years. The presented approach thus allows a fair classification of extreme temperatures within a year and between years and offers new possibilities to analyze daily air temperature. [ABSTRACT FROM AUTHOR]

Identifying the climatic characterization of a region and its spatial and temporal variation, as well as its changes in the face of climate change events, is essential for agrometeorological studies because they can assist in the planning of strategies that reduce the negative impacts generated in the cultures exposed to critical climatic conditions. Thus, this study aimed to characterize the climatic conditions of the MATOPIBA region and its changes in scenarios of climate change using the classification index of Thornthwaite. Daily time series of rainfall and temperature data in the 1950–1990 period were used, covering 467 points over the studied region. The data set was used to estimate climatological water balance and climate index Thornthwaite (1948), and obtain the trends climatological according to IPCC (2014) climate change projections, with changes in the average air temperature (+ 1.5 °C and − 1.5 °C) and precipitation (+ 30% and − 30%). The MATOPIBA region is characterized by its humid, dry subhumid, and moist subhumid climate, with the rainy seasons, between October and April, and drought, from May to September, well defined. In MATOPIBA climate change scenarios, climatic extreme indices tend to alter the pattern, frequency, and distribution of climate class, which can increase climate risk and impact crop production. Therefore, the results obtained can be used to develop strategies to mitigate the vulnerability of crops to climate change conditions. [ABSTRACT FROM AUTHOR]