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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]

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

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

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]

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]

This paper is about spatial–temporal variability of atmospheric temperature across Taiwan, an island with diverse local emission sources partly because of its Asian cultural characteristics. To develop a new land use regression (LUR) model for this study, we used the temperature data collected by the Taiwan Central Weather Bureau from 2000 and 2016, while using the data from 2017 as the external data verification to assess model reliability. Because incense and joss money burning is a cultural-specific emission source in Asia, we further included location of temples, cemeteries, and crematoriums as potential predictors. The overall model performance and tenfold cross-validated are R2 of 0.88 and R2 of 0.87, respectively, which presents a high level of prediction performance. Moreover, we used our LUR model to estimate urban heat islands intensity (UHII) for six metropolises in Taiwan and found Taichung City has the highest UHII value (4.60 °C) among them. These results provide important insights in expanding the remote sensing application on spatial–temporal variation of atmospheric temperature and its further application on UHI effects. [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]

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]

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

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

In recent years, climate change has attracted many researchers' attention around the world, among which the analysis of long-term surface air temperature (SAT) changes is essential. In this paper, we analyze the spatial and temporal variations of the SAT in Tarim Basin from 1961 to 2015. Our results show that warming of the SAT is evident at most weather stations in the Taklamakan Desert, except for those at Aketao, Kuche, and Tazhong. The overall warming rate is 0.25 °C/decade in the Taklamakan Desert. Mann–Kendall tests are adopted to detect abrupt changes in the SATs. It is demonstrated that nine out of 39 stations experienced a simultaneous abrupt change in 1996, which is consistent with the abrupt changes seen in the local vegetation cover. The relationships between the SAT changes and the underlying landscape and vegetation cover are also discussed in this paper. A moderate negative correlation is discovered between the SAT and the normalized difference vegetation index (NDVI) at oases. [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]

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]

This study presents a trend analysis of a two-decade (2000–2020) series of surface meteorological and air quality data measured in Rome (Italy). Data series are collected at three sites in Rome downtown and its coastal surroundings. This paper fills the gap due to missing or incomplete recent information about the meteorological and atmospheric composition tendencies in the Rome area. Datasets are subjected to in-depth quality control and to statistical analysis to verify the data homogeneity, whilst trend analysis is performed using the Seasonal Kendall test. The results show a statistically significant positive trend for average air temperature (0.07 °C year−1 in urban and coastal sites), whilst maximum and minimum temperatures increase more in urban (0.10 °C year−1) than in coastal (0.01 °C year−1) environment. The water vapour mixing ratio trend is higher in the city (0.10 g kg−1 year−1) than along the coast (0.03 g kg−1 year−1). The heat index tendency is more pronounced in the Rome centre (0.11 °C year−1) than in the coast (0.06 °C year−1). The monthly cumulative precipitations do not show statistically significant trends. On the other hand, air quality generally improved: surface pollutant concentrations show a significant decrease thanks to the reduction of local emissions (C6H6, − 0.12 µg m−3 year−1; SO2, − 0.09 µg m−3 year−1; CO, − 0.02 mg m−3 year−1; NOx, − 1.28 µg m−3 year−1; NO, − 0.38 µg m−3 year−1; NO2, − 0.60 µg m−3 year−1, PM10, − 0.35 µg m−3 year−1). Only O3 shows a statistically significant positive trend (0.15 µg m−3 year−1), in agreement with wider scale studies. The outcomes suggest that whilst local and national policies contribute to the improvement of air quality, the rising of temperature—as likely consequence of climate change—may be an emerging cause of concern for human thermal discomfort and potential effect on the conservation of tangible immovable heritage. [ABSTRACT FROM AUTHOR]

Boretti (Theor Appl Clim 114:567-573, ) presents an analysis of observed temperature trends in the Northern Territory, Australia, and claims that this analysis is inconsistent with trends reported by the Australian Bureau of Meteorology and thus that the Bureau of Meteorology results are flawed. This paper presents evidence that the results presented by Boretti () are at least partly attributable to his failure to account for inhomogeneities in the underlying station data and to the use of time periods for trends which are different to those in the Bureau of Meteorology analyses with which he is comparing them. The evidence, as presented in this paper, therefore fails to support his conclusions of inconsistencies between the Bureau of Meteorology analyses and the station data. [ABSTRACT FROM AUTHOR]

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

In this paper, the spatial and temporal characteristics of convective precipitation (CP) and large-scale precipitation (LSP) in southern China during 1980–2020 are analyzed using monthly mean precipitation data from MERRA-2. In addition, the possible effects of relative humidity on CP and LSP are explored. The results indicate the following. (1) The LSP dominates the proportion of total precipitation (TP). Both LSP and CP are more prevalent in the south and less prevalent in the north, but there is a difference in the regions of their maximum centers. (2) Significant interannual and seasonal variations are observed in precipitation. TP and LSP tended to be higher than average after the 1990s, while for the CP, a significant negative trend has dominated the past ten years. There are significant increasing trends for TP and LSP, with area-averaged linear trends of 78.1 mm/decade and 85.9 mm/decade, respectively, while that of CP is − 17.1 mm/decade. The increasing trends of LSP are mainly contributed by the precipitation of summer and autumn. (3) The variations of LSP are affected by relative humidity in the troposphere, while CP is only influenced by the changes in relative humidity due to air temperature or specific humidity. The trend of relative humidity is − 0.33%/decade, mainly due to rising temperature in the troposphere. (4) Changes in relative humidity caused by temperature or specific humidity alone act on large-scale precipitation through both interannual and interdecadal processes, causing large-scale precipitation to increase, and the convective precipitation is mainly affected by the interdecadal processes. [ABSTRACT FROM AUTHOR]

In this paper, we have analyzed the time series of daily values for three meteorological elements, two continuous and a discontinuous one, i.e., the maximum and minimum air temperature and the precipitation. The analysis was done based on the observations from seven stations in Serbia from the period 1951-2010. The main aim of this paper was to quantify the complexity of the annual values for the mentioned time series and to calculate the rate of its change. For that purpose, we have used the sample entropy and the Kolmogorov complexity as the measures which can indicate the variability and irregularity of a given time series. Results obtained show that the maximum temperature has increasing trends in the given period which points out a warming, ranged in the interval 1-2 °C. The increasing temperature indicates the higher internal energy of the atmosphere, changing the weather patterns, manifested in the time series. The Kolmogorov complexity of the maximum temperature time series has statistically significant increasing trends, while the sample entropy has increasing but statistically insignificant trend. The trends of complexity measures for the minimum temperature depend on the location. Both complexity measures for the precipitation time series have decreasing trends. [ABSTRACT FROM AUTHOR]

Sunshine duration is an important atmospheric indicator used in many agricultural, architectural, and solar energy applications (photovoltaics, thermal systems, and passive building design). Hence, it should be estimated accurately for areas with low-quality data or unavailable precise measurements. This paper aimed to obtain a sunshine duration measurement database in Algeria's south region and also to study the applicability of computational models to predict them. This work develops ensemble learning models for assessing daily sunshine duration with meteorological datasets that include daily mean relative humidity, daily mean air temperature, daily maximum air temperature, daily minimum air temperature, and daily temperature range as input. The study proposes a unique hybrid model, combining grey wolf and stochastic fractal search (GWO-SFS) optimization algorithms with the random forest regressor ensemble. A pre-feature selection process improved the newly suggested model. Various commonly adopted algorithms in relevant studies have been considered as references for evaluating the new hybrid algorithm. The accuracy of models was examined as a function of some frequently used statistical pointers, as well as the Wilcoxon rank-sum test. Besides, the models were evaluated according to the several input combinations. The numerical experiments show that the proposed optimization ensemble with feature preprocessing outperforms stand-alone models in terms of prediction accuracy and robustness, where relative root mean square errors are reduced by over 20% for all considered locations. In addition, all correlation coefficients are higher than 0.999. Moreover, the proposed model, with RMSEs lower than 0.4884 hours, shows significantly superior performances compared to previously proposed models in the literature. [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]

This paper examines monthly, seasonal, and annual trends in temperature and precipitation time series in Osijek during the period between 1900 and 2018. Two new methods, innovative trend analysis (ITA) and successive average methodology (SAM), together with the classic Mann–Kendall (M–K) and Sen's slope methods, have been applied to determine potential trends in the variables at different time scales. Moreover, time series decomposition using locally estimated scatterplot smoothing (STL) was conducted to determine trend, seasonality, and the relationship between the components of each variable. Regarding the air temperature, ITA showed a monotonic positive trend at relatively low (T ≤ 10 °C) and high (T ≥ 13 °C) temperature ranges in all seasons, excluding spring. A positive trend was also found in the medium temperature range in this season, which agrees with the results of M–K test. The highest Sen's slope was obtained in January, with the second highest in April. According to the results acquired for the observed precipitation time series, it was discovered that Osijek has experienced a decreasing trend in spring precipitation. However, there is no trend in annual precipitation at a 5% significance level. Differing from the M–K results, the ITA shows a decreasing trend in both spring and autumn seasons. Summer precipitation increases with a significant change in the high precipitating levels (p ≥ 100 mm). Comparing successive pairs of partial trends in both historical temperature and precipitation, our results show that trends in peak and trough change-points are very close to each other, indicating a slight positive trend in temperature and a negative change in precipitation over the past century. [ABSTRACT FROM AUTHOR]

Regional economies that depend predominantly on agriculture and livestock are heavily affected by changes in air temperature, one such case are the activities in La Plata river basin (LPB). Some studies suggest that variations in the seasonal cycle and season onset would affect efficiency in the use of radiation by vegetation. This paper evaluates the distribution of minimum temperature seasonality trends over LPB, describes the trends in the seasonal cycle, and detects changes of minimum temperature extremes characterized by the number of frost days and the frequency of warm and cold nights. The analysis includes absolute minimum temperature 0(TnMin) and minimum average temperature (TnMean) from ERA5 reanalysis for the 1980–2015 period. Significant positive trends in the amplitude of annual average TnMin and TnMean are observed over more than half the area (53.5% and 69.9% of the basin, respectively). Amplitude and phase parameters suggest that average minimum temperature underwent greater variation than absolute minimum temperature over LPB. The shifts in phase indicate that minimum temperatures occurred earlier than usual in the year considering the 35-year series. In general terms, there is a shift toward warmer conditions. This warming is evident in seasonal trends of minimum temperature as well as in the significant increase in the number of warm nights, a significant decrease of cold days and a significant decrease in the number of frost days in the highest Andes mountains in the west of the LPB. [ABSTRACT FROM AUTHOR]

Analyses of the variation characteristics for aridity index (AI) can further enhance the understanding of climate change and have effect on hydrology and agriculture. In this paper, based on the data of 283 standard meteorological stations, the temporal-spatial variations and the influences of climate factors on AI were investigated and the relationship between AI and two climate indices (the Arctic Oscillation (AO); El Nino-Southern Oscillation (ENSO)) were also assessed in northern China (NC) during the period from 1961 to 2012. The results revealed that the annual mean AI decreased at the rate of −0.031 per decade in the past 52 years and the trend was statistically significant at the 0.01 level. The Mann-Kendall (M-K) test presented that the percentages of stations with positive trends and negative trends for AI were 10 and 81.9 % (22.6 % statistically significant), respectively. Spatially, in the western part of 100° E, the extremely dry area declined and the climate tended to become wet obviously. In the eastern part of 100° E, dry area moved toward the east and the south, which resulted in the enhancement of semiarid area and the shrinkage of subhumid area. The contributions of sunshine duration and precipitation to the decline of AI are more than those of other meteorological variables in NC. Moreover, the average temperature has risen significantly and AI decreased in NC, which indicated the existence of 'paradox.' Relationship between climate indices (AO and ENSO) and AI demonstrated that the influence of ENSO on AI overweight the AO on AI in NC. [ABSTRACT FROM AUTHOR]

The aim of this paper is to understand the role of the macrocirculation factor in shaping the dynamics of long-term bioclimatic conditions, with reference to Kłodzko in the Polish part of the Central Sudetes. The variability of bioclimatic conditions in Kłodzko for the period 1966–2017 was analysed, exposing its local specificity. The Universal Thermal Climate Index (UTCI) was taken as an indicator of the analysed bioclimatic features. The influence of the North Atlantic Oscillation (NAO) on the variability of this index and its constituent meteorological elements, e.g. air temperature, vapour pressure, wind speed, and solar conditions, was examined. On the basis of the winter NAO Hurrell coefficient, the analysed years were divided into two opposing groups representing either a negative or positive Hurrell index. A comparison between these two year-groups for the mean values of UTCI and the examined meteorological elements showed a clear intergroup difference in these values. Except for cloudiness, the importance of the macrocirculation genesis of NAO was verified by the statistically significant correlation between the Hurrell index on the one hand and the tested UTCI index and its constituent meteorological elements on the other. [ABSTRACT FROM AUTHOR]

In this paper, we explored the trends of the atmospheric moisture budget, precipitation, and streamflow in summer during 1961 to 2005 and possible correlations between them by using the linear regression method in the Yangtze River basin, China. The results indicate that: (1) increasing tendencies can be detected in the atmospheric moisture budget, precipitation and streamflow in the Yangtze River basin; however, the significant increasing trends occur only in the atmospheric moisture budget and precipitation in the middle and lower Yangtze River basin; (2) both the ratio of summer moisture budget to annual moisture budget and the ratio of summer precipitation to annual precipitation exhibit a significant increasing trend in the Yangtze River basin. The ratio of summer streamflow to annual streamflow is in a significant increasing trend in Hankou station. Significant increasing summer precipitation can be taken as the major controlling factor responsible for the higher probability of flood hazard occurrences in the Yangtze River basin. The consecutively increasing summer precipitation is largely due to the consistently increasing moisture budget; (3) the zonal geopotential height anomaly between 1991 and 2005 and 1961 and 1990 is higher from the south to the north, which to a large degree, limits the northward propagation of the summer monsoon to north China. As a result, the summer moisture budget increases in the middle and lower Yangtze River basin, which leads to more summer precipitation. This paper sheds light on the changing properties of precipitation and streamflow and possible underlying causes, which will be greatly helpful for better understanding of the changes of precipitation and streamflow in the Yangtze River basin. [ABSTRACT FROM AUTHOR]

The paper concerns research on urban heat islands (UHIs) and presents case studies of two Slovak cities of different sizes: the capital, Bratislava, and the regional centre, Trnava. The model MUKLIMO_3 was applied to the UHI modelling and a heat wave that occurred between 15 and 23 August 2018 was chosen. Land cover/land use (LC/LU) classes based on the local climate zone (LCZ) concept were used as input for the modelling. The model was validated by data taken from five stations in Bratislava and two in Trnava, and a good rate of agreement between the modelled and measured data was statistically proven. Manifestations of UHI were studied during the whole of the mentioned period by summing the hours when threshold temperature values exceeded 25 and 30 °C. The results for Bratislava show greater variability related to the size of the city and terrain dissection compared with Trnava, which is smaller and situated in a flat terrain. A single representative day, that is, 20 August 2018, was chosen, for which UHI was modelled with three inputs of LC/LU classes: situation in 1998, 2007, and 2016 in order to assess the effect of change of LC/LU classes on the distribution of temperatures. Spatial manifestation of UHI was assessed in the frame of LC/LU classes for 2016 at 12:00, 18:00, and 24:00 in Central European Summer Time. The greatest heat load was modelled in densely built-up LC/LU classes, while forest areas were coolest. In Bratislava, due to a combination of a dissected terrain and UHI, great variability was modelled, with maximum temperature differences of up to 12 °C and, in the territory with homogeneous elevation, up to 6 °C. The UHI field in Trnava is less variable, with modelled temperature differences of up to 3 °C. A comparison of individual time horizons proved the effect of the urbanization process on the change of air temperature by several tenths of a degree Celsius on average. [ABSTRACT FROM AUTHOR]

In this paper, by using forecasts of the highest and lowest daily surface air temperatures from the Coupled Forecast System version 2 (CFSv2) over a period of 9 months, NCEP/DOE reanalysis data from the National Centers for Environmental Prediction (NCEP), and data from the U.S. NOAA Climate Prediction Center (CPC), three cold waves are identified during 2015–2016 in China, and the CFSv2 forecasts of these cold waves are evaluated. The results show that the high-resolution CPC data and the low-resolution NCEP/DOE data both very clearly show the temperature drops and temperature anomalies of these cold waves. The earlier the forecast lead time is, the greater the errors in the beginning and ending dates of the cold waves between the CFSv2 forecasts and the observations; there are errors of 1–2 days in the durations of the cold waves between the CFSv2 forecasts and the observations. CFSv2 exhibits certain abilities to forecast the overall temperature drops and temperature anomalies of cold waves at forecast lead times of 0, 5, 10, and 15 days, but the CFSv2 prediction ability is poor at forecast lead times exceeding 20 days. CFSv2 can predict the spatial distribution of temperature drops better than it can predict that of temperature anomalies, but the absolute errors in the observed temperature drops are greater than those in the observed temperature anomalies. The temperature drops from the CFSv2 forecasts are obviously underestimated in North China, South China, and their nearby areas but overestimated in Mongolia and Northeast China. The temperature anomalies from the CFSv2 forecasts are obviously underestimated in the inland areas of China but overestimated in the surrounding oceans and adjacent coastal areas. The difference fields at forecast lead times of 0 and 30 days are basically the same, but the absolute values of the differences increase with increases in the forecast lead time (with a few exceptions). [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]

This paper examines soil temperature (Ts) in two different plantation forests relative to an adjacent open land area in a tropical highland environment. To achieve the objectives, Ts in a Juniperus procera (JP) plantation, a Grevillea robusta (GR) plantation, and in the adjacent open land (OL) area were measured. One-way ANOVA and Pearson's correlation were used for the data analysis. The result indicated that the mean daily Ts in the OL area at 10-cm depth of the soil was greater than the mean daily Ts recorded in the JP and GR plantations by 5 °C and 6 °C, respectively. The OL area Ts recorded at different soil depths (10 cm and 20 cm) was significantly different (p < 0.05). Rainfall (RF) and air temperature (Ta) of the study area have a direct relationship with the Ts in the plantation forests. The RF and Ta do not have a significant relationship with the Ts in the OL area at 10-cm soil depth. Overall, the study indicated that the Ts in the open land area was highly fluctuating monotonically, while it is relatively constant in the plantation forests. [ABSTRACT FROM AUTHOR]

Surface air temperature is important for climate change, hydrological, and ecosystems undergoing climate warming in the permafrost regions on the Qinghai-Tibetan Plateau (QTP). We monitored daily air temperatures at five sites (alpine desert, alpine desert steppe, alpine steppe, alpine meadow, and alpine wet meadow) from 2011 to 2015 in permafrost regions on the QTP. Using this data, we evaluated the performance of five different reanalysis air temperature products (CFSR, ERA-Interim, GLDAS-NOAH, MERRA, JRA-55, and CMFD). These results demonstrated that the ERA-Interim air temperature products exhibited the best performance at the majority of sites. However, the original ERA-Interim air temperature products were found to overestimate air temperature at most sites. The calibration models were established by the observed daily air temperatures measured from 2013 to 2015 for the ERA-Interim air temperature products, which validated with observational data from 2011 to 2012. Following calibration, ERA-Interim products were found to be closer to the observations made across all sites. In addition, this paper demonstrated a warming trend present in month, seasonal, and annual mean air temperature from the calibration results collected from 1980 to 2015. These results demonstrated that the largest warming trend was observed in February, while the smallest warming trend was observed in March. We demonstrated that all air temperatures warmed fastest in the summer and winter, more slowly in autumn, and the slowest in spring at the majority of sites tested. In addition, we found that the average annual air temperature warming trends were 0.0398, 0.0394, 0.0288, 0.0259, and 0.0254 °C/year in the alpine desert steppe, alpine desert, alpine steppe, alpine meadow, and alpine wet meadow in the permafrost regions on the QTP, respectively. These findings generate a useful tool for the recognizing of the variation in permafrost under climate change in permafrost regions on the QTP. [ABSTRACT FROM AUTHOR]

The paper presents the variability of meteorological conditions: air temperature, wind speed and relative air humidity; and biometeorological indices: wind chill temperature, predicted clothing insulation and accepted level of physical activity on Franz Josef Land (in Teplitz Bay and Calm Bay) in the years 1899–1931. It employs meteorological measurements taken during four scientific expeditions to the study area. The analysis mainly covered the period October–April, for which the most complete data set is available. For that period of the year, which includes the part of the year with the Franz Josef Land's coldest air temperatures, the range and nature of changes in meteorological and biometeorological conditions between historical periods and the modern period (1981–2010) were studied. The data analysis revealed that during the three oldest expeditions (which took place in the years 1899–1914), the biometeorological conditions in the study area were more harsh to humans than in the modern period (1981–2010) or similarly harsh. In contrast, during the 1930/1931 expedition, which represents the Early Twentieth Century Warming (ETCW), conditions were clearly more favourable (including predicted clothing insulation being 0.3 clo lower and 4.0 °C higher wind chill temperature than conditions observed nowadays). [ABSTRACT FROM AUTHOR]

The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with "ground truth" are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. [ABSTRACT FROM AUTHOR]

In this paper, the fidelity of 28 models under Coupled Model Inter-comparison Project Phase-5 is examined for the Indian summer monsoon for the historical period from 1975 to 2005. It is found that all models simulate the spatial distribution of the seasonal mean surface air temperatures (Tas) quite well (pattern correlation > 0.75), whereas the simulation of precipitation is found to be relatively poor (correlation 0.1–0.7). Most models underestimate the Tas with more bias during winter and less bias during summer. In regard to precipitation, most models fail to capture the observed contribution ratio of convective and large-scale precipitation (LSP) and simulate more convective precipitation as compared to the LSP. Extremely large wet (dry) biases are noted in convective (large-scale) precipitation. The total precipitation is also noted to have a large dry bias in most models, which is mainly due to the large dry bias in the LSP. Contrary to the notion that better simulation of the contribution ratio would lead to better simulation of total precipitation or vice-versa, our results show that both of these notions are not valid for most models. In observations, the LSP dominates the annual cycle of the total precipitation, whereas in models, the convective component dominates. In few models, the annual cycle in the individual precipitation component is either weak or completely missing. None of the models are found to simulate the observed trend in precipitation and temperature. The model with the highest resolution, MIROC-4h, simulates many of the observed features better than the other models, thereby emphasizing the usefulness of finer resolutions in better simulation of Indian monsoon. A comprehensive list of models has been prepared on the basis of their capability in simulating various features of Indian summer monsoon. The multimodel mean of the better models identified in this study is expected to produce more reliable projections of the Indian monsoon. [ABSTRACT FROM AUTHOR]

The identification of causality is the core issue in climate change studies. In this paper, driving force analysis and causal influence of NAO for Central European air temperature is presented using slow feature analysis and convergent cross-mapping. Results showed that the driving force of the dominate 7–8 year scale was reconstructed with central European surface air temperature (SAT), this interannual variability may be driven by large-scale climate variability modes such as North Atlantic Oscillation (NAO) based on the previous studies. Then, the possible dynamical causal relation between NAO and SAT in central European was presented; it was indicative that the air temperature variability in Central European uncovers causal influence by NAO. [ABSTRACT FROM AUTHOR]

As the temperature and the diurnal temperature range (DTR) influence our life in many fields like agriculture and health, changes in either parameter may affect our current and future plans in such areas. This paper investigates the variability of temperature and DTR in the Levant and the Arabian Peninsula (172 stations in 12 countries) in different study periods of 10, 25, 40, 50, and 60 years. The data were tested for homogeneity using four standard tests, and we used the nonparametric statistics Mann-Kendall and Theil-Sen to calculate trend significance, direction, and magnitude in time series. Our results show a significant warming trend in 45–60% of the region with an average increase of 0.65, 0.43, and 0.30 °C/decade for the 25-, 40-, and 50-year study periods. The highest number of stations that have a significant increase in temperature occurs in spring and summer. On the other hand, decreasing DTR trends occur in about 31% of the region, mostly in the south of the Arabian Peninsula at 0.60 °C/decade. This work, additionally, provides an interactive online tool that shows the trends and temperature on zoomable maps, charts, and other visuals to station level. This tool could benefit researchers and strategic planners for the studied region. In many stations, we found that there are a significant increase in temperature and a decrease in DTR which reflect a severe change in the climate that should be considered in future planning. We recommend expanding this study to cover precipitation and other meteorological factors. [ABSTRACT FROM AUTHOR]

In this paper, the performance of 28 CMIP5 models in simulating the climate of the Himalaya-Tibetan Plateau (HTP) for the recent past (1975–2005) is evaluated using the observations from the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE). Many models realistically simulate the spatial distribution of surface air temperature (Tas) and precipitation with pattern correlation as high as 0.8; however, they possess severe biases in their magnitude. The biases in Tas appear to be associated with the biases in the surface elevation. All the models capture the observed phase of the annual cycle of the Tas but underestimate the amplitude. For precipitation, the phase is captured by most models (except few), but the amplitude is overestimated in all models. In the mid-intensity precipitation range (10–80 mm day−1), most of the models overestimate the probability of occurrence and show large intermodel differences. Most of the models fail to simulate the spatial distribution of the trend in Tas and precipitation. As compared to many individual models, the biases are noted to reduce when using multimodel means (MMMs); however, the MMMs also failed to capture the observed trends in both Tas and precipitation. Many models still struggle to capture the large-scale phenomena, such as the location and intensity of upper-level Asian anticyclone and middle troposphere temperature maximum over the HTP, which have large implications on the HTP as well as the Indian summer monsoon. The results show that none of the models capture all features of the HTP monsoon, and hence, further improvement in the parameterization schemes and resolution is required to gain more confidence in the projection of HTP climate using these models. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-driven approach is used to investigate the presence of spatial differences in the dynamic linkage between temperature and atmospheric carbon dioxide concentrations. This linkage seems to be latitude dependent. The main findings of the study are as follows. In the latitude belts surrounding the equator (0°− 24° N and 0°− 24° S), the link seems very similar. On the opposite, the patterns of the temperature CO2 link in the Arctic is very distant from those concerning the equatorial regions and other latitude bands in the South Hemisphere. This big distance is consistent with the so-called Arctic amplification phenomenon. Further, it is important to underline that this observational data-based analysis provides an independent statistical confirmation of the results from global circulation modelling. [ABSTRACT FROM AUTHOR]

The bioclimatic analysis in the context of urban environment and tourism activity is relatively new in Romania and highly important for the development of tourism. In the present study, the evidence of increasing air temperature by the Temperature-Humidity Index (THI) was conducted in the Focşani City, Romania, within the period of 17 years (2001–2017). Bioclimatic conditions were defined for this area by the relationship between air temperature, relative humidity, and THI. Tourism activities, such as nights spent in the city and arrivals of tourists in accommodations, were associated with the THI for the periods 2001–2016. As the THI values increased, higher tourist activity was recorded as measured by the overnight stays and arrivals in accommodations. The results of the bioclimatological analysis further revealed a high discomfort by the low winter temperatures and the high summer heat. The highest THI value, recorded in this period, was 38.5 °C in August 2017, while the lowest was − 6.8 °C, in January 2013. The bioclimatic comfort positively correlated with an increase in the number of arrivals and nights spent, in summer and in early autumn. In this paper, the analysis of THI in Focşani City (Romania) was used as a research example, to show that analysis of extreme temperatures can improve the future characterization of climatic events, its variability, and spatial pattern. Higuchi Dimension (DH) was used for additional assessment of THI, in order to better define its annual and monthly THI complexity. The innovative results consist of using the DH to determine the degree of complexity of THI oscillations and their impact on tourism activity. This study reports tools that efficiently define periods when conditions are usually unfavorable for tourism. This information facilitates the decision to increase the tourist offer for this period and thus may help decision-makers in the management of regional tourism. This can be the basis for future studies on the prevention of negative effects caused by the extreme weather conditions. [ABSTRACT FROM AUTHOR]

The paper discusses temporal changes in the configuration of vertical climatic belts in the Tatra Mountains as a result of current climate change. Meteorological stations are scarce in the Tatra Mountains; therefore, we modelled decadal air temperatures using existing data from 20 meteorological stations and the relationship between air temperature and altitude. Air temperature was modelled separately for northern and southern slopes and for convex and concave landforms. Decadal air temperatures were additionally used to delineate five climatic belts previously distinguished by Hess on the basis of threshold values of annual air temperature. The spatial extent and location of the borderline isotherms of 6, 4, 2, 0, and − 2 °C for four decades, including 1951–1960, 1981–1990, 1991–2000, and 2001–2010, were compared. Significant warming in the Tatra Mountains, uniform in the vertical profile, started at the beginning of the 1980s and led to clear changes in the extent and location of the vertical climatic belts delineated on the basis of annual air temperature. The uphill shift of the borderline isotherms was more prominent on southern than on northern slopes. The highest rate of changes in the extent of the climatic belts was found above the isotherm of 0 °C (moderately cold and cold belts). The cold belt dramatically diminished in extent over the research period. [ABSTRACT FROM AUTHOR]

In this paper, the dynamics of the structure of the rain profile as related to the zero-degree isotherm height and the implications for attenuation prediction along the Earth-space propagation links at locations in Nigeria, a tropical region, and South Africa, a subtropical region, are presented. Five-year (January 2010-December 2014) precipitation data on board the Tropical Rainfall Measuring Mission (TRMM) satellite have been analyzed over some selected locations in the two regions. The influences of the zero-degree isotherm height on some observed weather parameters are also discussed. The result on the influence of air temperature on rain height hr shows a significant increase in the tropical environment as compared with those in the subtropics. However, when hr results are compared with those obtained using rain height as recommended by the International Telecommunication Union (ITU), there is a significant difference at the 0.01% unavailability of the signal in a year particularly at higher frequencies. Further comparison with the slant path attenuation at 0.01% unavailability of the signal in a year shows a slight deviation (between 1.04 and 2.13 dB) in rain height than those acquired using the measured rain height in the tropical locations. Nevertheless, the result is slightly less than those obtained using the measured rain height in the subtropical locations with the differences in dB between − 0.49 and − 1.18. The overall results will be useful for estimating the link budgeting for digital radio satellite broadcasting. It will also be applicable for radar propagation systems at higher-frequency bands in Nigeria and South Africa. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the homogenised mean maximum ( T) and minimum ( T) temperatures. The data used in the analysis were collected at eight stations in the Autonomous Province of Vojvodina (Serbia) during the 1949-2008 period. The trends obtained from the slopes of the regression lines using the least square method show 0.9 °C/60 years for T and 1.1 °C/60 years for T; the non-parametric Mann-Kendall test was used to determine the statistically significant increasing trends of these two extreme parameters. In this paper, we analyse the influence of the Vangengeim-Girs classification of atmospheric circulation on the T and T trends in the Autonomous Province of Vojvodina (Serbia) using linear and quadratic models based on the least square method. Linear stepwise regression and the forward method reveal the highest dependence of T and T when the W or E circulation types are included in the model. Non-linear models show a greater contribution of T and T at W, E and C circulation types, respectively. The correction of the variance contribution of quadratic models ranges from approximately 16 to 44 % for T and 32 to 38 % for T. [ABSTRACT FROM AUTHOR]

A basic issue that arises when analysing data bases from weather networks is the correlation system that characterizes the set of weather stations. Some statistical models being used for simulating temperature and precipitation or estimating missing data often exploit the Pearson's correlation coefficient, whereby a selection of predictors is carried out. In this paper, a specific analysis was made to understand the relationship between the distances (between the stations) and the correlation structure (of the network) and to assess the evolution of the stations ranking over the time from the network establishment, given that they were ranked on the basis of their correlation coefficient values with a target station. This study was first carried out over the whole of the Veneto region in Northeast Italy, and subsequently, it was repeated, subdividing the area into three main climatic zones: mountain, plain and coast. The variables that are involved in this study are the following: daily precipitation and daily maximum, mean and minimum temperature. Generally, the correlation coefficients of the database of precipitation are, on average, inversely proportional to the mean distances from the target station. Considering that the same behaviour was not detected on analysing the temperature database, the main results of this work can be summarized as follows: (1) the most correlated stations of precipitation are generally closer to a target station than the most correlated stations of temperature (entire area); (2) starting from 5.5 years after the network was established, the temperature variable is characterized by a high stability (over time) of the correlation rankings, up to a wide radius from the target station; (3) this trend is not so clear in precipitation data. However, when taking into account the first result, (4) generally, the most correlated stations are placed within the radius of stability, more frequently so for precipitation than for temperature. [ABSTRACT FROM AUTHOR]

Characteristics and causes of global warming hiatus (GWH) phenomenon have received much attention in recent years. Monthly mean data of land surface air maximum temperature (Tmax), minimum temperature (Tmin), and mean temperature (Tmean) of 118 national stations since 1951 in Northeast China are used in this paper to analyze the changes of land surface air temperature in recent 64 years with an emphasis on the GWH period. The results show that (1) from 1951 to 2014, the warming trends of Tmax, Tmin, and Tmean are 0.20, 0.42, and 0.34 °C/decade respectively for the whole area, with the warming rate of Tmin about two times of Tmax, and the upward trend of Tmean obviously higher than mainland China and global averages; (2) in the period 1998-2014, the annual mean temperature consistently exhibits a cooling phenomenon in Northeast China, and the trends of Tmax, Tmin, and Tmean are −0.36, −0.14, and −0.28 °C/decade respectively; (3) in the GWH period, seasonal mean cooling mainly occurs in northern winter (DJF) and spring (MAM), but northern summer (JJA) and autumn (SON) still experience a warming, implying that the annual mean temperature decrease is controlled by the remarkable cooling of winter and spring; (4) compared to the global and mainland China averages, the hiatus phenomenon is more evident in Northeast China, and the cooling trends are more obvious in the cold season; (5) the Northeast China cooling trend occurs under the circulation background of the negative phase Arctic Oscillation (AO), and it is also closely related to strengthening of the Siberia High (SH) and the East Asian Trough (EAT), and the stronger East Asian winter monsoon (EAWM) over the GWH period. [ABSTRACT FROM AUTHOR]

In this paper, robust statistical methods (including the climatic tendency ratio, inverse distance weight (IDW), and Mann-Kendall’s non-parametric statistical tests) were applied to analyze the annual sequences of meteorological data for the period 1960-2014. The results indicated that the mean annual air temperature in the Pan-Hexi and the surrounding regions has increased by 0.9 °C. Over the 55-year period of study, this represented a warming rate that was significantly higher than the contemporaneous warming rate in China and the rest of the world. The warming trend in northeastern Tibet was the most pronounced. Here, the mean warming rate exhibited an increasing trend from the mid-late 1980s, and the cold season-dominated warming changed to year-round warming. The rise in the amplitude of the mean minimum temperature was significant, and the variation in the amplitude of the extreme minimum temperature was greater than that of the extreme maximum temperature, exhibiting a variation period of approximately 10 years. Since 1960, the mean precipitation in the study region and the surrounding areas first increased, and then decreased, and then increased again rapidly from 2007. Precipitation and relative humidity trend showed a 13- and a 10-year variation period, respectively. Crop evapotranspiration exhibited a decreasing trend, but from 1993 onwards, there was an increasing trend and a 9-year variation period was also notable based on observed datasets. In the most recent 55 years (1960-2014), the sunshine duration and mean wind velocity in the study area first showed a decreasing trend followed by an increasing trend, and then it exhibited a decreasing trend from east to west of the study region. The temporal variations in the climatic elements were similar to those of China, suggesting that the climate change signal in the study region over the last 55 years appeared to be well correlated with the global climate change signal. [ABSTRACT FROM AUTHOR]

Based on climatic data from 18 stations on the southern slopes of the eastern Himalayas in Bhutan for the period from 1996 to 2009, this paper investigates monthly characteristics of the near-surface air temperature lapse rate (TLR). The station elevations used in this study range from 300 to 2760 m a. s. l. TLRs were evaluated using a linear regression model. The monthly values of maximum TLRs were always smaller than those of the minimum TLRs, which is in contrast to results from the surrounding mountainous regions. In this study, annual patterns of TLRs were somewhat consistent, particularly in the summer; during the other seasons, patterns contrasted to results from the southeastern Tibetan Plateau (China) and were almost comparable to results from Nepal. The shallowest observed values for TLRs in summer are due to intense latent heating at the higher elevation, associated with water vapor condensation from moist convection and evapotranspiration, and decreasing sensible heating at lower elevation, due to heavier rainfall, cloud, and forest cover. When compared to summer, the steeper TLRs in the non-monsoon season are due to sensible heating at the lower elevations, corresponding to dry and clear weather seasons, as well as increasing cooling at higher elevations, particularly in winter due to snow and cloud cover. Owing to lower albedo and higher aerodynamic roughness of forested areas, the TLRs were considerably reduced in daytime because of the dissipation of sensible heat to the atmospheric boundary layer. The distinct variation in diurnal TLR range is due to the diurnal variation in net radiation associated with reduced turbulent heating in the day and increased turbulent heating in the night, in addition to the effect of moisture and cloud cover. The shallower values of TLRs in this study when compared with the surrounding mountainous regions are due to high humidity, as well as the differing elevations and local climates. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of two simulations by regional climate model (RCM) ALADIN-Climate/CZ with different sizes of integration domain and their driving simulation of global climate model (GCM) ARPÉGE-Climat over central Europe during the period 1961-2010. After a brief evaluation of seasonal means of air temperature characteristics (daily mean, maximum and minimum temperature) and their variability, we focus on the ability of the simulations to represent observed changes in seasonal mean air temperature and its variability. We distinguish between intrinsic and trend-induced variability. Moreover, the dependence on the size of the integration domain and the improvement brought about by the nested RCM compared to the driving GCM are analysed. Both the evaluation of temperature means and variability and the evaluation of their changes confirm that RCM simulation on a larger domain often differs more considerably from driving GCM than simulation on a smaller domain. The larger domain simulation usually produces smaller biases for mean temperature but has worse agreement with observations in terms of variability. Further, the areas with the greatest discrepancies are often in the south-eastern region. In addition, noticeable differences between RCM and GCM simulations mostly emerge for the Alpine region and eastern regions. A possible explanation is the complex terrain and larger distance from domain boundaries. Moreover, it was analysed whether removing of the linear trend enhances the representation of changes in variability. This was not generally confirmed. Only in summer the simulations underestimated the observed increase in intrinsic variability less considerably than for total variability. However, biases were still considerable in many cases. [ABSTRACT FROM AUTHOR]

This paper describes different ways of reducing urban air temperature and their results in two cities: Campinas, Brazil-a warm temperate climate with a dry winter and hot summer (Cwa), and Mendoza, Argentina-a desert climate with cold steppe (BWk). A high-resolution microclimate modeling system-ENVI-met 3.1-was used to evaluate the thermal performance of an urban canyon in each city. A total of 18 scenarios were simulated including changes in the surface albedo, vegetation percentage, and the H/W aspect ratio of the urban canyons. These results revealed the same trend in behavior for each of the combinations of strategies evaluated in both cities. Nevertheless, these strategies produce a greater temperature reduction in the warm temperate climate (Cwa). Increasing the vegetation percentage reduces air temperatures and mean radiant temperatures in all scenarios. In addition, there is a greater decrease of urban temperature with the vegetation increase when the H/W aspect ratio is lower. Also, applying low albedo on vertical surfaces and high albedo on horizontal surfaces is successful in reducing air temperatures without raising the mean radiant temperature. The best combination of strategies-60 % of vegetation, low albedos on walls and high albedos on pavements and roofs, and 1.5 H/W-could reduce air temperatures up to 6.4 °C in Campinas and 3.5 °C in Mendoza. [ABSTRACT FROM AUTHOR]

Meteorological and oceanographic data from ships of opportunity are the largest contributor to the world’s ocean surface database and thus are extensively used to estimate the change in climatic properties over the world’s oceans during the previous 150 years. The importance of these data for climate change studies underscores the need to fully understand the error associated with averages of these data. The sampling error problem is especially acute for ship data due to the fact that ships are moving platforms and, thus, report observations from constantly varying locations with time. This paper develops a theoretical framework for assessing the averaged sampling error associated with monthly, 1°×1° latitude-longitude box averaged ship data. It should be noted that the time-space distribution of ships within the averaging domain strongly affects the sampling error. This is shown in our derivation. The framework developed here can be used to improve upon existing methods for estimating the sampling error associated with three-dimensional box averages of meteorological and oceanographic data obtained from ship records. The framework is complimentary to existing methods of assessing biases and random error due to instrumentation, recording, etc. It is demonstrated mathematically that the uncertainty due to incomplete sampling is primarily a trade off between of the number of observations and their relative locations within the box as well as the inherent time-space correlation structure of the variable of interest. This work differs from other studies in that the three-dimensional interdependence of data is taken into account in deriving an expression for the sampling error. [ABSTRACT FROM AUTHOR]

The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble. [ABSTRACT FROM AUTHOR]

In this paper, the surface and lower tropospheric temperature trends in Greece and their relationship to the atmospheric circulation for the period 1955-2013 were examined, updating the study of Feidas et al. (Theor Appl Climatol 79:185-208, 2004) for data observed during the 12-year period 2002-2013. The trend analysis is based on a combination of three statistical tests. The trends are now examined for all the seasonal time series, new atmospheric circulation indices were added in the analysis, and maps with the spatial distribution of correlation between air temperature and atmospheric circulation were constructed and analysed. The series updated to 2013 for 18 stations reveal a clearer positive trend than that found for the period 1955-2001 on both the annual and the seasonal timescales. The warming signal detected only in summer in the study of Feidas et al. (Theor Appl Climatol 79:185-208, 2004) has now intensified and spread in other seasons. This warming appears to be mainly caused by the very high temperatures in the last decade (after 2004) of the record. At the national scale, there is now a match between surface temperature trends in Greece and Northern Hemisphere (NH) but only for summer, spring and annual time series, which are the only time series presenting a statistically significant warming trend in Greece. Satellite-induced lower tropospheric temperatures now show a statistically significant tropospheric temperature warming trend for the period 1979-2013, for both areas (Greece and NH). Lower tropospheric and surface air temperatures for the same period (1979-2013) show a very good agreement, with differences only in winter and summer for Greece. The influence of atmospheric circulation on the temperature variability in Greece was also examined using two more circulation indices: the Eastern Mediterranean Pattern Index (EMPI) and the North-Sea Caspian Pattern Index (NCPI). EMPI and especially NCPI explain better now the temperature variance in Greece, principally in winter. This connection, however, is not only developed during winter, as expected, but is also present for annual and other seasonal temperatures. [ABSTRACT FROM AUTHOR]