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In this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data. [ABSTRACT FROM AUTHOR]

The onset of periods with very high or low air temperatures has aroused general interest for a long time, which is understandable since it has many dangerous effects directly affecting humans. Such perilous climatic phenomena include exceptionally cold and exceptionally warm months, which this study investigates for all Europe and for its five physico‐geographic regions over the 190‐year period of 1831–2020. Therefore, the research in this paper includes two periods characteristic of the history of climate – the late Little Ice Age (LIA) and present‐day warming. The studies are based on average monthly air temperature values from 40 weather stations in Europe. In this paper, exceptionally cold months (ECMs) or exceptionally warm months (EWMs) are considered to have occurred when the average air temperature at a station differed from the respective long‐term average by at least two standard deviations. The highlights of the study include the identification of a drop in the number of ECMs by 20 over the entire 190‐year period, and a highly statistically significant increase in EWMs by 44 between 1980 and 2020. These changes proceeded with different intensities from one physico‐geographic region of Europe to another. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the relationships between soil moisture, cloud cover, solar radiation, air temperature and humidity, and precipitation during the warm half of the year in East‐Central Europe over the years 1971–2020. The temporal and spatial variability of these meteorological elements is presented in association with the occurrence of anticyclonic blocking events over the study area. It demonstrates that changes in soil moisture in East‐Central Europe point to the combined influence of many meteorological factors resulting from the atmospheric circulation, and are an indicator of the comprehensive relationships among those factors. The main factors affecting soil water content are precipitation and evapotranspiration, which in turn depend on air humidity, cloudiness, intensity of solar radiation and air temperature. The increase in the frequency and duration of sequences of days with blocking events in East‐Central Europe has contributed to an increased probability of longer periods with soil moisture negative anomalies. [ABSTRACT FROM AUTHOR]

This paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change. [ABSTRACT FROM AUTHOR]

Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30‐year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS‐TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30‐year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets. [ABSTRACT FROM AUTHOR]

Digital databases of monthly means of daily extreme, minimum (Tn) and maximum (Tx) air temperature collected in Ukraine were created. The databases contain temperature records of 371/340 (Tn/Tx) climatological stations which recorded data on the territory of Ukraine anytime during 1881–2015. The significant part of the data sets (~48/47%) was obtained after conducting a data rescue process, recovering historical paper records. Due to a great deal of missing data during World War I and II, homogenization was possible to perform only for 178 time series covering the period of 1946–2015. A homogenization procedure, conducted by means of the HOMER software, revealed 323 break points for minimum temperature (~1.8 breaks per station) and 310 for maximum temperature (~1.7 breaks per station). Approximately 37/33% (Tn/Tx) of the breaks can be explained by metadata. Shift amplitudes in Tn time series ranged between −1.18 and 1.71 °C, while for maximum temperature the range was slightly less from −0.99 to 1.15 °C. For both temperatures, mean values of shift amplitudes were near zero (−0.04/−0.05 °C). To validate the homogenization results, several statistical procedures were performed to compare inhomogeneous and homogeneous time series. The conducted statistical calculations provide the proofs that inhomogeneity of the Ukrainian extreme air temperature data should be taken into account when analysing regional climate change and variability. Detection of recent climate changes is a problem that is mainly solved based on analysis of long time series of observed/measured meteorological variables such as air temperature and precipitation. However, such the time series are usually affected by outliers and artificial abrupt shifts and/or gradual trends, which are not related to climate but reflect changes in observing practices and/or the surrounding environment. Furthermore, an amplitude of the shifts is frequently the same order of magnitude as an amplitude of a climate signal. Therefore, in order to conduct climatological studies of high quality and draw reliable conclusions it is necessary to use quality controlled and homogeneous data. In this paper, homogeneity of long monthly mean of daily extreme (minimum and maximum) air temperature time series collected in Ukraine has been investigated by means of the HOMER software. Revealed shifts and their influence on the time series have been studied in details. [ABSTRACT FROM AUTHOR]

Monitoring and quantifying high‐latitude atmospheric circulation variability and trends are important towards understanding the anatomy of extreme events and constraining their probabilities under continued Arctic change. The greater Alaska region stands out as one region of enhanced warming and environmental changes over the Arctic amplification era, which has shown proclivity to extreme events and speaks to the need to consistently monitor overlying atmospheric variability. In this paper, we describe the creation and analysis of an updated, daily mid‐tropospheric Alaska Blocking Index (ABI) time series from 1948 to 2020. Sensitivity testing and index modifications from the initial dataset are discussed, and the new ABI is evaluated over the full period and subperiods. Additionally, we assess the role of the ABI as a regional climate indicator by analysing its variability against surface air temperature and precipitation from 13 climate divisions across Alaska as well as broader‐scale hemispheric temperatures. Months of the highest ABI means mainly transpired in the most recent climatological period, 1991–2020, and most notably during the spring and autumn transition seasons, reflecting the nonstationary nature of the jet stream magnitude through time atop Alaska. ABI trends are positive across all months, seasons, and annually only in the 1991–2020 period. Contemporaneous ABI values are strongly and positively correlated with air temperature across all Alaskan climate divisions during the warm season, but negatively correlated with winter temperatures over southeastern Alaska. Meanwhile, the ABI is significantly anticorrelated with summer precipitation over Interior and southern Alaska, but only over southeastern Alaska in other seasons. The ABI is statistically differentiated from the primary modes of atmospheric variability as shown by its generally weak correlations with the Arctic Dipole and the Arctic Oscillation (r ≤ 0.40). [ABSTRACT FROM AUTHOR]

The rise in the Earth's global average surface temperature and the increase of extreme weather events have been the focus of scientific discussion in the last decades. Extreme heat combined with other environmental extremes, like high concentrations of air pollution, may induce health problems—especially in socioeconomically vulnerable populations. Spring in South America requires particular attention due to its association with hot, dry weather and air pollution in most parts of the continent. This paper intends to better comprehend the behaviour of heatwaves during the austral winter–spring transition. We propose identifying the spatial coverage, frequency and intensity of heatwaves in homogeneous areas of maximum air temperature near the surface (Tmax). We employed cluster analyses during the period between July and October for 41 years (1979–2019) through the ERA5 reanalysis. Homogeneous Tmax areas in South America were defined by cluster analyses that indicated three homogeneous Tmax regions, as follows: a larger area of the continent including the tropical region (Area 1), eastern and southeastern South America (Area 2) and southernmost and steep areas in the Andes (Area 3). The heatwave events identified via ERA5 reanalysis were classified according to their intensity (intense, moderate and weak events). Spatial frequency and trend analyses were also performed regarding the intensity and persistence of heatwave episodes. These methods allowed the identification of the behavioural aspect of heatwaves spanning the last four decades. The Mann–Kendall statistical test (MK) was applied in order to analyse the heatwave trend with a statistical significance level of 5%. A total of 191 heatwave episodes were identified. Of this total, 47.12, 35.60 and 17.28% of episodes occurred in Areas 2, 3 and 1, respectively. The hotter area extending from northeast to southwest in central South America stood out by its largest frequencies of intense heatwave episodes. Across the continent there was a significant increase in the intensity and persistence of heatwaves over the period of 1979 through to 2019. [ABSTRACT FROM AUTHOR]

This paper investigated the trends and variability in warmest daily maximum (TXx) and warmest daily minimum (TNx) air temperatures, and heatwave characteristics in Turkey in May during 1950–2020. It also analysed the climatological and synoptic meteorological evolutions of heatwave (HW) observed in May 2020. Trend analysis revealed significant increasing trends in TXx and TNx series and heatwave characteristics in May since 1950. Since the mid‐1980s, a continuously increasing trend in TXx and TNx series, and the number, frequency, and magnitude of the heatwaves were observed that accelerated with the mid‐1990s. The most severe heatwave in Turkey since 1950 in May was observed in 2020. The record‐breaking daily maximum (daily minimum) air temperatures were noted at 32 (23) of 96 stations in May 2020, mainly over Turkey's western and southern parts. Record‐high maximum (minimum) air temperature was 43.2°C (31.1°C) reached for the first time in May since 1950. The generalized extreme value model applied to the May TXx and TNx series revealed that the daily air temperatures in May 2020 exceeded 100 years' return periods at many stations of Turkey. This heatwave was linked to a regionalized strong anticyclonic blocking anomaly circulation and other associated atmospheric anomalies in May 2020. This also resulted in the centring of an unprecedented subsiding, calm, and stable warm air mass over Turkey. Adiabatic warming and drying developed very likely under subsidence control because of relatively weak circulation conditions. Such circumstances resulted in extremely hotter conditions in May 2020 compared to the long‐term averages. [ABSTRACT FROM AUTHOR]

There is growing interest in the international scientific community in characterizing climate variability in Antarctica because of the continent's fundamental role in regulating the world's climate. Researchers have intensively studied the Antarctic Peninsula since the warming that began in the mid‐1950s. This was followed by a subsequent cooling period over the last decades. For this paper, using the available data, we analysed the variability in surface air temperatures at five meteorological stations located on King George Island (KGI) (a subantarctic island that is part of the South Shetland Islands); we also investigated the relationships between the air temperatures and large‐scale atmospheric patterns from 1968 to 2019. In this study we found that summer temperatures are above 0°C from December to March and close to melting temperatures (extreme values) in spring and autumn; consequently, a small increase in temperature can have a significant impact on the cryosphere. The statistical analysis of the mean temperatures confirmed a trend toward cooling during the summer and in the mean monthly maximum temperatures over the 1990s at most of the weather stations whose data we analysed. Analysing the teleconnection patterns showed that the Southern Annular Mode (SAM) had strong, direct, and positive correlations during the autumn and less strong connections in spring, winter, and on an annual scale. Furthermore, we observed a lesser influence of El Niño‐Southern Oscillation (ENSO). [ABSTRACT FROM AUTHOR]

Air temperature has exhibited a clear positive trend over the past several decades throughout the arctic, including Alaska. Other variables, such as precipitation, have much more uncertain trends due to inhomogeneities in measurement and high internal variability. The use of linear regression to analyse precipitation in Alaska has resulted in often contradictory results. This paper proposes the use of Bayesian models such as the R package Rbeast to allow for the more nuanced analysis. The examples given in this paper show how Bayesian analysis can be used to detect subtle changes and better constrain the disagreement between data sources. Applied to gridded data, Bayesian analysis shows how precipitation has changed overtime across Alaska. Change has accelerated over the past decade, but only precipitation increase on the North Slope can be assigned high confidence. Overall, this analysis highlights how Bayesian techniques may be uniquely useful to climate research in regions with heterogeneous data sources and substantial internal variability. [ABSTRACT FROM AUTHOR]

This study investigates some of the uncertainties sources associated with the pseudo global warming (PGW) approach which was employed to project future patterns of tropical cyclones (TCs) over the Arabian Sea (AS). First, the climate variables controlling the patterns of tropical cyclones were extracted from reanalysis datasets of ERA5, ERAI, CFSR, and NCEP/NCAR. Then, each dataset was evaluated against long‐term measurements to identify the best‐performing reanalysis dataset. ERA5 showed the best performance for most of the variables. Outputs of 20 CMIP5 global climate models (GCMs) were then evaluated against the ERA5 data resulting in an ensemble of the best performing GCMs. A PGW framework was then used to project the changes in patterns of three significant historical cyclones: Gonu, Phet, and Ashobaa. In doing so, the signals of future climate variables were extracted from the GCMs ensemble to modify the initial and boundary conditions of the WRF model which was previously tuned for reproducing the historical TCs. Different tests were conducted to address the sources of uncertainty in the PGW approach, including the selection of the climate variables contributing to the computation of the signals, the selection of GCMs, and the spatial variation of signals. A considerable sensitivity of the projected track and intensity of TCs to the choice of GCMs was observed, acknowledging the importance of GCMs evaluation before calculating the signals. Moreover, it was found that among all variables, signals of sea surface temperature and air temperature have major effects on the cyclone's track and intensity. Apart from that, when the signals were applied to the domain of the WRF model uniformly, compared to applying spatially varying signals, different tracks and intensities for future TCs were also observed. Overall, the findings of this paper challenge the reliability of the projected changes in TCs patterns obtained from PGW. [ABSTRACT FROM AUTHOR]

This paper uses monthly surface air temperature (SAT) data from the Climatic Research Unit dataset and the Coupled Model Intercomparison Project phase 6 multimodel ensemble simulations to investigate the variations in the annual‐mean and four seasons SAT across the Eurasian and North Africa landmasses and the possible causes. The SAT over multiple timescales has significant seasonal and regional differences. The trend, variance of decadal variation (DV), and variance of interannual variability (IV) show that the differences between Siberia and mid‐low latitudes are much larger in winter and spring than in summer and autumn. The SAT exhibits a robust warming trend over Siberia in winter (0.21–0.42°C·decade−1) than over mid–low latitudes in summer (<0.07°C·decade−1). The cause is the combined diversity effects of greenhouse gases (GHG) and anthropogenic aerosols (AA) in four seasons over different regions. The warming trend of Siberia in winter is affected by the combination of strong warming effect by GHG and the weak cooling effect by AA, and the trend of mid–low latitudes in summer is opposite. The leading modes of the DV show a consistent spatial pattern over the whole Eurasia in four seasons while the IV show a meridional dipole pattern across 40°N except in summer. The internal climate variability is the main driver of the SAT IV and DV, but the effect of anthropogenic activity and natural forcing may reinforce the IV and DV in some regions. The combined role of GHG and AA forcings can significantly enlarge the DV in many parts of Eurasia, and the AA and natural forcing have significant effect on the IV in the high latitudes. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the effect of urbanization on the surface air temperature (SAT) from 1951 to 2018 in the Philippines. The daily minimum temperature (Tmin) and daily maximum temperature (Tmax) records from 34 meteorological stations were used to derive extreme temperature indices. These stations were then classified as urban or rural based on satellite night‐lights. The results showed a significant difference in the SAT trends between urban and rural stations, indicative of the effect of urbanization in the country. Larger and more significant warming trends were observed in indices related to Tmin than those related to Tmax. In particular, the effects of urbanization were significant in the annual index series of Tmin, diurnal temperature range, minimum Tmin, percentage of days when Tmin was less than the 10th percentile (TN10p), percentage of days when Tmin was greater than 90th percentile (TN90p), and the number of coldest nights. The effects of urbanization were not as clear on the index series of maximum Tmax (TXx), minimum Tmax (TXn), percentage of days when Tmax was less than 10th percentile (TX10p), and the number of hottest days. The effects of urbanization on the annual series of extreme temperature indices were statistically significant at the 95% confidence level, with the exception of Tmax, TXn, TXx, TX10p, and the number of hottest days. Further analysis revealed that the effect of urbanization was the greatest during the DJF (December–January–February) season. These findings serve as a baseline study that focuses on the countrywide effect of urbanization on SAT trends in the Philippines. [ABSTRACT FROM AUTHOR]

Arctic glaciers have been, and are predicted to remain, an important sea‐level rise contributor over the 20th and 21st centuries. However, multi‐annual observations of their basic meteorological characteristics, which drive melt processes, are very sparse, contributing to uncertainties in sea‐level rise projections. This paper presents a methodology to process and analyse 6 years (September 2011–September 2017) of glacier meteorology data collected by an automatic weather station on the ablation zone of a small valley glacier in Svalbard (~79°N). The study focuses on the microclimate of the study glacier and its differences with that of large glaciers in the region, namely slightly increased summer air temperature with steep near‐surface lapse rates, frequently superadiabatic, reduced wind speed and incoming shortwave radiation flux. These differences are likely related to the adjacent complex relief and non‐ice‐covered surfaces within the basin of the study glacier and, as such, pose a challenge for accurate simulation of alpine glacier microclimates in regional melt assessments in the Arctic. The processing routine applied in this paper might serve as a reference for future similar studies. [ABSTRACT FROM AUTHOR]

This paper presents a new dataset of quality controlled and homogenized monthly mean air temperature for Belgium. After in‐depth data quality control, monthly maximum and minimum temperature time series have been homogenized with the HOMER software on the basis of available metadata. Homogenization results are provided for 61 series over the period 1954–2015 and for 16 series starting before 1931 including eight covering the full time period 1880–2015. The new dataset of monthly homogenized series allows to make a robust analysis of the temperature evolution in Belgium at a local scale from 1880 to nowadays. Trends in maximum and minimum temperatures indicate on average a similar and statistically significant warming of about 0.3 °C/decade from 1954 and of about 0.15 °C/decade from 1880 with moderate spatial variations. An abrupt warming is observed at the end of the 1980s, especially in summer and spring seasons. This paper presents a new dataset of quality controlled and homogenized monthly mean air temperature for Belgium. It allows to make, for the first time, a robust analysis of the temperature evolution in Belgium at a local scale from 1880 to nowadays. Before this analysis, the quality control and the homogenization of the data are described in detail. [ABSTRACT FROM AUTHOR]

This paper investigates the elevation‐warming relationships across the Carpathian Mountains, using the 0.1° × 0.1° gridded daily air temperature dataset developed within the CARPATCLIM project, in order to understand the spatial patterns of annual and seasonal temperature trends and test the hypothesis of enhanced warming with elevation. Temperature trend and elevation‐warming analyses were conducted over the 50 years of the dataset (1961–2010). The vertical variations of five key isotherms relevant for the development of cold‐climate weathering, presence of permafrost (−2°C, 0°C, +2°C, +3°C) and spread of forest vegetation (July +10°C) were also examined. The Carpathian Mountains are under visible annual and seasonal warming (stronger in the summer and winter), both daytime and nighttime. The warming process shows a great spatial inhomogeneity, but is more pronounced in the lowlands of the region, generally below 1,000–1,200 m. The correlations between elevation and warming rates across the Carpathians have been found both to be positive and negative, while mostly significant at the 5% level. Evidence of enhanced warming with elevation was found to be related to the minimum temperature increase mostly across the NW and SW Carpathians, at both annual and seasonal scales (winter, summer and spring). Less prominent EDW signals have been identified in the NE, E and S Carpathians (summer), as well as in the NE Carpathians (spring). Parts of the detected temperature trends and EDW signals have been considered to be an effect of the changing atmospheric circulation (i.e. westerly intensification especially in the winter), although other mechanisms and processes could be involved (e.g. snow‐albedo feedbacks in the spring). These results could serve as a reference for further investigations of climate warming effects across the Carpathian Mountains region (e.g. ecological and geomorphic), especially in relation to the augmented summer heat stress (e.g. more frequent heatwaves) and milder winters (e.g. less freezing days). [ABSTRACT FROM AUTHOR]

Climate trends estimated using historical radiosounding time series may be significantly affected by the choice of the regression method to use, as well as by a subsampling of the dataset often adopted in specific applications. These are contributions to the uncertainty of trend estimations, which have been quantified in literature, although on specific pairs of regression methods, and in the not very recent past characterized by smaller trends in temperature than those observed over the last two decades. This paper investigates the sensitivity of trend estimations to four linear regression methods (parametric and nonparametric) and to the artificial subsampling of the same dataset using historical radiosounding time series from 1978 onwards, available in the version 2 of the Integrated Global Radiosonde Archive (IGRA). Results show that long‐term decadal trends may have not negligible uncertainties related to the choice of the regression method, the percentage of data available, the amount of missing data and the number of stations selected in the dataset. The choice of the regression methods increases uncertainties in the decadal trends ranging from −0.10 to −0.01 K⋅da–1 for temperature in the lower stratosphere at 100 hPa and from 0.2 to 0.8% da–1 for relative humidity (RH) in the middle troposphere at 300 hPa. Differences can also increase up to 0.4 K⋅da–1 at 300 hPa when the amount of missing data exceeds 50% of the original dataset for temperature, while for RH, significant differences are observed in the lower troposphere at 925 hPa for almost all datasets. Finally, subsampling effects on trend estimation are quantified by artificially reducing the size of the IGRA dataset: Results show that subsampling effects on trend estimations when at least 60 stations, up to 76% of data available, are considered for temperature and at least 40 stations for RH. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) is one of the most prominent patterns of atmospheric circulation variability in the Northern Hemisphere. Its climate impacts have been identified for regions extending from North America to northern Asia and the Middle East, with implications for sub‐seasonal variability and, in some cases, meteorological trends over multiple years to decades. The NAO itself, however, is the combined expression of two pressure centers: the Icelandic Low and the Azores High. Previous work has shown that when these "centers of action" (CoA) are separated out and studied they can often provide better insight and explanatory power than the NAO does on its own. Here, we analyse the influence that the CoA have on daily minimum air temperature in winter and daily maximum temperature in summer in eastern North America and Europe. The paper has two goals: (a) to characterize CoA associations with temperature on a range of timescales, including sub‐seasonal analyses that reflect short‐term dynamics of the CoA and that are frequently ignored in NAO teleconnection studies, and (b) to evaluate NAO and CoA influence on temperature across seasons (winter and summer) and in upstream and downstream regions (eastern North America and Europe) using a consistent combination of datasets and methods, in order to contextualize results from prior studies that targeted a single region or season. We find that associations with summertime daily maximum and wintertime daily minimum temperatures are strongest for seasonal averages, but select CoA indices inform explanation of temperature variability at subseasonal timescales. The strength of the Icelandic Low and the location of both CoA show strong associations in winter, while in summer the locations of the CoA show temperature associations that are distinct from and sometimes stronger than those found for CoA strength or NAO indices. [ABSTRACT FROM AUTHOR]

The spatial distribution of surface air temperatures is essential for understanding and modelling high‐relief environments. Good estimations of the surface temperature lapse rate (STLR) and the 0°C isotherm height (H0) are fundamental for hydrological modelling in mountainous basins. Although STLR changes in space and time, it is typically assumed to be constant leading to errors in the estimation of direct‐runoff volumes and flash‐floods risk assessment. This paper characterizes daily and seasonal temporal variations of the in‐situ STLR and H0 over the western slope of the subtropical Andes (central Chile). We use temperature data collected during 2 years every 10 min by a 16 sensors network in a small catchment with elevations ranging between 700 and 3,250 m. The catchment drains directly into Santiago, the Chilean capital with more than seven million inhabitants. Resulting values are compared against those obtained using off‐site, operational data sets. Significant intra‐ and inter‐day variations of the in‐situ STLR were found, likely reflecting changes in the low‐level temperature inversion during dry conditions. The annual average in‐situ STLR is −5.9°C/km during wet‐weather conditions. Furthermore, STLR and H0 estimations using off‐site gauges are extremely sensitive to the existence of gauging stations at high elevations. [ABSTRACT FROM AUTHOR]

This paper quantifies the potential benefit of limiting the global warming to 1.5°C above the preindustrial level on the high‐latitude climate of the Southern Hemisphere compared to 2.0°C and above. A set of Coupled Model Intercomparison Project Phase 5 global climate model experiments is used in order to determine the mean projected changes in surface air temperature, sea level pressure, surface winds and sea ice distribution over and around Antarctica. Alongside, changes in selected variables related to extremes (such as the annual number of extremely cold mornings and extremely cold days) are also quantified as a function of the Representative Concentration Pathway (RCP) considered reaching the given warming threshold. Physical mechanisms for the projected variations in sea ice are proposed, including surface air temperature, changes in low‐level air temperature advection and meridional winds. Results show that there would be significant differences in the atmospheric and sea ice variable changes depending on the warming threshold and RCP considered, and that indeed keeping the global warming under 1.5°C could have a noticeable impact particularly on the distribution of sea ice over the Southern Ocean. [ABSTRACT FROM AUTHOR]

The primary objective of the paper was the description of human‐biometeorological conditions during heat waves in Poland in the years 1966–2018. The description is based on the human‐biometeorological index Physiologically Equivalent Temperature (PET). The research was based on data obtained from the Institute of Meteorology and Water Management—National Research Institute for 30 stations in Poland. A heat wave was defined as a sequence of at least 3 days with a maximum daily air temperature of >30°C. The research revealed increasingly frequent occurrence of heat waves in the territory of Poland. The highest number of heat waves was recorded in the western and southern regions of the country, and the lowest in coastal stations. In a large majority of stations, the longest heat waves occurred at the turn of July and August 1994 and in the first half of August 2015. In the case of two longest heat waves, human‐biometeorological conditions were similar, although they were more strenuous during the heat wave in 1994. During that wave, considerably higher maximum PET values were recorded, but days with strong heat stress and extreme heat stress were also more frequent. [ABSTRACT FROM AUTHOR]

Two skilful 2‐month‐leading hybrid downscaling prediction schemes in October for winter surface air temperature (SAT) over China are proposed in this paper. The schemes are based on the year‐to‐year increment approach and the coupled climate patterns between the winter SAT over China and its predictors. Observed North Pacific sea surface temperature (SST) from the preceding July to September, Arctic sea ice concentration (SIC) in the preceding August and winter sea level pressure (SLP) over pan Eurasia from version 2 of the Climate Forecast System (CFSv2) are selected as the predictors based on the fundamental physics. Individual‐predictor schemes (IP‐schemes), that is, SLP‐scheme, SST‐scheme, SIC‐scheme, indicate that these predictors exhibit prediction skills in different regions. Multi‐predictor scheme I (MP‐schemeI) is developed by combining three predictors. However, MP‐schemeI shows limited skill in predicting SAT over Northeast China (NECTA), due to the limited skill of CFSv2 over the extratropics. Thus, MP‐schemeII is established, in which a hybrid downscaling model for NECTA is constructed. These two MP‐schemes have comparable prediction skill over China, but MP‐schemeII outperforms MP‐schemeI over NEC. The temporal (spatial) anomaly correlation coefficient (ACC) increases from 0.23 (0.15) in MP‐schemeI to 0.36 (0.21) in MP‐schemeII, and the ratio of the same sign of anomalous years (Anomalous‐RSS) improves from 39.1% to 56.5% over NEC. For the winter SAT over China, the MP‐schemes greatly enhance the prediction compared with CFSv2 outputs and the IP‐schemes. The temporal (spatial) ACC increases from 0.29 (0.05) in CFSv2 to 0.52 (0.29) in the MP‐schemes, and the station‐average root‐mean‐square error decreases by about 48.0% compared with CFSv2. Moreover, the RSS (Anomalous‐RSS) of the winter SAT over China is 55.9% (47.4%) in CFSv2 and 64.7% (63.2%) in the MP‐schemes. This indicates that the MP‐schemes perform better in predicting anomalous winters. [ABSTRACT FROM AUTHOR]

Daily and sub‐daily homogenization of climate variables have been intensively investigated in the last decades, but to the best of our knowledge, this is the first study on homogenization of hourly temperature in Romania. This paper describes the creation of a homogenized hourly air temperature data set at a country scale by combining data from four independent meteorological networks. The air temperature measurements for the period 2009 and 2017 were obtained from the following networks: Romanian National Meteorological Administration (ANM), National Network for Monitoring Air Quality (RNMCA), Regional Basic Synoptic Network (RBSN), and Meteorological Terminal Aviation Routine Weather Report network (METAR). The climatological limits, persistence, temporal variation (step test), and spatial consistency were the quality control tests used to isolate the errors due to malfunctioning of the temperature sensors, data coding or transmission. The Climatol homogenization method was successfully applied for identifying and correcting any suspicious values. The missing data were filled by considering the similarities between each station and the reference series. Comparing the output with the original data, it is apparent that the removal of the break points, correction and homogenization resulted in a new data set with statistical properties very similar to the raw data, but more reliable for climate research due to the increased homogeneity. Eventually, the procedure can be implemented in operational use for collecting more data from other networks. [ABSTRACT FROM AUTHOR]

This paper analyzes climate trends in satellite‐blended model reanalyses for the east Antilles islands of Guadeloupe, Dominica and Martinique (14–17°N, 62.5–60.5°W). Trends are studied for the dry winter (JFM) and wet summer (JAS) seasons, for parameters including: aerosol profile, tropospheric NO2 concentration, rainfall and hydrology, outgoing longwave radiation, potential evaporation, sea level, air and sea temperature and salinity in the upper ocean. Climate change emerges in the form of 2–4 and 6–8‐year surges in near‐surface temperature and mid‐tropospheric subsidence. While near‐surface specific humidity exhibits a rising trend especially in summer, the mid‐troposphere is drying. Coastal zones show little trend in rainfall, but cloud forests on the volcanic slopes are getting wetter. Faster rising trends are noted for sea surface height, and for sea temperatures in low salinity zones that affect tropical cyclone intensification. This work quantifies the spatial and temporal nature of climate variability in the east Antilles islands of Guadeloupe, Dominica and Martinique and advocates for increasing integration of mesoscale analysis into studies on local socio‐economic vulnerability. [ABSTRACT FROM AUTHOR]

This paper has examined the relative significance of uncertainty in future climate projections from a subset of the coupled model intercomparison project phase 5 (CMIP5) global climate models for the Prairie Provinces of western Canada. This was undertaken by determining: (a) the contribution of model and scenario uncertainty and natural variability to the total variance of these future projections, and (b) the timing of climate signal emergence from the background noise of natural climate variability. We examined future projections of mean temperature, precipitation and summer climate moisture index (CMI). In this region, natural climate variability plays an important role in future uncertainty until the end of this century, particularly for precipitation and to a lesser extent, summer CMI. Model uncertainty also contributes to total uncertainty for these variables throughout this century, while scenario uncertainty becomes more important towards the end of the century. For the region as a whole, significant climate change (i.e., signal/noise >2) occurs earliest for summer mean temperature, with median time of emergence around 2035 for the RCP8.5 radiative forcing scenario. Although the median precipitation signal emerges from the noise (i.e., signal/noise >1) around the 2070s in winter and the 2080s in spring, significant values do not occur in any season for this variable before 2100. For summer CMI, the median time of emergence for significant change is around 2085. At the grid scale, signal‐to‐noise ratios are significant for all seasons for mean surface air temperature, with earliest times of emergence occurring in summer. In contrast, the summer precipitation signal is not significant this century; for summer CMI, significant values are obtained in the eastern half of the region, occurring from about 2065 onwards. Median times of emergence are towards the end of the century for summer CMI in western Saskatchewan and in Alberta, although some areas of Alberta do not exhibit significant signals this century. [ABSTRACT FROM AUTHOR]

The primary objective of this paper was to determine the circulation conditions of the occurrence of cold spells in Poland and Germany. The observed inconsiderable warming slightly contributed to a change in the number of cold days and cold spells. The occurrence of cold spells in Poland and Germany was related to considerably more active and intensive pressure systems than on average in the winter season in the studied years. A strong high pressure system was located over much of the continent, blocking westerly circulation. During the occurrence of cold spells, negative anomalies of isobaric surface heights were recorded with the greatest anomalies being in the 300–250 hPa heights. The highest air temperature anomalies were recorded on the lowest isobaric levels, where they amounted to <−10°C. On average, anomalies on the 300 hPa isobaric level began developing approximately 9 days before the beginning of cold spells over the study area. The decay of cold spells was not related to rapid exchange of air masses, but rather to gradual weakening of anomalies in the upper troposphere, and their shifting westwards. [ABSTRACT FROM AUTHOR]

Hoar frost, one of the most common types of road slipperiness, can reduce road surface friction thereby adversely affecting traffic safety. The two main causes of hoar frost are warm air advection and radiative cooling. This paper investigates the impact of a changing climate on the formation of hoar frost in Sweden during the winter months due to warm air advection and radiative cooling. The analysis is based on in situ observations from 244 stations in the Swedish Road Weather Information System. The results show that in northern Sweden, hoar frost was mainly caused by warm air advection, while radiative cooling was the main cause of hoar frost in southern Sweden. Given the increase in the air temperature over land due to climate change during the period 2000–2016, the relative frequency of hoar frost due to warm air advection has significantly decreased, while the relative frequency of hoar frost due to radiative cooling has significantly increased. Further analysis showed that the weakened temperature gradient between land and ocean, which leads to weakened warm air advection, is the main cause of the changes. Locations of in situ RWIS stations and their respective clusters (triangles), and the centres of geostrophic wind calculated for the three regions (stars). The background shows the four traditional Swedish climate zones as shown in Wallman (2004). [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the variability and its temporal‐scale components of daily air temperature and precipitation simulated by two regional climate models (RCMs) and two driving global climate models (GCMs) from the EURO‐CORDEX and CMIP5 projects. The analysis was performed for eight geographical areas of central Europe for the period 1971–2000. After a brief evaluation of total variance, we focus on the ability of the simulations to represent short‐term, seasonal and long‐term variability components. This analysis was carried out for original time series as well as for series with the annual cycle removed. A fast Fourier transformation and a Kolmogorov–Zurbenko filter were used to separate the three temporal components. The two methods of time series decomposition generally agree, which confirms the robustness of the results. Although the observed total variability of daily air temperature and precipitation is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. Regarding the air temperature, the results for the time series with the annual cycle removed are generally somewhat worse than for original series. The simulations tend to underestimate the short‐term component of temperature variability, while the results for the other two components are inconclusive. Moreover, it was found that temperature variability caused by the annual cycle is overestimated by the simulation of HadGEM2 GCM and CCLM4‐8‐17 RCM driven by HadGEM2, which explains the relatively poor results of these simulations regarding air temperature total variance and short‐term as well as seasonal components in the original temperature series. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. Although the observed total variability of daily air temperature and precipitation over selected European areas is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. In the figure, location of the eight studied areas situated in the vicinity of central Europe and the orography of the E‐OBS data set. [ABSTRACT FROM AUTHOR]

In the paper we focus on the notes of botanist and meteorologist Gottfried Reyger on the weather conditions in Gdańsk, Poland. In our estimation, this chronicle, covering the period from December 1721 to June 1786 (the longest weather chronicle recorded by a single person), is a reliable source of information with the potential to contribute to the understanding of climate variability in 18th‐century Gdańsk, the significant old Hanseatic city in northern Poland. The temperature and precipitation series for the period 1721–1786 were indexed against contemporary conditions using the calculated percentages of cold, normal and warm decades. The climate in the 18th century in Gdańsk was colder than nowadays; the springs were much cooler, as were the winters, while the summers were warmer. A few relatively cold periods and several warm periods were identified. The first half of the analysed period was very humid, whereas the second was quite dry. The reconstructed indexed data series were compared to annual and seasonal mean values of air temperature and precipitation derived from regular instrumental observations made in Gdańsk since 1739. The linear regression method was applied to calibrate the reconstructed index series against anomalies from the 1961–1990 average. The correlation coefficient, standard error of estimate and the root‐mean‐square error statistics were used to diagnose the quality of calibration and verification. The highest correlation between the temperature indices and the observational series was found for winter. The reconstructed series for precipitation has a smaller variability than in reality. The reconstructed values for the years prior to 1739 indicate cold years for 1725–1732 and a wet period in the 1730s. The chronicle of botanist and meteorologist Gottfried Reyger, covering the period 1721–1786, characterizes the thermal and precipitational conditions in Gdańsk, Poland. The series were indexed against contemporary conditions, compared with instrumental data and reconstructed for early non‐instrumental period. The 18th‐century Gdańsk was colder than nowadays, particularly on case of springs, while the summers were warmer. The first decades of the period were very humid, whereas the latter ones quite dry. In the figure, annual air temperature (left panel) and precipitation (right panel) reconstruction for Gdańsk for the period 1722–1785 presented in the context of observational series, 1739–1785 for air temperature and 1739–1772 for precipitation. Solid line shows observational data; dotted line shows reconstructed series. [ABSTRACT FROM AUTHOR]

Studies assessing air temperature variations and their dependence on altitude from the eastern side of the Antarctic Peninsula are sparse. In this paper, we analyse air temperature and near‐surface lapse rates from the Ulu Peninsula (James Ross Island, Antarctic Peninsula). The temperature data were acquired from nine sites both in ice‐free areas and glaciers of the Ulu Peninsula in 2013–2016. The most important factors influencing air temperature differences were sea‐ice conditions, topography and ground surface properties. During the study period, air temperature decreased with height with the mean lapse rate of 0.40 °C 100 m−1 for the ice‐free sites (10–375 m a.s.l.) and 0.43 °C 100 m−1 for the glaciated sites (268–539 m a.s.l.). However, the values were lower in winter (ice‐free: 0.02 °C 100 m−1 and glacier: 0.31 °C 100 m−1) than in summer (ice‐free: 0.62 °C 100 m−1 and glacier: 0.51 °C 100 m−1) for both categories. The air layer between 10 and 56 m a.s.l. in the ice‐free area revealed an almost year‐round air temperature inversion resulting in a mean lapse rate of −0.79 °C 100 m−1 and an air temperature inversion frequency reaching up to 43%. The temperature inversion frequency was 10% lower for the lowest air layer for the glacier category, which was between 268 and 356 m a.s.l. The land surface air temperature was the highest when the wind was from the northwest and lowest when it was from the south–southwest. The near‐surface lapse rate–wind direction relationship was the most pronounced for the air layer between 10 and 56 m a.s.l. with the lowest mean lapse rate for west–southwest wind (−2.83 °C 100 m−1) and the highest for east–northeast wind (0.41 °C 100 m−1). Spatial variability of air temperature on the Ulu Peninsula, James Ross Island, from 2013–2016 was analysed based on data from nine sites. The sites were divided into two categories (ice‐free and glacier sites) and each category was analysed separately. Special attention was given to the dependence on altitude by calculating near‐surface lapse rates and air temperature inversion relative frequency as well as to the effect of wind direction. [ABSTRACT FROM AUTHOR]

ABSTRACT: Extreme precipitation events are widely thought to intensify in a warmer atmosphere through the Clausius‐Clapeyron equation. The temperature‐extreme precipitation scaling was proposed to analyse the temperature dependency of short‐duration extreme precipitation and since then, the concept has been widely used in climatology. Bao et al. (2017) suggest that the apparent scaling reflects not only how surface air properties affect extreme precipitation, but also reflects how synoptic conditions and localized cooling due to the storm itself affect the scaling – implying two‐way causality. We address here critical issues of this paper and provide evidence that dew point temperature drives extreme precipitation, with the direction of causality reversed only for the storm's peak intensity. This physical inference may serve as a basis to better quantify scaling rates and to help establish the relationship between extreme precipitation and environmental conditions in the current climate, and thereby provide insights into future changes to precipitation extremes due to climate change. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the relationship between near-surface air temperature (T) and precipitation (P) over Europe simulated by regional climate models (RCMs) from the EURO-CORDEX project. Simulations of two RCMs (RCA4 and CCLM4-8-17) driven by four different global climate models (GCMs) in the period 1971–2000 were analysed. Two different coefficients, the Pearson correlation coefficient based on monthly anomalies and the T-P index (TPI) based on daily values, were used for this evaluation. First, the comparison of T-P correlation and TPI patterns calculated from E-OBS data was made. Afterwards, simulated T-P relationship patterns were briefly evaluated. We especially focused on evaluating the RCM and GCM influence on the simulated T-P relationship pattern. Our results indicate that RCM simulations captured the basic seasonal dependence of the T-P relationship over Europe represented by both T-P correlation and TPI patterns. The simulated T-P relationship pattern is more strongly influenced by an RCM than a driving GCM in contrast to temperature means in winter and precipitation and temperatures changes, which are more strongly influenced by driving GCM as shown in previous studies. The simulations of the CCLM4-8-17 RCM demonstrated noticeably better agreement with observed T-P relationship fields than the RCA4 RCM simulations. The T-P relationship simulated by RCA4 tends to shift to positive values in comparison to E-OBS values. These findings were found for both TPI and T-P correlation. [ABSTRACT FROM AUTHOR]

In a changing climate, it is likely that precipitation patterns will experience shifts in amount and seasonality, with a tendency towards increased drought in many parts of Europe. In order to investigate what could be some of the feedbacks of dry conditions on temperature, this paper has investigated close to 30 European locations to assess the influence of observed dry summer days on daily maximum temperature ( Tmax) as well as a clustering of rainless days, and finally the temperature impact related to drought conditions as defined by the Standardized Precipitation Index. The results show that for the three main European climatic regions investigated ('Mediterranean', 'Maritime', 'Continental'), the influence of rainless days is discernible, with an average difference between mean summer Tmax and Tmax during rainless days only of up to 1.5 °C. A further aim of the study is to determine whether recent shifts in the occurrence of rainless days during the summer have a bearing on temperature trends. Results show that shifts in rainless days explain only a fraction of the observed temperature change at the locations studied, thereby hinting that the enhanced greenhouse effect remains the dominant mechanism for temperature change. Finally, using a regional climate model, very preliminary insight into a 'greenhouse climate' highlights the fact that changes in dry conditions seem to confirm the findings for current climate, i.e. they account for only a fraction of the increase in temperatures. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia (SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large-scale ocean-atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large-scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non-linear relationships between rainfall and large-scale ocean-atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location. [ABSTRACT FROM AUTHOR]

ABSTRACT The development of Adapted Caussinus-Mestre Algorithm for homogenising Networks of Temperature series ( ACMANT), one of the most successful homogenisation methods tested by the European project COST ES0601 ( HOME) has been continued. The third generation of the software package ' ACMANT3' contains six programmes for homogenising temperature values or precipitation totals. These incorporate two models of the annual cycle of temperature biases and homogenisation either on a monthly or daily time scale. All ACMANT3 programmes are fully automatic and the method is therefore suitable for homogenising large datasets. This paper describes the theoretical background of ACMANT and the recent developments, which extend the capabilities, and hence, the application of the method. The most important novelties in ACMANT3 are: the ensemble pre-homogenisation with the exclusion of one potential reference composite in each ensemble member; the use of ordinary kriging for weighting reference composites; the assessment of seasonal cycle of temperature biases in case of irregular-shaped seasonal cycles. ACMANT3 also allows for homogenisation on the daily scale including for break timing assessment, gap filling and ANOVA application on the daily time scale. Examples of efficiency tests of monthly temperature homogenisation using artificially developed but realistic test datasets are presented. ACMANT3 can be characterized by improved efficiency in comparison with earlier ACMANT versions, high missing data tolerance and improved user friendliness. Discussion concerning when the use of an automatic homogenisation method is recommended is included, and some caveats in relation to how and when ACMANT3 should be applied are provided. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper documents the changes in climate extremes over Indonesia during the past three decades (1983-2012) based on a subset of extreme indices recommended by the Expert Team on Climate Change Detection and Indices ( ETCCDI). The extreme indices were calculated based on the quality controlled daily observational data (minimum and maximum temperature and precipitation) from 88 weather stations. Overall, we found significant and spatially coherent trends of warming in the temperature indices over Indonesia, consistent with other studies conducted at different countries within the Southeast Asia. The frequency of cool days ( TX10p) and cools nights ( TN10p) had decreased whereas warm days ( TX90p) and warm nights ( TN90p) were observed more frequently. Averaged over the country, the annual mean of daily maximum ( TXmean) and minimum temperature ( TNmean) had increased significantly by 0.18 and 0.30 °C decade−1, respectively. Other temperature indices also showed significant warming trends. In contrast, trends in the precipitation extremes indices were generally not significant and less spatially coherent. However, a tendency towards wetter conditions was observed, in agreement with the results at the global scale. The daily precipitation intensity ( SDII) had increased significantly over the country by 0.21 mm day−1 decade−1 during the period studied. At the regional scale, we observed a significant wetting trend in the annual highest daily amount ( RX1day) and the rainfall amount contributed by the extremely very wet days (R99p) in the northern part of the country. The wetting trends of a number of extreme precipitation indices were depicted prominently in December-January-February ( DJF) and/or March-April-May ( MAM) seasons, both at country and regional levels. However, for the southern region of Indonesia, a drying tendency was observed for June-July-August ( JJA), September-October-November ( SON) and MAM. [ABSTRACT FROM AUTHOR]

ABSTRACT Sea surface temperature ( SST) is an important oceanic variable that impacts regional and global climate. This paper examines the relation of summer (June to August) surface air temperature ( SAT) variability over Saudi Arabia (1948-2013) with SST over the Atlantic, Pacific and Indian Oceans. The SAT over Saudi Arabia shows an abrupt increase around the 1980s (i.e. 1986). Partial correlations between SAT and different SST indices indicate that the Indo-Pacific warm pool ( IPWP) has a more significant effect on SAT than does the tropical North Atlantic ( TNA), tropical South Atlantic ( TSA) or Western Hemisphere warm pool ( WHWP). Hence, the change in IPWP SST can be considered as one of the factors linked to increase SAT over Saudi Arabia. The only significant relationship between El Niño and SAT is found in the Niño 4 region. Moreover, interactions between the tropical Atlantic meridional gradient mode and SAT are undetectable in the summer season, indicating that the TNA and TSA may have effectively independent roles on SAT over Saudi Arabia. The tropical Atlantic, through TNA and TSA, may influence the IPWP SST which modifies the strength of the Indian monsoon low, and in turn, has a strong influence on Saudi Arabia SAT variability. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper investigates the interannual variability of precipitation over the South Asian monsoonal region in May, during which the South Asian summer monsoon is generally established. The South Asian monsoonal region in this study is referred to as a broad area that includes the Arabian Sea, the Indian peninsula, the Bay of Bengal, and the Indochina peninsula. The precipitation exhibits a significant enhancement in May, compared to that in the previous month. The present results indicate that the interannual variability of May precipitation in this region is significantly regulated by two independent factors: The Europe-West Asia ( EWA) teleconnection and El Niño Southern Oscillation. The EWA teleconnection pattern is characterized by the see-saw pattern of upper-tropospheric geopotential height anomalies between the Europe and the Iranian plateau. Both the factors modulate the meridional gradient of tropospheric temperature and lower-tropospheric zonal winds, and thus affect the rainfall in the monsoonal region. Therefore, both the extratropical circulation teleconnection and tropical forcing are crucial for inducing rainfall variability in May over the monsoonal region. [ABSTRACT FROM AUTHOR]

There is an increasing focus on the usefulness of climate model--based seasonal precipitation forecasts as inputs for hydrological applications. This study reveals that most models from the North American Multi-Model Ensemble (NMME) have potential to forecast seasonal precipitation over 17 hydroclimatic regions in continental China. In this paper, we evaluated the NMME precipitation forecast against observations. The evaluation indices included the correlation coefficient (R), relative root-mean-square error (RRMSE), rank histogram (RH), and continuous ranked probability skill score (CRPSS).We presented the RRMSE-R diagram to distinguish differences between the performances of individual models. We find that the predictive skill is seasonally and regionally dependent, exhibiting higher values in autumn and spring and lower values in summer. Higher predictive skill is observed over most regions except the southeastern monsoon regions, which may be attributable to local climatology and variability. Among the 11 NMME models, CFS, especially CFSv2, exhibits the best predictive skill. The GFDL and NASA models, which are followed by CMC, perform worse than CFS. The performances of IRI and CCSM3 are relatively worse than that of the other models. The forecast skills are significantly improved in multi-model mean forecasts based on simple model averaging (SMA). The improvement is more obvious for Bayesian model averaging (BMA), which is employed to further improve the forecast skill and address model uncertainty using multiple model outputs, than individual model and SMA. [ABSTRACT FROM AUTHOR]

ABSTRACT The purpose of this paper is to describe the variability of sunshine duration and air temperature and to assess the relationship between these variables. The paper is based on daily means of air temperature and daily sunshine duration totals from the 1884-2012 period. The variability analyses of these variables for the whole period is supplemented by additional trend analyses for two separate time periods described in the literature as dimming (1958-1985) and brightening (1985-2012). All the data come from the Jagiellonian University Research Station in Krakow. In Krakow, there is a decrease in the number of sunshine hours in the 1950s to 1980s period followed by an increase in the last two decades of the 20th century. The long-term course of air temperature in Krakow confirms the results obtained in other places in the world and exhibits increasing warming of the climate. The temperature increase is visible in each month but is highest in winter (exceeding 1.5 °C per 100 years). Therefore, it may be assumed that the increase in air temperature, which has been particularly strong since the 1980s, showed the full effect of global warming in the brightening period that was previously masked by global dimming as shown in the sunshine duration records. The time course of air temperature for the whole study period (1884-2012) shows a statistically significant positive linear trend. The present warming is influenced probably by the air-quality improvement (brightening period) and an increase in sunshine duration in the years 1980-2012. [ABSTRACT FROM AUTHOR]

Tropospheric temperature inversions are thought to be an important feature of climate as well as a significant factor affecting air quality and low‐level cloud formation. The aim of this study is to investigate the temporal and spatial variability of the tropospheric temperature inversions, in particular so‐called elevated inversions, over Europe. The analysis is based on data gained from ERA‐Interim reanalysis for the period 1981–2015. The data consist of air temperature, and geopotential height from the entire vertical cross‐section of the troposphere, that is, from 1,000 to 100 hPa. The study examines the temporal (intra‐ and inter‐annual) variability of the temperature inversions based on their frequency, base height, depth, and strength. The analysis conducted revealed that the temperature inversions are a common phenomenon occurring in the lower troposphere. Their temporal and spatial variability is, however, determined by the inversion type. Surface‐based inversions (SBI) indicate a clear diurnal cycle, while the day–night variability of elevated inversions (EI) is far less pronounced. Two main regions of the most frequent EI occurrence may be distinguished. These are: (a) a marine area west of the Iberian Peninsula and (b) Eastern Europe. Both of them are located in areas which are under the influence of extensive high‐pressure systems—the permanent Azores High and semipermanent Siberian High, respectively. The development of EI should be therefore attributed to the large‐scale subsidence and adiabatic heating of air parcels. EI are also quite common over the other parts of the Atlantic Ocean, which is closely linked to the development of marine inversions. SBI tend to be stronger than EI—the mean seasonal inversion strength is usually substantially higher for SBI. In turn, EI reach higher values of the mean seasonal inversion depth as compared with SBI. [ABSTRACT FROM AUTHOR]

ABSTRACT This work introduces a new climate description scheme based on a long range statistical model fitted to local daily temperature anomaly series. The appropriate model can be considered as a sum of two processes, namely, stationary white noise ( WN) and nonstationary random walk ( RW). In the case of local series, the variance of the WN component appears to be much larger than that for the generator of RW (also a WN, but independent on the first one). Such a situation enables us to approximate the range of variability for local temperature anomalies by means of the standard deviation of the stationary WN component of the model. This means that the climate description splits into two parts; one describes ordinary and the other extreme weather events. This paper presents a detailed description of that approach using the air temperature series from Stockholm (1756-2011) as an example. The scheme produced here enables us to see that large weather variability does not always mean climate variability. It separates climate and weather scale variability, which is important for precise determination of climate variability and does not confuse the weather and climate time scales in climate description. [ABSTRACT FROM AUTHOR]

ABSTRACT Air temperature as a major climatic component is important in land evaluation, water resources planning and management, irrigation scheduling and agro-hydrologic planning. In this paper, the capabilities of Adaptive Neuro-Fuzzy Inference System ( ANFIS) and Artificial Neural Networks ( ANNs) were evaluated in predicting long-term monthly air temperature values at 30 weather stations of Iran. Monthly data of 20 weather stations were used for training and 10 stations' data were used for testing. Consequently, the periodicity component, station latitude, longitude and altitude values were introduced as input variable to predict the long-term monthly temperature values. The estimates of the ANFIS and ANN models were compared with each other with respect to root mean-squared error, mean absolute error and determination coefficient statistics. The ANN models generally performed better than the ANFIS model in the test period. For the ANN model, the maximum and minimum determination coefficient values were found to be 0.995 and 0.921 in Semnan and Bandar-e-Abbas meteorological stations, respectively. The maximum and minimum determination coefficient values were found as 0.999 and 0.876 for the ANFIS model in Shiraz and Bandar-e-Abbas stations. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper presents the new procedure MAC-D for the automated detection of undocumented Multiple Abrupt Changes in the mean value of Daily temperature series, recorded in a network of meteorological stations. MAC-D can be applied to series containing seasonality, multiple change points, outliers, and with a noise component that can be autocorrelated and non-normally distributed. The main novelties of the procedure are (1) the pretreatment of the observed series, to derive a series of daily values that complies with the theoretical requirements of the change point detection tests and in (2) the combined use of the reference series and pairwise comparison approaches. MAC-D consists of three phases in sequence. In phase 1, the seasonal and climatic fluctuations are estimated and removed, using the reference series approach. Phase 2 combines a linear filtering with a change point detection test in an iterative algorithm, which runs until full compliance between the characteristics of the filtered series and the test requirements is achieved. Phase 3 is aimed at removing the false change points, due to error propagation in the reference series analysis, by double checking the detected change points with the pairwise comparison approach. Monte Carlo estimations of the actual significance and overall performance of the procedure for different series features and test resolutions are provided. Results demonstrate that MAC-D performs very well with daily series having a wide range of different characteristics. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The present paper studies the variability and trends of Tropical Nights (>20 °C) (TNs) on the Iberian Peninsula (IP) over the 1961-2007 period during the extended summer (defined as June, July, August, and September, JJAS). The analyses were performed with the mean series calculated for the whole IP and for different subregions. We conducted a regionalisation by means of a S-Mode Principal Component Analysis (PCA), which identified three subregions where the variability of TNs behaves similarly: the Mediterranean (M) region, the centre-north of the Peninsula (CN) region, and the Atlantic (A) region. Throughout the entire period, the mean summer TN series for the whole IP shows a significant increase; there are, however, some regional differences, as the CN and M regions present higher rates of warming than the A region. Using Canonical Correlation Analysis (CCA) we detected the large-scale atmospheric circulation patterns controlling the interannual variability of TNs over the IP. The occurrence of TNs in the M and CN subregions is highly dependent on anticyclonic conditions over Western Europe in the mid-troposphere level. The increase in the positive phase of this canonical mode during the last three decades might explain part of the positive trend of the TNs detected in summer on the IP. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

We evaluated three definitions of heat wave (HW) and demonstrate that selection of the reference period and air temperature threshold used in the definition can affect the reported frequency of HWs. First, we propose an alternative definition of HW that is based on a moving reference period and specific air temperatures threshold for each calendar date. Then, we present a case study using data from five observatories in Malaga Province (Spain) from 1971 to 2016. We assessed the effect of a moving reference period (rather than a fixed reference period) and the definition of daily air temperatures threshold on the identification of anomalies in the definition of a HW. The results indicate that HWs can occur throughout the year. Finally, we propose incorporation of minimum daily air temperatures in two alternative definitions of HW. Our results indicate a high frequency of HWs in the summer months, in which HWs are mainly due to anomalous high Tmin and more frequent HWs in which maximum and minimum daily air temperature are exceeded from 2014 onwards. These new methods of defining a HW provide more accurate assessments of thermal discomfort. Aggregate number of HWs in different months (data for 2001–2016), based on AEMET, IPCC, and MRDT definitions. We demonstrate that selection of the reference period and threshold temperature used in the definition of heat waves (HWs) can affect the reported frequency. We propose an alternative definition of HW that is based on a moving reference period and specific threshold temperatures for each calendar date, and demonstrate it provides a more meaningful identification of HWs. We propose a second alternative definition, incorporating the minimum daily temperatures, in order to provide an accurate assessment of thermal discomfort, a main concept when measuring thermal anomalies. [ABSTRACT FROM AUTHOR]

This paper is devoted to the synoptic-climatic analysis with the application of circulation types and Geographic Information Systems (GIS). Construction of daily climatic air temperature maps for the Polish territory was the main aim of the work. Analysis was based on the mean daily temperature data from all synoptic and some climatological stations (73 stations totally) for the 50-year period of 1951-2000. The synoptic-climatic analysis was done using a well known circulation calendar developed by the German Meteorological Service 'Grosswetterlagen'. Residual kriging was chosen for the map constructions. Several geographic parameters, including elevation, latitude, longitude, and distance to the Baltic coast, were used as predictor variables for air temperature. However, the particular circulation type was the crucial predictor. Maps were created for all months for the Grosswetterlagen circulation types, which occurred more than 30 times during 1951-2000. Special attention was paid to the extreme thermal months of January and July. Maps constructed in the way described confirmed that application of circulation types was quite a good tool for improving accuracy of thermal daily maps. Accuracy was much higher for most of the maps constructed for particular circulation types than for the maps created without application of circulation types. There was only one exception: non-advective anticyclonic types during winter time. Then, relatively large variability of the temperature can be observed in the concave landforms and the constructed maps have only approximate meaning. Copyright © 2006 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

The land surface is a key component of climate models. It controls the partitioning of available energy at the surface between sensible and latent heat, and it controls the partitioning of available water between evaporation and runoff. The land surface is also the location of the terrestrial carbon sink. Evidence is increasing that the influence of the land surface is significant on climate and that changes in the land surface can influence regional- to global-scale climate on time scales from days to millennia. Further, there is now a suggestion that the terrestrial carbon sink may decrease as global temperatures increase as a consequence of rising CO2 levels. This paper provides the theoretical background that explains why the land surface should play a central role in climate. It also provides evidence, sourced from climate model experiments, that the land surface is of central importance. This paper then reviews the development of land surface models designed for climate models from the early, very simple models through to recent efforts, which include a coupling of biophysical processes to represent carbon exchange. It is pointed out that significant problems remain to be addressed, including the difficulties in parameterizing hydrological processes, root processes, sub-grid-scale heterogeneity and biogeochemical cycles. It is argued that continued development of land surface models requires more multidisciplinary efforts by scientists with a wide range of skills. However, it is also argued that the framework is now in place within the international community to build and maintain the latest generation of land surface models. Further, there should be considerable optimism that consolidating the recent rapid advances in land surface modelling will enhance our capability to simulate the impacts of land-cover change and the impacts of increasing CO2 on the global and regional environment. Copyright © 2003 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Precipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP. [ABSTRACT FROM AUTHOR]