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Time series forecasting of meteorological variables, such as the hourly air temperature, has multiple benefits for industry, agriculture, and the environment. Due to the high accuracy required for the associated short-term predictions, traditional methods cannot satisfy the requirements and generally ignore spatial dependencies. This paper proposes a deep Graph Convolutional Long Short Term Memory Neural Network (GCN-LSTM) technique to tackle the time series prediction problem in air temperature forecasting. In the proposed methodology, temporal and spatial-based imputation approaches have been employed to recover the weather variables missing values. The proposed approach is validated using real, open weather data from 37 meteorological stations in Spain. Performed analysis indicates that GCN-LSTM showed superior performance when compared with various state-of-the-art Deep Learning based models found in the literature, resulting in a more robust and computationally efficient model for forecasting air temperature in many meteorological stations simultaneously. [ABSTRACT FROM AUTHOR]

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007–2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov–Smirnov test, the corrected Akaike criterion AICc, the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28–32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. [ABSTRACT FROM AUTHOR]

Based on the daily data of relative humidity from 23 meteorological stations in the Tarim River Basin of northwest China during the period from 1961 to 2010, this paper analyzed the multifractal and long memory property of humidity process. Main findings are as follows: (1) The processes present scaling and multifractal property. (2) The left-skewed multifractal spectrum f( α) indicates that the time series of relative humidity is predominated by small fluctuations. (3) There exists long memory with the δ ∈ (0, 0.5) in the processes, except for Kalpin and Aksu's exhibiting non-stationary long memory with the parameter δ being 0.67 and 0.69 respectively. (4) We found that on the whole, the degree of multifractality exhibits a strengthening trend with the longitude and latitude increasing, but decreasing trend with elevation rising; For length of long memory, we investigated that on the whole, the δ values increased with the longitude and latitude increasing, which indicates that the bigger the longitude and latitude is, the longer the memory of humidity process is, but the higher the elevation is, the shorter the memory of humidity process is. [ABSTRACT FROM AUTHOR]

Rising surface air temperatures, coupled with delays in the onset of austral summer rains and increased fuel load have amplified forest fire risk over southern Africa. This study investigates interactions between climate change and fire risk in South Africa's northern savanna biome. We employ the CCAM model to simulate the reference climate and project future forest fire risk on the savanna. An ensemble of six CMIP5 GCMs were downscaled to 8 km to project climate change in the far-future (2080 to 2099) under RCP8.5 emission scenario. The models were validated using ERA5-Land reanalyses whilst future projections focused on the 10th, 50th and 90th percentiles. The frequency of high fire risk days was calculated using a McArthur Forest Fire Danger Index (FFDI) which links meteorological variables to fire danger. The ensemble simulated widespread temperature rises of between 4.5 and 6 °C across the savanna, whilst rainfall is projected to decline by up to 20 mm/month, with corresponding decreases in minimum relative humidity. Heat wave days are projected to increase to above 8 days per annum, whilst soil moisture deficiency increases by above 50 mm on the savanna. Consequently, mean annual high fire danger days are projected to reach a peak frequency of 25 days in October, with an autumnal secondary peak. Spatially, greater increases in high FFDI days were projected over the western savanna extending toward neighbouring Botswana. This study contributes to understanding fire risk under unprecedented temperature rises which appear to be modulating fire intensity in the study region. [ABSTRACT FROM AUTHOR]

The present study examines the role of large-scale climate modes and local hydro-meteorological variables, at 1–6 months lead, as drivers of streamflow and rainfall at four river basins in peninsular India. Bayesian Network was used to develop the conditional independence structure between the target variables (streamflow and rainfall) and large-scale climate modes as well as local hydro-meteorological variables. From the conditional independence structure, variables possessing a 'directed arc' from the target variable were selected as the potential predictors for developing the prediction models. The most important potential predictors influencing streamflow were found to be rainfall, u-wind, and soil moisture, while those influencing rainfall were u-wind, air temperature, geo-potential height, precipitable water, vertical velocity, and relative humidity. The conditional independence structure reveals that the information provided by the large-scale climate modes is generally dispensable for all the basins given the local meteorological variables barring the Pacific Decadal Oscillation and El-Niño Southern Oscillation which exert substantial influence on the targets. The network structure further revealed that about 87–97% of the initial information are redundant. The prediction performance is better for rainfall (Refined index of agreement MD: MDGDV = 0.81, MDUMB = 0.80, MDUNB = 0.79 and MDCAV = 0.61) than for streamflow (MDGDV = 0.78, MDUMB = 0.75, MDUNB = 0.67 and MDCAV = 0.67) and comparable across all the basins. Further, dry, intermediate, and wet months are satisfactorily categorised in each basin using two drought indices—Standardised Drought Index for streamflow and Standardised Precipitation Anomaly Index for rainfall. [ABSTRACT FROM AUTHOR]

In the context of "TO CHAIR" project, this work aims to improve the accuracy of short-term forecasts of maximum air temperature obtained from the https://weatherstack.com/ website. The proposed methodology is based on a state-space representation that incorporates the latent process, the state, which is estimated recursively using the Kalman filter. The proposed model linearly and stochastically relates the forecasts from the website (as a covariate) to the observations of the maximum temperature recorded at the study site. The specification of the state-space model is performed using the maximum likelihood method under the assumption of normality of errors, where empirical confidence intervals are presented. In addition, this work also presents a treatment of outliers based on the ratios between the observed maximum temperature and the website forecasts. [ABSTRACT FROM AUTHOR]

Climate change has great impacts on hydrological processes worldwide. The Tibetan Plateau (TP), the 'Water Tower' of Asia, poses significant influences on Asian climate and is also one of the most sensitive areas to climate change. Therefore, it is of importance to investigate the plausible future hydrological regimes in the TP based on the climate scenarios provided by General Circulation Models (GCMs). In this study, the Variable Infiltration Capacity model was coupled with Shuffled Complex Evolution developed at the University of Arizona to explore the responses of hydrological processes to climate change in the Lhasa River basin, the tributary of the Yarlung Zangbo River in the southern TP. A downscaling framework based on Automatic Statistical Downscaling was used to generate the future climate data from two GCMs (Echam5 and Miroc3.2_Medres) under three scenarios (A1B, A2 and B1) for the period of 2046-2065. Results show increases for both air temperature and annual precipitation in the future climate. Evaporation, runoff and streamflow will experience a rising trend, whereas spring snow cover will reduce dramatically. These changes present significant spatial and temporal variations. The alteration of hydrological processes may challenge the local water resource management. This study is helpful for policy makers to tackle climate change related issues in terms of mitigation and adaptation. [ABSTRACT FROM AUTHOR]

Near-surface air temperature is one of the most widely studied hydroclimatic variables, as both its regular and extremal behaviors are of paramount importance to human life. Following the global warming observed in the past decades and the advent of the anthropogenic climate change debate, interest in temperature's variability and extremes has been rising. It has since become clear that it is imperative not only to identify the exact shape of the temperature's distribution tails, but also to understand their temporal evolution. Here, we investigate the stochastic behavior of near-surface air temperature using the newly developed estimation tool of Knowable (K-)moments. K-moments, because of their property to substitute higher-order deviations from the mean with the distribution function, enable reliable estimation and an effective alternative to order statistics and, particularly for the outliers-prone distribution tails. We compile a large set of daily timeseries (30–200 years) of average, maximum and minimum air temperature, which we standardize with respect to the monthly variability of each record. Our focus is placed on the maximum and minimum temperatures, because they are more reliably measured than the average, yet very rarely analyzed in the literature. We examine segments of each timeseries using consecutive rolling 30-year periods, from which we extract extreme values corresponding to specific return period levels. Results suggest that the average and minimum temperature tend to increase, while overall the maximum temperature is slightly decreasing. Furthermore, we model the temperature timeseries as a filtered Hurst-Kolmogorov process and use Monte Carlo simulation to produce synthetic records with similar stochastic properties through the explicit Symmetric Moving Average scheme. We subsequently evaluate how the patterns observed in the longest records can be reproduced by the synthetic series. [ABSTRACT FROM AUTHOR]

The construction of valid and flexible cross-covariance functions is a fundamental task for modeling multivariate space–time data arising from, e.g., climatological and oceanographical phenomena. Indeed, a suitable specification of the covariance structure allows to capture both the space–time dependencies between the observations and the development of accurate predictions. For data observed over large portions of planet earth it is necessary to take into account the curvature of the planet. Hence the need for random field models defined over spheres across time. In particular, the associated covariance function should depend on the geodesic distance, which is the most natural metric over the spherical surface. In this work, we propose a flexible parametric family of matrix-valued covariance functions, with both marginal and cross structure being of the Gneiting type. We also introduce a different multivariate Gneiting model based on the adaptation of the latent dimension approach to the spherical context. Finally, we assess the performance of our models through the study of a bivariate space–time data set of surface air temperatures and precipitable water content. [ABSTRACT FROM AUTHOR]

Projections of changes in climate are important in assessing the potential impacts of climate change on natural and social systems. However, current knowledge on assembling different GCMs to estimate future climate change over the Pear River basin is still limited so far. This study examined the capability of BMA and arithmetic mean (AM) method in assembling precipitation and temperature from CMIP5 under RCP2.6, RCP4.5 and RCP8.5 scenarios over the Pearl River basin. Results show that the BMA outperforms the traditional AM method. Precipitation tends to increase over the basin under RCP2.6 and RCP4.5 scenarios, whereas decrease under RCP8.5. The most remarkable increase of precipitation is found in the northern region under RCP2.6 scenario. The linear trend of the monthly mean near-surface air temperature increases with the growing CO concentration. The warming trends in four seasons are distinct. The warming rate is prominent in summer and spring than that in other season, meanwhile it is larger in western region than in other parts of the basin. The findings can provide beneficial reference to water resources and agriculture management strategies, as well as the adaptation and mitigation strategies for floods and droughts under the context of global climate change. [ABSTRACT FROM AUTHOR]

In general, there are few studies that analyse the impact of low temperatures on mortality, and even fewer that extend this analysis to specific causes of mortality. This study had a twofold aim: Firstly, to analyse the trend in natural-, circulatory- and respiratory-cause mortality associated with cold waves in Castile-La Mancha (Spain) across a period of analysis of 34 years, which would confer an important degree of temporal representativeness on the results obtained; and secondly, to ascertain whether this impact had decreased over the years. Time series analysis using multivariate ARIMA models with data on daily natural-, circulatory- and respiratory-cause mortality in Castile-La Mancha. The independent variables were minimum daily temperature, mean daily pressure and mean daily relative humidity. We controlled for seasonalities and trend of the series, as well as influenza epidemics, cold-wave duration and chronological number in any given year. Data were stratified in three ten-year stages, i.e., 1975-1985, 1986-1996 and 1997-2008. The mortality trigger temperature was set at a minimum daily temperature of −2 °C, corresponding to the 4th ‰ of the minimum temperature series for the winter months considered. The impact on daily natural-cause mortality for each degree that the minimum daily temperature was below −2 °C was: 10.4 % (95 % CI 9.6-11.2) in the first decade; 11.9 % (95 % CI 11.0-12.8) in the second decade; and fell to 1.6 % (95 % CI 0.9-2.3) in the third. This same pattern was observed for circulatory- and respiratory-cause mortality, with the effect of cold being greater for respiratory causes. Socio-economic factors -both of adaptation and demographic- could account for this sharp decrease in mortality associated with low temperatures. These results question climate models which predict the effects of cold over long-term time horizons, while maintaining the risk attributable to low temperatures constant. Studies similar to ours should be undertaken in other regions to confirm whether it is solely local characteristics that explain this pattern or, on the contrary, whether the pattern is generalised. [ABSTRACT FROM AUTHOR]

This study investigates long-term trends in annual and seasonal precipitation at 16 stations in the upper Blue Nile River basin. The non-parametric Mann-Kendall test modified by effective sample size is used to detect linear trends in the precipitation time series. The trends magnitudes and starting time of significant trends are determined using the Sen's slope approach and the sequential Mann-Kendall test, respectively. Albeit annual precipitation shows a tendency to decrease in more than 80 % of the stations, statistically significant trends are found at only two stations. The significant decreasing trends of −40.3 and −168.1 mm/year per decade in annual precipitation at Debre Brihan and Gore stations started in the late 1970s and early 1980s respectively, which is consistent with the devastating droughts and famine during that period in the region. Owing to the great contribution of the main rainy season's (June to September: JJAS) precipitation to annual precipitation in the upper Blue Nile basin, the variation pattern of the JJAS precipitation is very similar to that of annual precipitation. Insignificant decreasing/increasing trends in the short rainy season's (March to May: MAM) precipitation are clearly predominant in the basin, where only one significant decreasing trend is detected in the time series. [ABSTRACT FROM AUTHOR]

The change in the mean temperature in Finland is investigated with a dynamic linear model in order to define the sign and the magnitude of the trend in the temperature time series within the last 166 years. The data consists of gridded monthly mean temperatures. The grid has a 10 km spatial resolution, and it was created by interpolating a homogenized temperature series measured at Finnish weather stations. Seasonal variation in the temperature and the autocorrelation structure of the time series were taken account in the model. Finnish temperature time series exhibits a statistically significant trend, which is consistent with human-induced global warming. The mean temperature has risen very likely over 2 °C in the years 1847-2013, which amounts to 0.14 °C/decade. The warming after the late 1960s has been more rapid than ever before. The increase in the temperature has been highest in November, December and January. Also spring months (March, April, May) have warmed more than the annual average, but the change in summer months has been less evident. The detected warming exceeds the global trend clearly, which matches the postulation that the warming is stronger at higher latitudes. [ABSTRACT FROM AUTHOR]

A joint model is proposed for analyzing and predicting the occurrence of extreme heat events in two temperature series, these being daily maximum and minimum temperatures. Extreme heat events are defined using a threshold approach and the suggested model, a non-homogeneous common Poisson shock process, accounts for the mutual dependence between the extreme events in the two series. This model is used to study the time evolution of the occurrence of extreme events and its relationship with temperature predictors. A wide range of tools for validating the model is provided, including influence analysis. The main application of this model is to obtain medium-term local projections of the occurrence of extreme heat events in a climate change scenario. Future temperature trajectories from general circulation models, conveniently downscaled, are used as predictors of the model. These trajectories show a generalized increase in temperatures, which may lead to extrapolation errors when the model is used to obtain projections. Various solutions for dealing with this problem are suggested. The results of the fitted model for the temperature series in Barcelona in 1951-2005 and future projections of extreme heat events for the period 2031-2060 are discussed, using three global circulation model trajectories under the SRES A1B scenario. [ABSTRACT FROM AUTHOR]