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The Southern Carpathians (Romania) are one of the highest, steepest and most massive sectors of the Carpathian Mountains, with a strong imprint of the Pleistocene glaciation, which are widely affected by a broad spectrum of natural hazards (avalanches, rock wall weathering and landslides, flash floods). This paper focuses on the detection of recent changes in annual temperature and precipitation extremes over the 1961–2018 period based on observational data, discussing their spatial distribution and dependencies (on altitude, latitude and longitude). Trend analysis was applied on a set of 16 indices selected from the core indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results show a significant warming trend, well reflected by the strong decline of frost days and icing days and a significant increase in absolute maximum temperatures, summer days and warm spell duration. Trends in precipitation extremes are more various, suggesting a climate drying trend, apparently stronger in the areas with a fast rise of maximum temperatures (evidence of coupled warming-drying climate change signal) and an increasing frequency of heavy precipitation events and resulted water amounts, in the areas affected by summer convection intensification. Except TNn with trends slopes revealing a winter nighttime amplification with elevation, high elevations (at above 2000 m) show only slight changes in temperature and precipitation extremes compared to the lower areas, explaining the general lack of elevation dependency of trends. Longitude explains 48 to 54% of trends in heavy precipitation indices. Changes in the large-scale atmospheric circulation (e.g. intensification of westerly circulation, increasing frequency of blocking conditions), cloudiness and summer convection have been identified among the main factors influencing trend behaviour and their spatial distribution.

The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs’ configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900–2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006–2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the South Central China (the Inner Mongolia), the North Eastern China (the South Central China), and the North Western China (the South Central China), under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively.

The three-cell model, including the Hadley, Ferrel, and Polar cells in each of two hemispheres, has been accepted for a long time and the strongest Hadley cell has been used to study the climate change in recent years. However, two questions, why the upper level flow of Ferrel cell does not match observations and how many cells exist in the two polar regions, still exist. Using three different reanalysis datasets for the last 30 years, this paper showed that there might be an additional cell in each of two polar regions. The analyses of meridional-vertical section streamline (MSS), meridional-mass stream function (MSF), and climatic vertical velocity provide some evidences to support the existence of the new Arctic and Antarctic cells located in the troposphere. Thus, an eight-cell model in the global troposphere is proposed in this study. The maximum intensity of the Hadley cell in the boreal winter indicated by MSF in the Northern Hemisphere (NH) is stronger than that of the Ferrel cell for about 4.8 times, so the upper level northeasterly wind of Ferrel cell is too weak to be detected when compared with the stronger southwesterly wind of the Hadley cell.

In this paper, the daily precipitation data measured at 58 meteorological stations were used, and the spatial and temporal variability of daily precipitation and precipitation extrema in the Yangtze River Delta (YRD) during 1958–2007 was investigated using the indicator of discrete wavelet entropy; then, the urbanization effects were further analyzed. Results indicate that the daily precipitation variability in YRD, especially in the mid YRD with highly urbanized city clusters, is determined by the comprehensive impacts of atmospheric circulation, urbanizations, and the Taihu Lake. Compared with the results in 1958–1985, the variability of daily precipitation and precipitation extrema becomes more complex in 1986–2007, and daily precipitation variability is more complex in the mid YRD relative to the north and south. The precipitation extrema with bigger magnitudes show more complex variability. Urbanizations cause more complexity and fluctuations of daily precipitation in the mid YRD in 1986–2007, reflecting more uncertainty of daily precipitation variability, while the urbanization effects vary with regions and precipitation magnitudes. The variability of precipitation extrema with maximum values is mainly determined by natural atmospheric circulation but has little relationship with urbanizations; however, the variability of those precipitation extrema with general values is determined by both urbanizations and the Taihu Lake in YRD over the last three decades.

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures (Tmax and Tmin) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948–2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001–2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for Tmax and Tmin for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO2 concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.

This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable was constructed in a relatively simple and transparent manner. For a specific atmospheric state, an ensemble of possible values was generated for the predictand following the Monte Carlo method. Such a stochastic simulation is essential to explore the uncertainties of climate change in the future through a series of random re-sampling experiments. The novel downscaling method was evaluated by downscaling daily precipitation over Southeast Australia. The large-scale predictors were extracted from the daily NCAR/NCEP reanalysis data, while the predictand was high-resolution gridded daily observed precipitation (1958–2008) from the Australian Bureau of Meteorology. The results showed that the method works reasonably well across a variety of climatic zones in the study area. Overall, there was no particular zone that stands out as a climatic entity where the downscaling skill in reproducing all statistical indices was consistently lower or higher across seasons than the other zones. The method displayed a high skill in reproducing not only the climatologic statistical properties of the observed precipitation, but also the characteristics of the extreme precipitation events. Furthermore, the model was able to reproduce, to a certain extent, the inter-annual variability of precipitation characteristics.

The aim of this paper is to introduce a new conditional statistical model for generating daily precipitation time series. The generated daily precipitation can thus be used for climate change impact studies, e.g., crop production, rainfall–runoff, and other water-related processes. It is a stochastic model that links local rainfall events to a continuous atmospheric predictor, moisture flux, in addition to classified atmospheric circulation patterns. The coupled moisture flux is proved to be capable of capturing continuous property of climate system and providing extra information to determine rainfall probability and rainfall amount. The application was made to simultaneously downscale daily precipitation at multiple sites within the Rhine River basin. The results show that the model can well reproduce statistical properties of daily precipitation time series. Especially for extreme rainfall events, the model is thought to better reflect rainfall variability compared to the pure CP-based downscaling approach.

The goal of the paper is to present and examine the method of classification of daily circulation patterns that allows (i) a fair comparison of groupings among different datasets (typically representing the observed climate and that simulated by a general circulation model, GCM) and (ii) huge datasets, common in GCM studies based on daily values, to be classified. The circulation classification method is shown to be a useful tool in GCM validation and analysis of climate change response, particularly in comparisons of (i) shapes of the mean type patterns, (ii) the frequency and persistence of the types, (iii) the probability of transitions from one type to another, and (iv) conditional surface temperature distributions. It is also shown that a simultaneous examination of multiple classifications is beneficial in eliminating subjectivity of any single classification and allowing a detailed inspection of differences between climates. The classification method is a modification of the T-mode principal component analysis (PCA). The T-mode refers to the input data matrix where gridpoint values are arranged in rows and daily patterns in columns. The classification procedure is applied to observed daily 500 hPa geopotential height patterns and those simulated by the control and ECHAM3 GCM runs.