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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.