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The paper concentrates on the response of the atmospheric circulation non-tropical regions of the Northern Hemisphere to considerable sea surface temperature anomalies regularly occurring in the equatorial Pacific Ocean, related to the El Nino event. It has been established that during El Nino, zonal western circulation and meridional southern circulation tend to decay (being −2.6% and −5.4% respectively), while meridional northern circulation intensifies (+8.0%). The paper examines regional peculiarities of the non-tropical atmospheric response to the processes taking in the tropics. It is shown that atmospheric circulation reconstruction is most essential in the Siberian (10.4%) and Pacific (10.2%) sections and is minimal (4.3%) in the Atlantic region. These peculiarities of the regional atmospheric circulation reconstruction may be taken into account to predict short-term climatic variations in the Northern Hemisphere.

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 spring of 2018 was the hottest on record since 1951 over eastern China based on station observations, being 2.5°C higher than the 1961–90 mean and with more than 900 stations reaching the record spring mean temperature. This event exerted serious impacts in the region on agriculture, plant phenology, electricity transmission systems, and human health. In this paper, the contributions of human-induced climate change and anomalous anticyclonic circulation to this event are investigated using the newly homogenized observations and updated Met Office Hadley Centre system for attribution of extreme events, as well as CanESM2 (Second Generation Canadian Earth System Model) simulations. Results indicate that both anthropogenic influences and anomalous anticyclonic circulation played significant roles in increasing the probability of the 2018 hottest spring. Quantitative estimates of the probability ratio show that anthropogenic forcing may have increased the chance of this event by ten-fold, while the anomalous circulation increased it by approximately two-fold. The persistent anomalous anticyclonic circulation located on the north side of China blocked the air with lower temperature from high latitudes into eastern China. Without anthropogenic forcing or without the anomalous circulation in northern China, the occurrence probability of the extreme warm spring is significantly reduced.

Knowledge of moisture sources is of great significance for understanding climatic change and landscape evolution in desert environments. In this paper, we aim to clarify moisture origins for the Alashan (Alxa) Sand Seas (ALSS) in western Inner Mongolia and their transport pathways during the Last Glacial Maximum (LGM) and the mid-Holocene using modern analogues and paleoclimatic simulations. Precipitation data for the period 1959–2015 from meteorological stations in the study area and wind and specific humidity data from the European Center for Medium-Range Weather Forecasts (ECMWF) daily re-analysis were adopted to determine the moisture sources of summer precipitation in the ALSS. In addition paleoclimate simulations under PMIP3/CMIP5 protocols were used to detect the atmospheric circulation and precipitation at 21 ka BP and 6 ka BP over the ALSS. We also reviewed paleoclimate records from the ALSS to acquire a semi-quantitative re-construction of the moisture history during the late Pleistocene and Holocene. Our results suggest that the summer monsoon transported water vapor from the Indian Ocean and the South China Sea to the ALSS during July and August, causing increased precipitation. The dominant moisture source was from the southwest monsoon, while the East Asian summer monsoon also partly contributed to precipitation in the ALSS. The increased humidity during the period 8.2–4.2 ka BP in the ALSS, as derived from both climate simulation outputs and sedimentary records, was caused by monsoons according to the outputs of simulations. At 21 ka BP, the moisture sources of the ALSS were greatly associated with the prevailing westerlies.

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.

The Southern Hemisphere (SH) annular mode (SAM) is the dominant mode of atmospheric circulation in the SH extratropics. The SAM regulates climate in many regions due to its large spatial scale. Exploration of the climatic impacts of the SAM is a new research field that has developed rapidly in recent years. This paper reviews studies of the climatic impact of the SAM on the SH and the Northern Hemisphere (NH), emphasizing linkages between the SAM and climate in China. Studies relating the SAM to climate change are also discussed. A general survey of these studies shows that signals of the SAM in the SH climate have been systematically investigated. On interannual scales, the SAM can influence the position of storm tracks and the vertical circulation, and modulate the dynamic and thermodynamic driving effects of the surface wind on the underlying surface, thus influencing the SH air-sea-ice coupled system. These influences generally show zonally symmetrical characteristics, but with local features. On climate change scales, the impacts of the SAM on SH climate change show a similar spatial distribution to those on interannual scales. There are also meaningful results on the relationship between the SAM and the NH climate. The SAM is known to affect the East Asian, West African, and North American summer monsoons, as well as the winter monsoon in China. Air-sea interaction plays an important role in these connections in terms of the storage of the SAM signal and its propagation from the SH to the NH. However, compared with the considerable knowledge of the impact of the SAM on the SH climate, the response of the NH climate to the SAM deserves further study, including both a deep understanding of the propagation mechanism of the SAM signal from the SH to the NH and the establishment of a seasonal prediction model based on the SAM.

On a hemispheric scale, it is now well established that stratospheric ozone depletion has been the principal driver of externally forced atmospheric circulation changes south of the Equator in the last decades of the 20th Century. The impact of ozone depletion has been felt over the entire hemisphere, as reflected in the poleward drift of the midlatitude jet, the southward expansion of the summertime Hadley cell and accompanying precipitation trends deep into the subtropics. On a regional scale, however, surface impacts directly attributable to ozone depletion have yet to be identified. In this paper we focus on South Eastern South America (SESA), a region that has exhibited one of the largest wetting trends during the 20th Century. We study the impact of ozone depletion on SESA precipitation using output from 6 different climate models, spanning a wide range of complexity. In all cases we contrast pairs of model integrations with and without ozone depletion, but with all other forcings identically specified. This allows for unambiguous attribution of the computed precipitation trends. All 6 climate models consistently reveal that stratospheric ozone depletion results in a significant wetting of SESA over the period 1960--1999. Taken as a whole, these model results strongly suggest that the impact of ozone depletion on SESA precipitation has been as large as, and quite possibly larger than, the one caused by increasing greenhouse gases over the same period.

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 atmospheric circulation patterns in the Southern Hemisphere have had a significant impact on the climate of the Antarctic and there is much evidence that these circulation patterns have changed in the recent past. This change is thought to have contributed to the warming trend observed at the Antarctic Peninsula over the last 50 years—one of the largest trends observed in this period on the planet. The trends associated with the continental Antarctic climate are less clear but are likely to be impacted less directly by atmospheric circulation changes. The circulation changes can be put into the context of longer timescales by considering atmospheric circulation reconstructions that have been performed using data from Antarctic ice cores. In this review paper we look at the main body of work examining: Antarctic climate trends; the understanding and impact of atmospheric circulation of the mid- to high-latitudes of the Southern Hemisphere; and the usefulness and reliability of atmospheric circulation reconstructions from Antarctic ice core data. Finally, beyond several of the more quantitative reconstructions, it is deemed that an assessment of their consistency is not possible due to the variety of circulation characteristics that the various reconstructions consider.

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.

The variation of warm pool ocean temperature in the equatorial western Pacific and its impacts on climatic change are studied in the present paper. The SSTs in the warm pool region not only have seasonal variation but also have interannual variation more clearly; The influence of anomalies of SST in the warm pool region on the East Asian monsoon is studied with data analysis; And the impact of SSTA in the warm pool region on the teleconnection (wave-train) in the atmospheric circulation is still investigated. The influence of ocean temperature anomalies in the warm pool subsurface on the occurrence of ENSO is also discussed by using data analysis and modelling with CGCM. All above-mentioned studies show that the situation of ocean temperature in the warm pool region in the equatorial western Pacific plays an important role in the global climatic variation.

In this paper, the two-layer IAP model with sea surface temperature anomalies in the equatorial central-eastern Pacific is used to investigate potential predictability of global short—term anomalous climate change caused by El Nino via the “switching” experiments. The experimental results show that short—term anomalous climate change in the tropics is mainly caused by instantaneous response of tropical atmosphere to SSTA in the tropics. The effective period of this kind of anomalous climate is shorter and about monthly scale. In the high latitudes, the anomalous climate is mainly caused by the lag response of atmosphere to SSTA in the tropics. The strongest influence appears in the month after a half year when the SSTA in the tropics disappears. Therefore, potential predictability of short-term anomalous climate change may be reached to one year; anomalous climate change in the middle-latitudes is not only. affected by instantaneous response to SSTA in the tropics, but also by lag response to that. Therefore, short-term climate change prediction with monthly time scale can be not only done by using SSTA in the tropics, but also prediction of short- term climate after a half year can be done and its effective predictable period may be reached to one year.

I Atmospheric radiatively-important chemical constituents (e.g., O3 and aerosols) are important to maintain the radiation balance of the Earth-atmosphere climate system, and changes in their concentration due to both natural causes and anthropogenic activities will induce climate changes. The distribution of these constituents is sensitive to the state of the climate (e.g., temperature, moisture, wind, and clouds). Therefore, rises in atmospheric temperature and water vapor, and changes in circulation and clouds in global warming can directly affect atmospheric chemistry with subsequent implications for these constituents. Although many coupling mechanisms are identified, the net effect of all these impacts on climate change is not well understood. In particular, changes in water vapor and clouds associated with the hydrologic cycle contain significant uncertainties. In 1997, a collaborative project with participating scientists from Taiwan, China, Norway, and the United States was initiated to study the “East Asian Climate and Environment (EACE)”. In addition to regular workshop, special journal issues were arranged to document research findings; for example, a collection of research papers concerning aircraft activities was published in the Journal of Terrestrial, Atmospheric and Oceanic Sciences in March 2001. The most recent workshop was the Sixth EACE meeting entitled “Climate-Chemistry Interaction: Importance of Atmospheric Circulation and Water Vapor”, held 19 21 September 2005 in Shanghai. The purpose of this special section is to document findings on topics and issues closely relevant to the climate and environment over East Asia, such as: transport of dust particles, radiative effect of clouds, and changes of atmospheric ozone, cirrus-contrails, and water vapor.

The climate simulations from atmospheric general circulation models (GCMs) are often used to analyze the potential effects of climate change on environmental resources. It has been demonstrated that there are differences among the simulations from various GCMs, on spatial scales ranging from global to regional. This paper quantifies the differences in temperature and precipitation simulated by three major GCMs for four specific regions: an agricultural region (the North American winter wheat belt), a hydrologic region (the Great Basin), a demographic region (the high-density population corridor of the northeast United States), and a political region (the state of Texas). Both the current (control) climate and the climatic response to a doubling of atmospheric carbon dioxide (CO2) are considered