Description: ABSTRACT An abrupt change occasionally occurs when the dynamical system suddenly shifts from one stable state to a new state, which can take place in many complex systems, such as climate, ecosystem, social system, and so on. In order to detect abrupt change, this article presents a novel method – sliding transformation parameter (STP) on the basis of skewness change and the Box–Cox transformation. Tests on model time series and 1000 simulated daily precipitation data show the ability of the present method to identify and detect abrupt change of probability density function. The applications of STP in daily precipitation data show that there is an abrupt climate change between 1979 and 1980 in the selected observational stations, which is almost the same with the result obtained by approximate entropy (ApEn). Furthermore, it is found that the sample sizes of sliding windows have some influence on the Lambda parameter of the Box–Cox transformation, but it does not significantly affect the varying trend of the parameter and the identification of the change point in annual or interannual time scale. Comparing STP with the coefficient of skewness and kurtosis, ApEn, and some statistics approaches (e.g. percentiles and annual maxima), we find that the performance of the present method is much better than that of these methods.Copyright © 2012 Royal Meteorological Society

Description: ABSTRACT In this study, the year-to-year variability of surface air temperature (SAT) over China in winter was investigated during 1961–2014. The results indicate that the year-to-year SAT variability can explain more than 30% of the SAT variance over most parts of China, with up to 60% variance over southern China, northeastern China and northwestern China. The leading pattern of year-to-year SAT exhibits homogeneous variability over most parts of China, except in small areas over the Tibetan Plateau and southwestern China. The circulation over the northern Pacific is a key factor of this homogeneous variability pattern. An anomalous anticyclonic circulation and weakening midlatitude westerly jet in the middle and high troposphere over the northern Pacific are associated with the homogeneous warmth over China. The second pattern shows a south–north dipole, with variability in northeastern China opposite to that south of 25°N in China and over the Tibetan Plateau. The south–north dipole pattern is part of a global year-to-year SAT anomaly pattern because it exhibits a significant relationship with the year-to-year SAT over large parts north of 50°N over the Eurasian landmass. The north-cold/south-warm pattern is accompanied by a significant weakening of the Arctic Oscillation. In comparison, the atmospheric circulation anomalies associated with the homogeneous variability pattern are much weaker than those with the south–north dipole pattern. The anomalous Indian Ocean dipole in the previous autumn and the snow cover around China in November are the two key causes of the homogeneous variability pattern. Many factors, such as the tropical central Pacific sea surface temperature (SST), stratospheric quasi-biennial oscillation (QBO), Okhotsk sea ice and western Siberia snow cover, can significantly influence the south–north dipole pattern. Compared to the tropical Pacific SST, the impact of the Indian Ocean SST on the winter SAT over China is much more important. The leading pattern of interannual variability of winter surface air temperature (SAT) over China is regional whereas the second pattern is continental. The effect of Indian Ocean dipole on the winter SAT over China is more important than that of sea surface temperature over tropical Pacific. (a) Spatial distribution of the correlation coefficients between the PC1 of year-to-year winter SAT over China and the global year-to-year winter SAT; (b) the same as (a) but for PC2. Shadings denote the correlations exceeding the 95% confidence level.

Description: ABSTRACT In this study, the variations of annual land surface air temperature (SAT) over Eurasia and the northern part of Africa (0°–180°E, 0°–90°N) were investigated using monthly SAT data from the Climatic Research Unit, University of East Anglia for 1901–2014 and the simulations from the Geophysical Fluid Dynamics Laboratory coupled model. The observed results suggested that the SAT variations exhibited robust non-uniform spatial features at multi-time scales. For the variations in inter-annual to decadal time scales (IDV), the intensity generally increased from south to north, with the strongest intensity being around Siberia and four times that of the weakest intensity found around China. The IDV leading pattern showed a north–south dipole across 40°N. The simulated results suggested that the north–south dipole and the northwards increase of the IDV were due to internal interactions within the complex nonlinear climate system, but the natural and greenhouse gas forcings could intensify the IDV. The warming trend of the SAT was generally homogeneous, but it showed distinctive multi-decadal fluctuations in different regions. The linear secular trends and robust multi-decadal variation around Siberia and China corresponded to the considerable acceleration and deceleration in the warming over the two regions, respectively. The warming around Siberia was mainly caused by greenhouse gases but its modulation due to natural forcing was also considerable because of the robust multi-decadal variations. Around China, the multi-decadal variation, contributed by the natural forcing, can explain more than half the variances in the warming. The warming trend around central Asia was intense and parabolic, and the multi-decadal variation over there was weak and showed few modulating effects. This study disclosed (1) the considerable effect of multi-decadal variations on the warming trend around Siberia and China. (2) The internal interactions within the complex nonlinear climate system lead to a south–north dipole pattern of Eurasian land surface air temperature in inter-annual to decadal time scales. (a) Multi-decadal variations, (b) secular trends and (c) the sum of multi-decadal variations and secular trends derived from EEMDs of the surface air temperature around Siberia (red), China (blue) and Central Asia (green).