Search

Climate zones are expected to shift in response to climate change, which significantly influences vegetation distribution and provides essential guidance for human activities including production, lifestyle and economic development. Quantifying the shifts in climate zones due to global warming is therefore crucial. The primary metric for categorizing climate zones is the number of days with a daily mean temperature above 10°C (DT10). Utilizing DT10 and the ERA5 reanalysis data, it is observed that climate zones in China have gradually shifted northward over the past 65 years. Notably, the interdecadal changes in the climate zones differ between the eastern and western regions divided by the 110°E longitude. The western regions show minimal shifts, whereas the eastern regions, particularly the central and southern parts of Northeast China, exhibit obvious northward shifts. Consequently, the simulation capabilities of 41 CMIP6 models for Chinese climate zones were assessed, and it was found that 11 models demonstrated robust performance. These models were further used to analyse interdecadal variations and project future shifts in climate zones in China. The results show that the spatial pattern of climate zones in China can be well captured by the CMIP6 models, except for ACCESS‐CM2, FGOALS‐g3 and GFDL‐CM4. Each CMIP6 model seems to be more suitable for specific climatic zones concerning trends and decadal variations in China. By 2100, a northward shift is projected for all climate zones in the east of 110°E under SSP2‐4.5 and SSP5‐8.5 emission scenarios, particularly in northern China. It should be noted that the potential disappearance of the northern subtropical belt, likely to be replaced by the middle subtropical belt in the future. [ABSTRACT FROM AUTHOR]

The western North Pacific Subtropical High (WNPSH) is a key circulation system regulating the East Asian climate, and its area has crucial indicative for summer precipitation in China. In this study, we established the model for classification prediction of summer WNPSH area via the Gaussian Naive Bayes (GNB). By setting different category proportions and different training set sample sizes, we investigated the prediction ability of GNB and its dependent on the data sample size. After comparing the prediction performance of GNB with tree models which were commonly used in short-term climate prediction, it was found that the accuracy scores (ACC) and balanced accuracy scores (BCC) of GNB were statistically significantly higher than tree models. Additionally, under different category classification criteria, the ACC and BCC of GNB could maintain above 0.77 and 0.75, respectively. Especially for anomalous categories, the recalls values could maintain above 0.5. These results indicate that the GNB had very strong prediction ability for the summer WNPSH area and could also better predict the degree of anomalous WNPSH area. Moreover, under different training set sample sizes, the ACC of GNB could be maintained above 0.6, which suggests that the GNB was less dependent on the data sample size and could reduce the limitation of abrupt interdecadal changes in climate on the available data sample size to some extent. This study reveals strong prediction ability of GNB for the summer WNPSH area, which also has high reference value for the research of other short-term climate prediction problems. [ABSTRACT FROM AUTHOR]

Many abrupt climate change events often cannot be detected timely by conventional abrupt detection methods until a few years after these events have occurred. The reason for this lag in detection is that abundant and long-term observational data are required for accurate abrupt change detection by these methods, especially for the detection of a regime shift. So, these methods cannot help us understand and forecast the evolution of the climate system in a timely manner. Obviously, spatial images, generated by a coupled spatiotemporal dynamical model, contain more information about a dynamic system than a single time series, and we find that spatial images show the fractal properties. The fractal properties of spatial images can be quantitatively characterized by the Hurst exponent, which can be estimated by two-dimensional detrended fluctuation analysis (TD-DFA). Based on this, TD-DFA is used to detect an abrupt dynamic change of a coupled spatiotemporal model. The results show that the TD-DFA method can effectively detect abrupt parameter changes in the coupled model by monitoring the changing in the fractal properties of spatial images. The present method provides a new way for abrupt dynamic change detection, which can achieve timely and efficient abrupt change detection results. [ABSTRACT FROM AUTHOR]

By using detrended fluctuation analysis (DFA), the present paper analyzed the nonlinearity and fractal properties of tree-ring records from two types of trees in northwestern China, and then we disclosed climate change characteristics during the past 500 years in this area. The results indicate that climate change in northwestern China displayed a long-range correlation (LRC), which can exist over time span of 100 years or longer. This conclusion provides a theoretical basis for long-term climate predictions. Combining the DFA results obtained from daily temperatures records at the Xi’an meteorological observation station, which is near the southern peak of the Huashan Mountains, self-similarities widely existed in climate change on monthly, seasonal, annual, and decadal timescales during the past 500 years in northwestern China, and this change was a typical nonlinear process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

On the basis of detrended fluctuation analysis (DFA), a new method, moving cut data-DFA (MC-DFA), was presented to detect abrupt dynamic change in correlated time series. The numerical tests show the capability of the presented method to detect abrupt change time-instants in model time series generated by Logistic map. Moving DFA (MDFA) and approximate entropy (ApEn) can provide some information such as a single time-instant of abrupt dynamic change, but both of them cannot exactly detect all of the abrupt change regions. Some traditional methods, such as moving t-test, Cramer method, Mann-Kendall test and Yamamoto method, even cannot provide any information of abrupt dynamic change in these model time series. Meanwhile, results showed that windows sizes and strong noise have some less effect on the MC-DFA results. In summary, MC-DFA provides a reliable measure to detect the abrupt dynamic change in correlated time series, and perfectively make up the deficiencies of MDFA and ApEn. The applications in daily surface air pressure records further verify the validity of the present method. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Using generalised Pareto distribution (GPD) function, the regional characteristic of extreme monthly low temperatures (EMLT) distribution during 1960-2007 in China is studied, and the potential impact of climate warming on EMLT regions in China is analysed. The results show that EMLT space distribution has latitudinal difference in China. Higher-value EMLT events are likely to happen in northeastern and southwestern China, while EMLT variability and occurrence probability of high-value EMLT events are less in the Yangtze and Huaihe River basin and the coast of southern China. The study indicates that climate warming impacts EMLT, and EMLT distribution responds to the climate warming over most areas of China. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]