Climatology and Trends of High Temperature Extremes across China in Summer

Based on the daily maximum air temperature data from 300 stations in China from 1958 to 2008, the climatological distribution of the number of days with high temperature extremes （HTEs, maximum temperatures higher than 35℃） are studied with a focus on the long-term trends. Although the number of HTE days display well-defined sandwich spatial structures with significant decreasing trends in central China and increasing trends in northern China and southern China, the authors show that the decrease of HTE days in central China occurs mainly in the early period before the 1980s, and a significant increase of HTE days dominates most of the stations after the 1980s. The authors also reveal that there is a jump-like acceleration in the number of HTE days at most stations across China since the mid 1990s, especially in South China, East China, North China, and northwest China.

GP Algorithm-Based Fourier Transform Infrared Spectrum Trend Term Removal Model

Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.

Spatial / Temporal Features of Antarctic Climate Change

Bused on January 1962-October 1993 mean value series of monthly mean temperature anomalies of 16 Antarctic stations on 10 standard isobanc surfaces from the surface to the 30 hPa,long term trends and periodic features of climate changes from the troposphere to the lower stratosphere over the Antarctic region are investigated by maximum entropy power spectrum analysis,and the relation between climate change of the stratosphere (troposphere) and tolal ozone (southern 500 hPa circulation) is discussed.

Restoration of Time-Spatial Scales in Global Temperature Data

The objective of this paper is to utilize images of spatial and temporal fluctuations of temperature over the Earth to study the global climate variation. We illustrated that monthly temperature observations from weather stations could be decomposed as components with different time scales based on their spectral distribution. Kolmogorov-Zurbenko (KZ) filters were applied to smooth and interpolate gridded temperature data to construct global maps for long-term (≥ 6 years) trends and El Nino-like (2 to 5 years) movements over the time period of 1893 to 2008. Annual temperature seasonality, latitude and altitude effects have been carefully accounted for to capture meaningful spatiotemporal patterns of climate variability. The result revealed striking facts about global temperature anomalies for specific regions. Correlation analysis and the movie of thermal maps for El Nino-like component clearly supported the existence of such climate fluctuations in time and space.