Appropriateness of correlated first-order auto-regressive processes for modeling daily temperature records

Nagarajan, Radhakrishnan; Govindan, Rathinaswamy B.

doi:10.1016/j.physa.2005.12.042

Physica A: Statistical Mechanics and its Applications

2006

DOI: 10.1016/j.physa.2005.12.042

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Appropriateness of correlated first-order auto-regressive processes for modeling daily temperature records

Radhakrishnan Nagarajan

Rathinaswamy B. Govindan

Abstract: The present study investigates linear and volatile (nonlinear) correlations of firstorder autoregressive process with uncorrelated AR (1) and long-range correlated CAR (1) Gaussian innovations as a function of the process parameter (θ). In the light of recent findings [1], we discuss the choice of CAR (1) in modeling daily temperature records. We demonstrate that while CAR (1) is able to capture linear correlations it is unable to capture nonlinear (volatile) correlations in daily temperature records.

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Cited by 3 publications

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Nonlinear correlations of daily temperature records over land

Bartos¹,

Jánosi²

2006

Nonlin. Processes Geophys.

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Abstract. We present a near global statistics on the correlation properties of daily temperature records. Data from terrestrial meteorological stations in the Global Daily Climatology Network are analyzed by means of detrended fluctuation analysis. Long-range temporal correlations extending up to several years are detected for each station. In order to reveal nonlinearity, we evaluated the magnitude of daily temperature changes (volatility) by the same method. The results clearly indicate the presence of nonlinearities in temperature time series, furthemore the geographic distribution of correlation exponents exhibits well defined clustering.

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Nonlinear correlations of daily temperature records over land

Bartos¹,

Jánosi²

2006

Nonlin. Processes Geophys.

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Distribution of most probable temperature of daily average temperature records in China and the slowing down of warming trend

Zhong-Hua¹,

Feng²,

Gong³

2010

Acta Phys. Sin.

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Most probable temperature (MPT) is defined according to the skewed function by using daily average temperature records of the National Meteorological Information Center from 1961 to 2008 in China, it can represent every stations background temperature explicitly. MPT interannual variations show that MPT in summer was relativelly low-temperature-major before the mid-1990s and from then on was warming with fluctuation, but since 2005 the trend slowed down, and that MPT in winter was relatively low-temperature-major in 1961—1986, and from 1987 to the beginning of the 21st century was warming up obviously, but since 2000 the warming trend slowed down. The warming amplitude in winter was stronger than that in summer and the time of beginning of warming in winter was earlier than that in summer by about 5—10 years. Studying three climate states of MPT changes of 1961—1990, 1971—2000 and 1981—2008, we found that in summer MPT got warmer obviously in the third climate state, while in winter in the second state. MPT warming trend was slowing down in the third state. Its worthy of note that in Sichuan, Guangdong and Guangxi MPT even began to decline in the last few years. Whether it means climatic transformation or not needs further research.

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Effect of global warming on law of record-breaking high temperature

Xu-ping¹,

Li²,

Kai-Guo³

et al. 2010

Acta Phys. Sin.

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Statistical characteristic of daily temperature series (1954—2004) of Xiamen station is analyzed by using Gaussian and skew distribution functions, and then the future probable trend of record temperature events (RBTE) is also simulated by using Monte-Carlo(MC) methods based on the Gaussian and skew distribution functions, respectively. Results show that the statistical property of nearly 50a daily observation temperature data in June of Xiamen station is more consistent with that obtained from the skew function. However, the theoretical study shows that the skew function and Gaussian function have the same limit of convergence, i.e. the Gumbel distribution function. The results also show that the MC simulation based on the skew distribution with global warming background can reveal the future probable extreme events well, and the Xiamen's daily temperature distribution of June in the next 10 a is predicted. The global warming background can lead the occurrence probabilities of high-temperature record-breaking event and the average daily temperature to increase. In addition, based on the observed date in China, the spatial temperature distribution of the occurrence with the max probability over China in coming 10 years is also presented.

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Appropriateness of correlated first-order auto-regressive processes for modeling daily temperature records

Cited by 3 publications

References 17 publications

Nonlinear correlations of daily temperature records over land

Nonlinear correlations of daily temperature records over land

Distribution of most probable temperature of daily average temperature records in China and the slowing down of warming trend

Effect of global warming on law of record-breaking high temperature

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