共查询到10条相似文献，搜索用时 38 毫秒
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Non-stationary time series such as global andhemispheric temperatures, greenhouse gasconcentrations, solar irradiance, and anthropogenicsulfate aerosols, may contain stochastic trends (thesimplest stochastic trend is a random walk) which, dueto their unique patterns, can act as a signal of theinfluence of other variables on the series inquestion. Two or more series may share a commonstochastic trend, which indicates that either oneseries causes the behavior of the other or that thereis a common driving variable. Recent developments ineconometrics allow analysts to detect and classifysuch trends and analyze relationships among seriesthat contain stochastic trends. We apply someunivariate autoregression based tests to evaluate thepresence of stochastic trends in several time seriesfor temperature and radiative forcing. The temperatureand radiative forcing series are found to be ofdifferent orders of integration which would cast doubton the anthropogenic global warming hypothesis.However, these tests can suffer from size distortionswhen applied to noisy series such as hemispherictemperatures. We, therefore, use multivariatestructural time series techniques to decomposeNorthern and Southern Hemisphere temperatures intostochastic trends and autoregressive noise processes. These results show that there are two independentstochastic trends in the data. We investigate thepossible origins of these trends using a regressionmethod. Radiative forcing due to greenhouse gases andsolar irradiance can largely explain the common trend.The second trend, which represents the non-scalarnon-stationary differences between the hemispheres,reflects radiative forcing due to tropospheric sulfateaerosols. We find similar results when we use the sametechniques to analyze temperature data generated bythe Hadley Centre GCM SUL experiment. 相似文献
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Identifying Nonstationarity in Turbulence Series 总被引：1，自引：1，他引：0
Edgar L Andreas Cathleen A. Geiger George Treviño Kerry J. Claffey 《Boundary-Layer Meteorology》2008,127(1):37-56
Because of rapid forcing by varying cloud and sky conditions, turbulence time series collected in the atmospheric surface
layer over land may often be nonstationary. The meteorological community, however, has no consensus definition of what nonstationarity
is and, thus, no consensus method for how to identify it. This study, therefore, adopts definitions for first-order and second-order
stationarity taken from the time series analysis literature and implements new analysis techniques and probabilistic tests
to quantify first-order and second-order nonstationarity. First-order nonstationarity manifests as a change in the series
mean; second-order nonstationarity, as a change in the variance. The analysis identifies nonstationarity in surface-level
turbulent temperature and water vapour series collected during two sample days with solar forcing influenced by cirrus and
cirrostratus clouds, but that nonstationarity is not as severe as expected despite the rapid thermal forcing by these clouds.
On the other hand, even with negligible cloud forcing, both sample days exhibited severe nonstationarity at night. 相似文献
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着重评述了目前国际上常用的3个全球平均温度序列：HadCRUT3、NCDC及GISS。后两个序列1982年之后引入了卫星观测资料。NCDC对资料作了插补,建立了1880-2009年覆盖面完整的序列。HadCRUT3序列所显示的近10年变暖的停滞可能主要是缺少极区资料造成的。NCDC与GISS两个序列依然保持约0.1℃/10a的增温。3个序列1910-2009年的变暖趋势十分接近,在0.70～0.75℃/100a之间。 相似文献
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Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series （1960-2006） 总被引：4，自引：2，他引：2
Homogenization of climate observations remains a challenge to climate
change researchers, especially in cases where metadata (e.g., probable dates of
break points) are not always available. To examine the influence of metadata on
homogenizing climate data, the authors applied the recently developed Multiple
Analysis of Series for Homogenization (MASH) method to the Beijing (BJ) daily
temperature series for 1960--2006 in three cases with different references: (1)
13M---considering metadata at BJ and 12 nearby stations; (2)
13NOM---considering the same 13 stations without metadata; and (3)
21NOM---considering 20 further stations and BJ without metadata. The estimated
mean annual, seasonal, and monthly inhomogeneities are similar between the 13M
and 13NOM cases, while those in the 21NOM case are slightly different. The
detected biases in the BJ series corresponding to the documented relocation
dates are as low as -0.71oC, -0.79oC, and -0.5oC
for the annual mean in the 3 cases, respectively. Other
biases, including those undocumented in metadata, are minor. The results
suggest that any major inhomogeneity could be detected via MASH, albeit with
minor differences in estimating inhomogeneities based on the different
references. The adjusted annual series showed a warming trend of 0.337, 0.316,
and 0.365oC (10 yr)-1 for the three cases, respectively, smaller
than the estimate of 0.453oC (10 yr)-1 in the original series,
mainly due to the relocation-induced biases. The impact of the MASH-type
homogenization on estimates of climate extremes in the daily temperature series
is also discussed. 相似文献
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On Reynolds Averaging of Turbulence Time Series 总被引：1，自引：1，他引：0
We show that validity of Reynolds averaging for estimating the (ensemble) mean of a turbulence time series requires that the
series values be both stationary and uncorrelated. In strict statistical terminology, these two conditions are jointly designated as independent identically distributed (i.i.d.). Moreover, we show that when the series values are correlated, knowledge of the correlation between the values is needed
to obtain a reliable estimate of the mean. Last, we contend that a viable averaging algorithm must be Reynolds number (Re) dependent, requiring one version for low Re (Gaussian) turbulence and another for high Re (non-Gaussian) turbulence. Alternatively the median (as opposed to the mean) is recommended as a measure of the central tendency of the turbulence probability density function. 相似文献
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Single-point, three-component turbulent velocity time series data obtained in the atmospheric boundary layer over the ocean reveal coherent structures that are consistent with a model of a steady linearly varying spatial velocity field that translates past the measurement point at constant velocity. The kinematic model includes both strain and rotation rates and has implications regarding vortex generation, vortex pairing, vortex break-up, and stability. While the complete specification of the dimensions, spatial velocity gradients, and translational velocity of the linear coherent structure (LCS) cannot be made from the single-point, three-component measurements, the model LCS velocity time series can be determined from least- squares fits to the data. The total turbulent kinetic energy is used to find in the record the initial and final times of a model LCS in the data, i.e., the time interval over which a model LCS is passing over the anemometer. Maxima in the kinetic energy removed from the data (by subtraction of the model LCS velocity functions from the data) are used to identify the most-energetic model LCSs. These model LCS velocity functions replicate the essential large-scale features of the time series of the three-component velocity fluctuations, most noticeably in the streamwise component. The model LCS decomposition was used to perform a scale analysis of the data, which was compared to the usual Fourier method. Time intervals of model LCSs were found successively in the data, after subtracting the previous fits. This process resulted in a series of 'levels with a number of LCSs found at each level. About six levels account for most of the kinetic energy. The model also allows the computation of the Reynolds stress components, for which six levels also are sufficient. The recomposition of the time series on a LCS-by-LCS basis compares well with the mode-by-mode Fourier recomposition for the average momentum fluxes and kinetic energy. 相似文献