The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

Total ozone trends are typically studied using linear regression models that assume a
first order autoregression of the residuals (so-called AR(1) models). We consider total ozone
time series over 60S-60N from 1979-2005 and show that most latitude bands exhibit
long-range correlated (LRC) behavior, meaning that ozone autocorrelation functions decay by
a power law rather than exponentially as in AR(1). At such latitudes the uncertainties of total
ozone trends are greater than those obtained from AR(1) models, and the expected time
required to detect ozone recovery correspondingly longer. We find no evidence of LRC
behavior in southern middle and high sub-polar latitudes (45-60S), where the long-term ozone
decline attributable to anthropogenic chlorine is the greatest. We thus confirm an earlier
prediction based on an AR(1) analysis that this region (especially the highest latitudes, and
especially the South Atlantic) is the optimal location for the detection of ozone recovery, with a
statistically significant ozone increase attributable to chlorine likely to be detectable by the end
of the next decade. In northern middle and high latitudes, on the other hand, there is clear
evidence of LRC behavior. This increases the uncertainties on the long-term trend attributable
to anthropogenic chlorine by about a factor of 1.5, and lengthens the expected time to detect
ozone recovery by a similar amount (from ~2030 to ~2045).
Crytic Period Analysis Model of Hydrological Process Based on Herteroskedasticity Test and Its
Application
Hongrui Wang
College of Water Sciences-Key Laboratory for Water and Sediment Sciences Ministry of Education,
<strong>Beijing</strong> Normal University, <strong>Beijing</strong>, 100875
henryzsr@bnu.edu.cn
Lin xin
College of Water Sciences-Key Laboratory for Water and Sediment Sciences Ministry of Education,
<strong>Beijing</strong> Normal University, <strong>Beijing</strong>, 100875
Yang Chi
College of Water Sciences-Key Laboratory for Water and Sediment Sciences Ministry of Education,
<strong>Beijing</strong> Normal University, <strong>Beijing</strong>, 100875
Qian Longxia
College of Water Sciences-Key Laboratory for Water and Sediment Sciences Ministry of Education,
<strong>Beijing</strong> Normal University, <strong>Beijing</strong>, 100875
<strong>The</strong> hydrological process is very complicated, which is influenced by both deterministic
and stochastic factors. It is difficult to analyze its critic period, because hydrological time series
is probably characterized by herteroskedasticity. To find out the cryptic period, a model is put
forward as follows: (1) to apply zero-mean-value to the data, to have ADF stationary test for
the sequence and to develop the corresponding AR(p) model, and then to have ARCH effects
test and white noise test for residual series, (2) for the time series unfit for ARCH test, to
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