final_program_abstracts[1]

11 IMSC Session Program
Non-linear trend technique to analyze non-stationary climate
series
Tuesday - Poster Session 10
Oleg Pokrovsky
Main Geophysical Observatory, St. Petersburg, Russia
A smoothing procedure to acquire non-linear trend (NLT) for a non-stationary time
series was developed. It is based on a combination of piecewise local polynomial data
approximation and smoothing by means of the regularization mechanism of
Tikhonov. A selection rule for impact nodes and data is governed by means of crossvalidation
criterion Wahba. The NLT provides a unbiased feature fulfillment similar
to those for linear trend (LT): estimates. Besides the NLT estimates delivers some
novel statistical benefits: (1) “white noise”-like behavior of the NLT deviations from
the observing data (2) delta-type of autocorrelation function for the deviation time
series in contrast to those for the LT; (3) lesser corresponding variances and widths
for confidential intervals with the same statistical significance levels than these for the
LT. Global annual surface air temperature (SAT) CRUTEM3 data set for 1850-2009
years was used for illustration of this method efficiency with account to LT and
moving average (MA) technique. The NLT smoothing permits to reveal SAT
wavelike oscillations with quasi-periodicity of 65-70 years. Independent wavelet
analysis confirms existence of this quasi-periodicity in data. Similar study for Atlantic
Multidecadal Oscillation (AMO) for winters in 1856-2009 years has proved that there
is a coherency between the AMO and the de-trended global SAT series.
Autocorrelation function for time series of the LT deviations from the SAT and AMO
data demonstrates pair wise swings just in intervals of its non-stationary behavior, e.g.
65-70 years. Autocorrelation functions responded to the NLT have a delta-like
structure. That means that corresponding NLT deviations from observations are close
to model of “white noise”. Wavelet analysis of the NLT SAT and AMO displays a
single anomaly in 2-D spectrum scales of 65-70 years, which looks like those in
spectrum for original data. Similar analysis carried out with the MA smoothing
procedure could not be so helpful in revealing of slow temporal data oscillation and
provides more blurred 2-D wavelet spectrums. Examples of NLT for other climate
parameters (solar activity, sea ice extent, sea surface temperature, Pacific Decadal
Oscillation, etc.) will be presented.
Abstracts 120