Download Issues with the Application of Empirical Mode

Global analysis of recent frequency
component changes in interannual
climate variability
Murray Peel1 & Tom McMahon1
1 Civil
& Environmental Engineering, The University of Melbourne,
Victoria, Australia
Outline

Background
 Temporal changes in frequency components
– Empirical Mode Decomposition
– Data set
– Results for dividing year = 1970
– Sensitivity of results to the dividing year

Conclusions
EGU 2006 - Session
AS1.07/CL040/CL007
Climate change impact on
climate variability

Mainly assessed at daily, monthly &
seasonal scales
– Changes in extreme event frequency
– Changes in the shape parameter of the daily
frequency distribution

Less attention has been paid to the annual
scale
EGU 2006 - Session
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Background

Potential modification of interannual
climate variability is important
– Multi-year drought severity
– Reservoir reliability
– Ecosystem dynamics
EGU 2006 - Session
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Interannual Climate Variability

What drives changes in the mean and
variance of an annual time series?
– Look at the components of a time series using
spectral analysis
Source: IPCC, Climate Change 2001
EGU 2006 - Session
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Spectral Analysis (EMD)

Empirical Mode Decomposition (EMD)
– Decomposes a time series into
 Intrinsic Mode Function(s) (IMFs)
 A residual (Trend)
– Locally adaptive algorithm
 Robust to non-linear / non-stationary data
– No data pre-processing (like removal of
“trend”)
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Spectral Components

EMD spectral components
– High Frequency (<10 years; Intra-Decadal)
 Sum of IMFs with average period < 10 years
– Low Frequency (>10 years; Inter-Decadal)
 Sum of IMFs with average period >= 10 years + the
residual
– Effectively using EMD as a high/low pass filter
EGU 2006 - Session
AS1.07/CL040/CL007
EMD Example
Robe, South Australia
Annual Precipitation (mm) .
1000
800
600
Observed
High Component
Low Component
400
200
0
-200
-400
1860
1880
1900
1920
1940
1960
Year

Can assess temporal changes in component
behaviour (pre and post a dividing date)
EGU 2006 - Session
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1980
2000
Data Set

Annual temperature and precipitation data from
the GHCN (version 2)
 Choose 1970 as the dividing year
– To maximise the number and spatial distribution of
stations with >= 15 years of unbroken record pre- and
post- the dividing year (N >= 30)
– Annual temperature

Stations = 1,524, average N = 63 years, ~1930 - 1993
– Annual precipitation

Stations = 2,814, average N = 74 years, ~1920 - 1993)
EGU 2006 - Session
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Results
Robe Example
VarRatio1970 = Variance>=1970 / Variance<1970
– Obs. = 0.65, High = 0.80, Low = 0.64
1000
Annual Precipitation (mm) .

800
600
Observed
High Component
Low Component
400
200
0
-200
-400
1860
1880
1900
1920
1940
Year
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1960
1980
2000
Results
Temperature – 1,524 Stations
VarRatio1970
Percentiles
% Stations
Obs.
High
Low
5%
0.41
0.45
0.09
50%
0.95
1.04
0.67
95%
1.97
2.08
5.36
>=1
44.3
53.3
38.3
<1
55.7
46.7
61.7
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Temperature
Observed VarRatio1970
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No. Stations (>=1) > (<1)
No. Stations (>=1) = (<1)
No. Stations (>=1) < (<1)
Results
Precipitation – 2,814 Stations
VarRatio1970
Percentiles
% Stations
Obs.
High
Low
5%
0.46
0.45
0.11
50%
0.95
0.98
0.79
95%
2.03
2.21
4.43
>=1
44.8
48.6
40.7
<1
55.2
51.4
59.3
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Precipitation
High Component VarRatio1970
EGU 2006 - Session
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No. Stations (>=1) > (<1)
No. Stations (>=1) = (<1)
No. Stations (>=1) < (<1)
Sensitivity to dividing year
Temperature
100%
80%
Obs
High
Low
Stations
1440
1280
70%
1120
60%
960
50%
800
40%
640
30%
480
20%
320
10%
160
0%
0
1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985
Dividing Year
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Stations
% of Stations with VarRatio (>=1)
90%
1600
Sensitivity to dividing year
Precipitation
4000
Obs
High
Low
Stations
% of Stations with VarRatio (>=1)
90%
80%
3600
3200
70%
2800
60%
2400
50%
2000
40%
1600
30%
1200
20%
800
10%
400
0%
0
1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985
Dividing Year
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Stations
100%
Conclusions – Temperature

VarRatio1970
– Observed: slight decrease
– High Frequency (intra-decadal): slight increase
– Low Frequency (inter-decadal): large decrease
 Variance moving from low to high frequency
component, over much of the last century
 Decreasing the long-term memory
 Increasing the degree of randomness
EGU 2006 - Session
AS1.07/CL040/CL007
Conclusions – Precipitation

VarRatio1970
– Observed: slight decrease
– High Frequency (intra-decadal): slight decrease
– Low Frequency (inter-decadal): large decrease
 Variance moving from low to high frequency
component, over much of the last century
 Decreasing the long-term memory
 Increasing the degree of randomness
– To a lesser extent than the temperature results
EGU 2006 - Session
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Overall Conclusions

Recent increase in intra-decadal fluctuations
maybe due to climate change
– Consistent with other research indicating that
the degree of randomness will increase under a
warmer climate

Recent decrease in inter-decadal
fluctuations may reduce the usefulness of
teleconnection based forecasting systems
EGU 2006 - Session
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Acknowledgements

The analysis presented forms part of a paper
under review at
– Geophysical Research Letters

Funded by
– Australian Research Council Discovery Grant

Useful Discussions
– Geoff Pegram
EGU 2006 - Session
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