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Global analysis of recent frequency component changes in interannual climate variability

Global analysis of recent frequency component changes in interannual climate variability. Murray Peel 1 & Tom McMahon 1 1 Civil & Environmental Engineering, The University of Melbourne, Victoria, Australia. Outline. Background Temporal changes in frequency components

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Global analysis of recent frequency component changes in interannual climate variability

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  1. 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

  2. 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

  3. 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 AS1.07/CL040/CL007

  4. Background • Potential modification of interannual climate variability is important • Multi-year drought severity • Reservoir reliability • Ecosystem dynamics EGU 2006 - Session AS1.07/CL040/CL007

  5. Source: IPCC, Climate Change 2001 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 EGU 2006 - Session AS1.07/CL040/CL007

  6. 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”) EGU 2006 - Session AS1.07/CL040/CL007

  7. 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

  8. EMD ExampleRobe, South Australia • Can assess temporal changes in component behaviour (pre and post a dividing date) EGU 2006 - Session AS1.07/CL040/CL007

  9. 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 AS1.07/CL040/CL007

  10. ResultsRobe Example • VarRatio1970 = Variance>=1970 / Variance<1970 • Obs. = 0.65, High = 0.80, Low = 0.64 EGU 2006 - Session AS1.07/CL040/CL007

  11. ResultsTemperature – 1,524 Stations EGU 2006 - Session AS1.07/CL040/CL007

  12. No. Stations (>=1) > (<1) No. Stations (>=1) = (<1) No. Stations (>=1) < (<1) TemperatureObserved VarRatio1970 EGU 2006 - Session AS1.07/CL040/CL007

  13. ResultsPrecipitation – 2,814 Stations EGU 2006 - Session AS1.07/CL040/CL007

  14. No. Stations (>=1) > (<1) No. Stations (>=1) = (<1) No. Stations (>=1) < (<1) PrecipitationHigh Component VarRatio1970 EGU 2006 - Session AS1.07/CL040/CL007

  15. Sensitivity to dividing yearTemperature EGU 2006 - Session AS1.07/CL040/CL007

  16. Sensitivity to dividing yearPrecipitation EGU 2006 - Session AS1.07/CL040/CL007

  17. 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

  18. 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 AS1.07/CL040/CL007

  19. 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 AS1.07/CL040/CL007

  20. 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 AS1.07/CL040/CL007

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