# Presentation: 907675

**   ****The role of Noise in El Niño has been widely studied.****  ****Noise contributes to **
**the irregularity and predictability of El Niño. ENSO is considered as a noise **
**driven phenomenon. The amplitude of the El Niño variability is determined by **
**the noise characteristics [Henk A. Dijkstra, 2002]. On the other hand, noise is **
**not important for long term forecasting El Niño [D. Chen,et al., 2004].**
**   ****Fractal dimension (e.g. Hurst coefficient**** “****H****”****)**** is**** an index for the noise **
**characters. The spatial fractal characters of sea surface temperature (****SST****) **
**have been documented in many articles. However, how do El Niño/La Niña **
**events affect the changes of**** ****noise characters of SST? **
**   ****Self-organized criticality (****SOC****) is one possible explanation for fractal **
**behaviors. There is an opinion that an El Niño event is a result of the SOC **
**phenomena. How can we demonstrate adequate ****scaling**** and ****critical features**** **
**of SOC for El Niño/ La Niña? **
** ****In this study, we applied the Discrete Daubechies Wavelet Transformation **
**Method (****DDWT****) to analyze 131 segments of Niño3.4 SST time series over a **
**period from 187****0**** to 2005. Each segment is 64 months long, starting from **
**January. The range of the frequencies analyzed in this study was from **
**2-month to 32-month. The timescale of 32-month was called Quasi-Biennial **
**(****QB****) in this study. **
**   ****From the view of SOC, d****ecadal/ inter-decadal variations provide a basic **
**background for El Niño. Inter-annual cycles directly affect El Niño. High **
**frequencies play as the first chain-reaction of setting out El Niño.**

**  ****A temporal-spectral analysis based on DDWT is carried out for the study. **

**  ****There do exist scaling behavior and critical state of SST oscillation during **
**El Niño episodes. But the exact critical points of spectrum variation for El **
**Niño/La Niña  are not clear yet. A wavelet based forecasting method should be **
**carried out for the further investigation.  Moreover, how does El ****Niño**** affect **
**Monsoon and NAO fractal characters?  **

**1. Introduction**

**4. Results**

**2. Data**

**3. Method**

**5. Discussion  and further work**

**   ****(2). During 1894-1923, and  1978-2000, the  oscillation patterns of  “H” and ENSO-event-**
**index were well matched. It could be a consequence of decadal/ inter-decadal variations **
**of background state.****                                                **
**(3). The unmatched oscillation patterns are normally due to the effluences of La Niña.**** **

**    ****Wavelet based fractal characters of El Niño/La Niña episodes**
*Zhiyong Huang (1), Hiroshi Morimoto (2)*
*(1), (2), **Departmental of Earth and Environmental* *Sciences, *
*Graduate School of Environmental Studies, Nagoya University, Japan *
**Corresponding author (1), mail to  s050109d@mbox.nagoya-u.ac.jp **

**2007/10/22-26**
**Tallahassee, FL**

**P2.8**

**The procedure of calculation:**
** ****Select a segment of SSTs time series with a length of  64-month, called time-window, starting from a January.**
** ****Apply DWT for averaged spectrum analysis ****[Rene Carmona et al., 1998].**
**(3) Calculate Hurst parameter ‘H’. It is a good tool [A.L. Wang et al., 2002].**
**(4) Shift time-window year by year.**
**(5) Repeat the steps from (1) to (4).**