# Presentation: 025316

## The Impact of ENSO on NAO  Variability

- Zhaohua Wu, B. P. Kirtman, E. K. Schneider
*Center for Ocean-Land-Atmosphere Studies*
- and
- N. E. Huang
*Goddard Space Flight Center, NASA*

**Notes:**
The title of my talk is “The impact of ENSO on NAO variability”. This work was done with Drs. Kirtman and Schneider at COLA.

## OUTLINE

- A question
- A natural filter based on EMD
- ENSO and NAO in observation
- Mechanism (incomplete)
- Summary

## AN OLD STORY

- 	“_Editorial Note_: *In the following suite of formal comments and responses we are following ****unusual**** ****procedure****; we are providing a venue for a valuable but ****unusual scientific exchange**** that involves multiple articles.*”
- 						BAMS, 1999, p2721
- Wunsch, BAMS, 1999, p245-255.
- Comments on “1” by Trenberth and Hurrell
- Reply to “2” by Wunsch
- Comments on “2” by Rajagoplan, Lall, and Cane
- Reply to “4” by Trenberth and Hurrell

## A QUESTION

- NAO Index

- Does white noise like NAO index contains the signals related to the interannual ENSO variability?
- (The lag one autocorrelation for the observed NAO index is 0.08.)

***X(t) = S(t) + N(t)***

**Notes:**
ENSO and NAO have been two of the most widely studied climate systems in the last two decades. Many have spent effort to reveal the relationship between them. However, some sort of consensus seems to be reached that there is little relationship between them. The most obvious reason to support such claims was that NAO has a white noise like temporal variation while ENSO has dominant interannual variability.  Therefore, I was a kind of dumb to try at the beginning to continue to examine if there are causal relations between them. Formally, my starting point is to impose a question that has been asked for many times.

## BAND PASS FILTERING

- Traditional Filters
- Subjective parameters
- “local” -> “global”
- Results
- Tuned
- fuzzy

**Notes:**
Before I show the results of relationships between them, I will make a detour to examine the band pass filtering method that has often been used to extract information from a time series. Traditional band pass filters can be schematically drawn as the figure. The horizontal axis is the frequency. For such filters, one needs to prescribe subjectively at least two parameters: the central location of the band and the band width. Because these filters are mostly based on Fourier transform, it is inevitable that the local signal in time domain is spread over the whole time domain of the time series. An easy example is the Delta function. When the band passed times series is recovered, it is often fuzzy even if the parameters are tuned.

## EMPIRICAL MODE DECOMPOSITION

**receiver**

## SIFTING PROCESS

## IMFs OF NAO INDEX

## STATISTICAL SIGNIFICANCE

## BAND PASS FILTER

- EMD Filter
- Adaptive without prescribed parameter
- “local” ->”local”
- Physical
- Results
- Clear
- Dyadic

## “LOCAL” -> “LOCAL”

## AN IDEALIZED MODEL

- NAO = Internal Variability + ENSO forced signal
- For simplicity, assuming
- When the standard deviation of the noise is one, *A* is 0.4, *T* is about 4.2 yr.

## DATA

## SIGNIFICANCE TEST

## PHYSICAL DECOMPOSITION

## ENSO/NAO IN OBSERVATION

## POSSIBLE ROUTES

## APPROACHES

- GCM Studies: Globally coupled *vs.* regionally uncoupled
- In the regional uncoupled cases, the atmospheric model is forced by climatological SST while the ocean model is driven by the interactive fluxes.

## WAVE CHAIN ROUTE

- NAO and SST variability in the North Atlantic will still have a significant simultaneous (or slightly delayed) correlation with ENSO cycle if the causal effect is through PNA type of wave chains in the North Atlantic uncoupled case.

## LOW LATITUDE ROUTE

- If the ENSO effect is through the low latitude route by affecting the wind stress that drives the subtropical gyre, the  correlation between ENSO and NAO will be missing.

## GCMS & EXPERIMENTS

- COLA Anomaly Coupled GCM:**  **
- ATMOSPHERE: COLA AGCM version 2, T42, 18 layers
- OCEAN: GFDL MOM 2,  1.5 ̊*0.5 ̊ in the tropics, 1.5 ̊*1.5 ̊ in the high latitude, 25 layers (15 layers in upper 250 m)
- ICE: observed climatology
- EXPERIMENTS:
- CONTROL: coupled globally
- PAC_UNCOUPLED: Tropical Pacific (between 30 ̊N and 30 ̊ S) uncoupled
- ATL_UNCOUPLED: North Atlantic (north of 15 ̊N) uncoupled
- Additional

## ENSO/NAO IN MODEL (I)

## ENSO/NAO IN MODEL (II)

**Notes:**
Emphasize NAO index in interannual timescale is indeed related to the ENSO variability.

## ENSO/NAO IN MODEL (III)

## STORY TO BE CONTINUE

- ...
- ...
- ...
- ...

## CONCLUSIONS

- The new natural filtering method provides a unique opportunity to examine the impact of ENSO on NAO variability on interannual timescale
- The observational evidence showed that NAO interannual variability is well correlated to ENSO variability in the tropical Pacific
- The modeling studies demonstrate that the relation of ENSO and NAO is hidden in the seemingly white noise like NAO variability
- More analysis need to be carried out to figure out causal relationships between ENSO and NAO