1. Mike McPhaden NOAA/PMEL Seattle, Washington
ENSO Observations
Mike McPhaden NOAA/PMEL Seattle, Washington
CLIVAR ENSO Workshop
Paris, France
17-19 November 2010

2. Required for Description, Understanding and Prediction
Observations
Required for Description, Understanding and Prediction 2

3. Initial Global Ocean Observing System for Climate Status against the GCOS Implementation Plan and JCOMM targets
Total in situ networks
60%
May 2008
87%
100%
62%
81%
100%
79%
43%
24%
48%
GOOS

4. A Short History of ENSO Observations
Paleo Proxies
Instrumental
1850s Matthew Fontaine Maury
1950s IGY & Bjerknes
1960s Satellite era for weather
1980s AVHRR, Geosat altimetry
 TOGA
1990s High precision altimetry, scatterometry
The climate data record in the Pacific for detailed ENSO studies begins around Only since then has there been sufficient data from below the surface and from space data to describe and diagnose observed variations.
 30 years of systematic subsurface ocean and satellite observations for describing, analyzing and developing forecasting capabilities for ENSO 4

5. Current Conditions
Global Tropical Array

6. Current Conditions
Global Tropical Array

7. El Niño vs La Niña
Global Tropical Array

8. Recharge Oscillator Theory (Wyrtki, 1985; Cane et al, 1986; Jin, 1997)
Meinen & McPhaden, 2000

9. Upper Ocean Heat Content and ENSO
Build up of excess heat content along equator is a necessary precondition for El Niño to occur.
El Niño purges excess heat to higher latitudes, which terminates the event.
The time between El Niños is determined by the time to recharge.
Note that heat content builds up prior to all El Ninos of the past 25 years.
The strongest El Nino in also had that largest prior build up.
During each El Nino heat content dropped often to large negative values.
Next El Nino did not occur until heat content anomaly was again in positive territory.
After Meinen & McPhaden, 2000
Heat content based on TAO/TRITON, XBT and Argo data

10. Global Tropical Array Upper Ocean Heat Content and ENSO
After Meinen & McPhaden, 2000
Heat content based TAO/TRITON, XBT and Argo data
Upper ocean heat content variations are the source of predictability for the ENSO cycle
Global Tropical Array

11. Lead Time Changes
Switch to WWV for exact comparison with MM; they used WWV. T300 and WWV are almost identical.

12. Seasonality of Lead Time Changes
McPhaden,, 2003: “Tropical Pacific Ocean heat content variations and ENSO persistence barriers.” GRL

13. Trends in Central Pacific El Niño SSTs
SST anomaly Dec 2009
Lee & McPhaden, Geophys. Res. Lett., 2010
Niño-4 SST
Central Pacific El Niños are increasing in frequency and amplitude

14. EP vs CP El Niños
3/5 EP
3/4 CP

15. Ratio of CP/EP El Niños Increases Under Global Warming
20th century simulations
21st century A1B scenario
TAO/TRITON: a partnership between NOAA/PMEL and the Japan Marine Earth Science and Technology Agency.
ATLAS moorings are the workhorse. These moorings were developed at PMEL.
Mean Thermocline Depth
Yeh et al, Nature, 2009

16. Changes in Background Conditions

17. Differences in El Niño CompositesEP CP
CP-EP

18. Mean State  El Niño Statistics?
ENSO ?

19. Summary
30 years of systematic subsurface ocean and satellite observations available for detailed studies of ENSO and its decadal modulation
Lead time of ocean heat content (WWV) a predictor of ENSO SST has decreased from 2-3 seasons to ~1 season in the first decade of the 21st century
Loss of predictability concentrated early in the calendar year
Changes correspond to increasing incidence of CP El Niños
Corresponds to decadal changes in background conditions (winds, thermocline depth, SST)
Natural variability? Influence of global warming?
CLIVAR ENSO Workshop
Paris, France
17-19 November 2010

20. The rationale for developing a forecasting capability is that skillful ENSO forecasts offer advance warning for protecting against, or taking advantage of, ENSO related changes in environmental conditions that affect life, property, and economic vitality.
ENSO is the most predictable year-to-year fluctuation of the climate system because of the way slow changes in ocean heat content precondition the system for warm and cold events to occur.
To make forecasts, one need models and real-time data to initialize them.
The first El Nino to be successfully predicted was the El Nino. Since then, considerable effort has been put into developing and improving ENSO forecast models. There are now about 20 groups world wide making routine forecasts
And in the future?

21. Global Tropical Array Global Tropical Moored Buoy Array:
TRITON
Global Tropical Moored Buoy Array:
A coordinated, sustained, multi-national effort to develop and implement moored buoy observing systems for climate research and forecasting throughout the global tropics
ATLAS
Key attributes:
Real-time
Ocean-atmosphere
High temporal resolution
Basin scale
A contribution to GOOS, GCOS, and GEOSS
Global Tropical Array

22. Eastern vs Central Pacific (“Modoki”) El Niños
Ashok, 2009
Eastern Pacific
Niño-3.4
Central Pacific(Modoki)
Niño-4

23. A Short History of ENSO Observations
Paleo proxy
Instrumental
1850s Matthew Fontaine Maury
1950s IGY & Bjerknes
1960s Satellite era for weather satellites
1980s AVHRR, Geosat altimetry
 TOGA
1990s High precision altimetry, scatterometry 23