1. El Nino – Southern Oscillation (ENSO) 圣婴现象和南方涛动 Mechanism, Prediction & Impacts December 1982 SST Anomaly

2. The white areas off the tropical coasts of South and North America indicate the pool of warm water

3. El Niño/La Niña-Southern Oscillation, or ENSO, is a quasiperiodic climate pattern that occurs across the tropical Pacific Ocean roughly every five years. It is characterized by variations in the temperature of the surface of the tropical eastern Pacific Ocean—warming or cooling known as El Niño and La Niña respectively—and air surface pressure in the tropical western Pacific—the Southern Oscillation. The two variations are coupled: the warm oceanic phase, El Niño, accompanies high air surface pressure in the western Pacific, while the cold phase, La Niña, accompanies low air surface pressure in the western Pacific. Mechanisms that cause the oscillation remain under study.

4. Discovering the Southern Oscillation

5. ENSO normal state ● Normal equatorial winds warm as they flow westward across the Pacific ● Cold water is pulled up along west coast of South America ● Warming water is pushed toward west side of Pacific

6. El Niño state Sea surface is warm in central and eastern Pacific Less cold water is pulled up along west coast of South America Hot air rises in central Pacific, travels east and west before cool

7. La NiñaLa Niña state Warm water accumulates in far western Pacific. Equatorial water is cooler than in the normal state

9. NINO3NINO3.4 NINO3.4 = ENSO index, measuring average SST anomaly within box 5S-5N; 170W-120W

10. NINO3 (or NINO3.4) is measure of oceanic part of ENSO. Southern Oscillation Index* (SOI) is measure of atmospheric part of ENSO. These two indices are highly correlated. *Traditional Version: SOI = SLP Tahiti - SLP Darwin (there’s also and equatorial version) Correlation > 0.9

11. Although they have similarities… ALL El Niño events are unique

12. Tropical Pacific – Average State Walker Circulation Mechanism - How it works: First understand the mean state

13. SST Winds Upper Ocean Gradient Structure Coupled Behavior in tropical Pacific (Thermocline)

14. Pacific Ocean Temperatures along Equator http://www.pmel.noaa.gov/tao/jsdisplayhttp://www.pmel.noaa.gov/tao/jsdisplay or http://www.tao.noaa.govhttp://www.tao.noaa.gov Based on these observations of equatorial temperatures: 1)Is eastern Pacific thermocline deeper or shallower than normal? 2)What direction are the zonal wind anomalies? 3)Will eastern Pacific SSTs get warmer or colder?

16. What is the direct (i.e. oceanic) impact of El Niño events on CO 2 variability?

17. “There is thus ample reason for a never-ending succession of alternating trends by air-sea interaction in the equatorial belt, but just how the turnabout between trends takes place is not yet quite clear.” J. Bjerknes 1969

18. Klaus Wyrti in early 1970s shows through observations of sea level that changes in upper ocean structure are related to ENSO variability, that can influence the initiation of El Nino or transition between El Nino and La Nina though ocean dynamics Decrease of sea level = Thermocline rise A dynamical response NOT surface heating Klaus Wyrtki

19. - - Wind Anomaly applied for 30 days Response of upper-ocean structure Dynes/cm**2 (Courtesy: Dave DeWitt, IRI) Warm SSTa +

20. Evolution of upper-ocean structure (or thermocline) anomalies Perturbations move eastward on the equator; westward off the equator Perturbations move slower as latitude increases (Courtesy: Dave DeWitt)

21. Continuing Evolution of upper-ocean structure (or thermocline) anomalies At western boundary, waves are reflected and channeled onto equator  Delayed negative feedback Warm SSTa (Courtesy: Dave DeWitt)

22. a b c d e Wind Stress Thermocline Anomalies Near peak El Nino Near peak La Nina Transition (neutral)

23. Main Points: * The tropical Pacific air-sea system is coupled, with the pattern of SSTs, the low-level winds and the thermocline slope all dynamically connected * El Nino & La Nina events result from coupled instability of the atmosphere/ocean system in the Tropical Pacific  Bjerknes Hypothesis of coupled growth + equatorial ocean dynamics * Among the fruits of the Bjerknes hypothesis, with Wyrtki’s contribution… ENSO events can be predicted ENSO events have been predicted The essence* of ENSO is understood *The “linear essence” at least

24. Zebiak-Cane Intermediate Coupled Ocean-Atmosphere Model Atmosphere Part – Low-level winds converge towards warmest SSTa, so atmospheric heating (SH & LH fluxes) are proportional to SSTa. This effect is amplified in regions where the mean SST is warm (mean convergence).

25. Zebiak-Cane Intermediate Coupled Ocean-Atmosphere Model Ocean Part – Very simplified ocean model (kind of like 2-layer fluid toy). Ekman transport in surface layer. Convergence or divergence in surface layer leads to changes in the depth of the thermocline, which sits at base of upper layer. Temperature anomaly in the sub-surface is determined by depth of the thermocline.

26. After Cane, Zebiak and Dolan - Nature 1986 and see Barnett, Graham, Cane, Zebiak, Dolan, O’Brien and Legler, Science 1988 Contours at 0.5°C First Successful [Documented] El Niño Prediction

27. Going back, they were able to get 1982/83:

28. Going forward, they were able to get 1990/91 (neutral):

29. Going forward, they were able to get 1991/92 (El Niño):

30. Going forward, they were NOT able to get 1993:

31. Factors limiting the current skill of forecasts: Model flaws Flaws in the way the data is used (data assimilation and initialization) Gaps in the observing system Inherent limits to predictability

32. Chen, et al 2004 Nature Some periods appear to be more predictable than others Prediction accuracy decreases at longer lead-times.

33. °C Example of Inherent Limit to Predictability  Sensitivity to Initial Conditions

34. °C

36. Another Example regarding inherent limits to predictability (and somewhat model flaws also) Evolution of the 2002-03 El Nino event compared to the 1997-98 El Nino event

37. “Signal” versus “Noise” issues ENSO is a slowly varying coupled ocean-atmosphere phenomenon with a timescale of a year or longer. Sub-seasonal weather acts rapidly on the coupled ocean-atmosphere system with a time scale of weeks to months. Eastward-propagating convective anomaly related to the MJO (Madden-Julian Oscillation) creates strong low-level winds.

38. 1996-1998 : Low Frequency

39. 1996-1998 : High Frequency

40. 2001-2003 : High Frequency

41. Even if a model has skill ENSO Prediction is Not a Guarantee

42. El Niño IMPACTS Source: Ropelewski & Halpert, 1987 J. Climate http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/impacts/warm.gif “Expectations” of climate anomalies during El Niño events

43. Monsoon Rainfall Index Red = warm NINO3 SSTA - El Niño Blue = cold NINO3 SSTA - La Niña

46. “Relative Frequency” of Climate Impacts (rainfall) due to El Niño Events Data & maps available through IRI Data Library: http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.Analyses/.ENSO-RP

47. Drought & El Nino Note: 5 month lag between max. NINO3.4 SSTA and extent peaks Spatial Extent of Tropical Drought Correlated with El Niño Source: B. Lyon, 2004, GRL

48. What is the indirect (i.e. through climate teleconnections) impact of El Niño events on CO 2 variability?

49. The basic ENSO mechanism is understood, and can be predicted, but gaps remain Role of MJO/WWBs, different “flavors” of ENSO, decadal differences in predictability Prediction skill is limited by Model flaws, data assimilation methods, limited data, inherent limits to predictability ENSO events have global impacts Many occur reliably, but most are just more likely with an El Niño or La Niña event ENSO events impact CO2 variability Summary

50. Extra Slides…

51. 1. Climate Mean State (focus on tropics):

55. Annual Mean Solar Radiation

56. Annual Mean Heat Flux into the Ocean

57. Pacific Annual SST

59. Steric Height relative to 2000m From T, S data at 1500m at 0m

60. Temperature along the equator

61. Equatorial Undercurrent

62. SST Anomalies: Dec 1997

64. 1997/98 El Niño

65. Economic “Cost” of El Nino 1982-83

66. Economic “Cost” of El Nino 1997-98 $14b USD: World Meteorological Organization $36b USD: NOAA OGP (excluding ’98 China floods) $45b USD : OFDA/CRED Int’l Disaster Database

67. Caution Must be Exercised when Attributing a “Cost” to an “Event” In the case of ENSO… Would the what is the baseline of ‘cost’? Or, What is the economic cost of disasters ENSO-neutral years?? Could the impact (or cost) have occurred in the absence of the event?

68. DROUGHTS Southern Africa

69. FLOODS Peru Southern California