Michael J. McPhaden NOAA/Pacific Marine Environmental Laboratory Seattle, Washington IAPSO Dynamic Planet Symposium Cairns, Australia 24 August 2005 ENSO and Intraseasonal Variability

El Nino/Normal ENSO and Intraseasonal Variability Outline:  Describe the interplay between large scale low frequency dynamics and stochastic intraseasonal forcing in the ENSO cycle.  Illustrate how intraseasonal variability can affect timing, amplitude, and predictability of ENSO.  Describe efforts to enhance observations in the Indian Ocean which can improve understanding of intraseasonal variability.

El Nino/Normal Anomalous Geostrophic Divergence Anomalous Geostrophic Convergence

Feedbacks Ocean-Atmosphere Feedback Loops During El Niño WindsSST Fast Positive Feedback Warms Thermocline Depth Slow Negative Feedback Cools

Feedbacks Ocean-Atmosphere Feedback Loops During La Niña WindsSST Fast Positive Feedback Cools Thermocline Depth Slow Negative Feedback Warms

NINO3.4 and SOI, Darwin Tahiti NINO-3.4 Correlation lag)=-0.9

 Build up of excess heat content along equator is a necessary precondition for El Niño to occur.  The time between El Niños is determined by the time to recharge.  El Niño purges excess heat to higher latitudes, which terminates the event. Upper Ocean Heat Content and El Niño (Recharge Oscillator Theory*) *Wyrtki, 1985; Cane et al, 1986; Jin, 1997

Weather Noise and Stochastic Forcing Episodic westerly wind forcing and downwelling Kelvin wave responses

Weather Noise and Stochastic Forcing June-July 2004 Westerly Wind Burst BEFORE: “It is…likely that ENSO-neutral conditions will continue for the next 3 months (through August 2004).” NOAA/NCEP 10 June 2004 AFTER: “El Niño conditions are expected to develop during the next 3 months.” NOAA/NCEP 5 August 2004

Niño 3.4 Sea Surface Temperature Forecasts from June 2004 Warm Cold International Research Institute, 2004 Observations

Evolution: March 2004-May 2005 QuickTime signal

Evolution: March 2004-May 2005 QuickTime

Effects of Westerly Wind Bursts on Equatorial SST  Westerly wind bursts cool the western Pacific, and warm the central and eastern Pacific Ocean (Shinoda & Hendon, 1998; Zhang, 2001; McPhaden, 2002).  Nonlinear processes can rectify these tendencies into lower frequency variations (Lukas and Lindstrom, 1991; Kessler et al, 1995; Kessler and Kleeman, 2000; Waliser et al, 2003)  Spatial structure resembles “optimal perturbations” in some coupled models of ENSO (Moore and Kleeman, 1999). Enhanced Surface Heat Fluxes Zonal Avection Suppressed Upwelling

Westerly Wind Bursts  Amplitude and Phase of ENSO

Westerly Wind Bursts  Amplitude and Phase of ENSO 29°C Stochastic forcing not entirely random

MJO Convection Indian | Pacific | Atlantic Mar 2004 May 2005 cloudy/wet clear/dry Cloudiness & Rainfall (OLR, 5°N-5°S) Convective flare-ups occur every days over the Indian Ocean. These flare- ups are characterized by towering cumulus clouds, rainfall, and westerly surface winds that propagate into the Pacific sector. Weather Noise and ENSO The Madden-Julian Oscillation (MJO)

Lau BAMS cover “…Understanding of the ENSO [may] not be complete without a better understanding of the day Oscillation.” Lau & Chan BAMS, 1986 A controversial topic:  Not all WWB related to MJO  ENSO and global MJO indices not linearly correlated  Noise may not be necessary for ENSO variability (unstable vs stable or damped oscillator) Weak El Niño largely noise driven

Ocean-Atmosphere Interaction and the MJO Flatau et al, 1997 MJO variability largely generated via internal atmospheric dynamics But, ocean-atmosphere interactions can improve MJO simulation in some atmospheric models (Flatau et al, 1997; Waliser et al, 2003) -Amplify and organize convection -Improve simulated phase speed & period -Seasonality more accurately represented Understanding ocean-atmosphere interactions, particularly in the Indian Ocean spawning ground of the MJO, is limited by lack of adequate data (ocean especially)

Indian Ocean Climate Science Drivers Improved description, understanding and ability to predict:  Intraseasonal oscillations: near field (monsoon rain)and far field (ENSO, west coast US rainfall, hurricane formation) impacts  Intraseasonal oscillations: near field (monsoon rain) and far field (ENSO, west coast US rainfall, hurricane formation) impacts  Seasonal monsoon variability  Monsoon ENSO interactions  Indian Ocean Dipole (El Niño-like phenomenon in the Indian Ocean)  Warming trends since the 1970s. Madden Julian Oscillation

Draft Strategy for Integrated Indian Ocean Observing System  Developed by CLIVAR/IOC Indian Ocean Panel ftp://ftp.marine.csiro.au/pub/meyers/Implementation%20Plan/  Multi-national, Multi-platform, Multi-year  Focus on in situ to complement existing and planned satellite missions

Draft Strategy for Indian Ocean Moored Buoy Array JAMSTEC ADCP

IAPSO Dynamic Planet Symposium Cairns, Australia 24 August 2005 Conclusions  The ENSO cycle results from the interplay between large scale low frequency coupled ocean-atmosphere dynamics and stochastic intraseasonal forcing [weak El Niño largely noise driven]  Intraseasonal variability can affect timing, amplitude, and predictability of ENSO  Amplitude and phase of ENSO feeds back on the statistics of the intraseasonal forcing  Some of the relevant intraseasonal variability is related to the MJO which originates over the Indian Ocean  New observations in the Indian Ocean may improve understanding of and ability to simulate intraseasonal variability, potentially contributing to improved predictability on intraseasonal and interannual time scales

Weather Noise and ENSO Stability  If ENSO is a freely oscillating instability of the ocean-atmosphere system governed by basin scale dynamics (Schopf & Suarez, 1988: Battisti & Hirst, 1989), weather “noise” is not essential but introduces irregularity.  If ENSO is a stable or weakly damped oscillator, external forcing in the form of weather noise is essential for initiation and development of warm events (Penland & Sardeshmukh, 1995; Moore & Kleeman, 1999; Kessler, 2002). Mean thermocline depth  Stability characteristics determined by strength of ocean- atmosphere coupling and may vary decadally with changing background conditions (Kirtman and Schopf, 1998; Fedorov and Philander, 2000) Fedorov & Philander, 2000 A= s; B= s

Peak Phase Janowiak et al (2003) rainfall, Quikscat wind velocity relative to ERS climatology Reynolds et al (2003) SST, Quikscat wind stress relative to ERS climatology DRY

Convective flare-ups occur every days over the Indian Ocean. These flare- ups are characterized by towering cumulus clouds, rainfall, and westerly surface winds that propagate into the Pacific sector. MJO Convection Indian | Pacific | Atlantic The Madden-Julian Oscillation (MJO) June 2001 Aug 2003 cloudy/wet clear/dry Cloudiness & Rainfall (OLR, 5°N-5°S)

Forecasts for the El Niño NOAA/National Centers for Environmental Prediction 9 month lead

Jan 2004-Aug 2005 June-July 2004 Westerly Wind Burst BEFORE: “…the potential for El Niño…is approximately equal to its historical, climatological probability of 25%.” IRI 17 June 2004 AFTER: “…the potential for El Niño conditions…carries a probability of 50%…” IRI 19 August 2004

ENSO Observing System Developed during the International Tropical Ocean-Global Atmosphere (TOGA) Program ( ) Consists of in-ocean & satellite observational components Provides information on key environmental parameters in real-time - Sea surface temperature -Surface winds -Upper ocean heat content -Sea level -Ocean currents

TAO/TRITON ATLAS Mooring TAO/TRITON: A U.S./Japan collaboration

ORV Sagar Kanya Cruise 9 October-17 November 2004  41 Day Cruise  4 ATLAS & 1 ADCP mooring PMEL in collaboration with the National Institute of Oceanography (NIO) and the National Center for Antarctic and Ocean Research (NCAOR), Goa, India.

Niño 3.4 Sea Surface Temperature Forecasts from June 2002 Warm Cold International Research Institute, 2002 Observations