The Influence of Tropical-Extratropical Interactions on ENSO Variability Michael Alexander NOAA/Earth System Research Lab

Pacific Ocean-Atmosphere Variability Nov-Mar Deser and Blackmon, 1995, J. Climate CI = 5 m Z500 EOF 1 SST

Response to tropical SSTAs SSTA °C Z 200 (m) Blackmon, Geisler and Pitcher 1983

Response to North Pacific SSTAs Z 200 (m) SSTA °C Pitcher, Blackmon, Bates, and Muñoz, 1988

Response to Tropical and North Pacific SSTs

Pacific SST Structure and Atmospheric Forcing/Feedback Pitcher et al.: Noted that past work indicated significant correlation between tropical and midlatitude SSTs and that the two might be related “For example, a tropical Pacific SST could produce an atmospheric anomaly which could produce a midlatitude SST anomaly” “Another possibility is that an enhanced atmospheric anomaly might occur if both a tropical SST anomaly and a favored midlatitude SST anomaly were to occur simultaneously” “Other permutations of possible cause and effects can be imagined.”

Oceanic Response to ENSO: the “Atmospheric Bridge” Alexander 1990 Climate Dyn.; Alexander 1992 J Climate Prescribed Climatological SSTs Mixed Layer Ocean Model L

ENSO’s impact on the North Pacific SST x 10 °C Q net W m -2

Response to Pacific SST Anomalies z200 (m) DJF Tropical North Shading: > 95% significance by t-test

Response to ENSO: Role of Ekman transport

El Niño – La Niña SST (˚C) JFM Alexander et al J. Climate; Alexander and Scott 2007

Z500 (m) Niño - Niña Composite Obs MLM EKM ∆

Niño - Niña SST, SLP,  JFM

Extratropical => Tropical Air Sea Interactions

Seasonal Foot Printing Mechanism (SFM) NPO in NDJ (-1)  Winds & Heat Flux  SST in MAM (0)  Tropical Winds  Bjerknes Feedback  El Nino in NDJ(0) (Vimont et al. 2001, 2003a&b J. Climate)

Model (Chang et al. 2007, GRL): AGCM: CCM3 Reduced Gravity Ocean (Cane-Zebiak) Model 30S-30N in Pacific. Slab model over remainder of the ocean Models are anomaly coupled 100-year Control run SFM Experiment Add additional heat flux forcing associated with the NPO 20°S-60°N; similar results when forcing > 10°N Initiate 60 heat flux anomaly runs from Nov in control run. Apply Heat flux anomaly during first Nov-Mar Then let model evolve with unperturbed fluxes for 12 more months. Compare ENSO evolution in perturbation and control runs. Note: model already includes SFM Experiment Design

Additional SFM Forcing NPO from AGCM –With Climatological SST Isolates intrinsic variability –2nd EOF of SLP EOF in North Pacific in Winter –Regress Sfc Heat flux on PC –double flux values Max values of ~30 Wm -2 Add identical/constant forcing in each of the experiments NovMar NovMar NovMar Control Exp n Exp n+1 Exp n+2 2nd EOF SLP & Qnet Nov-Mar SLP Qnet

Experiment - Control SST (°C) Winds & SST MJJ(0)

Experiment - Control SST (°C) NDJ(1) Thermocline depth (m)

Experiment - Control Nino 3.4 SST (°C) NDJ(1) In 43 of the 60 cases (~72%) SSTs warmed in the Nino 3.4 region in the subsequent winter after the forcing was applied. The Mean difference between Exp and Control is 0.47°C (significant at the 99% level). Forcing added 11 more warm events - Nino 3.4 NDJ > 1  control. Run number

Summary Connection Between tropical Pacific and extratropics: Atmospheric Bridge Global, including N. Pacific & N Atlantic Ekman transport important in generating SSTs Feedback of bridge-related SST anomalies on the atmosphere: ~1/3 of response to ENSO SSTs (signal/noise issues) May involve multiple bridges Nature of feedback depends on region/seasonal cycle Model dependence? Extratropics => Tropics Atmosphere Seasonal Footprinting Mechanisms Ocean Pathways Rossby waves Subtropical Cells

Additional Slides

Model Configration

Net Surface Heat FLux

Ekman

U200 (m/sec)

Composite Niño - Niña JFM Precipitation, 200 mb streamlines

Air-Sea Feedback N. Atlantic Peng, Robinson, Li, Hoerling, Alexander N. Atl: 50 m slab ocean N. Atl: 75 m slab + Ekman Trans Atmospheric Response 500 mb January SSTA N. Atl: Climatological SST Africa South America

Optimal Structure SST pattern that undergoes maximum growth - defined here as the domain integrated SSTA variance

When is Optimal Structure most likely to occur? Evolution of Air-Sea System based on OS: Difference maps between composite averages when the correlation between the OS and the observed SSTA field > 0.4 and < -0.4 during MAM. Starting in the winter before through the following winter

Composite Based on Optimal Structure in MAM

Composite Based on Optimal Structure II