Dynamics of the Indian Ocean response to boreal summer atmospheric intraseasonal oscillations Sara Vieira Committee members: Dr. Peter Webster Dr. Emanuele Di Lorenzo Dr. Judith Curry Dr. George Chimonas Dr. Greg Huey School of Earth and Atmospheric Sciences Georgia Institute of Technology

Overview Evidence of intraseasonal oscillation Rainfall, winds, sea level, SST Models used to study Indian Ocean response Reveal response on intraseasonal time scales Modeling Experiments: ROMS Isolate the response to atmospheric forcing on intraseasonal time scales Results show dynamic response in currents and sea level Spectra of rainfall anomalies

Boreal summer atmospheric intraseasonal oscillation Chatterjee and Goswami, 2004 Time scales between 10 and 90 days * 10 – 20 day oscillation * 30 – 60 day oscillation BoB rainfall Oscillations are related to monsoon active/break conditions Associated with significant fluctuations of surface winds, cloudiness and precipitation C.IO zonal winds Spectra of rainfall anomalies 30 – 60 day mode is characterized by a northward propagation Lawrence and Webster, 2002

Intraseasonal oscillation evidence in the Indian Ocean TOPEX/Poseidon power spectrum SLA along equator (1993-2002) Daily sea level data at Gan Island (0ºS – 73ºE) Han, 2005 Sea level observation show a primary spectral peak at 90 days and secondary peaks at 30 – 60 days Earlier studies show evidence of intraseasonal oscillations in time series of ocean currents [Knox, 1976; Mc Phaden, 1982, Reppin et al 1999]

Wave patterns in sea level Air-sea interaction Atmospheric intraseasonal oscillation force intraseasonal oscillations in SST Observations show intraseasonal fluctuations of SST and net heat flux Intraseasonal SST oscillations propagate northward coherent with OLR, surface wind speed and net heat flux In the eastern basin intraseasonal SST anomalies are mainly forced by Q fluctuations terminar Role played by SST on determining the amplitude and phase propagation ? Sengupta et al 2001

Indian Ocean response Indian Ocean broad scale response to intraseasonal atmospheric forcing has been done mainly in models [Qui et al, 1999; Han et al 1999; Kindle and Thompson, 1989] Coupled experiments improve simulation of amplitude and northward propagation of the intraseasonal oscillation in GCMs The ocean plays an important role in defining the characteristics of the intraseasonal oscillation Add influence colera chlorophyll A better understanding of how the Indian Ocean responds to the atmospheric intraseasonal oscillation is necessary.

Model Description ROMS: Regional Ocean Model System Primitive equations with potential temperature, salinity Hydrostatic approximation Orthogonal curvilinear coordinates S terrain following coordinates Domain: 30ºN to 30ºS, 28ºE to 120ºE Horizontal grid: 1/5º Vertical: 20 layers

Data Simple Ocean Data Assimilation (SODA) reanalysis Zonal and meridional velocity, sea surface height, temperature, salinity. Initial conditions, climatology, boundary conditions NCEP/NCAR reanalysis Surface forcing: wind stress Composite of winds stress associated with the atmospheric intraseasonal oscillation TOPEX/POSEIDON 10 day sea level anomalies Aclarar claculo de fluxes

Experiments Experiment 1 (CR) Climatological forcing run: model is forced with climatological daily wind stress Forcing at the equator Climatology Experiment 2 (IR) Intraseasonal oscillation run: model is forced with climatological daily wind stress plus the composite of wind stress anomaly associated with the IO. Climatology + ISO

Composite Hablar con carlos Agregar composite carlos..preguntar

ROMS simulation vs SODA Vertical salinity structure in the Arabian Sea Vertical salinity structure in the Bay of Bengal

Ocean response: currents Power spectra of surface band-passed zonal current from IR at 80ºE and 90ºE Spectra of zonal surface model currents along the equator

Ocean response: currents Primary peak occurs at 90 days The magnitude is maximum at the equator 80 ºE 90 ºE

Ocean response: Sea Level Anomalies From observations: TOPEX/Poseidon Power spectra of SLA along 70E Power spectra of SLA along 90E

Ocean response: Sea Level Anomalies Power spectra of SLA along 90E Power spectra of SLA along the Equator

Ocean response: Sea Level Anomalies Power spectra of SLA along 90E Power spectra of SLA along the Equator

Ocean response: Sea Level Anomalies Time longitude plot along the equator Time longitude plot along the 5N Velocidad de la kelvin Signature of oceanic Kelvin and Rossby waves associated with intraseasonal forcing Ekman Pumping forces additional intraseasonal variability North of the Equator by exciting open ocean Rossby Waves

Ocean response: Sea Level Anomalies mo

Wave patterns in sea level Upwelling Kelvin Wave Downwelling Kelvin Wave

Wave patterns in sea level Dissipation of Coastally trapped waves Upwelling and downwelling Kelvin waves along the equator. Coastal Kelvin waves propagate along the coast in the eastern boundary Equatorial Kelvin waves reflect at the eastern boundary and become Rossby waves.

Eddy variability in the Bay of Bengal It is possible that part of the wave energy along the coast in the Bay of Bengal is transferred into the mesoscale eddy field Seasonal cycle of eddy variance is very non-unifrom in space in the bay of bengal Understanding what drives the eddy variability and the role of the mesoscale eddy in the intraseasonal response needs further exploration

Summary Modeled zonal surface currents from the IR in the central and eastern basin exhibit a strong peak at 90 days and secondary peak between 40-60 days Period offset between ocean response and forcing can be attributed to the excitation of Kelvin and Rossby waves Patterns of equatorial Kelvin waves, Rossby waves and coastal Kelvin waves are observed in sea level anomaly fields. The 90-day peak in zonal currents suggest air-sea interaction on similar time scales. It is necessary a better understanding of how the SST feedbacks to the atmosphere

Summary The role of the mesoscale eddy in the intraseasonal response is yet to be fully explored Studying the intraseasonal oscillation influence on ocean biology, can lead to the prediction of ocean productivity in short time scales.