intra-seasonal thermocline variability in the bay of … thermocline variability in the bay of...

Intra-seasonal thermocline variability in the Bay of Bengal

M. Ravichandran ESSO-Indian National Centre for Ocean Information Services (ESSO-INCOIS)

Hyderabad, India

ravi@incois.gov.in

Outline

• What is the observed ISO thermocline variability in the Bay of Bengal

• What is the causative mechanism for the same

• What is the impact / role of thermocline variability

– Barrier layer and associated thermal inversion

– Chlorophyll-a productivity & Oxycline

– Cyclone genesis

RAMA

RAMA: Temperature (°C) and standard deviation

Observed thermocline variability in the Bay

D23

Girishkumar, M. S., M. Ravichandran and W. Han, J. Geophys. Res., 2013

Intra-seasonal (30-120 days) band-pass filtered vertical temperature profiles and its standard deviation (°C) at buoy locations in the BoB.

The Fourier amplitude spectra of RAMA buoy temperature (°C) in the BoB

8°N,90°E

12°N,90°E

15°N,90°E. Dominant peaks 30-70 days 90 days 120 days

Local or Remote forcing

• Ekman pumping velocity

• Equatorial forcing

Time series of band pass filtered (30- 70 day) Ekman pumping velocity (m day-1) (blue), rate of change of D23 (m day-1) (black) at RAMA buoy locations in the BoB.

Shoaling +Ve (Upwelling) Deepening – Ve (Downwelling)

Local Ekman pumping is not the dominant cause of the D23 at all ISO timescales

Time series of QuikSCAT surface wind stress (N/m2) (black line) averaged over the box bounded by 70° - 90°E, 1°N - 1°S (black box), SSHA (cm) (red line) averaged over the box bounded by 94° -

100°E, 1°N - 1°S, (red box) and D23 (m) from moored buoy located at 1.5°S, 90°E (green dot)

Equatorial Winds and Sea Level

D23 and SSHA variability in Equatorial Indian Ocean and BoB

r=0.68, 15N

r=0.77, 12N

r=0.86, 8N

r=0.75, 1.5S

Whether SSHA can be used as a proxy for D23?

(a) Depth-time section of salinity at the buoy location [15°N, 90°E].

Note the drop in salinity whenever SSHA and D23 match is poor

Equatorial Winds

u SSHA 8N 12N 15N

Equatorial winds and Sea Level

D23 Vs local forcing

Temporal evolution of 30-70 day filtered (Inverse Fourier transform)

Downwelling RW exists only at 8 N (Critical latitude) Importance of Local ekman pumping Possible role of alongshore winds in affecting the 30–70 day variability in the region

90-Day oscillations

Ekman Pumping +

Remote forcing

120-day oscillations

Ekman Pumping +

Remote forcing

Thermocline variability in the Bay of Bengal

• Pronounced persistent intraseasonal variability in the thermocline region with dominant periods in the range of 30–120 (30-70, 90, 120) days

• lSO variability of D23 in the BoB cannot be explained by local Ekman pumping velocity alone. Remote forcing by winds from the EIO plays an important role.

• 30-70 days: North and Central: Ekman (local)

South: Remote forcing + alongshore winds in the eastern BoB

• 90 and 120 days: strongly affected by Rossby waves driven by the equatorial zonal winds. The Ekman pumping velocity in the interior BoB has significant contributions to the westward enhancement of these signals (particularly northern Bay)

Temporal evolution of winds, temperature, salinity and BLT

MLD ILD D23

1. Barrier layer changes due to thermocline

Correlation between BLT and MLD & ILD

r

Total time period

BLT Vs MLD -0.39

BLT Vs ILD 0.45

During (Sep to May)

BLT Vs MLD -0.35

BLT Vs ILD 0.83

Ekman pumping Vs ILD

Daily evolution of Ekman pumping velocity (m/day) and rate of change of ILD (m/day,) at the buoy location.

Local Ekman pumping plays a minor role in modulating ILD

40-100 days band pass filtered (a) zonal wind along

the equator, (b) SSHA at equator

and off Sumatra coast

(c) SSHA along 8N in the southern Bay of Bengal and

(d) time series of ILD (unfiltered) at the buoy location

(e) Time series of ILD and SSHA (filtered) at the buoy location

Girishkumar et.al, JGR, 2011

Role of ILD in BL

2. Observed intra-seasonal variability of temperature inversion in the south central BoB

Temperature inversion

Temporal evolution of BLT and TI

Temperature inversions are large when the barrier layer is thick, not all thick barrier layers exhibit temperature inversions

Qpen Qnet+Hori.adv

Qnet Hori. adv

Temperature inversion

M. S. Girishkumar, M. Ravichandran, and M. J. McPhaden , J. Geophys. Res., 2013

Much of the variability in upper ocean thermal structure and hence the upper ocean heat budget in the BoB is forced remotely by winds in the EIO on intraseasonal to interannual time scales. Thus, understanding the controls on BoB SST is ultimately a problem that requires a basin scale perspective on wind, heat flux and fresh water forcing.

3. Oxycline depth from Altimeter based SLA?

Satya Prakash, Price Prakash and M. Ravichandran, Remote Sensing Letters, 2013

4. Easterly winds, Upwelling Rossby waves, increase in Chl and reduction in SST

Temp @ 8 N/ 90 E

Chl-a SSHA

WOA

M. S. Girishkumar, M. Ravichandran and Vimlesh Pant, Int. Jour of Remote Sensing, 2012

SSHA (cm) TCHP (KJ cm-2)

Heat content in the Bay during winter

Girishkumar and Ravichandran, J. Geophys. Res, 2012

5. Westerly winds, Downwelling Rossby wave, increase in HC and SST

Conclusion • Pronounced persistent intraseasonal variability in the thermocline region with

dominant periods in the range of 30–120 (30-70, 90, 120) days with maximum

variation in the southern BoB.

• It is due to both local and remote forcing

• Westerly winds in Equ (DW KW)SL and D23 increases (EEIO) DW RW and

Coastal KW HC, BL and TI increases (Cyclone genesis, low chlorophyll-a,

deep oxycline)

• Easterly winds in Equ (UW KW)SL and D23 decreases (EEIO) UW RW and

Coastal KW HC, BL and TI decreases Cyclone genesis, high chl-a, shallow

oxycline)

• Understanding the controls on BoB SST is ultimately a

problem that requires a basin scale perspective on wind,

heat flux and fresh water forcing.

Thank you for your attention References

• Girishkumar, M. S., M. Ravichandran, and W. Han (2013), Observed intraseasonal thermocline variability in the Bay of Bengal, J. Geophys. Res. Oceans, 118, 3336–3349, doi:10.1002/jgrc.20245.

• Girishkumar, M. S., and M. Ravichandran (2012), The influences of ENSO on tropical cyclone activity in the Bay of Bengal during October–December, J. Geophys. Res., 117, C02033, doi:10.1029/2011JC007417.

• Girishkumar, M. S., M. Ravichandran and V. Pant, (2012)Observed chlorophyll-a bloom in the southern Bay of Bengal during winter 2006–2007, International Journal of Remote Sensing, Volume 33, Issue 4, 2012 , pages 1264-1275, DOI:10.1080/01431161.2011.563251.

• Girishkumar, M. S., M. Ravichandran, M. J. McPhaden, and R. R. Rao (2011), Intraseasonal variability in barrier layer thickness in the south central Bay of Bengal, J. Geophys. Res., 116, C03009, doi:10.1029/2010JC006657

• Girishkumar, M. S., M. Ravichandran, and M. J. McPhaden (2013), Temperature inversions and their influence on the mixed layer heat budget during the winters of 2006–2007 and 2007–2008 in the Bay of Bengal, J. Geophys. Res. Oceans, 118, 2426–2437, doi:10.1002/jgrc.20192.

• Satya Prakash, Prince Prakash, and M. Ravichandran, (2013), Can oxycline depth be estimated using sea level anomaly (SLA) in the northern Indian Ocean?, Remote Sensing Letters, 2013, Vol. 4, No. 11, 1097–1106, http://dx.doi.org/10.1080/2150704X.2013.842284

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