intra-seasonal thermocline variability in the bay of … thermocline variability in the bay of...
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Intra-seasonal thermocline variability in the Bay of Bengal
M. Ravichandran ESSO-Indian National Centre for Ocean Information Services (ESSO-INCOIS)
Hyderabad, India
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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