Intra-seasonal thermocline
variability in the Bay of Bengal
M. Ravichandran
ESSO-Indian National Centre for Ocean Information Services (ESSO-INCOIS)
Hyderabad, India
[email protected]
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
Observed thermocline variability in the Bay
D23
RAMA: Temperature (°C) and standard deviation
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
Dominant peaks
30-70 days
90 days
120 days
15°N,90°E.
12°N,90°E
8°N,90°E
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
Equatorial Winds and Sea Level
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)
Whether SSHA can be used as a proxy for D23?
r=0.68, 15N
r=0.77, 12N
r=0.86, 8N
r=0.75, 1.5S
D23 and SSHA variability in Equatorial Indian Ocean and BoB
(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
Equatorial winds and Sea Level
u
SSHA
8N
12N
15N
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)
1. Barrier layer changes due to thermocline
MLD
ILD
D23
Temporal evolution of winds, temperature, salinity and BLT
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
BLT Vs ILD
-0.35
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
Qpen
Qnet
Qnet+Hori.adv
Hori. adv
Temperature inversions are large when the barrier layer is thick, not all thick barrier
layers exhibit temperature inversions
Temperature inversion
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.
M. S. Girishkumar, M. Ravichandran, and M. J. McPhaden , J. Geophys. Res., 2013
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
M. S. Girishkumar, M. Ravichandran and Vimlesh Pant, Int. Jour of Remote Sensing, 2012
WOA
SSHA
Heat content in the Bay during winter
5. Westerly winds, Downwelling Rossby wave, increase in HC and SST
SSHA (cm)
TCHP (KJ cm-2)
Girishkumar and Ravichandran, J. Geophys. Res, 2012
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
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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