Satellite derived chlorophyll a imageries are used to present a phytoplankton bloom in the Bay of Bengal during the northeast monsoon (November–February) and the mechanisms that can upwell nutrients to sustain the bloom are investigated using sea level anomalies and winds. OCTS and SeaWiFS chlorophyll a images show that there is a phytoplankton bloom in the southwestern part of the bay during November–January. The chlorophyll a concentration of the bloom can be as high as 2 mg m−3 compared to near zero value before the bloom. Open ocean upwelling driven by Ekman pumping causes the bloom. The cyclones which are common during this period lead to localized intense blooms in the western Bay of Bengal. The offshore extent and the intensity of the bloom varies from year to year. The bloom was absent during 1997 due to weak Ekman pumping.
[1] Intraseasonal variations (ISV) of sea surface temperature (SST) in the Bay of Bengal (BoB) is highest in its northwestern part. An Indian Ocean model forced by QuikSCAT winds and climatological river discharge (QR run) reproduces ISV of SST, albeit with weaker magnitude. Air-sea fluxes, in the presence of a shallow mixed layer, efficiently effect intraseasonal SST fluctuations. Warming during intraseasonal events is smaller (<1°C) for June -July period and larger (1.5°to 2°C) during September, the latter due to a thinner mixed layer. To examine the effect of salinity on ISV, the model was run by artificially increasing the salinity (NORR run) and by decreasing it (MAHA10 run). In NORR, both rainfall and river discharge were switched off and in MAHA10 the discharge by river Mahanadi was increased tenfold. The spatial pattern of ISV as well as its periodicity was similar in QR, NORR and MAHA10. The ISV was stronger in NORR and weaker in MAHA10, compared to QR. In NORR, both intraseasonal warming and cooling were higher than in QR, the former due to reduced air-sea heat loss as the mean SST was lower, and the latter due to enhanced subsurface processes resulting from weaker stratification. In MAHA10, both warming and cooling were lower than in QR, the former due to higher air-sea heat loss owing to higher mean SST, and the latter due to weak subsurface processes resulting from stronger stratification. These model experiments suggest that salinity effects are crucial in determining amplitudes of intraseasonal SST variations in the BoB.
Following the launch of the Global Precipitation Measurement (GPM) Core Observatory in early 2014, motivated from the successful Tropical Rainfall Measurement Mission (TRMM) satellite, an advanced and sophisticated global multi-satellite precipitation product -Integrated Multi-satellitE Retrievals for GPM (IMERG) was released at finer spatio-temporal scales. This precipitation product has been upgraded recently after several refinements and supposed to be superior to other existing global or quasi-global multi-satellite precipitation estimates. In the present study, IMERG precipitation is comprehensively evaluated for the first time against moored buoy observations over the north Indian Ocean at hourly scale for the study period of March 2014 to December 2015. IMERG precipitation performs considerably better over the Bay of Bengal than the Arabian Sea in both detection and estimation. The systematic error in IMERG is appreciably lower by about 14%, however, it generally overestimates in-situ precipitation and also exhibits noticeable false alarms. Furthermore, IMERG essentially shows an improvement over the TRMM Multi-satellite Precipitation Analysis (TMPA) at a daily scale over the north Indian Ocean. IMERG precipitation estimates show overall promising error characteristics, but there is still a need of substantial efforts for improvement in the satellite-based precipitation estimation algorithms especially over data sparse regions such as north Indian Ocean.
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