Abstract
This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 °C isotherm during January–March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January–March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Niño Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.
Highlights
We further compare the role of the January–March Ocean Mean Temperature (OMT) in the southwestern Indian Ocean in predicting Indian Summer Monsoon Rainfall (ISMR) to those using other oceanic indices, which are defined by January–March mean sea surface temperature (SST) and area-averaged regions where NINO3.4 (El Niño), El Niño Southern Oscillation Modoki Index (EMI), and Indian Ocean Dipole Mode Index (DMI) are conventionally defined
Since the OHC, representing the mean temperature of the surface to depth of 26 °C isotherm (D26) isotherm layer, has been recognized as an important parameter in cyclone forecasting, we extended the scope of this parameter to predict whether ISMR is above- or below-average value
Comparison of the correlation coefficient for ISMR Percentage Deviation (PD) versus SST and Ocean Mean Temperature Percent Deviation (OMT PD) separately for different months from January to March and for 3-month averages from January-March to March-May revealed that OMT PD correlates better with ISMR PD compared to SST PD
Summary
Since OHC cannot be integrated into purely atmospheric models, Ali et al.[17] suggested converting OHC to Ocean Mean Temperature (OMT) of the surface to D26 They studied the relationships of ISMR with OMT and SST at each 2.5° box in the North Indian Ocean for different months and concluded that OMT in the southwestern Indian Ocean region (50°E–70°E and 10°S–0°N; delimited by the rectangular box in Fig. 1a) has the highest correlation. OHC estimated with respect to the D26 reference depth is undefined or arbitrarily set to zero Both D26 and OHC during January–March are moderate in the southwestern Indian Ocean (the rectangular box in Fig. 1(a)), which is the region of interest in the present study. As the OHC from satellite and in situ derived observations has a good correlation having regression slope of close to one and a y-intercept of almost zero[28], we used the following equation to compute OMT from OHC and D26 following Ali et al.[17]
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