Predicting the Indian SW Monsoon

Spread the love

From Watts Up With That?

By Dr. M M Ali — June 7 2023

Why we should observe the ocean to better predict the atmosphere?

I may be embroiled in controversy with the monsoon professionals and modelers by writing this article.  However, I would like to trespass and put forth some of my ideas in meeting the challenges to predicting the mysterious Indian southwest monsoon rainfall by including ocean observations.

Monsoon rains in India are mystical and erratic, either with too little water causing drought or far too much flooding! Even worse, extreme rainfall events have been very common in recent years with the number of days with heavy rains increasing and longer dry spells stretching out in between. As a result, normal and steady rains that can reliably penetrate the soil are decreasing. Adding to this, increasing deforestation and urbanization continuously reduce the infiltration capacity of the soil. As a result, groundwater is withdrawn faster than rain can recharge it. This is an alarming situation for a country like India, which gets the maximum share of its water through the rain. In addition, [it is claimed – kh] climate change is now messing with the monsoon, making seasonal rains more intense and less predictable.

­Adding to these problems is the predictability or the unpredictability of the monsoon rainfall!

A monsoon is a seasonal reversal in the prevailing wind direction, that is usually initiated by the land sea temperature contrast.  The Indian summer monsoon, for example, is triggered when the land gets heated up more than the surrounding sea during the summer creating a pressure gradient between the land and the sea (Figure 1).  

Figure 1: Climatology of the southwest monsoon circulation during Indian summer (Source:

While efforts are underway to improve the understanding of the physics of the problem of monsoon rainfall prediction, it is worthwhile to look again at the efficiency of the input parameters presently used in models and to look for new approaches. Sea surface temperature (SST) is one such parameter that needs to be reconsidered for predicting the Indian summer monsoon rainfall (ISMR). SST is routinely used for predicting the weather phenomena such as monsoons or cyclones, while it is well established that the thermal energy required for atmospheric phenomena comes from the upper ocean, not from the thin layer of the ocean sometimes reflected in SST alone.  SST is restricted to a few millimeters of the top ocean layer, particularly when it is estimated from the satellites and is largely influenced by strong winds, evaporation, or thick clouds. Hence, it does not reflect the thermal energy available in the upper ocean.   Rapid (of the order of a day to a month) heating (such as strong solar heating) and cooling (such as more evaporation due to strong winds and/or clouds) events can quickly erase the thermal signature of subsurface warm or cold features (Pickard and Emery 1990), leading to SST misrepresenting the ocean thermal energy.  In contrast, ocean mean temperature (OMT), which is measured up to a depth of 26o C isotherm or up to a fixed depth, is more stable and consistent, the spatial spread of which is also less compared to SST. This is even evident from the average coefficient of variation, defined as the relative magnitude of the standard deviation to the average (1993–2017) value (Figure 2) for monthly SST (0.02) being double that for OMT (0.01) for the North Indian Ocean. 

 Figure 2: Coefficient of variation of SST (a) and OMT (b) during 1993–2017. The rectangle represents the south Indian ocean area that has a major influence in ISMR.  (courtesy: Venugopal et al. 2017).

The application of ocean thermal energy for cyclone studies was already demonstrated through several studies. For example, Mao et al. 2000 reported that the rate of intensification and final intensity of cyclones are sensitive to the initial spatial distribution of the mixed layer, a proxy for ocean thermal energy, rather than to SST alone. Similarly, Shay et al. (2000), Ali et al. (2007), Mainelli et al. (2008), Ali et al. (2013) and Lin et al. (2013) and Jaimes and Shay, (2015) demonstrated/suggested the importance of ocean thermal energy for cyclone studies.

Similarly, the role of the heat energy available in the sub-surface ocean layers in El Nino studies was confirmed by Smith et al. (1995), Ji et al. (1997) and Latif et al. (1998). They proved that even the El Niño forecast models could be improved by initializing the models with the observed ocean heat content (OHC). OHC is the amount of thermal energy available in the oceans from surface to a fixed depth, say, 100m or 200m given by equation (1).

where ρ is the density of the sea water, Cp is the specific heat capacity of the seawater at constant pressure, p; h1 the top depth, h2 the bottom depth, dz the thickness of the layer and T is the average temperature of the layer in oC. Although in situ temperature profiles are required to estimate this parameter, it can be indirectly inferred from the satellite-derived sea surface height anomaly (SSHA) and SST.

Based on this concept, Venugopal et al. (2018) analysed 25 years of OMT (ocean mean temperature) of the north Indian Ocean (NIO) from 1993 to 2017 spanning 30°S to 30°N and 40°E to 100°E, with a grid spacing of 0.25° × 0.25°. They computed OMT from the Tropical Cyclone Heat Potential (TCHP) and the depth of 26C (D26) obtained from the National Oceanic and Atmospheric Administration, Atlantic Oceanographic and Meteorological Laboratory ( The 26 degree C isotherm is seen at depths varying from 50–100 meters. During January–March, the mean 26 degree C isotherm depth in the Southwestern Equatorial Indian Ocean (SEIO), the rectangular area shown in figure 2 is 59 meters.  The researchers analysed 25-year OMT data from 1993 to 2017. They found that unlike SST, OMT of SEIO was able to correctly predict 20 out of 25 years (80% success rate) whether the amount of rainfall during the summer monsoon was more or less than the long-term mean, 887.5 mm. The prediction based solely on sea surface temperature was correct only for 15 out of 25 years (with a 60% success rate). Using this approach the monsoon was predicted to be more than the average during 2018-2022 with an 80% success rate.

In addition to better predictive score, the information on whether the amount of monsoon rainfall will be more or less than the long-term mean will be available by the beginning of April, two months before the southwest monsoon sets in. This is because OMT is analysed by measuring the ocean thermal energy during the period from January to March and the southwest monsoon sets in around June 1 each year in Kerala .

Out of the 10 years of observed rainfall, there are 6  below average and 4 above average rainfall years during 2013-2022 (Figure 3).  The all-India monsoon rainfall in 2014, 2015, 2016, 2017, 2018, and 2021 was below average during June–September but above average in 2013, 2019, 2020, and 2022.  Except in 2016, all the years were predicted correctly using OMT estimated during January-March.  Venugopal et al. (2018) claimed an accuracy of 80% for their 25 years study.    Since the predictions during 2018-2022  were also correct, the success rate has now increased from 80% to 83.3%. 

With this success rate, our prediction for the all-India June-September 2023 is that total rainfall is likely to be less than 887.5 mm.  We have to wait and watch what happens!

# # # # #

List of references:

Ali, M. M., P. S. V. Jagadeesh and S. Jain, Effects of eddies on Bay of Bengal cyclone intensity. Eos, Transactions American Geophysical Union 88(8): 93–95 (2007).

Ali, M. M., T. Kashyap and P. V. Nagamani, Use of sea surface temperature for cyclone intensity prediction needs a Relook. Eos, Transactions American Geophysical Union 94, 177–178 (2013).

Jaimes, B. and L.K. Shay, Enhanced wind-driven downwelling flow in warm oceanic eddy features during the intensification of tropical cyclone Isaac (2012): Observations and theory. J. Phys. Oceanogr. 45, 1667–1689. (2015).

Ji, M. and A. Leetmaa, Impact of data assimilation on ocean initialization and El Nino prediction. Monthly Weather Review 125(5), 742–753 (1997).

Latif, M., D. Anderson, T. Barnett, M. Cane, R. Kleeman and A. Leetmaa, E. Schneider, A review of the predictability and prediction of ENSO. Journal of Geophysical Research: Oceans 103(C7), 14375–14393 (1998).

Lin, I. I., G. J. Goni, J. A. Knaff, C. Forbes and M. M. Ali, Ocean heat content for tropical cyclone intensity forecasting and its impact on storm surge. Natural Hazards 66(3), 1481–1500 (2013).

Mainelli, M., M. DeMaria, L. K. Shay and G. J. Goni, Application of oceanic heat content estimation to operational forecasting of recent Atlantic category 5 hurricanes. Weather and Forecasting 23(1), 3–16 (2008).

Mao, Q., S. W. Chang and R. L. Pfeffer, Influence of large- scale initial oceanic mixed layer depth on tropical cyclones, Mon. Weather Rev. 1284058–4070 (2000).

Pickard, G. L. and W. J. Emery, Descriptive physical oceanography: An introduction. Elsevier (1990).

Shay, L. K., G. J. Goni and P. G. Black, Effects of a warm oceanic feature on Hurricane Opal, Mon.Weather Rev. 1281366–1383 (2000).

Smith, T. M., A. G. Barnston, M. Ji and M. Chelliah, The impact of Pacific Ocean subsurface data on operational prediction of tropical Pacific SST at the NCEP. Weather and forecasting 10(4), 708–714 (1995).

# # # # #

About the Author:

Dr. M M Ali is an Indian meteorologist and author/co-author of many peer-reviewed studies on the relationships between the oceans and the atmosphere. He is currently a Senior Scientist (courtesy) at the Center for Ocean-Atmospheric Prediction Studies, Florida State University (2015 to present). 

He is a co-author, with Venugopal Thandlam (as lead author) and others, of  “Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall”.   T. Venugopal computed the values for 2018-2023.

Dr. Ali wrote this piece for WUWT after reading “The Southwest Monsoon — More Erratic?”.

This essay has been lightly edited by Kip Hansen ( any editing errors are mine – kh )

# # # # #