A view of approaching rain clouds borne by monsoon winds, from a train departing Kanyakumari, 1995. Photo: jo_stafford/Flickr, CC BY 2.0.
As the monsoon season wraps up, the rainfall has been declared to be above the long term average by over 8%. Mumbai experienced the wettest monsoon in over 60 years and the excess was 41% over Goa. Above normal rainfall also occurred over much of Peninsular India.
And yet, various states ended the season with deficit rainfall amounts. Can the pattern be explained by the La Niña brewing in the tropical Pacific – which tends to bring excess rain over India? Does the averaging of rainfall over all of India hide more subtle relations between the external drivers such as El Niño and La Niña?
El Niño and La Niña are the warm and cold anomalies of sea surface temperature in the deep tropical Pacific Ocean. They typically occur to the east of the Dateline towards the coast of South America. They are caused by the shifts in the heavy rain centre located over the warm waters in the western tropical Pacific off of New Guinea and Australia. The normal state in the east is characterised by cold waters around Galapagos which are rich with phytoplankton and biodiversity. The west on the other hand is home to some of the warmest waters featuring the Great Barrier Reef and about five metres of rain per year. The cold waters in the east move westward at a few tens of kilometres per day and soaking up heat from the atmosphere on their way towards New Guinea.
Every two to seven years, the accumulated warm waters in the west are released because of wind changes, moving the rains towards the east with them. The unusually warm waters off the coast of South America arrive around Christmas time, which led the Spanish colonisers to name it the ‘Child’, or ‘El Niño’. The heat source over the warm waters in the west drives global weather and climate and thus its movement causes global weather and climate anomalies. Nearly 50% of the monsoon droughts tend to be associated with an El Niño.
La Niña is the opposite phase of El Niño, when the warm waters in the west get warmer than normal and the eastern tropical Pacific gets colder than normal. About 50% of the wet years over India can be blamed on La Niña in the Pacific.
Averaging rainfall over all of India does hide some important relations of the monsoon with El Niño and La Niña. Arindam Chakraborty of the Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru, had reported in 2018 that the previous winter’s El Niño Southern Oscillation (ENSO) can explain the patterns of rainfall quite skilfully especially when considered together with the transition to the simultaneous summer ENSO.
ENSO, more specifically El Niño, gets a lot of play during deficit years. This year’s monsoon offered more melodrama than normal with cyclones Amphan and Nisarga playing a role in dragging the monsoon trough onto India and avoiding a potential delay in the onset. But the spring warming over the Indian Ocean and a cooler subcontinent had set up a weak monsoonal circulation. The cooler temperatures over India could have been related to the reduced aerosols and greenhouse gases due to COVID-19 lockdowns across the globe. Excess pre-monsoon showers also brought some cooling which themselves may have been related to the warm ocean cool land pattern.
The entire West Coast of India had heavy deficit rainfall well into the month of July with only Peninsular India and the Northeast getting some excess rain during June and July. These could largely be attributed to the cyclones and the depressions.
A sudden emergence of La Niña seems to have changed the pace of the monsoon mid-way through the season even though we remain woefully ignorant about how the monsoon itself affects the evolution of El Niño and La Niña. Nonetheless, August and September lived up to a La Niña like excess rain over India and a slight delay in the withdrawal. But then there is the question of the spatial distribution of the rainfall. Can that also be explained by the growing La Niña?
Chakraborty’s study categorised the monsoon response to ENSO based on transitions in ENSO states from the previous winter into simultaneous summer, i.e., the monsoon season. Only strong El Niños tend to have a very clear impact simultaneously on the monsoon. Weaker ENSOs have a more complicated relation. Since ENSO affects the entire Indo-Pacific Ocean not only during the year of the occurrence of the ENSO event but also lingers on into the following spring, these winter to summer transitions of ENSO turn out to be critical.
For example, a La Niña state in the previous winter turning into an El Niño state in the simultaneous summer can produce a deficit of nearly 15% compared to just a 5% deficit if an El Niño state persists from the previous winter. As it turned out, the winter state of 2019 was a mild El Niño and it transitioned into a La Niña state during the summer of 2020. The monsoon rainfall pattern extracted by Chakraborty during such a transition from a previous winter’s El Niño to a summer La Niña state is eerily similar to the 2020 monsoon deficit pattern. And this despite the role of the cyclones at the beginning of the season.
This should at least motivate us to pay more attention to the ENSO states and their transitions.
The heavy reliance on only the simultaneous ENSO state may not be sufficient for skillful monsoon predictions beyond the all India averages. Predictions of spatial patterns of the upcoming season may benefit from ingesting the previous winter’s ENSO state as well as the predicted summer ENSO state.
How can that be done? One way could be to focus on the ensemble members that are consistent with the ENSO state transition. The India Meteorological Department has adopted a so-called Ensemble Prediction System. The initialisation of monsoon forecasts rely on preparing the ocean conditions for the start of the predictions by assimilating all available observations into the ocean model.
Since the ocean has a high heat capacity and is the main source of the memory for the climate system and its predictability beyond a few days or weeks, initialising the ocean accurately is critical. And then the atmospheric state can be perturbed to see how a small error may evolve into a very different monsoon prediction. Each such perturbed prediction is called an ensemble member. Making a large number of perturbed predictions provides a better sense of what the probabilistic evolution of the monsoon could be. We can think of ensemble members are potential paths nature can take and the actual state as the one that nature ended up taking.
It is traditional to simply average the ensemble members to provide a consensus forecast. But giving more weight to the ensemble members that are consistent with the transition from the known ENSO state of the previous winter to the predicted ENSO state of the upcoming summer may enhance the overall forecasts.
Performing predictions for the past years would be needed to test such alterations to the prediction system – which is not only time consuming but also computationally costly. But considering the suffering of the farmers experiencing deficit rainfall, it is critical to squeeze out every ounce of prediction skill from the Ensemble Prediction System.
Raghu Murtugudde is a professor of atmospheric and oceanic science and Earth system science at the University of Maryland. He is currently a visiting professor at IIT Bombay.