The characteristics of the Indian monsoon, such as its onset, withdrawal, active and break periods, and the presence of low-pressure systems are widely recognized. Global warming has impacted every aspect of the monsoon, leading to a consistent decline in total seasonal rainfall for over seven decades.
- Important Geophysical Phenomena
- Agricultural Resources
- Climate Change
The impact of global warming varies throughout the monsoon season in India. Analyse how different regions in India have shown a difference in their rainfall pattern change due to global warming. (15 Marks, 250 Words).
Background: Monsoons in India:
- India’s climate is characterized as the ‘monsoon‘ type, a classification predominantly observed in the southern and southeastern regions of Asia.
- Among the four seasonal divisions in India, the monsoon encompasses two specific divisions:
- The Southwest Monsoon Season: This period experiences seasonal rainfall from the southwest monsoons and occurs between June and September.
- The Retreating Monsoon Season: The months of October and November are notable for retreating monsoons, marking the conclusion of the monsoon season.
Recent Trend of Monsoons:
- This decrease is attributed to the differential heating of the land and ocean caused by global warming.
- Nevertheless, the impact of this trend varies throughout the monsoon season, evident in longer but less intense dry spells and more intense wet spells.
Dynamics of Monsoon Predictions in India:
- Despite advancements by the India Meteorological Department (IMD) in forecasting extremes, the complexity of multiple factors can still result in unpredictable and devastating heavy rain events.
- India’s monsoon predictions heavily rely on the relationship with El Niño and La Niña phenomena, though this connection is effective only about 60% of the time.
El Nino and La Nina:
- El Nino and La Nina represent intricate weather patterns resulting from fluctuations in ocean temperatures within the Equatorial Pacific Region.
- They constitute opposing phases within the El Nino-Southern Oscillation (ENSO) cycle, which outlines temperature variations between the ocean and atmosphere in the east-central Equatorial Pacific.
- Typically lasting between nine to 12 months, some instances of El Nino and La Nina can extend for several years. El Nino, identified as the “warm phase” of ENSO, involves the abnormal warming of surface waters in the eastern tropical Pacific Ocean. It occurs more frequently than La Nina.
- On the other hand, La Nina, recognized as the “cool phase” of ENSO, entails the unusual cooling of the tropical eastern Pacific.
- La Nina events may persist for a period ranging from one to three years, in contrast to El Nino, which usually has a duration of no more than a year.
- Both phenomena tend to reach their peak during the winter months of the Northern Hemisphere.
- While there are other global relationships, translating them into more accurate predictions necessitates meticulous modeling experiments.
- Researchers are actively seeking additional insights into various processes, particularly those associated with high-impact extreme rainfall events.
Extreme Rainfall in India:
- A recent study, in which the author participated, reveals that despite seemingly disparate changes in different aspects of monsoon dynamics, a noteworthy consistent element exists concerning the occurrence of synchronized extreme rainfall events.
- These large-scale extreme rainfall events, often simultaneous or nearly simultaneous heavy rain episodes, are scattered along a ‘highway’ stretching from parts of West Bengal and Odisha to areas of Gujarat and Rajasthan.
- The most significant discovery is that this corridor has remained unchanged from 1901 to 2019.
- Amidst the apparent chaos in various monsoon elements, the consistent confinement of extreme events to a relatively narrow corridor brings optimism for advancements in process understanding. This, in turn, is likely to enhance predictions of these synchronized extreme rainfall events.
Implications for the stability of the monsoon:
- Conventional statistical approaches often overlook the intricate relationships among multiple rainfall centers. Utilizing rainfall data from the India Meteorological Department (IMD) at a 25-km scale in latitude and longitude provides a detailed field for sophisticated network analysis.
- This study applied such analysis, revealing that the most active nodes have consistently followed a ‘highway’ for over a century. The link lengths between nodes, representing the scales of synchronicity, have remained nearly constant, averaging about 200 km.
- An examination of winds and other circulation features suggests that the monsoon domain has maintained a notable stability for the formation of these extremes, despite various influences from tropical oceans and pole-to-pole.
- Despite assertions by some researchers that stationary elements no longer persist in climate systems due to global warming, the Indian monsoon continues to defy expectations by coordinating heavy rainfall events and adhering to the established ‘highway’ for an extended period.
- This corridor is also the route for monsoon depressions, which have exhibited an increase at 3- to 10-day timescales and a decrease at lower frequencies of 10-60 days, as observed in the active and break periods mentioned earlier.
- The primary geographical factor likely responsible for trapping synchronized extreme rainfall is believed to be the mountain range running along the west coast and across Central India.
- Although this hypothesis requires testing in models, its undeniable implications for enhancing forecasts of such events are evident.
- Moreover, the findings suggest that, contrary to increasing model resolution and computational costs, the focus should be on understanding the dynamics of synchronization.
There is an enticing prospect of reducing risks at a smaller scale resulting from these large-scale extreme rainfall events, impacting areas such as agriculture, water resources, energy, transportation, and health. Fortunately, India possesses a robust modeling capacity and ample computational resources to fully exploit this potential.