A study published in Nature Geoscience has revealed that both strong and weak Indian Summer Monsoons (ISM) over the last 22,000 years have significantly impacted marine productivity in the Bay of Bengal.

• The study is significant given that several climate models warn of significant disruption to the monsoon, under the impact of human-caused warming.
• Methodology: Scientists analysed fossilised shells of foraminifera.
  • Foraminifera are tiny single-celled marine organisms that record environmental data in their calcium carbonate shells. 
  • These microfossils were retrieved from seafloor sediments by scientists aboard the JOIDES Resolution, a research ship operating under the International Ocean Discovery Program.

Key Points of the Study

• Significant Marine Contributor: The Bay of Bengal provides nearly 8% of global fishery production despite covering less than 1% of the global ocean area.
• Link Between Monsoon-Marine Productivity: Strong and weak monsoons caused major disruptions in ocean mixing, leading to a 50% reduction in food for marine life in the surface waters.
  • Extreme monsoon events hinder the vertical mixing of nutrient-rich deep water, reducing plankton growth which is the base of the marine food chain.
• Mechanism: Monsoon rainfall directly affects river run-off into the Bay of Bengal, altering ocean salinity and circulation.
  • Heavy monsoons increase freshwater at the surface, preventing nutrient circulation.
  • Weak monsoons reduce wind-driven mixing, also starving surface waters of nutrients.
  • Both extremes threaten marine resource availability
• Historical Evidence: Productivity dropped during Heinrich Stadial 1 (a cold phase between 17,500 and 15,500 years ago) and the early Holocene (10,500–9,500 years ago) due to abnormal monsoon intensity.
• Modern-Day Warning: Current climate trends resemble past patterns linked with productivity loss i.e warming seas, stronger river runoff, and weaker winds (Weaker winds = no mixing = fewer nutrients at surface = less plankton = fewer fish) .
• Food Security Risk: A decline in marine productivity threatens fish stocks and food security for millions dependent on fisheries in the region.

About Monsoon Variability

• The Indian Summer Monsoon is a large-scale seasonal wind system that dominates the Indian subcontinent from June to September, delivering around 70% of India’s annual rainfall.
• Monsoon variability refers to fluctuations in the timing, intensity, and spatial distribution of rainfall during the monsoon season.

Types of Monsoon Variability

• Onset and withdrawal variability: Changes in the timing of monsoon arrival and retreat.
• Spatial variability: Some regions may receive excess rainfall while others experience drought in the same year. 
  • Example: Northwest India (Rajasthan, Haryana) receives less rainfall.
  • Northeast and Western Ghats experience heavy rainfall.
• Intra-seasonal variability: Variations within a single season, such as active rainfall and break periods (Dry periods with reduced rainfall).
• Inter-annual variability: Year to year variation of monsoon rainfall over the large number of years is known as the interannual variability of monsoon.

Causes Of Monsoon Variability

• Monsoon Trough: It is an elongated low-pressure zone that extends from the heat low over Pakistan to the head of the Bay of Bengal during the Indian Summer Monsoon (June–September). 
  • It is a semi-permanent feature of the monsoon system.
  • The north-south oscillation of the monsoon trough drives active (southward shift) and break (northward shift) phases, influencing rainfall patterns.
• ENSO (El Nino Southern Oscillation): A warming (El Nino) or cooling (La Nina) of the Pacific Ocean can affect the monsoon.
  • El Nino weakens, while La Nina strengthens the Indian monsoon.
• Indian Ocean Dipole (IOD): It is an irregular oscillation of sea surface temperatures in the Indian Ocean, characterized by differences between the western and eastern parts. 
  • It has two phases: a positive phase, where the western Indian Ocean is warmer, and a negative phase, where it is cooler.
  • Positive IOD enhances monsoon, while negative IOD suppresses it.
• Himalayan Snow Cover: Higher snow cover is historically linked with weaker monsoons.
• Madden-Julian Oscillation (MJO): The Madden-Julian Oscillation (MJO) is a tropical atmospheric wave that moves eastward, with active phases boosting monsoon rainfall and suppressed phases causing dry spells
• Land-Atmosphere Feedbacks: Soil moisture, vegetation, and aerosols also impact local rainfall.
• Equatorial Indian Ocean Sea Surface Temperature (SST): Anomalous warming or cooling of sea surface temperatures affects convection and wind patterns, altering monsoon intensity.

Monsoon Forecasting In India

• Monsoon forecasting in India, vital for agriculture, water resources, and disaster preparedness, is primarily managed by the India Meteorological Department (IMD), which provides forecasts across various spatial and temporal scales.
• Spatial Scales
  • National Level: Forecasts for the whole country.
  • Regional/State: Broad or localized regions.
• Temporal Scales
  • Long Range Forecast (LRF): Seasonal/monthly (up to 4 months). It is based on the Statistical Ensemble Forecasting System (SEFS) using 8 climate predictors.
  • Extended Range Forecast (ERF): 10–30 days ahead.
  • Short to Medium Range: 1–10 days ahead.
  • Nowcast: Up to 6 hours ahead. The forecast is Issued by local meteorological centres.
    • Nowcasting targets localized weather events, especially intense rainfall and severe storms.

Monsoon Prediction Models In India

• Statistical Models: These models rely on historical data to predict trends and patterns, such as the onset of the monsoon and rainfall distribution.
• Dynamical Models: These models use numerical simulations based on global climate data to forecast weather conditions, including the monsoon.
• Multi-Model Ensemble Approach: To improve the accuracy of forecasts, IMD often uses multiple models to provide a more reliable prediction.

Recent Advances and Innovations in Monsoon Prediction In India

• Mission Mausam: Launched in 2024, this initiative aims to revolutionize weather forecasting by integrating Artificial Intelligence and machine learning into observational and modeling systems.
• Enhanced Monsoon Mission Coupled Forecasting System (MMCFSv2): The upgraded MMCFSv2 model incorporates advanced ocean and atmospheric components, improving the accuracy of monsoon predictions.
• Forecast Error Reduction: Between 2007 and 2024, the average absolute forecast error in seasonal rainfall predictions decreased by approximately 21%, indicating significant improvements in forecast reliability.

Agencies Involved in Monsoon Forecasting In India

• India Meteorological Department (IMD): It was established in 1875 and is the principal agency for weather and climate services under the Ministry of Earth Sciences. 
  • It is a Regional Climate Centre for South Asia and a key partner in the UN’s ‘Early Warning for All’ programme.

• Indian Institute of Tropical Meteorology (IITM): It is an autonomous institute located in Pune under the Ministry of Earth Sciences. It conducts research on the Ocean-Atmosphere Climate System to improve weather and monsoon forecasts.
• National Centre for Medium Range Weather Forecasting (NCMRWF): Provides medium-range forecasts and modeling support.
• Indian National Centre for Ocean Information Services (INCOIS): Offers oceanographic data crucial for monsoon predictions.
• National Institute of Ocean Technology (NIOT):  Supports ocean monitoring and data for climate studies.

Challenges In Monsoon Forecasting

• Complex Atmospheric Systems:Monsoons involve large-scale atmospheric circulation patterns such as Indian Ocean Dipole (IOD), El Nino, Madden-Julian Oscillation (MJO). 
  • Predicting the interaction between these systems is complex and often requires advanced modeling techniques.
    • Example: Positive IOD boosts monsoon rains, but its interaction with ENSO complicates predictions.
• Non-linear Atmospheric Interactions: Small changes in one factor can amplify unpredictability due to chaotic atmospheric behavior.
• Limitations in Observational Data: The India Meteorological Department (IMD) currently operates around 800 Automatic Weather Stations (AWS), 1,500 Automatic Rain Gauges (ARG), and 37 Doppler Weather Radars (DWR), falling short of the required 3,00,000 ground stations and 70 DWRs.
• Model Uncertainties and Forecasting Errors: Example: Dynamical models like Monsoon Mission Coupled Forecast System (MMCFS) face initial condition errors, where small inaccuracies amplify over time, reducing forecast reliability.
• Climate Change Impacts: Changing climate patterns lead to unpredictable monsoon behavior. Increased rainfall intensity, as seen in Kerala floods (2018), challenges forecasting.

Government Initiatives For Tackling Monsoon Variability

• National Monsoon Mission (2012): Monsoon Mission is a national programme launched by the Ministry of Earth Sciences (MoES) with a vision to develop state-of-the-art dynamical prediction systems for the monsoon rainfall in different time scales.
• Rashtriya Krishi Vikas Yojana (RKVY): States have been advised to keep aside about 5 to 10% of fund allocated under Rashtriya Krishi Vikas Yojana (RKVY) for undertaking interventions to minimize the adverse impact of an aberrant monsoon on the agriculture sector.
• Climate Resilient Agriculture (under NMSA): Promotes adaptive farming practices to deal with monsoon variability.

Way Forward

• Expansion of Observational Networks: Increase the number of Automatic Weather Stations (AWS), Doppler radars, and oceanic buoys to enhance data collection and improve forecast accuracy.
• High-Resolution Modeling: High-resolution climate models simulate atmospheric and oceanic processes at finer spatial and temporal scales, capturing localized weather phenomena (e.g., cloud bursts, cyclones, and regional rainfall variability).
  • Artificial Intelligence/Machine Learning can be integrated for better monsoon and extreme weather predictions
• Integrated Forecasting Systems: Combine statistical and dynamical models to create a more robust forecasting framework for improved accuracy.
• International Collaboration: Strengthen partnerships with organizations like World Meteorological Organization (WMO) and National Oceanic and Atmospheric Administration (NOAA) for improved data sharing and global forecasting capabilities.
• Open Access to Weather Data: Make weather data openly available for researchers and entrepreneurs to develop use cases for better understanding of the causes behind extreme weather events. 
  • It also helps in the creation of localised early warning tools. 
  • Example: United States, United Kingdom, France, and the European Union have made their weather forecasting data available on cloud for anyone to access.

Conclusion

Better monsoon prediction is crucial for safeguarding agriculture, marine ecosystems, and livelihoods in India. Strengthening forecasting systems will enhance preparedness and resilience against monsoon disruptions.

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