THE CONTEXT: The southwest monsoon, accounting for nearly 70% of India’s annual rainfall, is pivotal for agriculture, water resources, and the overall economy. Accurate forecasting enables timely sowing, efficient water management, and disaster mitigation, thereby safeguarding livelihoods and promoting sustainable development.
HISTORICAL TRAJECTORY
Colonial Foundations
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- 1875: Establishment of the IMD in response to devastating famines, notably the Great Famine of 1876-78.
- 1886: Henry Francis Blanford issued the first long-range monsoon forecast, correlating Himalayan snow cover with monsoon intensity.
- Early 1900s: Sir Gilbert Walker introduced statistical models, identifying the Southern Oscillation’s influence on monsoon variability.
Post-Independence Developments
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- 1988: Introduction of the Gowariker model, utilizing 16 atmospheric parameters for monsoon prediction.
- 2003: Adoption of a two-stage forecast strategy, issuing predictions in April and updates in June.
- 2007: Implementation of the Statistical Ensemble Forecasting System (SEFS), enhancing forecast accuracy through ensemble techniques.
- 2012: Launch of the Monsoon Mission Coupled Forecasting System (MMCFS), integrating ocean-atmosphere-land interactions for dynamic modeling.
- 2021: Introduction of the Multi-Model Ensemble (MME) approach, combining outputs from various global climate models for robust predictions.
KEY DRIVERS AND TELECONNECTIONS OF THE INDIAN SUMMER MONSOON:
1. El Niño–Southern Oscillation (ENSO)
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- Mechanism: ENSO involves periodic fluctuations in sea surface temperatures (SSTs) and atmospheric pressure across the equatorial Pacific Ocean. El Niño phases (warm SST anomalies) are typically associated with weakened monsoon activity over India, while La Niña phases (cool SST anomalies) often enhance monsoon rainfall.
- Impact: ENSO events alter the Walker Circulation, leading to subsidence over the Indian subcontinent during El Niño years, which suppresses convection and reduces rainfall.
2. Indian Ocean Dipole (IOD)
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- Mechanism: IOD refers to the oscillation of SSTs between the western and eastern Indian Ocean. A positive IOD (warmer western Indian Ocean) enhances the monsoon by strengthening the easterly winds and increasing moisture transport to India.
- Interaction with ENSO: The IOD can modulate the impact of ENSO on the monsoon. For instance, a positive IOD can offset the adverse effects of El Niño on the Indian monsoon.
3. Madden-Julian Oscillation (MJO)
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- Mechanism: MJO is an eastward-moving disturbance of clouds, rainfall, winds, and pressure that traverses the planet in the tropics and returns to its initial starting point in 30 to 60 days, on average. It influences the monsoon’s active and break periods.
- Impact: When the MJO is in a phase conducive to convection over the Indian Ocean, it can enhance monsoon rainfall. Conversely, when it is over the Pacific, it can suppress monsoon activity.
4. Quasi-Biennial Oscillation (QBO)
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- Mechanism: QBO is a regular variation of the winds that blow high above the equator in the stratosphere. These winds alternate between easterly and westerly phases approximately every 28 months.
- Impact: The QBO phase can influence the vertical wind shear in the tropics, thereby affecting the intensity and distribution of monsoon rainfall.
5. Eurasian Snow Cover and Albedo
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- Mechanism: The extent of snow cover over Eurasia during winter and spring affects the heating of the landmass, which in turn influences the strength of the monsoon.
- Impact: Excessive snow cover increases the albedo effect, leading to cooler land temperatures and a weaker monsoon due to reduced thermal contrast between the land and ocean.
6. North Atlantic and Pacific Oscillations
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- Mechanism: The North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) are patterns of climate variability that influence weather and climate over large areas.
- Impact: These oscillations can modulate the monsoon by altering the large-scale atmospheric circulation patterns, thereby affecting the transport of moisture and the position of the jet streams.
INSTITUTIONAL AND TECHNOLOGICAL ADVANCEMENTS
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- Statistical Ensemble Forecasting System (SEFS) – 2007: The IMD introduced SEFS, utilizing multiple statistical models to improve forecast reliability. This approach reduced the average absolute error in forecasts between 2007 and 2018 to 5.95% of the long-period average (LPA), compared to 7.94% during 1995-2006.
- Monsoon Mission Coupled Forecasting System (MMCFS) – 2012: MMCFS integrated ocean-atmosphere-land interactions, enhancing the dynamical modeling of monsoon systems. The latest version, MMCFSv2, improved the anomaly correlation coefficient by 17% and reduced precipitation biases, offering more accurate seasonal forecasts.
- Multi-Model Ensemble (MME) Approach – 2021: The MME system combines outputs from various global climate models, including IMD’s MMCFS, to generate probabilistic forecasts. This ensemble approach has further reduced forecast errors and improved spatial rainfall predictions.
IMPACT OF THE MMCFS REVOLUTION
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- Holistic Data Assimilation – Integrates SST, upper-ocean heat-content & land-soil moisture; handles non-stationary tele-connections.
- Higher Lead-Skill – Skillful forecasts ≥ 3 months ahead; aided correct calls on deficient 2014-15 & below-normal 2023 ISMR.
- Sectoral Pay-offs – Underpins Meghdoot Agromet advisories to 22 million farmers; calibrates PM-FBY premium matrices.
THE PERSISTENT CHALLENGES:
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- Cold Sea Surface Temperature (SST) Bias: MMCFS tends to simulate cooler SSTs in the equatorial Pacific, leading to the “cold tongue” bias. This misrepresentation affects the simulation of atmospheric convection and precipitation patterns.
- Double Intertropical Convergence Zone (ITCZ) Problem: The model often produces a spurious second ITCZ in the Southern Hemisphere, resulting in erroneous rainfall predictions over the Indian subcontinent.
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- ARGO Float Distribution: Less than 40% of the recommended ARGO floats are operational in the western Indian Ocean, leading to data deficiencies in this critical region.
- Impact on Madden-Julian Oscillation (MJO) Monitoring: The MJO significantly influences monsoon variability. Insufficient data hampers accurate tracking and modeling of MJO events, affecting forecast reliability.
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- Shifting Climate Patterns: Historical correlations between predictors and monsoon behavior are altering, reducing the efficacy of traditional statistical models.
- Need for Adaptive Models: Integrating machine learning and artificial intelligence can help develop models that adapt to evolving climate signals, enhancing forecast accuracy.
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- Probabilistic Forecast Interpretation: Many stakeholders, especially at the grassroots level, struggle to understand probabilistic forecasts, limiting their utility in decision-making .
- Localized Communication Strategies: Tailoring communication methods to local contexts, languages, and literacy levels is crucial for effective dissemination and utilization of forecast information.
IMPLICATIONS FOR GOVERNANCE & DEVELOPMENT
| Sector | Policy Lever | Monsoon-link |
|---|---|---|
| Agriculture | PM-KISAN timing, MSP procurement calendar, PM-FBY actuarial premiums | Early LRF curbs distress-sale & premium spikes |
| Prices & Macro-Stability | Reserve Bank inflation targeting; buffer-stock release | Above-normal rains 2024-25 aided 3.34 % CPI — five-year low |
| Water & Energy | National Hydrology Project; hydro-power dispatch | Dynamic reservoir rule-curves based on S2S outlook |
| Disaster Risk Reduction | NDMA Heat Action Plans; Flash-Flood Guidance Centers | Impact-based forecasts shift focus from “how much” to “so what” |
THE WAY FORWARD:
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- Seamless S2S-CMIP6 Suite Integration: Integrate the Monsoon Mission Coupled Forecast System version 2.0 (MMCFS-v2) with Coupled Model Intercomparison Project Phase 6 (CMIP6) baselines, enabling weekly 45-day forecast windows. This fusion enhances the accuracy of monsoon predictions by combining short-term variability with long-term climate trends.
- Establishment of a National AI Weather Hub: Create a centralized AI-driven platform akin to the ‘Gati Shakti’ initiative, pooling data from ISRO satellites, IMD radars, and crowdsourced observations. This hub would facilitate real-time data analysis and predictive modeling.
- Deployment of Ocean Observatories 2.0: Deploy advanced deep-water gliders and Argo floats in the Western Indian Ocean, particularly in the Indian Ocean Dipole (IOD) core areas. These instruments provide critical data on ocean temperatures, salinity, and currents.
- Hyper-local Downscaling for Agro-climatic Zones: Utilize the Weather Research and Forecasting (WRF) model to create 2 km resolution grids for India’s 115 agro-climatic zones. This fine-scale modeling supports the Digital Agriculture Mission by offering localized advisories.
- Embedding State Climate Cells in SDRFs: Integrate dedicated climate cells within State Disaster Response Funds (SDRFs) and develop a ‘Monsoon Preparedness Index’ to guide Finance Commission devolution. This approach promotes cooperative federalism in climate adaptation.
THE CONCLUSION:
India’s journey in monsoon forecasting reflects a blend of scientific innovation and adaptive governance. While significant strides have been made, continuous efforts in research, infrastructure development, and stakeholder engagement are essential to address emerging challenges and harness the full potential of monsoon predictions for national development.
UPSC PAST YEAR QUESTION:
Q. How far do you agree that the behavior of the Indian monsoon has been changing due to humanizing landscapes? Discuss. 2015
MAINS PRACTICE QUESTION:
Q. Probabilistic monsoon forecasts pose challenges for governance. Discuss these challenges and propose policy interventions to translate seasonal outlooks into resilient agricultural and fiscal strategies
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