Overview

Previous Year UPSC-CSE Questions By the end you will be able to draft model answers for the following UPSC questions. Each question carries a collapsible framework showing how to approach it in the exam.

  1. UPSC Mains 2014 GS-IIIDrought has been recognized as a disaster in view of its spatial expanse, temporal duration, slow onset, and lasting effects on vulnerable sections. With a focus on the September 2010 guidelines from the National Disaster Management Authority (NDMA), discuss the mechanisms for preparedness to deal with likely El Nino and La Nina fallouts in India.
    How to structure the answer in the exam

    Directive verb: Discuss · Approach: Define drought as a slow-onset disaster, then walk through its four defining attributes (spatial expanse, temporal duration, slow onset, lasting effects on the vulnerable), ground the answer in the September 2010 NDMA guidelines, and close on preparedness mechanisms for El Nino and La Nina fallouts. · Word count: 250

    Introduction: Drought is a slow-onset hydro-meteorological disaster marked by prolonged rainfall deficiency that compounds across spatial scales and stresses vulnerable communities through cumulative food, water, and livelihood impact. The September 2010 NDMA guidelines frame India's institutional preparedness for ENSO-linked monsoon failure.

    Body (sub-themes to develop):

    • Spatial expanse: A single ENSO-driven drought can simultaneously affect Maharashtra, Karnataka, Andhra Pradesh, Telangana, and Rajasthan, covering a large share of India's cropped area.
    • Temporal duration: Unlike a cyclone or flood, drought builds over months and may persist across more than one monsoon season, as in the 2014 to 2016 multi-year deficits.
    • Slow onset: No single event triggers drought; cumulative monsoon-deficit indicators are read from IMD long-range forecasts and verified by satellite vegetation maps.
    • Lasting effects on vulnerable sections: Small and marginal farmers, landless labourers, women, and scheduled tribes bear disproportionate impact through income loss, migration distress, debt, and food insecurity.
    • NDMA preparedness mechanisms: Drought was brought under the Disaster Management Act 2005; response is now framework-driven through the September 2010 NDMA guidelines, MGNREGA wage relief, and PMFBY crop insurance for El Nino and La Nina fallouts.

    Conclusion: Treating drought as a notified disaster shifts the state from reactive relief to anticipatory preparedness, with IMD forecasts, NDMA guidelines, and crop insurance forming the backbone of India's response to ENSO-driven monsoon failure.

  2. UPSC Mains 2024 GS-IThe groundwater potential of the Gangetic valley is on a serious decline. How may it affect the food security of India?
    How to structure the answer in the exam

    Directive verb: Examine · Approach: Establish how the Gangetic valley aquifer underpins the wheat-paddy rotation, trace the decline mechanism, then link the decline to national food security through output, buffer stocks, and the ENSO-driven recharge overlay, closing on policy responses. · Word count: 250

    Introduction: The Gangetic valley aquifer supports the wheat-paddy rotation that feeds northern India. Its decline, driven by over-extraction and erratic monsoon recharge under climate change and ENSO variability, threatens national food security.

    Body (sub-themes to develop):

    • Decline mechanism: Over-pumping for paddy and wheat depletes the aquifer, while rainfall variability under ENSO interrupts natural recharge.
    • Food security stakes: Punjab, Haryana, and Uttar Pradesh contribute a large share of India's wheat and rice, so an aquifer crash threatens the national buffer stock.
    • Atal Bhujal Yojana: The 2020 World Bank-assisted scheme targets thousands of water-stressed gram panchayats for community-led groundwater management.
    • Climate-change overlay: ENSO-driven monsoon failures compound the recharge crisis, and IPCC AR6 projects continued aquifer-recharge variability.
    • Policy response: PMKSY 'per drop more crop', ICAR climate-resilient cultivars, FCI buffer stocks, and the National Mission for Sustainable Agriculture under the NAPCC build resilience.

    Conclusion: Securing Gangetic groundwater is inseparable from food security; demand-side efficiency, recharge investment, and ENSO-aware crop planning together protect the northern grain bowl from monsoon variability.

  3. UPSC Mains 2016 GS-IIn what way micro-watershed development projects help in water conservation in drought-prone and semi-arid regions of India?
    How to structure the answer in the exam

    Directive verb: Explain · Approach: Define the micro-watershed approach, set out how its structures capture runoff and recharge aquifers, then show how this builds drought resilience in ENSO-exposed semi-arid India, with Indian examples and the policy scheme map. · Word count: 200

    Introduction: Micro-watershed development is the catchment-area approach to water conservation that captures rainwater locally, recharges aquifers, and builds drought resilience in semi-arid India, where ENSO-driven monsoon failures recur.

    Body (sub-themes to develop):

    • Mechanism: Small check dams, percolation tanks, contour bunds, and field bunds capture surface runoff within a watershed unit.
    • Aquifer recharge: Captured water percolates to recharge shallow aquifers, raising the groundwater table and improving well yield.
    • Drought resilience: The built infrastructure provides an irrigation buffer when the monsoon fails during El Nino years and reduces reliance on government drought relief.
    • Indian examples: Ralegan Siddhi and Hiware Bazar in Maharashtra and Sukhomajri in Haryana demonstrate the model in practice.
    • Policy schemes: The PMKSY Watershed Development Component, NWDPRA, IWMP, DPAP, and DDP carry the watershed mandate.

    Conclusion: Micro-watershed development converts erratic ENSO-modulated rainfall into stored, rechargeable water, making it a low-cost, community-led pillar of drought resilience in semi-arid India.

India's monsoon-dependent economy is exceptionally exposed to the El Niño Southern Oscillation (ENSO). Approximately 60 per cent of net sown area remains rainfed. Hydropower depends on monsoon runoff. The Kharif harvest sets the rural-income floor. Over 70 per cent of annual rainfall falls in the four monsoon months. When ENSO swings to its warm phase (El Niño), the Walker Circulation that draws moisture eastward over the Indian Ocean weakens. Southwest monsoon rainfall is suppressed, triggering drought conditions that ripple across food production, inflation, hydropower generation, and rural distress. When ENSO swings to its cold phase (La Niña), the same coupling enhances monsoon rainfall and sometimes triggers flood events. ENSO is the single largest external climate driver of year-to-year variability in Indian rainfall, agriculture, and rural economy.

Background: Why ENSO Matters Disproportionately to India

Why it matters. ENSO is the single most important external driver of the Indian summer monsoon. The monsoon, in turn, drives Indian agricultural output, rural household income, food inflation, and reservoir hydropower. UPSC Mains GS-III questions on agriculture, food security, and disaster management repeatedly draw on ENSO-India linkages. Prelims questions test ENSO-monsoon-IOD relationships through the Walker circulation and Indian Ocean Dipole.

Sir Gilbert Walker's identification of the Southern Oscillation in the 1920s was prompted by repeated Indian monsoon failures and the colonial revenue stakes attached to them. The 1877-78 Great Famine struck during a major El Niño. The 1965-67 droughts, and the 1972, 1982, 1987, 2002, 2009, 2014, 2015, and 2023 monsoon-deficit years extend the historical pattern. The link between ENSO and Indian monsoon outcome has been a central operational concern for the India Meteorological Department, the Ministry of Agriculture, and successive Five-Year Plans.

The picture is not deterministic. The 1997 strong El Niño produced a near-normal Indian monsoon. The 2014 moderate El Niño produced a substantial deficit. The correlation has also weakened in recent decades.

Part 1 of this series covered the mechanism: Walker Circulation, El Niño and La Niña phases, Bjerknes feedback. Part 2 mapped the global teleconnection footprint and climate-change effects on ENSO itself. Part 3 (this part) covers what ENSO does to India: which regions feel it, which crops suffer, and what the economy absorbs. It also examines how cyclone activity in the Bay of Bengal and Arabian Sea responds.

ENSO and Indian Monsoon: Walker Circulation Coupling, El Niño Drought, La Niña Excess, and the Weakening Recent Correlation

ENSO-Monsoon Relationship

What is the significance of the ENSO-Indian-monsoon coupling? The relationship operates through Walker circulation modulation. El Niño weakens the Pacific-Indian Ocean trade-wind contrast that drives monsoon convection, while La Niña strengthens it. Approximately 60 percent of historical Indian drought years coincide with El Niño phases, and the coupling shapes agriculture, hydropower, and inflation outcomes for the year.

The Indian southwest monsoon and the Walker Circulation share the same set of physical drivers: trade-wind strength across the equatorial Pacific, the sea-surface-temperature gradient between the western and eastern Pacific, and the position of the ascending atmospheric branch over the warm-pool region. When ENSO shifts the Walker Circulation, the monsoon's moisture-supply geometry changes too. Sir Gilbert Walker discovered the Southern Oscillation in the 1920s while studying exactly this connection from India Meteorological Department offices in Shimla.

How the Walker Circulation shapes monsoon moisture

Under normal conditions, the western Pacific warm pool (including the maritime continent of Indonesia) generates vigorous ascending air. This deep convection is part of the same atmospheric flow that brings the southwest monsoon's moisture northwest from the Indian Ocean toward the Indian subcontinent.

During El Niño the ascending branch shifts eastward to the central Pacific, the maritime continent dries, and the moisture-pump that supplies the Indian monsoon is partially diverted. During La Niña the western-Pacific ascending branch strengthens, the moisture-pump intensifies, and Indian monsoon rainfall tends to exceed climatological normals.

Walker Circulation shift and Indian monsoon couplingNormal / La NiñaIndiaIndian Ocean warm poolMaritime continent (Indonesia)Pacificmonsoon moisture flowstrong ascending branchEl NiñoIndiaIndian Ocean (reduced gradient)Maritime continent driesPacific (warm anomaly)monsoon flow weakenedascending branch shifted eastEl Niño displaces the western-Pacific ascending branch eastward, weakening the moisture pump that supplies the Indian monsoon.
Walker Circulation shift and Indian monsoon: under normal/La Niña conditions the western-Pacific ascending branch enhances the Indo-Pacific warm pool and the monsoon moisture pump; under El Niño the ascending branch shifts to the central Pacific, weakening moisture transport to the Indian subcontinent.

Why El Niño tilts India toward drought

  • Major Indian drought years coinciding with El Niño: 1877-78, 1899-1900, 1918, 1965, 1972, 1982, 1987, 2002, 2009, 2014, 2015, 2018.
  • Counter-examples (El Niño without major Indian drought): 1997 strong El Niño produced near-normal monsoon; 2006 moderate El Niño produced near-normal rainfall.
  • Reverse counter-examples (Indian drought without El Niño): 2000 was neutral-ENSO yet produced drought conditions due to other factors.
  • Implication: El Niño is a probability-shifter, not a deterministic trigger. Most strong El Niños are associated with monsoon deficit, but several have not been.

How La Niña lifts monsoon rainfall

ENSO phase versus typical Indian southwest monsoon outcome. Probability-shifter, not deterministic.
ENSO phase Typical monsoon outcome Drought / flood risk Representative years
Strong El Niño Below-normal rainfall, deficit risk High drought risk 1972, 1982, 1987, 2009, 2014, 2015, 2018
Weak/moderate El Niño Spatially variable; central and northwest impact Moderate drought risk 1991, 2002, 2004, 2006
ENSO-neutral Near-climatology Other drivers (IOD, MJO) dominate Most years
Weak/moderate La Niña At-or-above normal Slight flood risk in concentrated regions 1995, 2007, 2017
Strong La Niña Above-normal rainfall, sometimes excess Flood risk in east and central India 1988, 1998-2000, 2010-2011, 2020-2022

How El Niño suppresses the southwest monsoon

El Niño years systematically suppress the south-west monsoon over India. The disrupted Walker Circulation reduces the moisture pull from the Indian Ocean, the monsoon trough develops weakly, and the all-India rainfall departs negatively from normal. El Niño years 1972, 1987, 2002, 2009 and 2015 each produced rainfall deficits in the range of ten to fourteen per cent on the all-India seasonal record, with corresponding kharif yield losses and food-grain price pressure across the country.

ENSO and Monsoon Variability

How the ENSO-monsoon correlation changed over time

The historical correlation between ENSO state (measured by Niño 3.4 SST anomaly or Southern Oscillation Index) and All-India Summer Monsoon Rainfall was strongly negative over much of the 20th century. In the 1950s-1970s, El Niño years almost always produced deficit monsoons. The correlation provided high-skill seasonal forecasts for Indian agricultural planning and was a cornerstone of IMD's long-range forecast methodology.

Beginning in the late 1970s and most clearly from the 1990s, the ENSO-monsoon correlation has weakened. Kumar, Rajagopalan, and Cane published a landmark 1999 paper in Science documenting this decoupling. Possible explanations cluster into four strands.

The first is a shift toward more Modoki-type (central-Pacific) El Niño events whose teleconnection over India differs. The second is the rising influence of the Indian Ocean Dipole, which sometimes counteracts ENSO impact. The third is warming of the Indian Ocean itself, altering the monsoon's local moisture budget; the fourth is changes in extratropical circulation. The 1997 strong El Niño with a near-normal Indian monsoon was the canonical break-point example.

ENSO-Indian-Monsoon correlation, 1950 to 20250-0.4-0.81950s1970s1990s2010s2020sCorrelation coefficientstrong negativeweakCorrelation weakens post-1980 as Modoki events rise + IOD influence grows.Conceptual; see Kumar, Rajagopalan and Cane 1999 (Science) for the original empirical decoupling result.
Schematic of ENSO-Indian-Monsoon correlation strength over time. Strong negative correlation through the 1970s; weakening from the 1980s onward as Modoki events become more frequent and IOD influence rises.

Regional Impacts on India: Northwest, Peninsular, Northeast Monsoon, Himalayan Climate, and Extreme Rainfall

ENSO and Spatial Distribution of Rainfall

ENSO's impact on Indian rainfall is not spatially uniform. The strongest drought signal during El Niño is over the northwest and central plains, where the southwest monsoon delivers the bulk of seasonal rainfall. Peninsular India and the northeast respond differently because of distinct rainfall regimes.

Impact on Northwest India

Northwest India (Rajasthan, Punjab, Haryana, western Uttar Pradesh, Gujarat) shows the clearest El Niño drought signal. The region depends heavily on the southwest monsoon. June-September supplies 75-80 per cent of annual rainfall in parts of Rajasthan. El Niño-related Walker Circulation weakening reduces moisture inflow. The result is rainfall deficits that translate into reduced Kharif sowing, lower wheat-water reserves for the subsequent Rabi season, and water-table stress.

How ENSO drives extreme rainfall events

Indian rainfall departure under a strong El Niño (schematic)Northweststrong deficitCentraldeficitPeninsularmoderate deficitTamil NaduNE monsoon: enhancedEast HimadeficitLegendSW monsoon deficitNE monsoon enhancedSchematic; exactspatial patternvaries per event
Schematic spatial distribution of Indian rainfall departure during a strong El Niño year. Northwest and central India: large negative departures (drought). Peninsular interior: moderate negative. Coastal Kerala: minor. Northeast monsoon belt (Tamil Nadu): enhanced. Eastern Himalaya: deficit.

Impact on Peninsular India

Peninsular India experiences pronounced El Niño rainfall deficits, particularly across Karnataka, Andhra Pradesh, Telangana, and the Vidarbha region of Maharashtra. The combined effect of weakened westerly monsoon flow and suppressed Bay of Bengal depression activity reduces rainfall by ten to twenty per cent in deficit years, intensifying drought conditions in the Deccan rain-shadow zone.

Impact on the Northeast Monsoon

The north-east monsoon over Tamil Nadu and Coastal Andhra Pradesh responds in the opposite direction to ENSO. El Niño years often produce above-normal October-November rainfall along the Coromandel coast due to enhanced Bay of Bengal cyclone activity. The 2015 El Niño coincided with the catastrophic Chennai floods that delivered record November rainfall and city-scale infrastructure failure.

ENSO and Himalayan Climate

The Himalayan climate shows mixed ENSO signals. El Niño winters tend to bring stronger Western Disturbance activity through an enhanced subtropical westerly jet, often producing above-normal snowfall over Himachal Pradesh and Uttarakhand. The 2014-15 winter recorded extended snow spells during the moderate El Niño. La Niña winters often see weaker jet meanders and reduced Western Disturbance frequency over the western Himalaya.

ENSO and Indian Agriculture: Kharif and Rabi Crops, Drought, Food Grain Variability, Irrigation, Food Security

Impact on Indian Agriculture

Impact on Kharif Crops

Kharif crops (sown June-July, harvested October-November) are directly dependent on southwest monsoon rainfall. Rice, maize, jowar, bajra, cotton, and pulses dominate the Kharif portfolio. El Niño-driven monsoon deficits hit Kharif hardest. Delayed sowing reduces yield. Mid-season dry spells stress crops at critical growth stages: tillering in rice, silking in maize. Harvest-time rainfall failures compress the harvesting window.

Impact on Rabi Crops

Rabi crops (sown October-November, harvested March-April) are less directly exposed to the southwest monsoon but are indirectly affected through soil-moisture carryover and reservoir levels. Wheat, mustard, gram, and lentils dominate the Rabi portfolio. A failed southwest monsoon leaves reservoirs depleted and soil moisture low at Rabi sowing time, reducing Rabi-irrigated yields. Conversely, La Niña years often produce strong Rabi seasons on the back of full reservoirs.

Agricultural Droughts during El Niño

  • 2002 El Niño drought: All-India rainfall deficit of about 19 per cent; food grain production fell by about 30 million tonnes.
  • 2009 El Niño drought: All-India deficit about 22 per cent; food grain output dropped sharply; pulse prices spiked.
  • 2014-2015 consecutive El Niño-affected years: Back-to-back deficits of about 12 per cent and 14 per cent; one of the few back-to-back drought periods in IMD records.
  • 2023 El Niño drought: All-India deficit about 6 per cent; spatially concentrated in northwest and central India; pulse and oilseed output affected.

Implications for Food Security

Crop sensitivity to ENSO state. Kharif crops are directly exposed to southwest monsoon failure; Rabi crops are indirectly exposed via soil moisture and reservoir levels.
Season Sowing-Harvest window Major crops ENSO-state sensitivity
Kharif Jun-Jul sowing → Oct-Nov harvest Rice, maize, jowar, bajra, cotton, sugarcane, pulses HIGH , direct SW monsoon dependence; El Niño deficit = yield loss
Rabi Oct-Nov sowing → Mar-Apr harvest Wheat, mustard, gram, lentil, peas MEDIUM , indirect through soil moisture, reservoir levels carrying SW monsoon outcome
Zaid (summer) Mar-Apr sowing → Jun-Jul harvest Watermelon, cucumber, fodder MEDIUM , irrigated; reservoir-level dependent

How food-grain output swings with ENSO

Indian food-grain production shows direct sensitivity to ENSO. Strong El Niño years have historically reduced national kharif output by five to ten per cent, with paddy and pulses most affected. The 2009 El Niño triggered a paddy production drop of approximately ten per cent against the MoA Directorate of Economics and Statistics baseline. FCI buffer stocks under the NFSA framework cushion price shocks but cannot fully insulate household food security.

How ENSO stresses irrigation and water resources

Reservoir storage levels across the country decline sharply during El Niño years. Central Water Commission monitoring showed that 91 major reservoirs ran significantly below normal end-September levels during the 2015 El Niño, with deficit-zone reservoirs particularly stressed. PMKSY irrigation extension and groundwater management under Atal Bhujal Yojana are designed to absorb such shocks but stress the rural water budget significantly.

ENSO and Cropping Patterns

Farmers in drought-prone zones increasingly shift to climate-resilient cropping patterns when IMD long-range forecasts signal El Niño risk. ICAR NICRA has released 150 to 200 climate-resilient cultivars across cereals, pulses, oilseeds, and horticulture, with the programme demonstrated in over 1,800 villages through Krishi Vigyan Kendra district networks. Insurance-driven crop switching through PMFBY signals is an emerging mechanism for climate-shock buffering.

ENSO and Indian Economy: GDP, Rural Economy, Inflation, Hydropower, Fisheries, Disaster Costs

Impact on Indian Economy

ENSO and GDP Growth

Agriculture contributes about 18 per cent of Indian GDP but employs roughly 45 per cent of the workforce. ENSO-driven monsoon failures therefore have an outsized effect on rural incomes and consumption demand. El Niño drought years (2002, 2009, 2014, 2015) shaved an estimated 0.3 to 1.0 per cent off annual GDP growth via the agriculture channel. The consumption-demand channel of rural-to-urban spillover added a further indirect drag.

Impact on Rural Economy

The rural economy bears the front-line cost of ENSO-driven monsoon failure. Reduced crop incomes constrain household consumption. Agricultural wage employment shrinks. Migration to urban centres rises. The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) was designed in part as a counter-cyclical wage safety net that ramps up during drought years. Demand for MGNREGA workdays has spiked in major El Niño drought years.

What ENSO adds to disaster-management costs

ENSO transmission channels to the Indian economyEl Niño / La Niñamonsoon deficit or excessAgricultureKharif yield, Rabi moistureInflationfood prices, pulses, CPIHydropowerreservoir levels, river flowDisaster costdrought relief, MGNREGA spikeGDP growth and fiscal balanceconsolidated macroeconomic impact
Channels through which ENSO transmits to the Indian economy: agriculture (Kharif/Rabi yields), inflation (food prices), hydropower (reservoir levels), rural employment (MGNREGA), trade balance (food imports), and fiscal pressure (disaster relief).

Inflationary Pressures due to Monsoon Failure

Monsoon failure during El Niño years drives food inflation through the supply-chain stress on pulses, vegetables, and dairy. The RBI monetary policy committee tracks CPI food inflation closely against the headline-CPI four-per-cent target with a two-per-cent tolerance band; El Niño-induced spikes have historically pushed headline CPI to the upper bound, forcing a rate-policy reaction. The 2015 episode saw tur dal prices roughly double across twelve months according to Department of Consumer Affairs price-monitoring data.

Impact on Hydropower Generation

India's hydropower fleet, concentrated in the Himalayan and Western Ghats catchments, shows generation reductions during El Niño years through below-normal reservoir inflows from deficient monsoon rainfall. Lower plant load factors force greater coal-fired backup generation, with associated climate-trade-off and electricity-price implications for the power sector.

ENSO and Energy Demand

Concurrent heat-wave intensification during El Niño summers raises peak electricity demand from residential and commercial cooling loads. Northern and central India saw record peak demand during the 2015 El Niño summer, stressing the transmission grid. Demand-side management programmes coordinate with IMD long-range forecasts to forewarn distribution utilities.

Fisheries and Marine Productivity

Marine fisheries along the Indian coast show ENSO sensitivity through plankton-bloom modulation. El Niño years reduce upwelling along the south-west coast, lowering small-pelagic catch including the Indian oil sardine. Central Marine Fisheries Research Institute documents the link and provides advisories to coastal fishing communities for catch-allocation adjustment.

ENSO and Extreme Weather: Droughts, Floods, Heat Waves, Cyclones, Forest Fires, Global Connections

Impact on Extreme Weather Events

ENSO and Droughts in India

Strong El Niño years account for a disproportionate share of major Indian droughts. The drought-state-correlation is one of the most robust ENSO-impact signals on the subcontinent. The 2014-15 back-to-back drought-state Maharashtra/Karnataka/Telangana sequence is a recent canonical example.

ENSO and Flood Events

La Niña years raise the probability of flood events in central and eastern India through enhanced southwest monsoon rainfall. The 2010 Leh cloudburst, the 2013 Uttarakhand floods, and the 2020-22 Bangladesh-Assam flood sequences all overlapped with La Niña conditions, though attribution is complex. Urban flooding (Mumbai 2005, Chennai 2015, Bengaluru 2022) is partly driven by extreme rainfall events whose frequency ENSO modulates.

ENSO and Heat Waves

Indian extreme weather events by ENSO state. The pattern illustrates ENSO's role as a probability shifter across drought, flood, heat-wave, cyclone, and fire dimensions, not a deterministic driver.
Event type ENSO state favouring Indian examples Mechanism
Drought El Niño 1972, 1987, 2002, 2009, 2014-15, 2018, 2023 Walker Circulation shift weakens monsoon moisture-pump
Flood (rural) La Niña 2010 Leh, 2013 Uttarakhand, 2020-22 Assam Enhanced southwest monsoon rainfall and soil saturation
Heat wave (pre-monsoon) El Niño 2015 south India (about 2,500 deaths) Reduced cloud cover and altered circulation
Bay of Bengal cyclones La Niña 1999 Odisha, 2020 Amphan Reduced vertical wind shear over the basin
Arabian Sea cyclones Independent of ENSO Tauktae 2021, Biparjoy 2023 Driven by Arabian Sea warming and IOD
Forest fires El Niño 2016 Uttarakhand Dry fuel loads in dry-deciduous forest

ENSO and Tropical Cyclones

ENSO modulates tropical cyclone activity in both ocean basins around India. El Niño tends to suppress cyclogenesis in the Bay of Bengal while activating Arabian Sea systems through warmed SSTs, as seen in cyclone Biparjoy 2023 which struck the Gujarat coast during El Niño development. NDMA cyclone-preparedness frameworks integrate ENSO-state forecasts into pre-season risk assessments for coastal districts.

ENSO and Forest Fires

Dry conditions during El Niño years raise forest-fire risk across Uttarakhand, Himachal Pradesh, Odisha, and the central-Indian sal belt. Forest Survey of India fire-alert satellite monitoring documents pronounced spikes in active-fire detections during deficit-monsoon years. MoEFCC and state forest departments tighten pre-monsoon fire-line preparedness during El Niño-flagged years.

How ENSO drives global concurrent extremes

Beyond India, ENSO drives global concurrent extremes. El Niño years 2015 and 2023 produced concurrent droughts across Southern Africa, severe wildfires across Indonesia and the Amazon, heatwave records in Europe, and East African flooding through the positive Indian Ocean Dipole co-development. WMO global outlooks integrate ENSO state into joint risk advisories for affected regions.

ENSO and Tropical Cyclones: Cyclogenesis Modulation, Bay of Bengal vs Arabian Sea, Cyclone Tracks, ENSO-IOD

ENSO Influence on Cyclone Activity

ENSO Influence on Cyclogenesis

ENSO modulates the large-scale environmental factors that govern tropical cyclogenesis: sea-surface temperature, vertical wind shear, mid-tropospheric moisture, and low-level cyclonic vorticity. El Niño typically raises vertical wind shear over the North Indian Ocean, suppressing cyclogenesis. La Niña typically reduces wind shear, favouring cyclogenesis. The Atlantic basin shows the same pattern in reverse, which is why El Niño years are quieter Atlantic hurricane seasons.

Why El Niño suppresses Bay of Bengal cyclones

Bay of Bengal cyclone counts tend to be lower during El Niño years. The 1997 strong El Niño season was a notably quiet Bay of Bengal cyclone year. The mechanism is the increased vertical wind shear from El Niño's altered upper-tropospheric circulation; high shear tears developing cyclones apart before they organise. There are counter-examples, since cyclones can still form in El Niño years given favourable local conditions, but the climatological signal is suppression.

How the ENSO-IOD interaction shapes cyclone formation

ENSO state versus tropical cyclone activity in the North Indian Ocean (climatological tendencies; individual years vary).
ENSO state Bay of Bengal Arabian Sea Mechanism
Strong El Niño Suppressed Reduced (slightly) High vertical wind shear; altered upper troposphere
ENSO-neutral Climatological mean Climatological mean Local factors dominate
Strong La Niña Enhanced Mixed (basin warming dominates) Reduced shear favours cyclogenesis
ENSO state and North Indian Ocean cyclonesCyclone countStrong El NiñoENSO-neutralStrong La Niña3-4 storms/yr5 storms/yr avg6-7 storms/yrBay of BengalArabian SeaEl Niño suppresses BoB cyclones via higher vertical wind shear; La Niña enhances. Arabian Sea drift dominated by basin warming.
ENSO state modulates tropical cyclone activity in the North Indian Ocean. El Niño raises vertical wind shear, suppressing Bay of Bengal cyclones. La Niña reduces shear, enhancing activity. Arabian Sea cyclones show a recent post-2017 surge that overlaps with La Niña but is mostly attributed to basin warming.

How La Niña and warming shift Arabian Sea cyclones

La Niña years historically strengthened the Bay of Bengal monsoon while keeping the Arabian Sea relatively quieter. The recent climate-change overlay is shifting this pattern: rising Arabian Sea SSTs are increasing cyclogenesis there even during neutral and La Niña years. Cyclone Tauktae struck the Gujarat coast in May 2021 during the La Niña phase, reflecting the climate-shifted baseline of Arabian Sea cyclone risk.

ENSO and Cyclone Tracks

Cyclone track patterns respond to ENSO-state through changes in mid-tropospheric steering flow. El Niño-driven jet displacement tends to push Bay of Bengal cyclone tracks toward the Tamil Nadu and Andhra coast; La Niña years see more landfalls along the Odisha and West Bengal coast. IMD operational forecasts incorporate ENSO-state in track-uncertainty cones.

ENSO-India coupling in the recent record and current Indian monitoring frameworks

The 2023-24 El Niño contributed to a southwest monsoon deficit of approximately 6 percent below the long-period average. Concentrated rainfall deficits hit Karnataka, Kerala, and parts of Tamil Nadu and Andhra Pradesh. The 2020-2022 triple-dip La Niña overlapped with three consecutive normal-to-above-normal Indian monsoons. Strong La Niña persistence continues to support Indian agricultural output.

Three institutions carry the ENSO-India forecasting load. The India Meteorological Department publishes its Long-Range Forecast for the southwest monsoon in April and updates it in June. The forecast integrates ENSO state with the Indian Ocean Dipole and the new Mission Mausam framework.

The Indian Institute of Tropical Meteorology (IITM Pune) has operated the dynamical Monsoon Mission seasonal forecast system since 2012. The Indian National Centre for Ocean Information Services (INCOIS) contributes ocean-state monitoring in the Bay of Bengal and Arabian Sea, and this monitoring feeds the cyclone and monsoon outlook.

Prelims MCQ practice

Each question below tests one specific concept on the topic. Click to reveal the answer and a full option-wise explanation.

Q1. Consider the following statements about the ENSO-Indian Monsoon teleconnection:

  1. El Nino conditions in the equatorial Pacific are statistically associated with deficit Indian Southwest Monsoon rainfall.
  2. La Nina conditions are statistically associated with normal-to-excess Indian Southwest Monsoon rainfall.
  3. The ENSO-monsoon teleconnection is a deterministic one-to-one relationship with no exceptions.

Which of the statements given above are correct?

  1. 1 and 2 only
  2. 2 and 3 only
  3. 1 and 3 only
  4. 1, 2 and 3
Show answer and explanation

Answer: 1 and 2 only

Explanation.

Correct: a (1 and 2 only). Statement 1 is correct: El Nino is statistically associated with Indian monsoon deficit. Statement 2 is correct: La Nina is associated with normal-to-excess monsoon. Statement 3 is wrong: the ENSO-monsoon link is statistical, not deterministic; exceptions occur (1997 strong El Nino had a near-normal monsoon, while a moderate El Nino in 2002 coincided with severe drought, because the IOD and other factors modulate the outcome).

Q2. Consider the following statements about historical strong El Nino events and Indian Monsoon outcomes:

  1. El Nino events always produce drought in India without exception.
  2. The 1997-98 El Nino was among the strongest on record but the Indian monsoon was near-normal that year.
  3. The 2009 El Nino was associated with severe Indian monsoon deficit.

Which of the statements given above are correct?

  1. 1 and 2 only
  2. 2 and 3 only
  3. 1 and 3 only
  4. 1, 2 and 3
Show answer and explanation

Answer: 2 and 3 only

Explanation.

Correct: b (2 and 3 only). Statement 1 is wrong: El Nino does NOT always produce drought in India; the relationship is statistical, modulated by the IOD and other factors. Statement 2 is correct: 1997-98 was a record-strong El Nino, yet the Indian monsoon that year was near-normal, a famous exception attributed to a strong positive Indian Ocean Dipole. Statement 3 is correct: the 2009 El Nino was associated with a severe Indian monsoon deficit of about 22 per cent below the long-period average.

Q3. Consider the following statements about Indian Ocean Dipole (IOD) and the Indian Monsoon:

  1. Positive IOD (warmer western Indian Ocean) tends to support stronger Indian Southwest Monsoon.
  2. Strong positive IOD can partially offset the deficit-monsoon impact of El Nino as seen in some recent years.

Which of the statements given above is/are correct?

  1. 1 only
  2. 2 only
  3. Both 1 and 2
  4. Neither 1 nor 2
Show answer and explanation

Answer: Both 1 and 2

Explanation.

Correct: c (Both 1 and 2). Statement 1 is correct: positive IOD supports a stronger Indian Southwest Monsoon. Statement 2 is correct: a strong positive IOD has historically offset El Nino-induced deficit, as in 1997 when a record-strong positive IOD kept the monsoon near-normal despite a record El Nino, and again during the 2019 positive-IOD-aided recovery.

Q4. Consider the following statements about Indian Meteorological Department (IMD) monsoon-forecast classification:

  1. IMD classifies the Southwest Monsoon as deficient when seasonal rainfall is more than 10 per cent below the Long Period Average (LPA).
  2. IMD's seasonal monsoon forecast uses dynamic and statistical models incorporating ENSO and IOD as inputs.

Which of the statements given above is/are correct?

  1. 1 only
  2. 2 only
  3. Both 1 and 2
  4. Neither 1 nor 2
Show answer and explanation

Answer: Both 1 and 2

Explanation.

Correct: c (Both 1 and 2). Statement 1 is correct: IMD's deficient category is more than 10 per cent below LPA. Statement 2 is correct: IMD seasonal forecasts use both dynamic (numerical weather prediction) and statistical (multi-parameter regression) models incorporating ENSO, IOD, Eurasian snow cover, and other predictors.

Q5. Consider the following statements about ENSO impact on Indian agriculture:

  1. El Nino-deficit-monsoon years typically produce reduced kharif foodgrain production in rainfed Peninsular and central India tracts.
  2. PMFBY (Pradhan Mantri Fasal Bima Yojana) provides crop-insurance cover for monsoon-deficit-induced crop loss.
  3. El Nino has no impact on Indian agricultural production at any time.

Which of the statements given above are correct?

  1. 1 and 2 only
  2. 2 and 3 only
  3. 1 and 3 only
  4. 1, 2 and 3
Show answer and explanation

Answer: 1 and 2 only

Explanation.

Correct: a (1 and 2 only). Statement 1 is correct: El Nino-deficit-monsoon years typically reduce kharif foodgrain production in rainfed Peninsular and central India. Statement 2 is correct: PMFBY provides crop-insurance cover for monsoon-deficit-induced crop loss. Statement 3 is wrong: El Nino has a demonstrable impact on Indian agricultural production via monsoon-deficit transmission to rainfed and partially-irrigated cropping.

Q6. Consider the following statements about ENSO-Monsoon research coordination in India:

  1. The Indian Institute of Tropical Meteorology (IITM) at Pune is a leading Indian institute for monsoon and tropical-climate research.
  2. IITM operates under the Ministry of Earth Sciences (MoES) and contributes to the IMD monsoon-forecast framework.

Which of the statements given above is/are correct?

  1. 1 only
  2. 2 only
  3. Both 1 and 2
  4. Neither 1 nor 2
Show answer and explanation

Answer: Both 1 and 2

Explanation.

Correct: c (Both 1 and 2). Statement 1 is correct: IITM at Pune is a leading Indian monsoon and tropical-climate research institute. Statement 2 is correct: IITM operates under MoES and contributes to the IMD seasonal-monsoon-forecast framework through coupled-model research and ENSO-IOD-monsoon studies.

Sources and Further Reading

Editorial Disclaimer

This article explains how the El Niño Southern Oscillation shapes India's monsoon, agriculture, rural economy, inflation, and cyclone activity for UPSC preparation. Figures for drought years, rainfall deficits, and economic impact are drawn from IMD, NOAA, IITM Pune, RBI, and NDMA sources listed above. Readers should consult the latest IMD long-range forecast for current-season figures.

Part 3 of 3 · ENSO

All 3 parts in this cluster
  1. 1 Part 1: Pacific Ocean-Atmosphere Coupling, El Nino-La Nina Cycle, Mechanism and Dynamics
  2. 2 Part 2: Interactions, Global Impacts and Climate Change
  3. 3 Part 3: India Monsoon Impact, Agriculture, Economy and Cyclones (this article)