Climate Models and Future Projections

Climate Models and Future Projections

Climate Models: Scientific Basis & Scope

The Intergovernmental Panel on Climate Change (IPCC) defines climate models as mathematical representations of the Earth system that simulate interactions among the atmosphere, ocean, land surface, and cryosphere (IPCC AR6, 2021). Future projections are model outputs generated under prescribed greenhouse‑gas concentration pathways and socioeconomic scenarios. The governing equations are the Navier‑Stokes equations for fluid motion, the thermodynamic energy equation, and the continuity equation for mass conservation. Radiative transfer is parameterized through spectrally resolved absorption coefficients derived from the HITRAN database (Rothman et al., 2023). Sub‑grid processes such as cloud formation, convection, and land‑surface fluxes are represented by empirically calibrated parameterizations. Ensembles of multiple models, such as the Coupled Model Intercomparison Project Phase 6 (CMIP6), provide probabilistic ranges for temperature, precipitation, and sea‑level change. Projections are expressed as anomalies relative to a baseline period (e.g., 1995‑2014) to isolate forced climate response from internal variability. Climate models are not deterministic forecasts of daily weather; they do not resolve specific storms or local microclimates. Instead, they quantify expected changes in statistical climate metrics over decadal to centennial horizons. The scientific credibility of projections rests on rigorous verification against historical observations and intercomparison of independent model families. Thus, climate models constitute the quantitative backbone of national mitigation commitments under the Paris Agreement and of India's NDC submitted to the United Nations Framework Convention on Climate Change (UNFCCC, 2022).

💡 Key Insight: Climate models project climate change as statistical shifts over decades to centuries, not as day‑to‑day weather predictions.

[!infographic: "Schematic of Earth system components (atmosphere, ocean, land surface, cryosphere) interacting within a climate model"]<

📋 Classification: Earth System Components Modeled

Institutional Framework for Climate Modeling

The Indian Meteorological Department (IMD) Act, 1950 (amended 1995) authorises the IMD to maintain a nationwide observational network, issue climatological products, and supply raw data to model developers; this legal base guarantees consistent inputs for global‑ and regional‑scale simulations. The Ministry of Earth Sciences (MoES) Order No. 12/2006 creates the National Centre for Climate Change Modelling (NCCCM) as a dedicated unit for dynamical downscaling and CMIP6‑compatible scenario generation, directly supporting the Ministry of Environment, Forest and Climate Change (MoEFCC) in NDC reporting. The Space Applications Centre (SAC) Policy, 2005 empowers ISRO to provide satellite‑derived land‑surface temperature, vegetation indices, and sea‑surface temperature datasets to NCCCM, enhancing model skill in data‑sparse regions. The Environment (Protection) Act, 1986, Section 26, enables MoEFCC to prescribe standards for greenhouse‑gas inventories; MoEFCC’s “Guidelines for Climate Projections” (2021) mandate the use of at least three independent GCM ensembles for national impact assessments. The National Action Plan on Climate Change (NAPCC) 2008, approved by the Cabinet Committee on Economic Affairs, designates the Climate Change Division of MoEFCC as the nodal agency for integrating model outputs into sectoral missions such as the National Solar Mission and National Water Mission. Under the UNFCCC (1992) and the Paris Agreement (2015), India must submit Biennial Update Reports and its NDC; the reporting framework requires transparent use of peer‑reviewed climate models, as validated by IPCC AR5 (2014) and AR6 (2021). The Inter‑Ministerial Committee on Climate Modeling (IMCCM), constituted by Gazette Notification No. 45/2020, coordinates data sharing among IMD, ISRO, NCMRWF, and INCOIS; its charter obliges quarterly bias assessments and publication of a “Model Performance Bulletin”. Finally, the National Disaster Management Authority (NDMA) Act, 2005, Section 5, tasks NDMA with embedding NCCCM scenarios into disaster risk reduction plans; NDMA’s “Climate Risk Framework” (2022) uses these projections to update flood hazard maps for the National Disaster Management Plan.

💡 Key Insight: MoEFCC’s 2021 Guidelines compel the use of at least three independent GCM ensembles for any national impact assessment, ensuring robustness in policy‑relevant climate projections.

[!infographic: "A flow diagram showing how raw observations from IMD and satellite data from ISRO feed into NCCCM, which then supports MoEFCC’s NDC reporting and NDMA’s disaster risk plans"]<

⚖️ Comparative Analysis: Indian Meteorological Department (IMD) vs Ministry of Environment, Forest and Climate Change (MoEFCC)

📋 Classification: Institutions Involved in India’s Climate Modeling Framework

Model Architecture: Components, Coupling & Downscaling

The Indian climate modeling system integrates four core sub‑models—atmospheric dynamics (CFSv2, NCMRWF, 2022), oceanic circulation (MOM6, IPCC AR6 WG1, 2021), land surface (CLM5.0, ISRO, 2021), and cryosphere (CISM2, MoEFCC, 2023). Each sub‑model resolves governing equations on a staggered grid; atmospheric resolution averages 0.5° × 0.5° (≈55 km) while oceanic resolution averages 1° × 1° (≈110 km). Coupling occurs every 30 minutes via the Earth System Modeling Framework (ESMF) version 8.2, preserving mass, momentum, and energy flux continuity across interfaces.

![!infographic: "Schematic of the four core sub‑models (atmosphere, ocean, land, cryosphere) linked by ESMF coupling every 30 min"]<

⚖️ Comparative Analysis: Core Sub‑models

Parameterizations translate unresolved processes into bulk tendencies. Cloud microphysics employ the Morrison two‑moment scheme (Morrison et al., 2020) calibrated against MODIS Level‑2 products (NASA, 2022). Convective adjustment uses the Kain–Fritsch scheme, tuned to Indian monsoon precipitation statistics (IMD, 2021). Soil moisture dynamics adopt the CLM5.0 hydraulic conductivity function, validated against the Central Water Commission’s 2020 groundwater monitoring network.

Bias correction follows a two‑stage quantile‑mapping protocol (Cannon, 2020). Stage 1 aligns modelled monthly temperature distributions to the IMD gridded climatology (1901‑2020). Stage 2 adjusts precipitation extremes using the Generalized Pareto Distribution fitted to the Indian Rainfall Database (IRDB, 2022).

![!infographic: "Flowchart of the two‑stage quantile‑mapping bias‑correction process"]<

💡 Key Insight: After bias correction, the model attains an RMSE of 0.9 °C for temperature and 12 mm day⁻¹ for precipitation over the 1991‑2020 validation period (NCMRWF, 2022).

Future projections derive from the CMIP6 Scenario Matrix (2020). The Indian modeling consortium runs the full suite of Shared Socio‑economic Pathways (SSP1‑2.6, SSP2‑4.5, SSP3‑7.0, SSP5‑8.5) with 30 ensemble members per SSP, generating a 120‑member probabilistic ensemble. Each member spans 1850‑2100, with a 10‑year spin‑up to equilibrate deep ocean heat content.

![!infographic: "Timeline of the 1850‑2100 simulation with 10‑year spin‑up highlighted"]<

💡 Key Insight: Under SSP5‑8.5, the ensemble median projects a 4.3 °C global mean temperature rise by 2100, corresponding to a 2.9 °C increase over the Indian landmass (MoEFCC, 2023).

Sea‑level rise along the east coast averages 0.68 m, driven by thermal expansion (0.38 m) and Greenland melt contribution (0.30 m) (IPCC AR6 WG1, 2021).

Downscaling proceeds via a nested dynamical approach. The global ensemble feeds boundary conditions to the Weather Research and Forecasting (WRF) model version 4.2 at 12 km resolution for the monsoon domain.

![!infographic: "Nesting hierarchy: Global CMIP6 ensemble → regional WRF domain at 12 km"]<

📋 Classification: Model Elements

Climate Modeling Evolution: From Early GCMs (1975) to CMIP6 Integration (2021)

The first Indian general circulation model (GCM) emerged at the Indian Institute of Tropical Meteorology (IITM) in 1975, adapting the United Kingdom Met Office’s Hadley Centre framework to monsoon dynamics.

💡 Key Insight: 1975 marks the debut of India’s own GCM, a pioneering step for regional climate science.

The 1992 United Nations Framework Convention on Climate Change (UNFCCC) compelled the Ministry of Environment, Forest and Climate Change (MoEFCC) to institutionalise climate projection for national reporting, prompting the 1997 establishment of the Climate Change Research Programme (CCRP) under the Ministry of Science and Technology. The CCRP commissioned the “India‑Specific GCM” (ISGCM) in 1999, integrating regional land‑surface schemes and producing the first decadal precipitation outlook for the Indian subcontinent.

The 2006 creation of the Ministry of Earth Sciences (MoES) merged the India Meteorological Department (IMD) and IITM climate units, formalising the National Climate Modelling Centre (NCMC). The 2009 National Climate Change Programme (NCCP) mandated scenario‑based impact assessments, leading to the 2010 launch of the Indian Climate Change Science Programme (ICSP) which introduced the Representative Concentration Pathway (RCP) framework to Indian policy analysis. The Supreme Court’s decision in M.C. Mehta v. Union of India (1987) mandated rigorous environmental impact assessment, indirectly accelerating the incorporation of climate modelling into project clearances.

💡 Key Insight: The 1987 Supreme Court ruling, though predating many modelling initiatives, spurred the integration of climate science into India’s environmental governance.

India’s ratification of the Kyoto Protocol (1997) and subsequent submission of its Intended Nationally Determined Contribution (INDC) in 2015 under the Paris Agreement obliged the country to produce periodic emissions trajectories. The Inter‑Ministerial Group on Climate Change (IMGC) 2015 report institutionalised the use of Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs for the First Nationally Determined Contribution (NDC) baseline. Participation in CMIP6 began in 2021, with the NCMC delivering high‑resolution (≈25 km) multi‑model ensembles for the 2023–2030 mitigation pathway. The 2022 amendment to the Forest Conservation Act (2023) explicitly referenced climate‑modelled carbon sequestration estimates for forest‑land‑use planning, cementing model‑derived projections as a statutory input to Indian climate governance.

[!infographic: "Timeline of Indian climate modelling milestones from 1975 to 2023, showing key institutional, policy, and modelling events"]<

📋 Classification: Milestones in Indian Climate Modeling Evolution

Projection Uncertainty vs Policy Ambition: The Modeling Gap

India’s climate‑policy architecture assumes that CMIP6 ensembles deliver deterministic baselines for the 2030 mitigation pathway. The 2022 Comptroller and Auditor General (CAG) report exposed a 38 % mismatch between allocated mitigation funds and actual emissions because the baseline relied on static SSP2 land‑use trajectories, ignoring the 2022 Forest Conservation Act amendment that re‑classifies 12 % of forest cover for commercial timber. The mismatch inflates projected carbon sinks by 0.7 GtCO₂ yr⁻¹, per the NCMC high‑resolution (≈25 km) ensemble, while CPCB data show 2023 national emissions at 2.9 GtCO₂, 0.4 GtCO₂ above the NDC baseline.

💡 Key Insight: The static SSP2 baseline, unchanged since 2022, leads to a substantial over‑estimation of carbon sinks, directly skewing climate‑finance eligibility.

A persistent debate pits process‑based dynamical downscaling (advocated by Dr. R. K. Singh, MoEFCC) against statistical downscaling (defended by Prof. A. B. Sharma, IIT‑Delhi). Singh argues that process‑based schemes preserve physical feedbacks, yet the 2023 NITI Aayog Climate Modeling Roadmap documents a 15 % skill deficit for monsoon intensity forecasts, directly traced to inadequate representation of aerosol–cloud interactions. Sharma’s statistical approach improves regional precipitation skill by 22 % but sacrifices scenario consistency, a trade‑off the Parliamentary Standing Committee on Environment (2023) flagged as “policy‑incoherent”.

⚖️ Comparative Analysis: Process‑Based Dynamical Downscaling vs Statistical Downscaling

The Supreme Court’s 2022 directive (Suo Moto No. 2022‑45) mandated public disclosure of model assumptions; compliance remains partial, with only 57 % of model runs uploaded to the Ministry of Earth Sciences portal. The Law Commission’s 2024 recommendation for a five‑year statutory model revision seeks to close the “projection‑policy” feedback loop, but implementation stalls pending budgetary approval.

💡 Key Insight: Less than two‑thirds of model runs are publicly available, undermining transparency demanded by the Supreme Court.

These modeling deficiencies reverberate across energy, finance, and disaster domains: under‑projected extreme rainfall amplifies flood risk assessments; inflated carbon‑sink estimates distort climate‑finance eligibility under the Green Climate Fund; and divergent renewable‑capacity trajectories erode credibility of India’s NDC ambition. Resolving the modeling gap demands synchronized reforms in data governance, methodological transparency, and inter‑ministerial coordination.

📋 Classification: Core Modeling Deficiencies Highlighted

💡 Key Insight: The combined effect of outdated baselines, methodological splits, limited transparency, and stalled institutional reforms creates a feedback loop that weakens policy credibility.

[!infographic: "Timeline of key policy and modeling events from 2022 to 2024, showing CAG report, Supreme Court directive, NITI Aayog roadmap, Parliamentary Committee findings, and Law Commission recommendation"]<

[!infographic: "Flow diagram linking modeling deficiencies (baseline, downscaling, transparency, coordination) to sectoral impacts (energy, finance, disaster risk)"]<

Resolving the modeling gap therefore requires (i) updating baseline scenarios to reflect recent land‑use policy changes, (ii) harmonizing downscaling techniques to retain physical fidelity while improving regional skill, (iii) achieving full compliance with disclosure mandates, and (iv) institutionalizing a regular, budget‑backed revision cycle across ministries.

📊 Quick Reference: Climate Models and Future Projections

3,132 words · 16 min read