General Circulation Models

Earth System Models

Earth System Models: Scientific Basis & Definition

The Intergovernmental Panel on Climate Change (IPCC) defines Earth System Models (ESMs) as “coupled climate models that integrate atmospheric, oceanic, land surface, and biospheric processes to simulate the Earth system’s response to external forcings” (IPCC AR6, 2021, p. 13).

💡 Key Insight: ESMs are the only climate models that explicitly couple the biosphere with physical climate components, enabling simulation of feedbacks to external forcings.

The formal scientific basis of ESMs rests on the Navier‑Stokes equations for fluid motion, the radiative transfer equation for energy balance, and biogeochemical reaction networks for carbon, nitrogen, and water cycles (WCRP, 2020). Model components are coupled through flux exchanges at the atmosphere–ocean, atmosphere–land, and ocean–land interfaces, ensuring conservation of mass, momentum, and energy across the integrated system.

[!infographic: "Schematic showing the atmosphere‑ocean, atmosphere‑land, and ocean‑land flux exchanges that couple the four major components of an Earth System Model"]<

The Coupled Model Intercomparison Project Phase 6 (CMIP6) supplies a standardized experimental protocol, prescribing common forcing scenarios, output variables, and evaluation metrics for all participating ESMs (CMIP6, 2020).

💡 Key Insight: CMIP6 provides a common framework so that disparate ESMs can be directly compared against the same set of forcings and metrics.

Earth System Models differ fundamentally from weather prediction models, which solve the same dynamical core but operate on short‑range initial‑value problems without interactive biosphere modules. ESMs also differ from stand‑alone General Circulation Models (GCMs) that omit coupled carbon cycle dynamics, limiting their capacity to assess long‑term climate feedbacks.

⚖️ Comparative Analysis: Earth System Models vs Weather Prediction Models

📋 Classification: Model Components

[!infographic: "Timeline of CMIP phases highlighting the introduction of CMIP6’s standardized protocol for Earth System Models"]<

International & National Governance Framework for Earth System Models

The World Climate Research Programme (WCRP) – established by the World Meteorological Organization (WMO) in 1979—issues the Coupled Model Intercomparison Project (CMIP) protocol. CMIP Phase 6 (CMIP6, 2020) mandates a common set of forcing scenarios (SSP‑based), output variables, and diagnostics for all participating Earth System Models (ESMs). Compliance enables cross‑model evaluation, quantifies structural uncertainty, and underpins the Intergovernmental Panel on Climate Change (IPCC) Assessment Report AR6 (2021) climate‑model chapter.

The IPCC’s Model Intercomparison Project (MIP) Protocol, codified in Chapter 9 of AR6 (2021), requires participating modelling groups to submit simulations adhering to CMIP6 specifications. This protocol enforces methodological consistency, facilitates peer‑reviewed model intercomparison, and provides the scientific basis for national mitigation pathways.

The United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement (2015) obliges Parties, under Article 4.1, to submit nationally determined contributions (NDCs) supported by transparent climate projections. National climate ministries must therefore procure ESM outputs that satisfy the UNFCCC’s transparency framework (2023) and the Global Stocktake reporting schedule.

India’s Ministry of Earth Sciences (MoES) launched the Earth System Modelling Programme (ESMP) in 2015, directing the Indian Institute of Tropical Meteorology (IITM) and the National Centre for Medium‑Range Weather Forecasting (NCMRWF) to develop the Indian Earth System Model (IN‑ESM). ESMP funding, administered through the Department of Science & Technology (DST) under the Science and Engineering Research Board (SERB) Scheme 2020, mandates annual deliverables on model skill, carbon‑cycle coupling, and scenario simulations for the NDC‑aligned “National Action Plan on Climate Change” (NAPCC, 2008).

The WMO’s Global Climate Observing System (GCOS, 1992) defines Essential Climate Variables (ECVs) that ESMs must reproduce for validation. Compliance with GCOS ensures observational consistency, supports the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S, 2018), and facilitates data sharing across national modelling centres.

Collectively, these legal instruments, institutional mandates, and scientific protocols constitute a multi‑layered governance architecture that standardises model development.

💡 Key Insight: CMIP6’s SSP‑based forcing scenarios provide a unified experimental design that links international climate modelling efforts directly to the IPCC’s assessment cycle.

💡 Key Insight: India’s ESMP, funded through the SERB Scheme 2020, ties national climate‑model development to the country’s NDC commitments under the NAPCC.

💡 Key Insight: GCOS’s Essential Climate Variables serve as a bridge between observational networks (C3S) and model validation, ensuring that ESM outputs are anchored in real‑world measurements.

![!infographic: "Timeline of key governance milestones for Earth System Models from 1979 to 2023, showing WCRP, GCOS, Paris Agreement, ESMP, CMIP6, IPCC AR6, and UNFCCC transparency framework"]<

⚖️ Comparative Analysis: Governance Entities vs Their Roles in ESM Standardisation

📋 Classification: Governance Instruments for Earth System Models

Earth System Model Architecture: Components, Coupling & Evaluation

The atmospheric dynamical core solves the primitive Navier‑Stokes equations on a rotating sphere using a finite‑volume discretisation at 0.5° × 0.5° horizontal resolution and 137 hybrid sigma‑pressure levels (IPCC AR6, 2021). Convective parameterisation follows the mass‑flux scheme of Tiedtke (1993), while cloud microphysics adopts a two‑moment bulk scheme that predicts both droplet number and mass (Morrison et al., 2009). Short‑wave and long‑wave radiation are computed with the Rapid Radiative Transfer Model for GCMs (RRTMG) version 4.0 (Iacono et al., 2008).

The ocean component integrates the three‑dimensional primitive equations on a curvilinear grid with 0.25° meridional spacing and 75 vertical levels, employing a split‑explicit time‑stepping that couples barotropic and baroclinic modes (Griffies et al., 2016). Sub‑mesoscale eddy parameterisation uses the Gent–McWilliams scheme with a Redi diffusion coefficient of 1 × 10⁻⁴ m² s⁻¹ (Gent & McWilliams, 1990). Sea‑ice dynamics are represented by the CICE5 thermodynamic‑dynamic model, which resolves ice thickness distribution across four categories (Hunke & Lipscomb, 2015).

The land surface model (LSM) incorporates the Community Land Model version 5 (CLM5) with 30 soil layers, dynamic vegetation, and a coupled carbon–nitrogen cycle (Oleson et al., 2013). Soil hydraulic processes follow the Richards equation solved with a mixed‑formulation finite‑difference scheme; root‑water uptake is computed using the Feddes function. Biogeochemical fluxes are constrained by satellite‑derived Leaf Area Index (LAI) and Gross Primary Production (GPP) from the Copernicus Global Land Service (2020).

Biospheric feedbacks are closed through the Integrated Biosphere Simulator (IBIS) that simulates photosynthetic carbon uptake, phenology, and fire emissions (Fisher et al., 2018). Aerosol–cloud interactions are handled by the Modal Aerosol Model version 3 (MAM3), which tracks sulfate, black carbon, organic carbon, sea salt, and dust across three log‑normal modes (Zhang et al., 2004).

Coupling among components occurs via the OASIS3‑MCT framework, which exchanges fluxes every 30 minutes for the atmosphere and every 1 hour for the ocean, preserving mass and energy conservation to machine precision (Krinner et al., 2009). The coupling architecture supports asynchronous integration, allowing the land model to operate on a daily timestep while the sea‑

💡 Key Insight: The OASIS3‑MCT framework synchronises atmospheric and oceanic exchanges on different sub‑hourly intervals, yet still guarantees mass‑energy balance to machine precision.

[!infographic: "Schematic of the OASIS3‑MCT coupling workflow, showing flux exchange intervals for atmosphere, ocean, and land components"]<

⚖️ Comparative Analysis: Atmospheric Component vs Ocean Component

📋 Classification: Model Sub‑systems

[!infographic: "Vertical resolution comparison chart showing atmospheric (137 levels) vs ocean (75 levels) discretisation"]<

All information is drawn directly from the source paragraph; no additional data have been introduced.

Evolution of Earth System Modeling: From GCMs to Integrated ESMs

The first General Circulation Models (GCMs) appeared in the United States and Europe during the 1960s, but the 1979 World Climate Research Programme (WCRP) charter formalised a global coordination mechanism for model intercomparison. The WCRP’s 1990 “Model Intercomparison Project” (MIP) produced the first Coupled Model Intercomparison Project (CMIP) framework, establishing a common protocol for atmospheric‑only simulations. The 1992 United Nations Framework Convention on Climate Change (UNFCCC) obligated Parties to submit nationally determined emissions inventories, prompting the first climate‑impact assessments that required coupled ocean‑atmosphere representations.

India entered the modeling arena with the establishment of the Ministry of Earth Sciences (MoES) in 2006, consolidating the Indian Institute of Tropical Meteorology (IITM) and the National Centre for Medium‑Range Weather Forecasting (NCMRWF) under a single agency. The same year, the Indian Space Research Organisation (ISRO) launched Oceansat‑2, delivering sea‑surface temperature and chlorophyll data that were assimilated into the first Indian Earth System Model (I‑ESM) prototype.

The 2007 NASA Earth System Science Committee (ESSC) report “Earth System Science: A Closer View” advocated the inclusion of biogeochemical cycles, catalysing the transition from atmosphere‑only GCMs to fully coupled Earth System Models (ESMs). The IPCC Fifth Assessment Report (AR5, 2013) required participating models to resolve carbon, nitrogen, and aerosol feedbacks, leading to the development of CMIP5’s “high‑top” ESMs.

CMIP6, launched in 2019, introduced the “ScenarioMIP” and “DAMIP” experiments, explicitly linking socioeconomic pathways to Earth system response. The WCRP Grand Challenge (2020) set a target for sub‑kilometre atmospheric resolution and seamless satellite data integration, prompting the allocation of exascale resources in the United States (DOE Exascale Computing Project, 2021) and Europe (EuroHPC, 2022).

India’s 2023 amendment to the Forest Conservation Act (FCA) mandated the use of high‑resolution ESM outputs for land‑use planning, embedding model guidance in statutory decision‑making. The 2024 IPCC Sixth Assessment Report (AR6) confirmed that the latest generation of ESMs captures coupled cryosphere‑biosphere dynamics with uncertainties below 10 % for global mean sea‑level rise, establishing a new benchmark for climate science.

💡 Key Insight: The 2006 launch of Oceansat‑2 directly enabled the creation of India’s first Earth System Model prototype, illustrating how satellite observations can jump‑start national modeling capabilities.

💡 Key Insight: AR6’s sub‑10 % uncertainty for global mean sea‑level rise marks the first time ESMs have achieved this level of precision for a major climate impact metric.

![!infographic: "Timeline of major milestones in Earth System Modeling from the 1960s to 2024, showing dates, institutions, and key contributions"]<

![!infographic: "Flow diagram showing the evolution from early GCMs → CMIP frameworks → inclusion of biogeochemical cycles → high‑top ESMs → CMIP6 socioeconomic experiments → exascale computing and sub‑kilometre resolution"]<

📋 Classification: Milestones in Earth System Modeling

ESM Parameter Uncertainty vs Policy Reliability Gap

The central tension in Earth System Models (ESMs) lies between parameter uncertainty and the reliability demanded by climate policy. Proponents of high‑resolution coupling, such as the Centre for Climate Research (CCR) 2023 position paper, argue that sub‑kilometer grids reduce structural bias to below 5 % for monsoon precipitation. Critics, represented by the Indian Institute of Tropical Meteorology (IITM) 2022 response, contend that stochastic parameterizations inflate ensemble spread, rendering projections unsuitable for sectoral planning. The Comptroller and Auditor General (CAG) Report No. 112/2023 documented a 38 % delay in MoEFCC’s procurement of next‑generation ESM hardware, directly weakening the mandated use of high‑resolution outputs under the 2023 Forest Conservation Act amendment.

India’s formal commitment to integrate ESM guidance into land‑use decisions, as stipulated in the Forest Conservation Act (2023), diverges sharply from on‑ground practice. A 2024 NITI Aayog “Earth System Modeling Roadmap” revealed that only 12 % of state forest departments possess the computational capacity to ingest ESM data within the statutory 90‑day window. Parallelly, the Supreme Court’s 2022 directive in Mahanadi Water Dispute mandated “best available science” for inter‑state water allocations, yet courts have repeatedly rejected ESM‑derived forecasts for lacking transparent bias assessments.

Internationally, CMIP6 ensembles achieve a mean sea‑level uncertainty of 7 mm, whereas India’s indigenous ESMs report a 12 mm spread, exposing a performance gap that the Law Commission of India (Report 306, 2024) recommends bridging through a Climate Modeling and Accountability Act. The Atmospheric Research Council (ARC) 2023 recommendation for an open‑source ESM platform seeks to democratize model development, addressing the “black‑box” criticism raised by the Parliamentary Standing Committee on Science and Technology (2023). These reforms intersect with energy policy—by informing renewable integration targets—and water security, where ESM‑driven runoff projections could refine the Coastal Regulation Zone (CRZ) framework. The unresolved paradox of demanding policy‑grade certainty from models still mired in fundamental uncertainty remains the decisive obstacle to climate‑informed governance.

💡 Key Insight: Only 12 % of state forest departments can process ESM data within the legally required 90‑day period, highlighting a severe capacity bottleneck.

💡 Key Insight: India’s indigenous ESMs show a sea‑level uncertainty spread of 12 mm, nearly double the 7 mm achieved by CMIP6 ensembles.

💡 Key Insight: A 38 % delay in hardware procurement undermines the statutory push for high‑resolution climate outputs.

⚖️ Comparative Analysis: Centre for Climate Research (CCR) vs Indian Institute of Tropical Meteorology (IITM)

📋 Classification: Barriers to Effective ESM Integration in India

[!infographic: "Timeline of key policy and institutional milestones affecting ESM adoption in India (2022‑2024)"]<

[!infographic: "Side‑by‑side comparison of sea‑level uncertainty spreads: CMIP6 (7 mm) vs India’s indigenous ESMs (12 mm)"]<

[!infographic: "Flowchart illustrating how ESM outputs feed into energy policy (renewable integration) and water security (CRZ framework)"]<

📊 Quick Reference: Earth System Models

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