Running a coupled model to equilibrium is easy—until you include ice sheets. BIG-MITgcm tackles that head-on, using a new global-scale ice sheet model and a new coupled framework to let slow components catch up.

Reporting by Helen Hill for MITgcm

BIG (Biogeodynamics-Ice sheet-Geneva)-MITgcm, introduced by Moinat et al. (2026), represents a thoughtful extension of the MITgcm framework into the challenging space between high-end Earth System Models and highly simplified climate models. The motivation is familiar to many in the MITgcm community: capturing the coupled evolution of atmosphere, ocean, ice, and biosphere across the vastly different timescales that govern Earth’s climate. While CMIP-class models offer comprehensive process representation, they are generally too computationally expensive to integrate to equilibrium over tens of thousands of years. Simpler Earth Models of Intermediate Complexity can reach such equilibria, but often do so with coarse resolution and heavily parameterized physics. BIG-MITgcm is designed to bridge this gap, retaining a dynamical core based on MITgcm while adopting strategies that make multi-millennial integrations feasible.

At the heart of BIG-MITgcm is a coupled MITgcm configuration that will look familiar to many users: atmosphere, ocean, thermodynamic sea ice, and a simple land surface model, all running on a cubed-sphere grid at roughly 2.8° resolution. The atmospheric component uses the SPEEDY physics package, trading some complexity for computational efficiency, while the ocean relies on standard MITgcm parameterizations such as KPP vertical mixing and Gent–McWilliams eddy closures. This balance allows the system to maintain realistic large-scale dynamics while running fast enough to support the long integrations required to equilibrate slow components like the deep ocean.

The key conceptual advance in BIG-MITgcm is how it incorporates slow components—ice sheets and vegetation—through an asynchronous (offline) coupling strategy. Rather than evolving all components simultaneously, the model alternates between phases: MITgcm is first run to a quasi-steady climate state, and the resulting fields are then used to update vegetation distributions and ice sheet geometry. These updated boundary conditions are fed back into the coupled model, and the process is repeated until the system converges. This approach allows the model to capture long-timescale feedbacks among climate, ice sheets, and the biosphere without the computational burden of fully synchronous coupling.

A particularly notable addition is the new global ice sheet model, MITgcmIS, which operates on the same cubed-sphere grid as the parent model. MITgcmIS uses the shallow-ice approximation for ice dynamics and a Positive Degree Day method—with an added percolation layer—to estimate surface mass balance. The design reflects a deliberate choice to focus on processes that can be meaningfully resolved at coarse resolution, omitting small-scale features such as basal sliding and ice streams. While simplified, this formulation captures the large-scale climatic role of ice sheets, including their influence on albedo, topography, and freshwater fluxes, which are central to long-term climate equilibria.

The framework is completed by coupling to the BIOME4 vegetation model and a hydrological routing scheme based on pysheds (an open-source Python library designed for processing Digital Elevation Models and conducting hydrologic analysis), enabling consistent treatment of land surface processes under evolving climates and topographies. Together, these components allow BIG-MITgcm to simulate fully coupled climate states that include atmosphere, ocean, cryosphere, and biosphere feedbacks. The model has been evaluated in present-day and historical configurations, where it reproduces many large-scale climate features and is able to generate plausible ice sheets starting from ice-free conditions. It has also been applied to deep-time scenarios, such as Permian–Triassic paleogeography, demonstrating flexibility in exploring radically different climate regimes.

To find out more, contact Laure

Story image: BIG-MITgcm graphical abstract [courtesy the researchers]

About the Researcher

Laure Moinat is a PhD student in climate physics at the University of Geneva, Switzerland. In this study, she worked with long-time MITgcm associates Dan Goldberg (Subglacial Melting) and Maura Brunetti (Tracking Down Climate’s Tipping Points)

This Month’s Featured Publication

Other New Publications last month

 

Aladrah, Nicola (2026), Extending Oceananigans.jl Ocean Model Toward Regional Applications: Evaluation and Implementation of Radiative Open Boundary Schemes, Master’s Dissertation, Scuola Internazionale Superiore di Studi Avanzati, Italy

Atwater, D. P., Wongpan, P., O’Farrell, S., Hobbs, W., Spence, P., Plante, M., Lemieux, J.-F., Bradley, A. C., Adams, C., and Fraser, A. D., (2026), Impact of sea ice rheological parameters and grounded iceberg distribution on Antarctic landfast sea ice: a sensitivity study with CICE version 6.4.1, EGUsphere, doi: 10.5194/egusphere-2026-1541

Bai, X., Dong, Z., Wang, K., Jiang, X., and Zhang, W. (2026), High-Resolution Modelling of Landfast Ice Formation and Midseason Breakout on the Siberian Shelf, EGUsphere, doi: 10.5194/egusphere-2026-826

Bhattacharjee, Ayantika et al (2026), Estimating the Kinetic Energy Spectrum from the Second-Order Velocity Structure Function using a Regularized Fitting Approach, arXiv: 2604.27200

Chen, S.‐Y. S., Marchal, O., Andres, M., Gardner, W., Yang, J., & Peacock, T.(2026), Deep cyclones and benthic storms in the western North Atlantic: New insights from a regional circulation model, Journal of Geophysical Research: Oceans, doi: 10.1029/2025JC023298

De Le Court, M. et al (2026), An efficient multi-GPU implementation for the Discontinuous Galerkin ocean model SLIM, arXiv: 2605.16082

Deng, Mingjun et al (2026), Physics-Enhanced Multi-Level Residual Model for Sea Surface Temperature Prediction, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2026.3689608

Fecanin, James A. et al (2026), Exploring the Impact of Tilted Magnetic Dipoles on the Atmospheric Dynamics of Hot Jupiters: Towards an Improved Magnetohydrodynamic Framework, arXiv: 2604.25043

Frazier, Robert C. et al (2026), The Days Drag On on WASP-121 b: Interpreting its NIRISS Spectroscopic Phase Curve with General Circulation Models, arXiv: 2605.01589

Gao, Zhen et al (2026), Extratropical forcing of tropical subsurface oxygenation under future warming, via ESS Open Archive, doi: 10.22541/essoar.15002870/v1

Giordano, F., Ličer, M., Querin, S., Salon, S., and Vodopivec, M. (2026), Hidden Heat: The case of 2023 Gulf of Trieste Bottom Marine Heatwave, EGUsphere [preprint], doi: 10.5194/egusphere-2026-2567

Han, Lei (2026), Differentiating Eulerian and Lagrangian Tendencies in the Ocean Interior via a Dynamical Overturning Decomposition, arXiv: 2605.23277

Jin, He et al (2026), Changes of the Indonesian Throughflow Spreading in the Indian Ocean During Extreme Indian Ocean Dipole Events, JGR Oceans, doi: 10.1029/2026JC023998

Li, HY., Meng, XH. & Xu, XG. (2026), Novel Internal Wave Propagation Simulation for a Two–layered Generalized Nonlinear Schrödinger Equation, Int J Theor Phys, doi: 10.1007/s10773-026-06336-y

Lowery, K., Holland, P. R., Dutrieux, P., Hogg, A. E., & Gourmelen, N. (2026), Drivers of basal melt variability for Pine Island Glacier Ice Shelf: Ocean forcing versus geometric feedback, Geophysical Research Letters, doi: 10.1029/2025GL121404

Matsuta, Takuro et al (2026), Drag-Controlled Regime Transitions in the Eddy Saturation Mechanism of the Antarctic Circumpolar Current, arXiv: 2605.13895

Mehta, Nishil et al (2026), The tale of the 3 planets: 3D cloud feedback enhances the spectral diversity of warm Jupiters, arXiv: 2604.26911

Menemenlis, D., Molod, A., Hill, C.N. et al. (2026), The GEOS/ECCO C1440-LLC2160 Coupled Atmosphere-Ocean Simulation Dataset, Sci Data,  doi: 10.1038/s41597-026-07349-2

Miyamoto, A., Xie, S.‐P., & Peng, Q. (2026), Simulating SST variability by forcing a coupled model with observed wind stress, Journal of Advances in Modeling Earth Systems, doi: 10.1029/2025MS005672

Monkman, Tatsu et al (2026), Spatiotemporal Mapping of Sea Surface Height and Temperature Fields from Satellite Observations Using Score-based Data Assimilation, ESS Open Archive, doi: 10.22541/essoar.15003398/v1

Moses, William S. et al (2026), DJ4Earth: Differentiable, and Performance-portable Earth System Modeling via Program Transformations, via ESS Open Archive, doi: 10.22541/essoar.176314951.18114616/v2

Narayanan, Aditya et al (2026), Compound drivers of Antarctic sea ice loss and Southern Ocean destratification,  Sci. Adv., doi: 10.1126/sciadv.aeb0166

Neme, Julia et al (2026), Two Decades of Ross and Weddell Gyre Variability from Observations, via ESS Open Archive, doi: 10.22541/essoar.15002625/v1

Navarra, G.G. et al (2026), Causal drivers of Southern Ocean greening in the Antarctic Circumpolar Current, via ResearchSquare, doi: 10.21203/rs.3.rs-9621820/v1

Ni, X., Zhang, Y., Wang, W. (2026), Subsurface messenger for the annual maximum lifetime maximum intensity of tropical cyclones in the western North Pacific, Nat Commun, doi: 10.1038/s41467-026-72770-5

Noel, Mathias et al (2026), Investigating Two Antarctic Coastal Polynyas in a High-Resolution Ocean-Sea Ice-Atmosphere Coupled Model, via ESS Open Archive, doi: 10.22541/essoar.15002648/v1

Pamentier, Vivien et al (2026), Horizontal transport as a source of disequilibrium chemistry on the nightside of a hot exoplanet, Nature Astronomy, doi: 10.1038/s41550-026-02845-2

Prajapati, Jagdish et al (2026), A machine learning framework for sea surface salinity prediction at short time scales, Environmental Research Communications, doi: 10.1088/2515-7620/ae6d8d

Santos, D. M. C., Van Caspel, M., Timmermann, R., and Sato, O. T. (2026), Impact of Grid Resolution on the Abyssal Ocean Representation in Numerical Models: A Focus on Vema Channel, EGUsphere, doi: 10.5194/egusphere-2026-2319

Schwab, Melissa et al (2026), Arctic condensed aromatic carbon budget reveals efficient fluvial transfer and shelf storage, via ResearchSquare, doi: 10.21203/rs.3.rs-9476681/v1

Sun, Junchuan et al (2026), El Niño-Southern Oscillation Drives Interannual Extremes of Surface Salinity in the Red Sea, JGR Oceans, doi: 10.1029/2025JC023637

Valsala, Vinu et al (2026), Investigating climate-driven variability of a small pelagic fishery off the Indian west coast using a population dynamic model, Ecological Modelling, doi: 10.1016/j.ecolmodel.2026.111655

Vanderborght, Elian et al (2026), Multi-stability of Atlantic and Pacific overturning: The role of Freshwater Forcing Asymmetries and the Hydrological Cycle, arXiv: 2605.15699

Wang, O., Lee, T., Frederikse, T. et al (2026), Subpolar North Atlantic heat flux drives projected U.S. East Coast sea-level trend in a climate model, Commun Earth Environ, doi: 10.1038/s43247-026-03632-7

Wang, S., Jing, Z., Wang, H. et al. (2026), Future changes of upscale ocean kinetic energy transfer under greenhouse warming, npj Clim Atmos Sci, doi:  10.1038/s41612-026-01429-1

Xie, Hanzhou et al (2026), A case study of anomalous horizontal heat transport by an anticyclonic warm eddy in the Canada Basin, Atmospheric and Oceanic Science Letters, doi: 10.1016/j.aosl.2026.100838

Xu, Jiexin et al (2026), Mechanisms of Chlorophyll Enhancement Driven by internal tides in the Oligotrophic Northern South China Sea, JGR Oceans, doi: 10.1029/2025JC023161

Xu, Yilang et al (2026), Influence of Subinertial Variability on Dense Overflows Across the Iceland–Faroe Ridge, via ESS Open Archive, doi: 10.22541/essoar.15003334/v1

Yadav, D.K., Pant, M., Mandal, B. et al. (2026), Pre-monsoon Indian Ocean subsurface salinity and potential temperature anomalies associated with monsoon onset over Kerala Ocean Dynamics, doi: 10.1007/s10236-026-01817-4

Yang, Z. et al (2026), Understanding the Mesoscale Eddy Vertical Tilt Based on Mode Decomposition, JGR Oceans, doi: 10.1029/2025JC023199

Youngs, M.K., Stewart, A.L., Si, Y. et al (2026), Antarctic ice-shelf basal melt shaped by competing feedbacks, Nat. Geosci., doi: 10.1038/s41561-026-01975-6

Zheng, Shuo et al  (2026), Mass-induced seasonal sea level variability from geodetic observations: a case study along the eastern coast of China, Geophysical Journal International, doi: 10.1093/gji/ggag172
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Do you have news about research using MITgcm? We are looking for contributions to these pages. If you have an interesting MITgcm project (ocean, atmosphere, sea-ice, physics, biology or otherwise) that you want to tell people about, get in touch. To make a post, contact Helen