reporting by Helen Hill for MITgcm

New technique accelerates ocean biogeochemical model optimization, making it easier to enhance predictions of ocean health and carbon cycles.

In a new study, a team of researchers (Sophy Oliver from the National Oceanography Centre, Samar Khatiwala and Coralia Cartis from the University of Oxford, Ben Ward from the University of Southampton and Iris Kriest from GEOMAR Helmholtz Centre for Ocean Research Kiel) has developed a novel approach to speed up the optimization of ocean biogeochemical models—critical tools for predicting the impacts of climate change on marine ecosystems and the global carbon cycle. The work is published in JAMES (Journal of Modeling Earth Systems).

Ocean biogeochemical models simulate how nutrients, carbon, and oxygen flow through the ocean. These models are essential for understanding how marine ecosystems respond to climate change and how the ocean regulates carbon dioxide levels in the atmosphere. However, these models are often too computationally expensive to systematically optimize due to the need for long “spin-up” periods—simulations that allow the model to stabilize before real testing begins.

To carry out their research, the team used transport matrices extracted from the MIT General Circulation Model (MITgcm). By incorporating their shortened spin-up method, they demonstrated that the optimized model’s performance remained robust, even with reduced simulation time. The high efficiency of using transport matrices from MITgcm made it the perfect tool to test and validate their optimization method.

“Spin-up times are a major bottleneck when calibrating ocean biogeochemical models to observations,” says Sophy Oliver, a climate scientist at the UK’s National Oceanography Center. “By showing these times can be reduced, we can refine and calibrate our models faster, which should ultimately lead to more reliable projections of how marine ecosystems react to climate change.”

The team tested their method by applying it to a commonly used ocean biogeochemical model, speeding up its spin-up phase from 3000 years down to 2000 years. The results showed that even with a shorter spin-up it was possible to reliably optimize the model, a result that the authors believe is broadly applicable to other ocean models.

This breakthrough is crucial because improving the speed and efficiency of model optimization allows scientists to explore a wider range of scenarios, from predicting the future state of ocean ecosystems to understanding past climate conditions. These models are key to projecting how much carbon the ocean can absorb as global temperatures rise, and how marine life, from plankton to fish populations, will respond.

“Climate models are only as good as their ability to capture the complexities of the ocean, which plays a vital role in regulating Earth’s climate,” Dr. Oliver explains. “A more efficient optimization process allows researchers to fine-tune their models so they are closer to reality.”

Samar Khatiwala adds that “we’re currently exploring whether it is possible to reduce the spin-up time further, perhaps even down to a couple of hundred years, which would be transformational”. This ongoing research exploits a new acceleration technique developed by Dr. Khatiwala published recently in Science Advances (https://www.science.org/doi/10.1126/sciadv.adn2839). That study also used MITgcm to test the algorithm.

The team’s work holds promise for a wide range of applications in climate science, from enhancing predictions of harmful algal blooms to improving estimates of how much carbon the oceans can sequester. It also offers practical benefits for computational research, allowing labs to run larger simulations with fewer computational resources.

To find out more about this work​ contact Sophy

This month’s story image was generated using Google’s Gemini AI tool.

Note: Microsoft’s generative AI tool Copilot was used to provide partial assistance in drafting a preliminary version of this text. All generated content was thoroughly reviewed and edited for accuracy, including in consultation with the authors of the research being summarized.

This Month’s Featured Publication

Other New Publications last month

Ali Muhamed Ali et al (2024), Ocean Currents Velocity Hindcast and Forecast Bias Correction Using a Deep-Learning Approach, Journal of Marine Science and Engineering, doi: 10.3390/jmse12091680

Maxime Ballarotta et al (2024), Integrating wide swath altimetry data into Level-4 multi-mission maps, EGU Sphere, doi: 10.5194/egusphere-2024-2345

Hayley Beltz and Emily Rauscher (2024), Comparative Planetology of Magnetic Effects in Ultrahot Jupiters: Trends in High Resolution Spectroscopy, arXiv: 2409.13840 [astro-ph.EP]

Maxime Benoît-Gagné, Stephanie Dutkiewicz, Inge Deschepper, Christiane Dufresne, Dany Dumont, et al. (2024), Exploring controls on the timing of the phytoplankton bloom in western Baffin Bay, Canadian Arctic. Elementa: Science of the Anthropocene, In press. ffhal-04695952

Bertino, L., Heimbach, P., Blockley, E., and Ólason, E. (2024), Numerical Models for Monitoring and Forecasting Sea Ice: a short description of present status, State Planet Discuss., doi:  10.5194/sp-2024-24

Romain Caneill (2024), From alpha to beta ocean, Doctoral Dissertation University of Gottenburg

Chericoni, M., Fosser, G., Flaounas, E., Sannino, G., and Anav, A. (2024), Extreme Mediterranean cyclones and associated variables in an atmosphere-only vs an ocean-coupled regional model, EGUsphere , doi: 10.5194/egusphere-2024-2829

Čerkasova, N., Mėžinė et al (2024), Exploring variability in climate change projections on the Nemunas River and Curonian Lagoon: coupled SWAT and SHYFEM modeling approach, Ocean Sci., doi: 10.5194/os-20-1123-2024

Elipot, S. et al (2024), An integrated dataset of near-surface Eulerian fields and Lagrangian trajectories from an ocean model, Sci Data, doi: 10.1038/s41597-024-03813-z

He Gao et al (2024), Deep learning solver unites SDGSAT-1 observations and Navier–Stokes theory for oceanic vortex streets, Remote Sensing of the Environment, doi: 10.1016/j.rse.2024.114425

Kaushal Gianchandani (2024), Recirculation through western boundary currents varies nonlinearly with the ocean basin’s aspect ratio featured, Physics of Fluids, doi: 10.1063/5.0226883

Hamdeno, M. et al (2024), Investigating the long-term variability of the Red Sea marine heatwaves and their relationship to different climate modes: focus on 2010 events in the northern basin, Ocean Sci., doi: 10.5194/os-20-1087-2024

Hyder, Ali (2024), The Interplay of Moist Convective and Diffusive Transport in the Jovian Atmosphere, New Mexico State University ProQuest Dissertations & Theses,  2024. 31336120

Yanhong Lai et al (2024), Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model, The Planetary Science Journal, doi: 10.3847/PSJ/ad7111

Mi-Ling Li et al (2024), Global fishing patterns amplify human exposures to methylmercury, PNAS, doi: 10.1073/pnas.2405898121

Huang, Minghai (2024), Mean Currents, Eddies, and Their Interactions in the Northwestern Atlantic Ocean and Its Marginal Seas,  University of Delaware ProQuest Dissertations & Theses,  2024. 31234644

Martin, M. J., Hoteit, I., Bertino, L., and Moore, A. M. (2024), Data assimilation schemes for ocean forecasting: state of the art, State Planet Discuss., doi: 10.5194/sp-2024-20

Zhao Li et al (2024), Environmental Loading Products, and Their Contributions to Nonlinear Variations of Global Navigation Satellite System (GNSS) Coordinate Time Series, Engineering, doi: 10.1016/j.eng.2024.09.001

Liu, Xueqi et al (2024), Characteristics and dynamics of the interannual eddy kinetic energy variation in the Western Equatorial Pacific Ocean, Progress in Oceanography, doi: 10.1016/j.pocean.2024.103358

Lukas Lundgren et al (2024), A potential energy conserving finite element method for turbulent variable density flow: application to glacier-fjord circulation, arXiv: 2409.00972

Donna K. McCullough (2024), Hydrogen peroxide impacts on marine phytoplankton community on community structure: A modeling story, University of Tennessee Doctoral Dissertation

Nicole Neumann, C Spencer Jones (2024), Impact of Isopycnal Mixing on Southern Ocean Overturning Geometry, ESS Open Air, doi: 10.22541/essoar.172589400.04452674

Reiss, R.S. et al (2024), Strong bottom currents in large, deep Lake Geneva generated by higher vertical-mode Poincaré waves, Commun Earth Environ, doi: 10.1038/s43247-024-01653-8

Oleg A. Saenko, Neil F. Tandon (2024), Interannual Variability of the Heat Budget in the Tropical Pacific Ocean and Its Link to the Overturning Circulation, JGR Oceans, doi: 10.1029/2024JC020981

San, S.‐C. et al (2024), Why is Decadal Climate Variability predominantly observed in the Niño4 region? Geophysical Research Letters, doi: 10.1029/2024GL110457

Stephenson, D., Amrhein (2024), Three atmospheric patterns dominate decadal North Atlantic overturning variability, Geophysical Research Letters, doi: 10.1029/2024GL109193

Tajouri, S. et al (2024), Simulated impact of time‐varying river runoff and Greenland freshwater discharge on sea level variability in the Beaufort Gyre over 2005–2018, Journal of Geophysical Research: Oceans, doi:10.1029/2024JC021237

Zhiwei Tian et al (2024), Tide simulation in a global eddy-resolving ocean model, Acta Oceanologica Sinica, doi: 10.1007/s13131-024-2352-5

Vasou, Panagiotis (2024), Exchanges through the Strait of Hormuz and their Impact on the Arabian Gulf’s Thermohaline Variability, Doctoral Dissertation, KAUST

Odette A. Vergara et al (2024), The influence of the Biobio Canyon on the circulation and coastal upwelling/downwelling off central Chile, Continental Shelf Research, doi: 10.1016/j.csr.2024.105335

Chae-Hyun Yoon et al (2024), Deciphering Super El Nino: Development of a Novel Predictive Model Integrating Local and Global Climatic Signals, arXiv: 2409.06161

Zahn, M. J. et al. (2024). Consistent seasonal hydrography from moorings at Northwest Greenland glacier fronts, Journal of Geophysical Research: Oceans, doi: 10.1029/2024JC021046

Yuxin Zhao et al (2024), Hierarchical stacked spatiotemporal self-attention network for sea surface temperature forecasting, Ocean Modeling, doi: 10.1016/j.ocemod.2024.102427

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