Duncan, James P C, Elynn Wu, Surya Dheeshjith, Adam Subel, Troy Arcomano, Spencer K Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W Andre Perkins, William Gregory, Carlos Fernandez-Granda, Julius J M Busecke, Oliver Watt-Meyer, William J Hurlin, Alistair Adcroft, Laure Zanna, and Christopher S Bretherton, June 2026: SamudrACE: Fast and accurate coupled climate modeling with 3D ocean and atmosphere emulators. Geophysical Research Letters, 53(11), DOI:10.1029/2025GL119340. Abstract
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning-based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries-long simulations at 1-degree horizontal, 6-hourly atmospheric, and 5-daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top-of-atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode.
Coats, Sloan, Philip R Thompson, Christopher G Piecuch, John Fasullo, Benjamin D Hamlington, Kristopher B Karnauskas, R Steven Nerem, Angelica R Rodriguez, Jacob M Steinberg, and Julius J M Busecke, October 2025: Understanding the role for internal variability in driving past and future ocean dynamic sea level trends in CMIP6 simulations. Journal of Climate, 38(20), DOI:10.1175/JCLI-D-24-0336.15685-5699. Abstract
Herein, spatial variations of sea level trends from the altimeter record are compared to contemporaneous (1993–2014) and future trends in ocean dynamic sea level from state-of-the-art climate models. A multiclimate model ensemble of CMIP6 historical simulations is analyzed (n = 560), and little agreement is found in the global pattern of ocean dynamic sea level trends across the ensemble. While some simulations have regional ocean dynamic sea level trends that are a close match to the altimeter record, none are a good match globally (maximum pattern correlation globally of 0.47 and 5%–95% range from −0.20 to 0.26), and simultaneously matching the altimeter record in the tropical and North Pacific and tropical and North Atlantic is particularly challenging. Our focus in this study is on differences across the individual historical simulations and the role for internal variability, external forcing, and structural factors in driving these differences. A close relationship is found between patterns of sea surface temperature trends and those in sea level, and both can be related to the trajectories of common modes of atmosphere–ocean variability, with centers of action in the Indian Ocean and the tropical and North Pacific. Using preindustrial control simulations, we determine where external forcing has and will produce local (i.e., gridpoint level) ocean dynamic sea level trends that are significant relative to internal variability. At the present (1992–2023), climate models suggest that ocean dynamic sea level trends over ∼15% of the ocean area are significant relative to internal variability, with this number increasing to 37% by 2050 under a high-emission scenario (33% under a low-emission scenario).
Dheeshjith, Surya, Adam Subel, Alistair Adcroft, Julius J M Busecke, Carlos Fernandez-Granda, Shubham Gupta, and Laure Zanna, May 2025: Samudra: An AI global ocean emulator for climate. Geophysical Research Letters, 52(10), DOI:10.1029/2024GL114318. Abstract
AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi-depth levels of ocean data. We show that the ocean emulator—Samudra—which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work.
Global ocean oxygen loss is projected to persist in the future, but Earth system models (ESMs) have not yet provided a consistent picture of how it will influence the largest oxygen minimum zone (OMZ) in the tropical Pacific. We examine the change in the Pacific OMZ volume in an ensemble of ESMs from the CMIP6 archive, considering a broad range of oxygen (O2) thresholds relevant to biogeochemical cycles and ecosystems (5–160 µmol/kg). Despite OMZ biases in the historical period of the simulations, the ESM ensemble projections consistently fall into three regimes across ESMs: an expansion of low oxygenated waters (+0.8 [0.6, 1.0] × 1016 m3/century for O2 ≤ 120 µmol/kg, ESM median and interquartile range); a slight contraction of the OMZ core although more uncertain across ESMs (−0.1 [−0.5, 0.0] × 1016 m3/century for O2 ≤ 20 µmol/kg); and at the transition from contraction to expansion regimes, a spatial redistribution but near-zero change in the volume of hypoxic waters (0.0 [−0.3, +0.1] × 1016 m3/century for O2 ≤ 60 µmol/kg). Changes in circulation and biology dictate the shift from expansion to contraction. Specifically, reduced subtropical ventilation controls the expansion of low oxygenated waters, while a combination of circulation and biological changes explains the contraction of the core (likely changes in mixing, reduced intermediate ventilation and oxygen demand). Increased model complexity (e.g., ecosystem dynamics and equatorial circulation) likely stabilize the OMZ response, suggesting that future changes might lie in the lower bound of current projections. The expansion of low oxygenated waters which delimit the optimum habitat of numerous marine species would severely impact ecosystems and ecosystem services.
Warming‐driven expansion of the oxygen minimum zone (OMZ) in the equatorial Pacific would bring very low oxygen waters closer to the ocean surface and possibly impact global carbon/nutrient cycles and local ecosystems. Global coarse Earth System Models (ESMs) show, however, disparate trends that poorly constrain these future changes in the upper OMZ. Using an ESM with a high‐resolution ocean (1/10°), we show that a realistic representation of the Equatorial Undercurrent (EUC) dynamics is crucial to represent the upper OMZ structure and its temporal variability. We demonstrate that coarser ESMs commonly misrepresent the EUC, leading to an unrealistic “tilt” of the OMZ (e.g., shallowing toward the east) and an exaggerated sensitivity to EUC changes overwhelming other important processes like diffusion and biology. This shortcoming compromises the ability to reproduce the OMZ variability and could explain the disparate trends in ESMs projections.
Mesoscale turbulence in the ocean strongly affects the circulation, water mass formation, and transport of tracers. Little is known, however, about how mixing varies on climate timescales. We present the first time-resolved global dataset of lateral mesoscale eddy diffusivities at the ocean surface, obtained by applying the suppressed mixing length theory to satellite-observed velocities. We find interannual variability throughout the global ocean, regionally correlated with climate indices such as ENSO, NAO, DMI, and PDO. Changes in mixing length, driven by variations in the large-scale flow, often exceed the effect of variations in local eddy kinetic energy, previously thought of as the primary driver of variability in eddy mixing. This mechanism, not currently represented in global climate models, could have far-reaching consequences for the distribution of heat, salt, and carbon in the global ocean, as well as ecosystem dynamics and regional dynamics such as ENSO variance.
