Article
Adcroft A, Anderson W, Balaji V, Blanton C, Bushuk M, Dufour CO, Dunne JP, Griffies SM, Hallberg R, Harrison MJ (2019) The GFDL global ocean and sea ice model OM4.0: model description and simulation features. J Adv Model Earth Syst 11:3167–3211. doi:10.1029/2019MS001726
10.1029/2019MS001726Bakker DCE, Pfeil B, Landa CS, Metzl N, O'brien KM, Olsen A, Smith K, Cosca C, Harasawa S, Jones SD (2016) A multi-decade record of high-quality fCO2 data in version 3 of the surface ocean CO2 atlas (SOCAT). Earth Syst Sci Data 8:383–413. doi:10.5194/essd-8-383-2016
10.5194/essd-8-383-2016Beech N, Rackow T, Semmler T, Jung T (2024) Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the southern ocean. Geosci Model Dev 17:529–543. doi:10.5194/gmd-17-529-2024
10.5194/gmd-17-529-2024Bleichrodt F, Bisseling RH, Dijkstra HA (2012) Accelerating a barotropic ocean model using a GPU. Ocean Modell 41:16–21. doi:10.1016/j.ocemod.2011.10.001
10.1016/j.ocemod.2011.10.001Chang I, Ho Kim Y, Park Y-G, Jin H, Pak G, Kwon J-I, Chang Y-S (2024) Assessment of high-resolution regional ocean reanalysis K-ORA22 for the northwest pacific. Prog Oceanogr 229:103359. doi:10.1016/j.pocean.2024.103359
10.1016/j.pocean.2024.103359Ciżnicki M, Kierzynka M, Kopta P, Kurowski K, Gepner P (2012) Benchmarking data and compute intensive applications on modern CPU and GPU architectures. Procedia Comput Sci 9:1900–1909. doi:10.1016/j.procs.2012.04.208
10.1016/j.procs.2012.04.208De Luca P, Galletti A, Giunta G, Marcellino L (2021) Recursive filter based GPU algorithms in a Data assimilation scenario. J Comput Sci 53:101339. doi:10.1016/j.jocs.2021.101339
10.1016/j.jocs.2021.101339Delmas V, Soulaïmani A (2022) Multi-GPU implementation of a time-explicit finite volume solver using CUDA and a CUDA-Aware version of OpenMPI with application to shallow water flows. Comput Phys Commun 271:108190. doi:10.1016/j.cpc.2021.108190
10.1016/j.cpc.2021.108190Evensen G (1994) Sequential data assimilation with a nonlinear quasi‐geostrophic model using monte carlo methods to forecast error statistics. J Geophys Res-Oceans 99:10143–10162. doi:10.1029/94JC00572
10.1029/94JC00572Evensen G (2003) The ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics 53:343–367. doi:10.1007/s10236-003-0036-9
10.1007/s10236-003-0036-9Fay AR, Gregor L, Landschützer P, Mckinley GA, Gruber N, Gehlen M, Iida Y, Laruelle GG, Rödenbeck C, Roobaert A (2021) SeaFlux: harmonization of air-sea CO2 fluxes from surface pCO2 data products using a standardized approach. Earth Syst Sci Data 13:4693–4710. doi:10.5194/essd-13-4693-2021
10.5194/essd-13-4693-2021Häfner D, Nuterman R, Jochum M (2021) Fast, cheap, and turbulent-Global ocean modeling with GPU acceleration in python. J Adv Model Earth Syst 13:e2021MS002717. doi:10.1029/2021MS002717
10.1029/2021MS002717Harris CR, Millman KJ, Van Der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ (2020) Array programming with NumPy. Nature 585:357–362. doi:10.1038/s41586-020-2649-2
10.1038/s41586-020-2649-232939066PMC7759461Houtekamer PL, Mitchell HL (2001) A sequential ensemble Kalman Filter for atmospheric data assimilation. Mon Weather Rev 129:123–137. doi:10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2Iida Y, Kojima A, Takatani Y, Nakano T, Sugimoto H, Midorikawa T, Ishii M (2015) Trends in pCO2 and sea–air CO2 flux over the global open oceans for the last two decades. J Oceanogr 71:637–661. doi:10.1007/s10872-015-0306-4
10.1007/s10872-015-0306-4Jin H, Kim YH, Park Y-G, Chang I, Chang Y-S, Park H, Pak G (2024) Simulation characteristics of ocean predictability experiment for marine environment (OPEM): A western north pacific regional ocean prediction system. Ocean Sci J 59:71. doi:10.1007/s12601-024-00195-6
10.1007/s12601-024-00195-6Jones SD, Le Quéré C, Rödenbeck C (2012) Autocorrelation characteristics of surface ocean pCO2and air-sea CO2 fluxes. Global Biogeochem Cy 26:GB2042. doi:10.1029/2010GB004017
10.1029/2010GB004017Keckler SW, Dally WJ, Khailany B, Garland M, Glasco D (2011) GPUs and the future of parallel computing. IEEE micro 31:7–17. doi:10.1109/MM.2011.89
10.1109/MM.2011.89Landschützer P, Laruelle GG, Roobaert A, Regnier P (2020) A uniform pCO2 climatology combining open and coastal oceans. Earth Syst Sci Data 12:2537–2553. doi:10.5194/essd-12-2537-2020
10.5194/essd-12-2537-2020Martin MJ, Hoteit I, Bertino L, Moore AM (2025) Data assimilation schemes for ocean forecasting: state of the art. State of the Planet 5:9. doi:10.5194/sp-2024-20
10.5194/sp-2024-20Navarro CA, Hitschfeld-Kahler N, Mateu L (2014) A survey on parallel computing and its applications in Data-Parallel problems using GPU architectures. Commun Comput Phys 15:285–329. doi:10.4208/cicp.110113.010813a
10.4208/cicp.110113.010813aPatterson DA, Hennessy JL (2016). Computer organization and design ARM edition: the hardware software interface. Morgan kaufmann.
Porter AR, Heimbach P (2025) Unlocking the power of parallel computing: GPU technologies for ocean forecasting. State of the Planet 5-opsr:23. doi:10.5194/sp-2024-32
10.5194/sp-2024-32Rödenbeck C, Bakker DCE, Gruber N, Iida Y, Jacobson AR, Jones S, Landschützer P, Metzl N, Nakaoka S, Olsen A (2015) Data-based estimates of the ocean carbon sink variability-first results of the surface ocean pCO2 mapping intercomparison (SOCOM). Biogeosciences 12:7251–7278. doi:10.5194/bg-12-7251-2015
10.5194/bg-12-7251-2015Roobaert A, Regnier P, Landschützer P, Laruelle GG (2024) A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020. Earth Syst Sci Data 16:421–441. doi:10.5194/essd-16-421-2024
10.5194/essd-16-421-2024Shchepetkin AF, Mcwilliams JC (2005) The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modell 9:347–404. doi:10.1016/j.ocemod.2004.08.002
10.1016/j.ocemod.2004.08.002Silvestri S, Wagner GL, Constantinou NC, Hill CN, Campin J-M, Souza AN, Bishnu S, Churavy V, Marshall J, Ferrari R (2025) A GPU-Based ocean dynamical core for routine mesoscale-resolving climate simulations. J Adv Model Earth Syst 17:e2024MS004465. doi:10.1029/2024MS004465
10.1029/2024MS004465Vance TC, Wengren M, Burger E, Hernandez D, Kearns T, Medina-Lopez E, Merati N, O’brien K, O’neil J, Potemra JT (2019) From the oceans to the cloud: opportunities and challenges for data, models, computation and workflows. Front Mar Sci 6:211. doi:10.3389/fmars.2019.00211
10.3389/fmars.2019.00211Vanderbauwhede W, Davidson G (2018) Domain-specific acceleration and auto-parallelization of legacy scientific code in FORTRAN 77 using source-to-source compilation. Comput Fluids 173:1–5. doi:10.1016/j.compfluid.2018.06.005
10.1016/j.compfluid.2018.06.005Xu S, Huang X, Oey LY, Xu F, Fu H, Zhang Y, Yang G (2015) POM.gpu-v1.0: a GPU-based princeton ocean model. Geosci Model Dev 8:2815–2827. doi:10.5194/gmd-8-2815-2015
10.5194/gmd-8-2815-2015Yuan Y, Yu F, Chen Z, Li X, Hou F, Gao Y, Gao Z, Pang R (2024) Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0. Geosci Model Dev 17:6123–6136. doi:10.5194/gmd-17-6123-2024
10.5194/gmd-17-6123-2024- Publisher :Korea Institute of Ocean Science and Technology
- Publisher(Ko) :한국해양과학기술원
- Journal Title :Ocean and Polar Research
- Journal Title(Ko) :Ocean and Polar Research
- Volume : 48
- Pages :1-10
- Received Date : 2025-09-25
- Revised Date : 2025-12-22
- Accepted Date : 2025-12-29
- Published Date : 2026-01-14
- DOI :https://doi.org/10.4217/OPR.2026001


Ocean and Polar Research







