Received date: 2024-01-17
Revised date: 2024-04-10
Online published: 2024-06-03
Supported by
the National Natural Science Foundation of China(42242018)
The global Ocean General Circulation Model (OGCM) is a critical component of Earth system modeling and plays an essential role in climate projections and marine environmental forecasting. Herein, the history of global OGCM models is systematically reviewed and significant scientific and recent technological advancements are summarized. This review covers three topics involving the core technology of OGCMs: the dynamical core, physics or physical parameterization, and soft-hardware configuration. In the dynamic core, the latest developments in horizontal discretization methods, vertical coordinate schemes, and multi-resolution strategies are explored. Regarding physics, the focus has been on the progress of mesoscale, sub-mesoscale, and boundary-layer mixing parameterizations. In the soft-hardware configuration section, the current status and prospects for the application of heterogeneous computing architectures and artificial intelligence technology in global OGCMs are discussed. The advancement of the LASG/IAP Climate System Ocean Model, a fully autonomous Chinese global OGCM, is also highlighted. Based on key trends and novel ideas in the field of global OGCMs, suggestions are provided for Chinese researchers and relevant policymakers to comprehensively advance R&D strategies and long-term planning for fully autonomous global OGCMs.
Jingwei XIE , Hailong LIU , Weipeng ZHENG , Pengfei LIN , Jinfeng MA , Yiwen LI , Zipeng YU , Jiangfeng YU , Jiahui BAI . Advances in Global Ocean General Circulation Models[J]. Advances in Earth Science, 2024 , 39(5) : 454 -465 . DOI: 10.11867/j.issn.1001-8166.2024.040
| 1 | SYMONDS M E. Faculty opinions recommendation of IPCC， 2021： summary for policymakers ［R］// Climate change 2021： the physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. 2021. |
| 2 | CHASSIGNET E， le SOMMER J， WALLCRAFT A. General circulation models ［M］// COCHRAN K J， BOKUNIEWICZ H J， YAGER P L. Encyclopedia of ocean sciences， 2019， 5： 486-490. |
| 3 | BRYAN K. A numerical method for the study of the circulation of the world ocean［J］. Journal of Computational Physics， 1969， 4（3）： 347-376. |
| 4 | GRIFFIES S， STOUFFER R， ADCROFT A， et al. A historical introduction to MOM ［EB/OL］. 2015. ［2023-11-20］. . |
| 5 | SMITH R D， DUKOWICZ J K， MALONE R C. Parallel ocean general circulation modeling［J］. Physica D： Nonlinear Phenomena， 1992， 60（1/2/3/4）： 38-61. |
| 6 | BLECK R. An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates［J］. Ocean Modelling， 2002， 4（1）： 55-88. |
| 7 | MARSLAND S J， HAAK H， JUNGCLAUS J H， et al. The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates［J］. Ocean Modelling， 2003， 5（2）： 91-127. |
| 8 | GURVAN M， the NEMO team. NEMO ocean engine ［M］. Note du P?le de modélisation de l'Institut Pierre-Simon Laplace No. 27，2017. |
| 9 | MARSHALL J， ADCROFT A， HILL C， et al. A finite-volume， incompressible Navier Stokes model for studies of the ocean on parallel computers［J］. Journal of Geophysical Research： Oceans， 1997， 102（C3）： 5 753-5 766. |
| 10 | ZHANG X H， LIANG X Z. A numerical world ocean general circulation model［J］. Advances in Atmospheric Sciences， 1989， 6（1）： 44-61. |
| 11 | LIU H L， ZHANG X H， LI W， et al. An eddy-permitting oceanic general circulation model and its preliminary evaluation［J］. Advances in Atmospheric Sciences， 2004， 21（5）： 675-690. |
| 12 | LI Y W， LIU H L， DING M R， et al. Eddy-resolving simulation of CAS-LICOM3 for phase 2 of the ocean model intercomparison project［J］. Advances in Atmospheric Sciences， 2020， 37（10）： 1 067-1 080. |
| 13 | LIN P F， YU Z P， LIU H L， et al. LICOM model datasets for the CMIP6 ocean model intercomparison project［J］. Advances in Atmospheric Sciences， 2020， 37（3）： 239-249. |
| 14 | LIU H L， LIN P F， YU Y Q， et al. The baseline evaluation of LASG/IAP Climate system Ocean Model （LICOM） version 2［J］. Acta Meteorologica Sinica， 2012， 26（3）： 318-329. |
| 15 | WANG Pengfei， JIANG Jinrong， LIN Pengfei， et al. The GPU version of LASG/IAP Climate System Ocean Model version 3 （LICOM3） under the Heterogeneous-compute Interface for Portability （HIP） framework and its large-scale application ［J］. Geoscientific Model Development， 2021， 14（5）： 2 781-2 799. |
| 16 | WEI J L， JIANG J R， LIU H L， et al. LICOM3-CUDA： a GPU version of LASG/IAP climate system ocean model version 3 based on CUDA［J］. The Journal of Supercomputing， 2023， 79（9）： 9 604-9 634. |
| 17 | GRIFFIES S M， BIASTOCH A， B?NING C， et al. Coordinated Ocean-ice Reference Experiments （COREs）［J］. Ocean Modelling， 2009， 26（1）： 1-46. |
| 18 | TONANI M， BALMASEDA M， BERTINO L， et al. Status and future of global and regional ocean prediction systems［J］. Journal of Operational Oceanography， 2015， 8（）： s201-s220. |
| 19 | LIU Na， WANG Hui， LING Tiejun， et al. Review and prospect of global operational ocean forecasting［J］. Advances in Earth Science， 2018， 33（2）： 131-140. |
| 19 | 刘娜， 王辉， 凌铁军， 等. 全球业务化海洋预报进展与展望［J］. 地球科学进展， 2018， 33（2）： 131-140. |
| 20 | ZHANG Yongchui， CHEN Shiyao， WANG Ning， et al. Progress of global operational ocean forecasting systems［J］. Advances in Earth Science， 2022， 37（4）： 344-357. |
| 20 | 张永垂， 陈诗尧， 王宁， 等. 全球业务化海洋预报系统进展［J］. 地球科学进展， 2022， 37（4）： 344-357. |
| 21 | HEWITT H T， ROBERTS M， MATHIOT P， et al. Resolving and parameterising the ocean mesoscale in Earth system models［J］. Current Climate Change Reports， 2020， 6（4）： 137-152. |
| 22 | DONG J H， FOX-KEMPER B， ZHANG H， et al. The seasonality of submesoscale energy production， content， and cascade［J］. Geophysical Research Letters， 2020， 47（6）. DOI：10.1029/2020GL087388 . |
| 23 | SU Z， WANG J B， KLEIN P， et al. Ocean submesoscales as a key component of the global heat budget［J］. Nature Communications， 2018， 9. DOI：10.1038/s41467-018-02983-w . |
| 24 | RINGLER T， PETERSEN M， HIGDON R L， et al. A multi-resolution approach to global ocean modeling［J］. Ocean Modelling， 2013， 69： 211-232. |
| 25 | DANILOV S， SIDORENKO D， WANG Q， et al. The Finite-volumE Sea ice-Ocean Model （FESOM2） ［J］. Geoscientific Model Development， 2017， 10（2）： 765-789. |
| 26 | KORN P， BRüGGEMANN N， JUNGCLAUS J H， et al. ICON-O： the ocean component of the ICON earth system model—global simulation characteristics and local telescoping capability［J］. Journal of Advances in Modeling Earth Systems， 2022， 14（10）. DOI：10.1029/2021MS002952 . |
| 27 | ARBIC B K. Incorporating tides and internal gravity waves within global ocean general circulation models： a review［J］. Progress in Oceanography， 2022， 206. DOI：10.7302/4882 . |
| 28 | H?FNER D， NUTERMAN R， Fast JOCHUM M.， cheap， and turbulent—global ocean modeling with GPU acceleration in python ［J］. Journal of Advances in Modeling Earth Systems， 2021， 13（12）. DOI： 10.1029/2021MS002717 . |
| 29 | SILVESTRI S， WAGNER G， HILL C， et al. Oceananigans. jl： a model that achieves breakthrough resolution， memory and energy efficiency in global ocean simulations ［J］. arXiv preprint arXiv： 230906662， 2023. DOI：10.48550/arXiv.2309.06662 . |
| 30 | GRIFFIES S， DANABASOGLU G， DURACK P， et al. OMIP contribution to CMIP6： experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project［J］. Geoscientific Model Development， 2016， 9（9）： 3 231-3 296. |
| 31 | TSUJINO H， URAKAWA L S， GRIFFIES S M， et al. Evaluation of global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 （OMIP-2）［J］. Geoscientific Model Development， 2020， 13（8）： 3 643-3 708. |
| 32 | CHASSIGNET E P， YEAGER S G， FOX-KEMPER B， et al. Impact of horizontal resolution on global ocean-sea ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 （OMIP-2）［J］. Geoscientific Model Development， 2020， 13（9）： 4 595-4 637. |
| 33 | TREGUIER A M， de BOYER M C， BOZEC A， et al. The mixed layer depth in the Ocean Model Intercomparison Project （OMIP）： impact of resolving mesoscale eddies ［J］. EGUsphere， 2023， 16（13）： 3 849-3 872. |
| 34 | WANG Q， SHU Q， BOZEC A， et al. Impact of high resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 （OMIP-2） ［J］. Geoscientific Model Development Discussions， 2023. DOI：10.5194/gmd-2023-123 . |
| 35 | NI Q B， ZHAI X M， WILSON C， et al. Submesoscale eddies in the South China Sea［J］. Geophysical Research Letters， 2021， 48（6）. DOI：10.1029/2020GL091555 . |
| 36 | TANG H B， SHU Y Q， WANG D X， et al. Submesoscale processes observed by high-frequency float in the western South China Sea［J］. Deep Sea Research Part I： Oceanographic Research Papers， 2022， 192（7 550）. DOI：10.1016/j.dsr.2022.103896 . |
| 37 | OSCAR V， ROSEMARY M， ISABELLE P M， et al. Global submesoscale diagnosis using along-track satellite altimetry［J］. Ocean Science， 2023， 19（2）： 363-379. |
| 38 | LI Jun. Review on current Chinese supercomputing industry［J］. Bulletin of Chinese Academy of Sciences， 2019， 34（6）： 617-624. |
| 38 | 历军. 中国超算产业发展现状分析［J］. 中国科学院院刊， 2019， 34（6）： 617-624. |
| 39 | MURRAY R J. Explicit generation of orthogonal grids for ocean models［J］. Journal of Computational Physics， 1996， 126（2）： 251-273. |
| 40 | KORN P. Formulation of an unstructured grid model for global ocean dynamics［J］. Journal of Computational Physics， 2017， 339： 525-552. |
| 41 | ARAKAWA A， LAMB V R. Computational design of the basic dynamical processes of the UCLA general circulation model［M］// Methods in computational physics： advances in research and applications. Amsterdam： Elsevier， 1977： 173-265. |
| 42 | DUKOWICZ J K. Mesh effects for Rossby waves［J］. Journal of Computational Physics， 1995， 119（1）： 188-194. |
| 43 | WAJSOWICZ R C. Free planetary waves in finite-difference numerical models［J］. Journal of Physical Oceanography， 1986， 16（4）： 773-789. |
| 44 | BARHAM W， BACHMAN S， GROOMS I. Some effects of horizontal discretization on linear baroclinic and symmetric instabilities［J］. Ocean Modelling， 2018， 125： 106-116. |
| 45 | MCDOUGALL T. Neutral surfaces ［J］. Journal of Physical Oceanography， 1987， 17（11）： 1 950-1 964. |
| 46 | VERONIS G. The role of models in tracer studies ［J］. Numerical Models of Ocean Circulation， 1975： 133-146. |
| 47 | ADCROFT A， HALLBERG R. On methods for solving the oceanic equations of motion in generalized vertical coordinates［J］. Ocean Modelling， 2004， 11（1）： 224-233. |
| 48 | FOX-KEMPER B， ADCROFT A， B?NING C， et al. Challenges and prospects in ocean circulation models ［J］. Frontiers in Marine Science， 2019， 6： 65-102. |
| 49 | HALLBERG R. Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects［J］. Ocean Modelling， 2013， 72： 92-103. |
| 50 | SEIN D V， KOLDUNOV N V， DANILOV S， et al. Ocean modeling on a mesh with resolution following the local rossby radius［J］. Journal of Advances in Modeling Earth Systems， 2017， 9（7）： 2 601-2 614. |
| 51 | BERGER M J， CALHOUN D A， HELZEL C， et al. Logically rectangular finite volume methods with adaptive refinement on the sphere［J］. Philosophical Transactions Series A， Mathematical， Physical， and Engineering Sciences， 2009， 367（1 907）： 4 483-4 496. |
| 52 | BLAYO E， DEBREU L. Adaptive mesh refinement for finite-difference ocean models： first experiments ［J］. Journal of Physical Oceanography， 1999， 29（6）： 1 239-1 250. |
| 53 | KEVLAHAN N K R， DUBOS T， AECHTNER M. Adaptive wavelet simulation of global ocean dynamics using a new Brinkman volume penalization［J］. Geoscientific Model Development， 2015， 8（12）： 3 891-3 909. |
| 54 | POPINET S， RICKARD G. A tree-based solver for adaptive ocean modelling［J］. Ocean Modelling， 2006， 16（3）： 224-249. |
| 55 | ZHANG Yan， WANG Xuantong， SUN Yuhao， et al. Ocean Modeling with Adaptive REsolution （OMARE， version 1.0）-refactoring NEMO model （version 4.0.1） with the parallel computing framework of JASMIN. Part 1： adaptive grid refinement in an idealized double-gyre case ［J］. EGUsphere， 2022. DOI： 10.5194/gmd-16-679-2023 . |
| 56 | BIASTOCH A， SEIN D， DURGADOO J V， et al. Simulating the Agulhas system in global ocean models-nesting vs. multi-resolution unstructured meshes［J］. Ocean Modelling， 2018， 121： 117-131. |
| 57 | DANILOV S， WANG Q. Resolving eddies by local mesh refinement［J］. Ocean Modelling， 2015， 93： 75-83. |
| 58 | ENGWIRDA D. JIGSAW-GEO （1.0）： locally orthogonal staggered unstructured grid generation for general circulation modelling on the sphere［J］. Geoscientific Model Development， 2017， 10（6）： 2 117-2 140. |
| 59 | HOANG T T P， LENG W， JU L L， et al. Conservative explicit local time-stepping schemes for the shallow water equations［J］. Journal of Computational Physics， 2019， 382： 152-176. |
| 60 | LIU Hailong. LASG/IAP climate system ocean model （LICOM 1.0） reference manual［M］. Beijing： Science Press， 2004. |
| 60 | 刘海龙. LASG/IAP气候系统海洋模式（LICOM 1.0）参考手册［M］. 北京： 科学出版社， 2004. |
| 61 | XIE J W， LIU H L， LIN P F. A multifaceted isoneutral eddy transport diagnostic framework and its application in the southern ocean［J］. Journal of Advances in Modeling Earth Systems， 2023， 15（7）. DOI：10.1029/2023MS003728 . |
| 62 | WANG Bin， JI Zhongzhen. A new numerical method in atmospheric science and its application［M］. Beijing： Science Press， 2006. |
| 62 | 王斌， 季仲贞. 大气科学中的数值新方法及其应用［M］. 北京： 科学出版社， 2006. |
| 63 | GENT P R， MCWILLIAMS J C. Isopycnal mixing in ocean circulation models［J］. Journal of Physical Oceanography， 1990， 20（1）： 150-160. |
| 64 | REDI M H. Oceanic isopycnal mixing by coordinate rotation［J］. Journal of Physical Oceanography， 1982， 12（10）： 1 154-1 158. |
| 65 | VISBECK M， MARSHALL J， HAINE T， et al. Specification of eddy transfer coefficients in coarse-resolution ocean circulation models［J］. Journal of Physical Oceanography， 1997， 27（3）： 381-402. |
| 66 | BACHMAN S D， FOX-KEMPER B， PEARSON B. A scale-aware subgrid model for quasi-geostrophic turbulence［J］. Journal of Geophysical Research： Oceans， 2017， 122（2）： 1 529-1 554. |
| 67 | JANSEN M F， HELD I M. Parameterizing subgrid-scale eddy effects using energetically consistent backscatter［J］. Ocean Modelling， 2014， 80： 36-48. |
| 68 | JANSEN M F， HELD I M， ADCROFT A， et al. Energy budget-based backscatter in an eddy permitting primitive equation model［J］. Ocean Modelling， 2015， 94： 15-26. |
| 69 | BACHMAN S D. The GM+E closure： a framework for coupling backscatter with the Gent and McWilliams parameterization［J］. Ocean Modelling， 2019， 136： 85-106. |
| 70 | FOX-KEMPER B， MENEMENLIS D. Can large eddy simulation techniques improve mesoscale-rich ocean models？［J］. Geophysical Monograph Series， 2008， 177： 319-338. |
| 71 | GRAHAM J P， RINGLER T. A framework for the evaluation of turbulence closures used in mesoscale ocean large-eddy simulations［J］. Ocean Modelling， 2013， 65： 25-39. |
| 72 | LEITH C E. Stochastic models of chaotic systems［J］. Physica D， 1996， 98（2/3/4）： 481-491. |
| 73 | KHANI S， DAWSON C N. A gradient based subgrid-scale parameterization for ocean mesoscale eddies［J］. Journal of Advances in Modeling Earth Systems， 2023， 15（2）. DOI：10.1029/2022MS003356 . |
| 74 | BOLTON T， ZANNA L. Applications of deep learning to ocean data inference and subgrid parameterization［J］. Journal of Advances in Modeling Earth Systems， 2019， 11（1）： 376-399. |
| 75 | GUILLAUMIN A P， ZANNA L. Stochastic-deep learning parameterization of ocean momentum forcing［J］. Journal of Advances in Modeling Earth Systems， 2021， 13（9）. DOI：10.1029/2021MS002534 . |
| 76 | FREZAT H， le SOMMER J， FABLET R， et al. A posteriori learning for quasi-geostrophic turbulence parametrization［J］. Journal of Advances in Modeling Earth Systems， 2022， 14（11）. DOI： 10.1029/2022MS003124 . |
| 77 | FOX-KEMPER B， DANABASOGLU G， FERRARI R， et al. Parameterization of mixed layer eddies. III： implementation and impact in global ocean climate simulations［J］. Ocean Modelling， 2011， 39（1/2）： 61-78. |
| 78 | FOX-KEMPER B， FERRARI R， HALLBERG R. Parameterization of mixed layer eddies. part I： theory and diagnosis［J］. Journal of Physical Oceanography， 2008， 38（6）： 1 145-1 165. |
| 79 | ZHANG J C， ZHANG Z W， QIU B. Parameterizing submesoscale vertical buoyancy flux by simultaneously considering baroclinic instability and strain-induced frontogenesis［J］. Geophysical Research Letters， 2023， 50（8）. DOI：10.1029/2022GL102292 . |
| 80 | LARGE W G， MCWILLIAMS J C， DONEY S C. Oceanic vertical mixing： a review and a model with a nonlocal boundary layer parameterization［J］. Reviews of Geophysics， 1994， 32（4）： 363-403. |
| 81 | MELLOR G L， YAMADA T. Development of a turbulence closure model for geophysical fluid problems［J］. Reviews of Geophysics， 1982， 20（4）： 851-875. |
| 82 | REICHL B， HALLBERG R. A simplified energetics based Planetary Boundary Layer （ePBL） approach for ocean climate simulations［J］. Environmental Science， Physics， 2018. DOI： DOI：10.1016/J.OCEMOD.2018.10.004 . |
| 83 | BURCHARD H， BOLDING K. Comparative analysis of four second-moment turbulence closure models for the oceanic mixed layer［J］. Journal of Physical Oceanography， 2001， 31（8）： 1 943-1 968. |
| 84 | GRIFFIES S， LEVY M， ADCROFT A， et al. Theory and numerics of the community ocean vertical mixing （CVMix） project ［Z］. Draft from March， 2015. |
| 85 | van ROEKEL L， ADCROFT A J， DANABASOGLU G， et al. The KPP boundary layer scheme for the ocean： revisiting its formulation and benchmarking one-dimensional simulations relative to LES［J］. Journal of Advances in Modeling Earth Systems， 2018， 10（11）： 2 647-2 685. |
| 86 | ARBIC B K， ALFORD M H， ANSONG J K， et al. A primer on global internal tide and internal gravity wave continuum modeling in HYCOM and MITgcm［M］// New frontiers in operational oceanography. GODAE OceanView， 2018. |
| 87 | LOGEMANN K， LINARDAKIS L， KORN P， et al. Global tide simulations with ICON-O： testing the model performance on highly irregular meshes［J］. Ocean Dynamics， 2021， 71（1）： 43-57. |
| 88 | von STORCH J S， HERTWIG E， LüSCHOW V， et al. Open-ocean tides simulated by ICON-O， version icon-2.6.6［J］. Geoscientific Model Development， 2023， 16（17）： 5 179-5 196. |
| 89 | YU Y， LIU H L， LAN J. The influence of explicit tidal forcing in a climate ocean circulation model［J］. Acta Oceanologica Sinica， 2016， 35（9）： 42-50. |
| 90 | ROCHA C B， CHERESKIN T K， GILLE S T， et al. Mesoscale to submesoscale wavenumber spectra in drake passage［J］. Journal of Physical Oceanography， 2016， 46（2）： 601-620. |
| 91 | ROCHA C， GILLE S， CHERESKIN T， et al. Seasonality of submesoscale dynamics in the Kuroshio Extension［J］. Geophysical Research Letters， 2016， 43（21）. DOI：10.1002/2016GL071349 . |
| 92 | MüLLER M， CHERNIAWSKY J Y， FOREMAN M G G， et al. Global M2 internal tide and its seasonal variability from high resolution ocean circulation and tide modeling［J］. Geophysical Research Letters， 2012， 39（19）. DOI：10.1029/2012GL053320 . |
| 93 | SONG Pengyang， SIDORENKO Dmitry， SCHOLZ Patrick， et al. The tidal effects in the Finite-volumE Sea ice-Ocean Model （FESOM2.1）： a comparison between parameterised tidal mixing and explicit tidal forcing ［J］. Geoscientific Model Development， 2023， 16（1）： 383-405. |
| 94 | WEI Z X， SUN J C， TENG F， et al. A harmonic analyzed parameterization of tide-induced mixing for ocean models［J］. Acta Oceanologica Sinica， 2018， 37（7）： 1-7. |
| 95 | XU S， HUANG X， OEY L Y， et al. POM.gpu-v1.0： a GPU-based princeton ocean model［J］. Geoscientific Model Development， 2015， 8（9）： 2 815-2 827. |
| 96 | JIANG J R， LIN P F， WANG J， et al. Porting LASG/IAP climate system ocean model to gpus using OpenAcc［J］. IEEE Access， 2019， 7： 154 490-154 501. |
| 97 | PARK M S， KIM M， LEE M I， et al. Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees［J］. Remote Sensing of Environment， 2016， 183： 205-214. |
| 98 | ZHANG W， LEUNG Y， CHAN J C L. The analysis of tropical cyclone tracks in the western North Pacific through data mining. part I： tropical cyclone recurvature［J］. Journal of Applied Meteorology and Climatology， 2013， 52（6）： 1 394-1 416. |
| 99 | JIANG G Q， XU J， WEI J. A deep learning algorithm of neural network for the parameterization of typhoon-ocean feedback in typhoon forecast models［J］. Geophysical Research Letters， 2018， 45（8）： 3 706-3 716. |
| 100 | ZHANG Tao， XIE Feng， XUE Wei， et al. Quantification and optimization of parameter uncertainty in the grid-point atmospheric model GAMIL2［J］. Chinese Journal of Geophysics， 2016， 59（2）： 465-475. |
| 100 | 张涛， 谢丰， 薛巍， 等. 格点大气环流模式GAMIL2参数不确定性的量化分析与优化［J］. 地球物理学报， 2016， 59（2）： 465-475. |
| 101 | ZHU J C， HU S， ARCUCCI R， et al. Model error correction in data assimilation by integrating neural networks［J］. Big Data Mining and Analytics， 2019， 2（2）： 83-91. |
| 102 | BRENOWITZ N D， BRETHERTON C S. Prognostic validation of a neural network unified physics parameterization［J］. Geophysical Research Letters， 2018， 45（12）： 6 289-6 298. |
| 103 | WILLIS M J， von STOSCH M. Simultaneous parameter identification and discrimination of the nonparametric structure of hybrid semi-parametric models［J］. Computers and Chemical Engineering， 2017， 104： 366-376. |
| 104 | LIU H L， LIN P F， ZHENG W P， et al. A global eddy-resolving ocean forecast system in China-LICOM Forecast System （LFS）［J］. Journal of Operational Oceanography， 2023， 16（1）： 15-27. |
| 105 | ZHENG W P， LIN P F， LIU H L， et al. An assessment of the LICOM Forecast System under the IVTT class 4 framework［J］. Frontiers in Marine Science， 2023， 10. DOI：10.3389/fmars.2023.1112025 . |
| 106 | ZHANG Z W， LIU Y L， QIU B， et al. Submesoscale inverse energy cascade enhances Southern Ocean eddy heat transport［J］. Nature Communications， 2023， 14. DOI： 10.1038/s41467-023-36991-2 . |
| 107 | JING Z， WANG S P， WU L X， et al. Maintenance of mid-latitude oceanic fronts by mesoscale eddies［J］. Science Advances， 2020， 6（31）. DOI： 10.1126/sciadv.aba7880 . |
| 108 | WANG S P， JING Z， WU L X， et al. El Ni?o/Southern Oscillation inhibited by submesoscale ocean eddies［J］. Nature Geoscience， 2022， 15： 112-117. |
| 109 | LIN H Y， LIU Z Y， HU J Y， et al. Characterizing meso- to submesoscale features in the South China Sea［J］. Progress in Oceanography， 2020， 188. DOI：10.1016/j.pocean.2020.102420 . |
| 110 | LIU C Y， WANG X W， LIU Z Y， et al. On the formation of a subsurface weakly sheared laminar layer and an upper thermocline strongly sheared turbulent layer in the eastern equatorial Pacific： interplays of multiple-time-scale equatorial waves［J］. Journal of Physical Oceanography， 2020， 50（10）： 2 907-2 930. |
| 111 | PENG S Q， LIAO J W， WANG X W， et al. Energetics-based estimation of the diapycnal mixing induced by internal tides in the andaman sea［J］. Journal of Geophysical Research： Oceans， 2021， 126（4）. DOI：10.1029/2020JC016521 . |
| 112 | BRYAN F， KAUFFMAN B， LARGE W， et al. NCAR CSM flux coupler. Technical note［Z］. 1996. |
| 113 | BALAJI V. The FMS manual： a developer’s guide to the GFDL Flexible Modeling System ［Z］. 2002. |
| 114 | VALCKE S. The OASIS3 coupler： a European climate modelling community software［J］. Geoscientific Model Development， 2013， 6（2）： 373-388. |
/
| 〈 |
|
〉 |
甘公网安备62010202000687
