Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2781-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-2781-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 May 2021
Development and technical paper |
| 18 May 2021
| 18 May 2021
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
Pengfei Wang
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
Center for Monsoon System Research (CMSR), Institute of Atmospheric
Physics, Chinese Academy of Sciences, Beijing 100190, China
Jinrong Jiang
CORRESPONDING AUTHOR
Computer Network Information Center, Chinese Academy of Sciences,
Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Pengfei Lin
CORRESPONDING AUTHOR
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Mengrong Ding
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
Junlin Wei
Computer Network Information Center, Chinese Academy of Sciences,
Beijing 100190, China
Feng Zhang
Computer Network Information Center, Chinese Academy of Sciences,
Beijing 100190, China
Lian Zhao
Computer Network Information Center, Chinese Academy of Sciences,
Beijing 100190, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
Zipeng Yu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
Weipeng Zheng
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Yongqiang Yu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Xuebin Chi
Computer Network Information Center, Chinese Academy of Sciences,
Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics
(IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Related authors
Kai Xu, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Xiang Han, Jiaying Song, Mingyao Geng, Jinrong Jiang, Hailong Liu, Pengfei Wang, and Pengfei Lin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2231, https://doi.org/10.5194/egusphere-2025-2231, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
swLICOM represents a significant advancement in kilometer-scale resolution ocean general circulation models on heterogeneous computing architectures. Our optimization efforts addressed a series of challenges that are particularly crucial for high-resolution modeling. We use swLICOM with a horizontal resolution of 2 km to conduct a short-term simulation test. The 2-km resolution global simulation shows the high capacity of swLICOM to capture the oceanic meso- to submesoscale processes.
Kai Xu, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Xiang Han, Jiaying Song, Mingyao Geng, Jinrong Jiang, Hailong Liu, Pengfei Wang, and Pengfei Lin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2231, https://doi.org/10.5194/egusphere-2025-2231, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
swLICOM represents a significant advancement in kilometer-scale resolution ocean general circulation models on heterogeneous computing architectures. Our optimization efforts addressed a series of challenges that are particularly crucial for high-resolution modeling. We use swLICOM with a horizontal resolution of 2 km to conduct a short-term simulation test. The 2-km resolution global simulation shows the high capacity of swLICOM to capture the oceanic meso- to submesoscale processes.
Fan Yang, Jiahui Bai, Hailong Liu, Weihang Zhang, Yi Wu, Shuhao Liu, Chunxiang Shi, Tao Zhang, Min Zhong, Zitong Zhu, Changqing Wang, Ehsan Forootan, Jiangfeng Yu, Zipeng Yu, and Yun Xiao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-81, https://doi.org/10.5194/essd-2025-81, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We introduce China’s first de-aliasing computation platform, incorporating China’s Atmospheric Reanalysis and an in-house ocean circulation model. This platform produces CRA-LICOM, a high-frequency atmospheric and oceanic gravity de-aliasing product with a 6-hourly, 50 km resolution covering 2002–2024 globally. This product is reliable for de-aliasing, signal separation in satellite gravity missions, and climate change studies.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Xiaoxu Shi, Martin Werner, Carolin Krug, Chris M. Brierley, Anni Zhao, Endurance Igbinosa, Pascale Braconnot, Esther Brady, Jian Cao, Roberta D'Agostino, Johann Jungclaus, Xingxing Liu, Bette Otto-Bliesner, Dmitry Sidorenko, Robert Tomas, Evgeny M. Volodin, Hu Yang, Qiong Zhang, Weipeng Zheng, and Gerrit Lohmann
Clim. Past, 18, 1047–1070, https://doi.org/10.5194/cp-18-1047-2022, https://doi.org/10.5194/cp-18-1047-2022, 2022
Short summary
Short summary
Since the orbital parameters of the past are different from today, applying the modern calendar to the past climate can lead to an artificial bias in seasonal cycles. With the use of multiple model outputs, we found that such a bias is non-ignorable and should be corrected to ensure an accurate comparison between modeled results and observational records, as well as between simulated past and modern climates, especially for the Last Interglacial.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
Short summary
Short summary
The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Bette L. Otto-Bliesner, Esther C. Brady, Anni Zhao, Chris M. Brierley, Yarrow Axford, Emilie Capron, Aline Govin, Jeremy S. Hoffman, Elizabeth Isaacs, Masa Kageyama, Paolo Scussolini, Polychronis C. Tzedakis, Charles J. R. Williams, Eric Wolff, Ayako Abe-Ouchi, Pascale Braconnot, Silvana Ramos Buarque, Jian Cao, Anne de Vernal, Maria Vittoria Guarino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina A. Morozova, Kerim H. Nisancioglu, Ryouta O'ishi, David Salas y Mélia, Xiaoxu Shi, Marie Sicard, Louise Sime, Christian Stepanek, Robert Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 17, 63–94, https://doi.org/10.5194/cp-17-63-2021, https://doi.org/10.5194/cp-17-63-2021, 2021
Short summary
Short summary
The CMIP6–PMIP4 Tier 1 lig127k experiment was designed to address the climate responses to strong orbital forcing. We present a multi-model ensemble of 17 climate models, most of which have also completed the CMIP6 DECK experiments and are thus important for assessing future projections. The lig127ksimulations show strong summer warming over the NH continents. More than half of the models simulate a retreat of the Arctic minimum summer ice edge similar to the average for 2000–2018.
Chris M. Brierley, Anni Zhao, Sandy P. Harrison, Pascale Braconnot, Charles J. R. Williams, David J. R. Thornalley, Xiaoxu Shi, Jean-Yves Peterschmitt, Rumi Ohgaito, Darrell S. Kaufman, Masa Kageyama, Julia C. Hargreaves, Michael P. Erb, Julien Emile-Geay, Roberta D'Agostino, Deepak Chandan, Matthieu Carré, Partrick J. Bartlein, Weipeng Zheng, Zhongshi Zhang, Qiong Zhang, Hu Yang, Evgeny M. Volodin, Robert A. Tomas, Cody Routson, W. Richard Peltier, Bette Otto-Bliesner, Polina A. Morozova, Nicholas P. McKay, Gerrit Lohmann, Allegra N. Legrande, Chuncheng Guo, Jian Cao, Esther Brady, James D. Annan, and Ayako Abe-Ouchi
Clim. Past, 16, 1847–1872, https://doi.org/10.5194/cp-16-1847-2020, https://doi.org/10.5194/cp-16-1847-2020, 2020
Short summary
Short summary
This paper provides an initial exploration and comparison to climate reconstructions of the new climate model simulations of the mid-Holocene (6000 years ago). These use state-of-the-art models developed for CMIP6 and apply the same experimental set-up. The models capture several key aspects of the climate, but some persistent issues remain.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
Short summary
This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Josephine R. Brown, Chris M. Brierley, Soon-Il An, Maria-Vittoria Guarino, Samantha Stevenson, Charles J. R. Williams, Qiong Zhang, Anni Zhao, Ayako Abe-Ouchi, Pascale Braconnot, Esther C. Brady, Deepak Chandan, Roberta D'Agostino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, Ryouta O'ishi, Bette L. Otto-Bliesner, W. Richard Peltier, Xiaoxu Shi, Louise Sime, Evgeny M. Volodin, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 16, 1777–1805, https://doi.org/10.5194/cp-16-1777-2020, https://doi.org/10.5194/cp-16-1777-2020, 2020
Short summary
Short summary
El Niño–Southern Oscillation (ENSO) is the largest source of year-to-year variability in the current climate, but the response of ENSO to past or future changes in climate is uncertain. This study compares the strength and spatial pattern of ENSO in a set of climate model simulations in order to explore how ENSO changes in different climates, including past cold glacial climates and past climates with different seasonal cycles, as well as gradual and abrupt future warming cases.
Cited articles
Baker, A. H., Hammerling, D. M., Levy, M. N., Xu, H., Dennis, J. M., Eaton, B. E., Edwards, J., Hannay, C., Mickelson, S. A., Neale, R. B., Nychka, D., Shollenberger, J., Tribbia, J., Vertenstein, M., and Williamson, D.: A new ensemble-based consistency test for the Community Earth System Model (pyCECT v1.0), Geosci. Model Dev., 8, 2829–2840, https://doi.org/10.5194/gmd-8-2829-2015, 2015.
Balaji, V., Maisonnave, E., Zadeh, N., Lawrence, B. N., Biercamp, J.,
Fladrich, U., Aloisio, G., Benson, R., Caubel, A., Durachta, J.,
Foujols, M.-A., Lister, G., Mocavero, S., Underwood, S., and Wright, G.:
CPMIP: measurements of real computational performance of Earth system models
in CMIP6, Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, 2017.
Canuto, V., Howard, A., Cheng, Y., and Dubovikov, M.: Ocean turbulence. Part I: One-point closure model – Momentum and heat vertical diffusivities, J. Phys. Oceanogr., 31, 1413–1426, https://doi.org/10.1175/1520-0485(2001)031<1413:OTPIOP>2.0.CO;2, 2001.
Canuto, V., Howard, A., Cheng, Y., and Dubovikov M.: Ocean turbulence, Part II: Vertical diffusivities of momentum, heat, salt, mass, and passive scalars, J. Phys. Oceanogr., 32, 240–264, https://doi.org/10.1175/1520-0485(2002)032<0240:OTPIVD>2.0.CO;2, 2002.
Chassignet, E. P., Sommer, J. L., and Wallcraft A. J: General Circulation
Models, in: Encyclopedia of Ocean Sciences, 3rd edn., edited by:
Cochran, K. J., Bokuniewicz, H. J., and Yager P. L., Academic Press, 5, 486–490,
https://doi.org/10.1016/B978-0-12-409548-9.11410-1, 2019.
Chassignet, E. P., Yeager, S. G., Fox-Kemper, B., Bozec, A., Castruccio, F., Danabasoglu, G., Horvat, C., Kim, W. M., Koldunov, N., Li, Y., Lin, P., Liu, H., Sein, D. V., Sidorenko, D., Wang, Q., and Xu, X.: 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), Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, 2020.
Chelton, D. B., Schlax, M. G., and Samelson R. M.: Global observations of nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216, https://doi.org/10.1016/j.pocean.2011.01.002, 2011.
Craig, A. P., Vertenstein, M., and Jacob, R.: A new flexible coupler for earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012.
Ferreira, D., Marshall, J., and Heimbach, P.: Estimating eddy stresses by fitting dynamics to observations using a residual-mean ocean circulation model and its adjoint, J. Phys. Oceanogr., 35, 1891–1910, https://doi.org/10.1175/JPO2785.1, 2005.
Fox-Kemper, B. and Menemenlis D.: Can Large Eddy Simulation Techniques Improve Mesoscale-Rich Ocean Models?, in: Ocean Modeling in an Eddying Regime, edited by: Hecht, M. W. and Hasumi, H., American Geophysical Union, 177, 319–338, https://doi.org/10.1029/177GM19, 2008.
Fuhrer, O., Chadha, T., Hoefler, T., Kwasniewski, G., Lapillonne, X., Leutwyler, D., Lüthi, D., Osuna, C., Schär, C., Schulthess, T. C., and Vogt, H.: Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0, Geosci. Model Dev., 11, 1665–1681, https://doi.org/10.5194/gmd-11-1665-2018, 2018.
Gent, P. R. and Mcwilliams, J. C.: Isopycnal mixing in ocean circulation models, J. Phys. Oceanogr., 20, 150–155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990.
Griffies, S. M., Winton, M., Anderson, W. G., Benson, R., Delworth, T. L., Dufour, C. O., Dunne, J. P., Goddard, P., Morrison, A. K., Rosati, A., Wittenberg, A. T., Yin, J., and Zhang, R.: Impacts on ocean heat from transient mesoscale eddies in a hierarchy of climate models, J. Climate, 28, 952–977, https://doi.org/10.1175/JCLI-D-14-00353.1, 2015.
Gupta, S., Patel, T., Engelmann, C., and Tiwari, D.: Failures in Large Scale
Systems: Long-term Measurement, Analysis, and Implications, in: Proceedings of
SC17, Denver, CO, USA, 12–17 November 2017, https://doi.org/10.1145/3126908.3126937, 2017.
Hallberg, R.: Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects, Ocean Model., 72, 92–103, https://doi.org/10.1016/j.ocemod.2013.08.007, 2013.
He, B., Yu, Y., Bao, Q., Lin, P. F., Liu, H. L., Li, J. X., Wang, L.,
Liu, Y. M., Wu, G., Chen, K., Guo, Y. Zhao, S., Zhang, X., Song, M., and
Xie, J.: CAS FGOALS-f3-L model dataset descriptions for CMIP6 DECK
experiments, Atmospheric and Oceanic Science Letters, 13, 582–588,
https://doi.org/10.1080/16742834.2020.1778419, 2020.
Hewitt, H. T., Bell, M. J., Chassignet, E. P., Czaja, A., Ferreira, D., Griffies, S. M., Hyder, P., McClean, J. L., New, A. L., and Roberts, H. J.: Will high-resolution global ocean models benefit coupled predictions on short-range to climate timescales?, Ocean Model., 120, 120–136, https://doi.org/10.1016/j.ocemod.2017.11.002, 2017.
Jiang, J., Lin, P., Wang, J., Liu, H., Chi, X., Hao, H., Wang, Y., Wang, W., and Zhang, L. H.: Porting LASG/IAP Climate System Ocean Model to GPUs Using OpenAcc, IEEE Access, 7, 154490–154501, https://doi.org/10.1109/ACCESS.2019.2932443, 2019.
Large, W. G. and Yeager, S. G.: The global climatology of an interannually varying air-sea flux data set, Clim. Dynam., 33, 341–364, https://doi.org/10.1007/s00382-008-0441-3, 2009.
Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., Dong, L., Zhou, T.,
Liu, L., Wang, L., Pu, Y., Chen, X. L., Chen, L., Xie, Z. H., Liu, H. B.,
Zhang, L. X., Huang, X., Feng, T., Zheng, W. P., Xia, K., Liu, H. L.,
Liu, J. P., Wang, Y., Wang, L. H., Jia, B. H., Xie, F., Wang, B., Zhao, S. W.,
Yu, Z. P., Zhao, B. W., and Wei, J. L.: The Flexible Global Ocean Atmosphere
Land System Model Grid Point Version 3 (FGOALS-g3): Description and
Evaluation, J. Adv. Model Earth Sy., 12, e2019MS002012, https://doi.org/10.1029/2019MS002012,
2020.
Li, Y. W., Liu, H. L., Ding, M. R., Lin, P. F., Yu, Z. P., Meng, Y., Li, Y. L., Jian, X. D., Jiang, J. R., Chen, K. J., Yang, Q., Wang, Y. Q., Zhao, B. W., Wei, J. L., Ma, J. F., Zheng, W. P., and Wang, P. F.: Eddy-resolving Simulation of CAS-LICOM3 for Phase 2 of the Ocean Model Intercomparison Project, Adv. Atmos. Sci., 37, 1067–1080, https://doi.org/10.1007/s00376-020-0057-z, 2020.
.
Lin, P., Liu, H., Xue, W., Li, H., Jiang, J., Song, M., Song, Y., Wang, F., and Zhang, M. H.: A Coupled Experiment with LICOM2 as the Ocean Component of CESM1, J. Meteorol. Res., 30, 76–92, https://doi.org/10.1007/s13351-015-5045-3, 2016.
Lin, P., Yu, Z., Liu, H., Yu, Y., Li, Y., Jiang, J., Xue, W., Chen, K., Yang, Q., Zhao, B. W., Wei, J. L., Ding, M. R., Sun, Z. K., Wang, Y. Q., Meng, Y., Zheng, W. P., and Ma, J. F.: LICOM Model Datasets for the CMIP6 Ocean Model Intercomparison Project, Adv. Atmos. Sci., 37, 239–249, https://doi.org/10.1007/s00376-019-9208-5, 2020.
Liu, H.: The updated dataset of figures for “The GPU version of LICOM3 under HIP framework and its large-scale application”, Zenodo [Data set], https://doi.org/10.5281/zenodo.4542544, 2021
Liu, H., Lin, P., Yu, Y., and Zhang X.: The baseline evaluation of LASG/IAP climate system ocean model (LICOM) version 2, Acta Meteorol. Sin., 26, 318–329, https://doi.org/10.1007/s13351-012-0305-y, 2012.
Liu, H., Wang, P., Jiang, J., and Lin, P.: The GPU version of LICOM3 under HIP framework and its large-scale application (updated) (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.4302813, 2020.
Liu, H. L., Lin, P., Zheng, W., Luan, Y., Ma, J., Mo, H., Wan L., and Tiejun
Ling: A global eddy-resolving ocean forecast system – LICOM Forecast System
(LFS), J. Oper. Oceanogr., https://doi.org/10.1080/1755876X.2021.1902680,
2021.
Madec, G. and Imbard, M.: A global ocean mesh to overcome the North Pole singularity, Clim. Dynam., 12, 381–388, https://doi.org/10.1007/s003820050115, 1996.
Murray, R. J.: Explicit generation of orthogonal grids for ocean models, J. Comput. Phys., 126, 251–273, https://doi.org/10.1006/jcph.1996.0136, 1996.
Ohlmann, J. C.: Ocean radiant heating in climate models, J. Climate, 16,
1337–1351, https://doi.org/10.1175/1520-0442(2003)16<1337:ORHICM>2.0.CO;2, 2003.
Palmer, T.: Climate forecasting: Build high-resolution global climate models, Nature News, 515, 338–339, https://doi.org/10.1038/515338a, 2014.
Redi, M. H.: Oceanic isopycnal mixing by coordinate rotation, J. Phys. Oceanogr., 12, 1154–1158, https://doi.org/10.1175/1520-0485(1982)012<1154:OIMBCR>2.0.CO;2, 1982.
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-scale climate models: Prospects and challenges, B. Am. Meteorol. Soc., 101, E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020.
St. Laurent, L., Simmons, H., and Jayne, S.: Estimating tidally driven mixing in the deep ocean, Geophys. Res. Lett., 22, 21-1–21-4, https://doi.org/10.1029/2002GL015633, 2002.
Su, Z., Wang, J., Klein, P., Thompson, A. F., and Menemenlis, D.: Ocean submesoscales as a key component of the global heat budget, Nat. Commun., 9, 1–8, https://doi.org/10.1038/s41467-018-02983-w, 2018.
Tsujino, H., Urakawa, S., Nakano, H., Small, R., Kim, W., Yeager, S.,
Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W.,
Bozec, A., Chassignet, E. P., Curchitser, E., Dias, F. B., Durack, P. J.,
Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C.,
Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J.,
Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: JRA-55 based surface dataset for driving ocean - sea-ice models (JRA55-do), Ocean Model., 130, 79–139, https://doi.org/10.1016/j.ocemod.2018.07.002, 2018.
Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., Arsouze, T., Bentsen, M., Bernardello, R., Böning, C. W., Bozec, A., Chassignet, E. P., Danilov, S., Dussin, R., Exarchou, E., Fogli, P. G., Fox-Kemper, B., Guo, C., Ilicak, M., Iovino, D., Kim, W. M., Koldunov, N., Lapin, V., Li, Y., Lin, P., Lindsay, K., Liu, H., Long, M. C., Komuro, Y., Marsland, S. J., Masina, S., Nummelin, A., Rieck, J. K., Ruprich-Robert, Y., Scheinert, M., Sicardi, V., Sidorenko, D., Suzuki, T., Tatebe, H., Wang, Q., Yeager, S. G., and Yu, Z.: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, 2020.
Váňa, F., Düben, P., Lang, S., Palmer, T., Leutbecher, M., Salmond, D., and Carver, G.: Single Precision in Weather Forecasting Models: An Evaluation with the IFS, Mon. Weather Rev., 145, 495–502, https://doi.org/10.1175/mwr-d-16-0228.1, 2017.
Wang, S., Jing, Z., Zhang, Q., Chang, P., Chen, Z., Liu, H. and Wu L.: Ocean Eddy Energetics in the Spectral Space as Revealed by High-Resolution General Circulation Models, J. Phys. Oceanogr., 49, 2815–2827, https://doi.org/10.1175/JPO-D-19-0034.1, 2019.
Xiao, C.: Adoption of a two-step shape-preserving advection scheme in an OGCM
and its coupled experiment, Master thesis, Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, 78 pp., 2006.
Xu, S., Huang, X., Oey, L.-Y., Xu, F., Fu, H., Zhang, Y., and Yang, G.: POM.gpu-v1.0: a GPU-based Princeton Ocean Model, Geosci. Model Dev., 8, 2815–2827, https://doi.org/10.5194/gmd-8-2815-2015, 2015.
Yang, C., Xue, W., Fu, H., You, H., Wang, X., Ao, Y., Liu, F., Gan, L.,
Xiu, P., and Wang, L.: 10M-core scalable fully-implicit solver for
nonhydrostatic atmospheric dynamics, Paper presented at SC'16: Proceedings of
the International Conference for High Performance Computing, Networking,
Storage and Analysis, 13–18 November 2016, Salt Lake City, Utah, IEEE, 2016.
Yashiro, H., Terai, M., Yoshida, R., Iga, S., Minami, K., and Tomita, H.: Performance analysis and optimization of nonhydrostatic icosahedral atmospheric model (NICAM) on the K computer and TSUBAME2, 5, Paper presented at Proceedings of the Platform for Advanced Scientific Computing Conference, 8–10 June 2016, EPFL, Lausanne, Switzerland, 2016.
Yu, Y., Tang, S., Liu, H., Lin, P., and Li, X.: Development and evaluation of
the dynamic framework of an ocean general circulation model with arbitrary
orthogonal curvilinear coordinate, Chinese Journal of Atmospheric Sciences,
42, 877–889,
https://doi.org/10.3878/j.issn.1006-9895.1805.17284,
2018.
Yuan, Y., Shi, F., Kirby, J. T., and Yu, F.: FUNWAVE-GPU: Multiple-GPU Acceleration of a Boussinesq-Type Wave Model, J. Adv. Model. Earth Sy., 12, e2019MS001957, https://doi.org/10.1029/2019MS001957, 2020.
Zhang, H., Zhang, M., Jin, J., Fei, K., Ji, D., Wu, C., Zhu, J., He, J., Chai, Z. Y.,
Xie, J. B., Dong, X., Zhang, D. L., Bi, X. Q., Cao, H., Chen, H. S.,
Chen, K. J., Chen, X. S., Gao, X., Hao, H. Q., Jiang, J. R., Kong, X. H.,
Li, S. G., Li, Y. C., Lin, P. F., Lin, Z. H., Liu, H. L., Liu, X. H., Shi, Y.,
Song, M. R., Wang, H. J., Wang, T. Y., Wang, X. C., Wang, Z. F., Wei, Y.,
Wu, B. D., Xie, Z. H., Xu, Y. F., Yu, Y. Q., Yuan, L., Zeng, Q. C.,
Zeng, X. D., Zhao, S. W., Zhou, G. Q., and Zhu, J.: CAS-ESM2: Description and
Climate Simulation Performance of the Chinese Academy of Sciences (CAS) Earth
System Model (ESM) Version 2, J. Adv. Model. Earth Sy., 12,
e2020MS002210. https://doi.org/10.1029/2020MS002210, 2020.
Zhang, S., Fu, H., Wu, L., Li, Y., Wang, H., Zeng, Y., Duan, X., Wan, W., Wang, L., Zhuang, Y., Meng, H., Xu, K., Xu, P., Gan, L., Liu, Z., Wu, S., Chen, Y., Yu, H., Shi, S., Wang, L., Xu, S., Xue, W., Liu, W., Guo, Q., Zhang, J., Zhu, G., Tu, Y., Edwards, J., Baker, A., Yong, J., Yuan, M., Yu, Y., Zhang, Q., Liu, Z., Li, M., Jia, D., Yang, G., Wei, Z., Pan, J., Chang, P., Danabasoglu, G., Yeager, S., Rosenbloom, N., and Guo, Y.: Optimizing high-resolution Community Earth System Model on a heterogeneous many-core supercomputing platform, Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020, 2020.
Zhang, X. and Liang, X.: A numerical world ocean general circulation model,
Adv. Atmos. Sci., 6, 44–61, https://doi.org/10.1007/BF02656917, 1989.
Short summary
Global ocean general circulation models are a fundamental tool for oceanography research, ocean forecast, and climate change research. The increasing resolution will greatly improve simulations of the models, but it also demands much more computing resources. In this study, we have ported an ocean general circulation model to a heterogeneous computing system and have developed a 3–5 km model version. A 14-year integration has been conducted and the preliminary results have been evaluated.
Global ocean general circulation models are a fundamental tool for oceanography research, ocean...
