Articles | Volume 8, issue 9
https://doi.org/10.5194/gmd-8-2815-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2815-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| Highlight paper
| 
09 Sep 2015
Development and technical paper | Highlight paper |
| 09 Sep 2015
POM.gpu-v1.0: a GPU-based Princeton Ocean Model
S. Xu
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
L.-Y. Oey
Institute of Hydrological & Oceanic Sciences, National Central University, Jhongli, Taiwan
Program in Atmospheric & Oceanic Sciences, Princeton University, Princeton, New Jersey, USA
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
H. Fu
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
Y. Zhang
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
G. Yang
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-10, https://doi.org/10.5194/gmd-2024-10, 2024
Revised manuscript under review for GMD
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The hardware-related perturbations caused by the heterogeneous many-core architectures can blend with software or human errors, which can affect the accuracy of the model consistency verification. We develop a deep learning-based consistency test tool for ESMs on the heterogeneous systems (ESM-DCT) and evaluate it in CESM on new Sunway system. The ESM-DCT can detect the existence of software or human errors when taking hardware-related perturbations into account.
Jiaxu Guo, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Xianwei Wu, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-264, https://doi.org/10.5194/gmd-2022-264, 2022
Revised manuscript accepted for GMD
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To further improve the efficiency of experiments using SCAM, we train a neural network-based surrogate model to support large-scale sensitivity analysis and tuning of combinations of multiple parameters. Using a hybrid method, we explore the joint sensitivity of multi-parameter combinations to typical cases and identify the most sensitive three-parameter combination out of eleven, and perform a tuning process that reduces the error of precipitation in these cases.
Lei Lin, Hao Liu, Xiaomeng Huang, Qingjun Fu, and Xinyu Guo
Hydrol. Earth Syst. Sci., 26, 5207–5225, https://doi.org/10.5194/hess-26-5207-2022, https://doi.org/10.5194/hess-26-5207-2022, 2022
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Earth system (climate) model is an important instrument for projecting the global water cycle and climate change, in which tides are commonly excluded due to the much small timescales compared to the climate. However, we found that tides significantly impact the river water transport pathways, transport timescales, and concentrations in shelf seas. Thus, the tidal effect should be carefully considered in earth system models to accurately project the global water and biogeochemical cycle.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
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To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Qingyang Xiao, Yixuan Zheng, Guannan Geng, Cuihong Chen, Xiaomeng Huang, Huizheng Che, Xiaoye Zhang, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 21, 9475–9496, https://doi.org/10.5194/acp-21-9475-2021, https://doi.org/10.5194/acp-21-9475-2021, 2021
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We used both statistical methods and a chemical transport model to assess the contribution of meteorology and emissions to PM2.5 during 2000–2018. Both methods revealed that emissions dominated the long-term PM2.5 trend with notable meteorological effects ranged up to 37.9 % of regional annual average PM2.5. The meteorological contribution became more beneficial to PM2.5 control in southern China but more unfavorable in northern China during the studied period.
Shaoqing Zhang, Haohuan Fu, Lixin Wu, Yuxuan Li, Hong Wang, Yunhui Zeng, Xiaohui Duan, Wubing Wan, Li Wang, Yuan Zhuang, Hongsong Meng, Kai Xu, Ping Xu, Lin Gan, Zhao Liu, Sihai Wu, Yuhu Chen, Haining Yu, Shupeng Shi, Lanning Wang, Shiming Xu, Wei Xue, Weiguo Liu, Qiang Guo, Jie Zhang, Guanghui Zhu, Yang Tu, Jim Edwards, Allison Baker, Jianlin Yong, Man Yuan, Yangyang Yu, Qiuying Zhang, Zedong Liu, Mingkui Li, Dongning Jia, Guangwen Yang, Zhiqiang Wei, Jingshan Pan, Ping Chang, Gokhan Danabasoglu, Stephen Yeager, Nan Rosenbloom, and Ying Guo
Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020, https://doi.org/10.5194/gmd-13-4809-2020, 2020
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Science advancement and societal needs require Earth system modelling with higher resolutions that demand tremendous computing power. We successfully scale the 10 km ocean and 25 km atmosphere high-resolution Earth system model to a new leading-edge heterogeneous supercomputer using state-of-the-art optimizing methods, promising the solution of high spatial resolution and time-varying frequency. Corresponding technical breakthroughs are of significance in modelling and HPC design communities.
Zhuang Liu, Yi Zhang, Xiaomeng Huang, Jian Li, Dong Wang, Mingqing Wang, and Xing Huang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-158, https://doi.org/10.5194/gmd-2020-158, 2020
Revised manuscript not accepted
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This paper describes several techniques for the parallelization and performance optimization of
an unstructured-mesh global atmospheric model. The purpose of this research is to facilitate the rapid iterative model development. These techniques are general and can be used for other parallel modeling on unstructured meshes.
Xiaomeng Huang, Xing Huang, Dong Wang, Qi Wu, Yi Li, Shixun Zhang, Yuwen Chen, Mingqing Wang, Yuan Gao, Qiang Tang, Yue Chen, Zheng Fang, Zhenya Song, and Guangwen Yang
Geosci. Model Dev., 12, 4729–4749, https://doi.org/10.5194/gmd-12-4729-2019, https://doi.org/10.5194/gmd-12-4729-2019, 2019
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We designed a simple computing library (OpenArray) to decouple ocean modelling and parallel computing. OpenArray provides 12 basic operators featuring user-friendly interfaces and an implicit parallelization ability. Based on OpenArray, we implement a practical ocean model with an enhanced readability and an excellent scalable performance. OpenArray may signal the beginning of a new frontier in future ocean modelling through ingesting basic operators and cutting-edge computing techniques.
Wenfang Lu, Lie-Yauw Oey, Enhui Liao, Wei Zhuang, Xiao-Hai Yan, and Yuwu Jiang
Ocean Sci., 14, 1303–1320, https://doi.org/10.5194/os-14-1303-2018, https://doi.org/10.5194/os-14-1303-2018, 2018
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In this study, we investigate the physical factors controlling the biological production in a coastal upwelling system, the Vietnam boundary upwelling system in the South China Sea. We found that, in addition to the effects from the wind (as a major factor driving the ocean), the ocean circulation could also contribute positively to the production here, which is distinct from other major coastal upwelling systems.
Xiaomeng Huang, Qiang Tang, Yuheng Tseng, Yong Hu, Allison H. Baker, Frank O. Bryan, John Dennis, Haohuan Fu, and Guangwen Yang
Geosci. Model Dev., 9, 4209–4225, https://doi.org/10.5194/gmd-9-4209-2016, https://doi.org/10.5194/gmd-9-4209-2016, 2016
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Refining model resolution is helpful for representing climate processes. With resolution increasing, the computational cost will become very huge. We designed a new solver to accelerate the high-resolution ocean simulation so as to reduce the computational cost and make full use of the computing resource of supercomputers. Our results show that the simulation speed of the improved ocean component with 0.1° resolution achieves 10.5 simulated years per wall-clock day on 16875 CPU cores.
Allison H. Baker, Yong Hu, Dorit M. Hammerling, Yu-heng Tseng, Haiying Xu, Xiaomeng Huang, Frank O. Bryan, and Guangwen Yang
Geosci. Model Dev., 9, 2391–2406, https://doi.org/10.5194/gmd-9-2391-2016, https://doi.org/10.5194/gmd-9-2391-2016, 2016
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Software quality assurance is critical to detecting errors in large, complex climate simulation codes. We focus on ocean model simulation data in the context of an ensemble-based statistical consistency testing approach developed for atmospheric data. Because ocean and atmosphere models have differing characteristics, we develop a new statistical tool to evaluate ocean model simulation data that provide a simple, subjective, and systematic way to detect errors and instil model confidence.
Cheng Zhang, Li Liu, Guangwen Yang, Ruizhe Li, and Bin Wang
Geosci. Model Dev., 9, 2099–2113, https://doi.org/10.5194/gmd-9-2099-2016, https://doi.org/10.5194/gmd-9-2099-2016, 2016
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We propose a butterfly implementation for data transfer. Although the butterfly implementation outperforms the existing implementation (the P2P implementation) in many cases, it degrades the performance in some cases. So we design and implement an adaptive data transfer library that automatically chooses an optimal implementation between the P2P one and the butterfly one and also further improves the performance based on the butterfly implementation through skipping some butterfly stages.
R. Li, L. Liu, G. Yang, C. Zhang, and B. Wang
Geosci. Model Dev., 9, 731–748, https://doi.org/10.5194/gmd-9-731-2016, https://doi.org/10.5194/gmd-9-731-2016, 2016
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In this paper, we show that different compiling setups can achieve exactly the same (bitwise identical) results in Earth system modeling, and a set of bitwise identical compiling setups of a model can be used across different compiler versions and different compiler flags. Moreover, we shows that new test cases can be generated based on differences of bitwise identical compiling setups between different models, which can help detect software bugs and finally improve the reliability.
L. Liu, S. Peng, C. Zhang, R. Li, B. Wang, C. Sun, Q. Liu, L. Dong, L. Li, Y. Shi, Y. He, W. Zhao, and G. Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-4375-2015, https://doi.org/10.5194/gmdd-8-4375-2015, 2015
Revised manuscript has not been submitted
L. Liu, R. Li, C. Zhang, G. Yang, B. Wang, and L. Dong
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-2403-2015, https://doi.org/10.5194/gmdd-8-2403-2015, 2015
Revised manuscript not accepted
L. Liu, G. Yang, B. Wang, C. Zhang, R. Li, Z. Zhang, Y. Ji, and L. Wang
Geosci. Model Dev., 7, 2281–2302, https://doi.org/10.5194/gmd-7-2281-2014, https://doi.org/10.5194/gmd-7-2281-2014, 2014
L. Liu, R. Li, C. Zhang, G. Yang, and B. Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-7-4429-2014, https://doi.org/10.5194/gmdd-7-4429-2014, 2014
Revised manuscript not accepted
X. M. Huang, W. C. Wang, H. H. Fu, G. W. Yang, B. Wang, and C. Zhang
Geosci. Model Dev., 7, 93–103, https://doi.org/10.5194/gmd-7-93-2014, https://doi.org/10.5194/gmd-7-93-2014, 2014
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Carl Svenhag, Moa Kristina Sporre, Tinja Olenius, Daniel Yazgi, Sara Marie Blichner, Lars Peter Nieradzik, and Pontus Roldin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2665, https://doi.org/10.5194/egusphere-2023-2665, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we showed that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacted the number of particles in the air and clouds. It also changed the radiation balance in the same magnitude as man-made greenhouse emissions.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Cited articles
Allen, J. S. and Newberger, P. A.: Downwelling Circulation on the Oregon Continental Shelf. Part I: Response to Idealized Forcing, J. Phys. Oceanogr., 26, 2011–2035, https://doi.org/10.1175/1520-0485(1996)026<2011:DCOTOC>2.0.CO;2, 1996.
Berntsen, J. and Oey, L.-Y.: Estimation of the internal pressure gradient in σ-coordinate ocean models: comparison of second-, fourth-, and sixth-order schemes, Ocean Dynam., 60, 317–330, 2010.
Blumberg, A. F. and Mellor, G. L.: Diagnostic and prognostic numerical circulation studies of the South Atlantic Bight, J. Geophys. Res.-Oceans, (1978–2012), 88, 4579–4592, 1983.
Blumberg, A. F. and Mellor, G. L.: A description of a three-dimensional coastal ocean circulation model, Coast. Est. Sci., 4, 1–16, 1987.
Browne, S., Dongarra, J., Garner, N., Ho, G., and Mucci, P.: A portable programming interface for performance evaluation on modern processors, Int. J. High Perf. Comp. Appl., 14, 189–204, 2000.
Chang, Y.-L. and Oey, L.-Y.: Instability of the North Pacific subtropical countercurrent, J. Phys. Oceanogr., 44, 818–833, 2014.
Chapman, B., Jost, G., and Van Der Pas, R.: Using OpenMP: portable shared memory parallel programming, vol. 10, The MIT Press, 2008.
Ezer, T. and Mellor, G. L.: A numerical study of the variability and the separation of the Gulf Stream, induced by surface atmospheric forcing and lateral boundary flows, J. Phys. Oceanogr., 22, 660–682, 1992.
Gopalakrishnan, S., Liu, Q., Marchok, T., Sheinin, D., Surgi, N., Tuleya, R., Yablonsky, R., and Zhang, X.: Hurricane Weather Research and Forecasting (HWRF) model scientific documentation, edited by: Bernardet, L., 75, 2010.
Gopalakrishnan, S., Liu, Q., Marchok, T., Sheinin, D., Surgi, N., Tong, M., Tallapragada, V., Tuleya, R., Yablonsky, R., and Zhang, X.: Hurricane Weather Research and Forecasting (HWRF) model: 2011 scientific documentation, edited by: Bernardet, L., 2011.
Govett, M., Middlecoff, J., and Henderson, T.: Running the NIM next-generation weather model on GPUs, in: Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on, 792–796, IEEE, 2010.
Gropp, W. D., Lusk, E. L., and Thakur, R.: Using MPI-2: Advanced features of the message-passing interface, vol. 2, Globe Pequot, 1999.
Guo, X., Miyazawa, Y., and Yamagata, T.: The Kuroshio Onshore Intrusion along the Shelf Break of the East China Sea: The Origin of the Tsushima Warm Current, J. Phys. Oceanogr., 36, 2006.
Henderson, T., Middlecoff, J., Rosinski, J., Govett, M., and Madden, P.: Experience applying Fortran GPU compilers to numerical weather prediction, in: Application Accelerators in High-Performance Computing (SAAHPC), 2011 Symposium, 34–41, IEEE, 2011.
Huang, S.-M. and Oey, L.: Right-side cooling and phytoplankton bloom in the wake of a tropical cyclone, J. Geophys. Res.-Oceans, 2015.
Huang, X. M., Wang, W. C., Fu, H. H., Yang, G. W., Wang, B., and Zhang, C.: A fast input/output library for high-resolution climate models, Geosci. Model Dev., 7, 93–103, https://doi.org/10.5194/gmd-7-93-2014, 2014.
Isobe, A., Kako, S., Guo, X., and Takeoka, H.: Ensemble numerical forecasts of the sporadic Kuroshio water intrusion (kyucho) into shelf and coastal waters, Ocean Dyn., 62, 633–644, 2012.
Jordi, A. and Wang, D.-P.: sbPOM: A parallel implementation of Princenton Ocean Model, Environ. Model. Softw., 38, 59–61, 2012.
Kagimoto, T. and Yamagata, T.: Seasonal transport variations of the Kuroshio: An OGCM simulation, J. Phys. Oceanogr., 27, 403–418, 1997.
Korres, G., Hoteit, I., and Triantafyllou, G.: Data assimilation into a Princeton Ocean Model of the Mediterranean Sea using advanced Kalman filters, J. Marine Syst., 65, 84–104, 2007.
Kurihara, Y., Bender, M. A., Tuleya, R. E., and Ross, R. J.: Improvements in the GFDL hurricane prediction system, Mon. Weather Rev., 123, 2791–2801, 1995.
Kurihara, Y., Tuleya, R. E., and Bender, M. A.: The GFDL hurricane prediction system and its performance in the 1995 hurricane season., Mon. Weather Rev., 126, 1306–1322, 1998.
Leutwyler, D., Fuhrer, O., Cumming, B., Lapillonne, X., Gysi, T., Lüthi, D., Osuna, C., and Schär, C.: Towards Cloud-Resolving European-Scale Climate Simulations using a fully GPU-enabled Prototype of the COSMO Regional Model, in: EGU General Assembly Conference Abstracts, vol. 16, p. 11914, 2014.
Lin, X., Xie, S.-P., Chen, X., and Xu, L.: A well-mixed warm water column in the central Bohai Sea in summer: Effects of tidal and surface wave mixing, J. Geophys. Res.-Oceans, 111, C11017, https://doi.org/10.1029/2006JC003504, 2006.
Linford, J. C., Michalakes, J., Vachharajani, M., and Sandu, A.: Multi-core acceleration of chemical kinetics for simulation and prediction, in: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, p. 7, ACM, 2009.
Mak, J., Choboter, P., and Lupo, C.: Numerical ocean modeling and simulation with CUDA, in: OCEANS 2011, 1–6, IEEE, 2011.
McCalpin, J. and Wonnacott, D.: Time skewing: A value-based approach to optimizing for memory locality, Tech. rep., Technical Report DCS-TR-379, Department of Computer Science, Rugers University, 477–480, 1999.
Michalakes, J. and Vachharajani, M.: GPU acceleration of numerical weather prediction, Parallel Proc. Lett., 18, 531–548, 2008.
Miyazawa, Y., Zhang, R., Guo, X., Tamura, H., Ambe, D., Lee, J.-S., Okuno, A., Yoshinari, H., Setou, T., and Komatsu, K.: Water mass variability in the western North Pacific detected in a 15-year eddy resolving ocean reanalysis, J. Oceanogr., 65, 737–756, 2009.
Newberger, P. and Allen, J. S.: Forcing a three-dimensional, hydrostatic, primitive-equation model for application in the surf zone: 1. Formulation, J. Geophys. Res.-Oceans, (1978–2012), 112, 2007a.
Newberger, P. A. and Allen, J. S.: Forcing a three-dimensional, hydrostatic, primitive-equation model for application in the surf zone: 2. Application to DUCK94, J. Geophys. Res.-Oceans, 112, 2007b.
NVIDIA: CUDA C Best Practices Guide, available at: http://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#coalesce
Oey, L., Chang, Y.-L., Lin, Y.-C., Chang, M.-C., Xu, F.-H., and Lu, H.-F.: ATOP-the Advanced Taiwan Ocean Prediction System based on the mpiPOM Part 1: model descriptions, analyses and results, Terr Atmos Ocean Sci, 24, 2013.
Oey, L.-Y.: A wetting and drying scheme for POM, Ocean Modelling, 9, 133–150, 2005.
Oey, L.-Y.: Geophysical Fluid Modeling with the mpi version of the Princeton Ocean Model (mpiPOM). Lecture Notes, 70 pp., ftp://profs.princeton.edu/leo/lecture-notes/OceanAtmosModeling/Notes/GFModellingUsingMpiPOM.pdf (last access: January 2014), 2014.
Oey, L.-Y. and Chen, P.: A model simulation of circulation in the northeast Atlantic shelves and seas, J. Geophys. Res.-Oceans, 97, 20087–20115, 1992a.
Oey, L.-Y. and Chen, P.: A nested-grid ocean model: With application to the simulation of meanders and eddies in the Norwegian Coastal Current, J. Geophys. Res.-Oceans, (1978–2012), 97, 20 063–20 086, 1992b.
Oey, L.-Y., Mellor, G. L., and Hires, R. I.: A three-dimensional simulation of the Hudson-Raritan estuary. Part I: Description of the model and model simulations, J. Phys. Oceanogr., 15, 1676–1692, 1985a.
Oey, L.-Y., Mellor, G. L., and Hires, R. I.: A three-dimensional simulation of the Hudson-Raritan estuary. Part II: Comparison with observation, J. Phys. Oceanogr., 15, 1693–1709, 1985b.
Oey, L.-Y., Mellor, G. L., and Hires, R. I.: A three-dimensional simulation of the Hudson-Raritan estuary. Part III: Salt flux analyses, J. Phys. Oceanogr., 15, 1711–1720, 1985c.
Oey, L.-Y., Lee, H.-C., and Schmitz, W. J.: Effects of winds and Caribbean eddies on the frequency of Loop Current eddy shedding: A numerical model study, J. Geophys. Res.-Oceans, 108, 3324, https://doi.org/10.1029/2002JC001698, 2003.
Shimokawabe, T., Aoki, T., Muroi, C., Ishida, J., Kawano, K., Endo, T., Nukada, A., Maruyama, N., and Matsuoka, S.: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 1–11, An 80-fold speedup, 15.0 TFlops full GPU acceleration of non-hydrostatic weather model ASUCA production code, 2010.
Siewertsen, E., Piwonski, J., and Slawig, T.: Porting marine ecosystem model spin-up using transport matrices to GPUs, Geosci. Model Dev., 6, 17–28, https://doi.org/10.5194/gmd-6-17-2013, 2013.
Smolarkiewicz, P. K.: A fully multidimensional positive definite advection transport algorithm with small implicit diffusion, J. Comp. Phys., 54, 325–362, 1984.
Sun, J., Oey, L., Xu, F., Lin, Y., Huang, S., and Chang, R.: The Influence of Ocean on Typhoon Nuri (2008), in: AGU Fall Meeting Abstr., 1, L3360, available at: http://adsabs.harvard.edu/abs/2014AGUFM.A33L3360S, 2014.
Sun, J., Oey, L.-Y., Chang, R., Xu, F., and Huang, S.-M.: Ocean response to typhoon Nuri (2008) in western Pacific and South China Sea, Ocean Dynam., 65, 735–749, 2015.
Varlamov, S. M., Guo, X., Miyama, T., Ichikawa, K., Waseda, T., and Miyazawa, Y.: M2 baroclinic tide variability modulated by the ocean circulation south of Japan, J. Geophys. Res.-Oceans, 2015.
Wonnacott, D.: Using time skewing to eliminate idle time due to memory bandwidth and network limitations, in: Parallel and Distributed Processing Symposium, 2000. IPDPS 2000, Proceedings, 14th International, 171–180, IEEE, 2000.
Xu, F.-H. and Oey, L.-Y.: The origin of along-shelf pressure gradient in the Middle Atlantic Bight, J. Phys. Oceanogr., 41, 1720–1740, 2011.
Xu, F.-H. and Oey, L.-Y.: State analysis using the Local Ensemble Transform Kalman Filter (LETKF) and the three-layer circulation structure of the Luzon Strait and the South China Sea, Ocean Dynam., 64, 905–923, 2014.
Xu, F.-H. and Oey, L.-Y.: Seasonal SSH variability of the Northern South China Sea, J. Phys. Oceanogr., 45, 1595–1609, 2015.
Xu, F.-H., Oey, L.-Y., Miyazawa, Y., and Hamilton, P.: Hindcasts and forecasts of Loop Current and eddies in the Gulf of Mexico using local ensemble transform Kalman filter and optimum-interpolation assimilation schemes, Ocean Model., 69, 22–38, 2013.
Yang, C., Xue, W., Fu, H., Gan, L., Li, L., Xu, Y., Lu, Y., Sun, J., Yang, G., and Zheng, W.: A peta-scalable CPU-GPU algorithm for global atmospheric simulations, in: Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming, 1–12, ACM, 2013.
Yin, X.-Q. and Oey, L.-Y.: Bred-ensemble ocean forecast of Loop Current and rings, Ocean Model., 17, 300–326, 2007.
Zavatarelli, M. and Mellor, G. L.: A numerical study of the Mediterranean Sea circulation, J. Phys. Oceanogr., 25, 1384–1414, 1995.
Zavatarelli, M. and Pinardi, N.: The Adriatic Sea modelling system: a nested approach, Ann. Geophys., 21, 345–364, 10.5194/angeo-21-345-2003, 2003.
Zhenya, S., Haixing, L., Xiaoyan, L., and Zhao, W.: The Application of GPU in Ocean General Circulation Mode POP, Comp. Appl. Softw., 27, 27–29, 2010.
Short summary
In this paper, we redesign the mpiPOM with GPUs. Specifically, we first convert the model from its original Fortran form to a new CUDA-C version, POM.gpu-v1.0. Then we optimize the code on each of the GPUs, the communications between the GPUs, and the I/O between the GPUs and the CPUs.
We show that the performance of the new model on a workstation containing 4 GPUs is comparable to that on a powerful cluster with 408 standard CPU cores, and it reduces the energy consumption by a factor of 6.8.
In this paper, we redesign the mpiPOM with GPUs. Specifically, we first convert the model from...
