Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-1791-2019
https://doi.org/10.5194/gmd-12-1791-2019
Methods for assessment of models
 | 
06 May 2019
Methods for assessment of models |  | 06 May 2019

Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques

Dan Lu and Daniel Ricciuto

Related authors

A model-independent data assimilation (MIDA) module and its applications in ecology
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021,https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
LIVVkit 2.1: automated and extensible ice sheet model validation
Katherine J. Evans, Joseph H. Kennedy, Dan Lu, Mary M. Forrester, Stephen Price, Jeremy Fyke, Andrew R. Bennett, Matthew J. Hoffman, Irina Tezaur, Charles S. Zender, and Miren Vizcaíno
Geosci. Model Dev., 12, 1067–1086, https://doi.org/10.5194/gmd-12-1067-2019,https://doi.org/10.5194/gmd-12-1067-2019, 2019
Short summary
The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources
Anthony P. Walker, Ming Ye, Dan Lu, Martin G. De Kauwe, Lianhong Gu, Belinda E. Medlyn, Alistair Rogers, and Shawn P. Serbin
Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018,https://doi.org/10.5194/gmd-11-3159-2018, 2018
Short summary
Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods
Dan Lu, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger
Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017,https://doi.org/10.5194/bg-14-4295-2017, 2017
Short summary

Related subject area

Climate and Earth system modeling
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024,https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024,https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024,https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024,https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024,https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary

Cited articles

Agarap, A. F. M.: Deep learning using Rectified Linear Units (ReLU), https://arxiv.org/pdf/1803.08375 (last access: 7 February 2019), 2018. 
Archambeau, C., Valle, M., Assenza, A., and Verleysen, M.: Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures, IEEE, ISCAS 2006, 21–24 May 2006, Island of Kos, Greece, https://doi.org/10.1109/ISCAS.2006.1693317, 2006. 
Bardenet, R. and Kegl, B.: Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm, in: International Conference on Machine Learning, 21–24 June 2010, Haifa, Israel, 55–62, 2010. 
Basu, A., De, S., Mukherjee, A., and Ullah, E.: Convergence guarantees for rmsprop and adam in nonconvex optimization and their comparison to nesterov acceleration on autoencoders, arXiv preprint arXiv:1807.06766, available at: https://arxiv.org/abs/1807.06766 (last access: 10 March 2019), 2018. 
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281–305, 2012. 
Download
Short summary
This work uses machine-learning techniques to advance the predictive understanding of large-scale Earth systems.