Articles | Volume 11, issue 3
https://doi.org/10.5194/esd-11-835-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-11-835-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Sep 2020
Research article |
| 18 Sep 2020
| 18 Sep 2020
Reconstructing coupled time series in climate systems using three kinds of machine-learning methods
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
Lichao Yang
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
Zuntao Fu
CORRESPONDING AUTHOR
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
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Cited
20 citations as recorded by crossref.
- Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine-learning methods Z. Zhang et al.
- Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? P. Sun et al.
- Application of swarm-based deep neural networks and ensemble models for reconstruction of specific conductance data A. Mahdavi-Meymand et al.
- pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning S. Flecha et al.
- Loss of Earth system resilience during early Eocene transient global warming events S. Setty et al.
- Deep Learning-Based Time Series Analysis for Environment Changes A. Anand et al.
- Estimating prediction horizon of reservoir computer on L63 system when observed variables are incomplete Y. Huang & Z. Fu
- A new method of nonlinear causality detection: Reservoir computing Granger causality M. Wang & Z. Fu
- Reconstructing historical climate fields with deep learning N. Bochow et al.
- MoHiPr-TB: A Monthly Gridded Multi-Source Merged Precipitation Dataset for the Tarim Basin Based on Machine Learning P. Chen et al.
- Seasonal prediction of Indian summer monsoon onset with echo state networks T. Mitsui & N. Boers
- Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation S. Weng et al.
- A Dual-Decomposition Graph-Mamba-Transformer Framework for Ultra-Short-Term Wind Power Forecasting J. Gao et al.
- Dynamical system analysis of a data-driven model constructed by reservoir computing M. Kobayashi et al.
- Forecasting the forced van der Pol equation with frequent phase shifts using Reservoir Computing S. Kuno & H. Kori
- Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter T. Jinno et al.
- Seasonal predictions of energy-relevant climate variables through Euro-Atlantic Teleconnections I. Cionni et al.
- Machine learning approach reveals strong link between obliquity amplitude increase and the Mid-Brunhes transition T. Mitsui & N. Boers
- Lyapunov analysis of data-driven models of high dimensional dynamics using reservoir computing: Lorenz-96 system and fluid flow M. Kobayashi et al.
- A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons K. Hunt & S. Harrison
20 citations as recorded by crossref.
- Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine-learning methods Z. Zhang et al.
- Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? P. Sun et al.
- Application of swarm-based deep neural networks and ensemble models for reconstruction of specific conductance data A. Mahdavi-Meymand et al.
- pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning S. Flecha et al.
- Loss of Earth system resilience during early Eocene transient global warming events S. Setty et al.
- Deep Learning-Based Time Series Analysis for Environment Changes A. Anand et al.
- Estimating prediction horizon of reservoir computer on L63 system when observed variables are incomplete Y. Huang & Z. Fu
- A new method of nonlinear causality detection: Reservoir computing Granger causality M. Wang & Z. Fu
- Reconstructing historical climate fields with deep learning N. Bochow et al.
- MoHiPr-TB: A Monthly Gridded Multi-Source Merged Precipitation Dataset for the Tarim Basin Based on Machine Learning P. Chen et al.
- Seasonal prediction of Indian summer monsoon onset with echo state networks T. Mitsui & N. Boers
- Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation S. Weng et al.
- A Dual-Decomposition Graph-Mamba-Transformer Framework for Ultra-Short-Term Wind Power Forecasting J. Gao et al.
- Dynamical system analysis of a data-driven model constructed by reservoir computing M. Kobayashi et al.
- Forecasting the forced van der Pol equation with frequent phase shifts using Reservoir Computing S. Kuno & H. Kori
- Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter T. Jinno et al.
- Seasonal predictions of energy-relevant climate variables through Euro-Atlantic Teleconnections I. Cionni et al.
- Machine learning approach reveals strong link between obliquity amplitude increase and the Mid-Brunhes transition T. Mitsui & N. Boers
- Lyapunov analysis of data-driven models of high dimensional dynamics using reservoir computing: Lorenz-96 system and fluid flow M. Kobayashi et al.
- A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons K. Hunt & S. Harrison
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
We investigate the applicability of machine learning (ML) on time series reconstruction and find that the dynamical coupling relation and nonlinear causality are crucial for the application of ML. Our results could provide insights into causality and ML approaches for paleoclimate reconstruction, parameterization schemes, and prediction in climate studies.
We investigate the applicability of machine learning (ML) on time series reconstruction and find...
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