Articles | Volume 17, issue 4
https://doi.org/10.5194/bg-17-1033-2020
© 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/bg-17-1033-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2020
Research article |
| 26 Feb 2020
| 26 Feb 2020
Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach
Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
Laboratory of Hydrology and Water Management, Ghent University, Ghent 9000, Belgium
Jakob Runge
German Aerospace Center, Institute of Data Science, 07745 Jena, Germany
Diego G. Miralles
Laboratory of Hydrology and Water Management, Ghent University, Ghent 9000, Belgium
Mirco Migliavacca
Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
Oscar Perez-Priego
Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
Tarek El-Madany
Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
Arnaud Carrara
Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), 46980 Paterna, Spain
Miguel D. Mahecha
Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany
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Total article views: 4,693 (including HTML, PDF, and XML)
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- Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior L. Hernandez Rodriguez & P. Kumar 10.1063/5.0131468
- Research on delay propagation mechanism of air traffic control system based on causal inference L. Zeng et al. 10.1016/j.trc.2022.103622
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- Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage L. Zou et al. 10.1007/s11269-022-03381-0
- Dynamic relationships between gross primary production and energy partitioning in three different ecosystems based on eddy covariance time series analysis V. Cicuéndez et al. 10.3389/ffgc.2023.1017365
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29 citations as recorded by crossref.
- Discovering causal relations and equations from data G. Camps-Valls et al. 10.1016/j.physrep.2023.10.005
- Functional convergence of biosphere–atmosphere interactions in response to meteorological conditions C. Krich et al. 10.5194/bg-18-2379-2021
- Spatiotemporal dynamics of dengue in Colombia in relation to the combined effects of local climate and ENSO E. Muñoz et al. 10.1016/j.actatropica.2021.106136
- Sequential Interaction of Biogenic Volatile Organic Compounds and SOAs in Urban Forests Revealed Using Toeplitz Inverse Covariance-Based Clustering and Causal Inference Y. Long et al. 10.3390/f14081617
- Spatiotemporal Dynamics of NDVI, Soil Moisture and ENSO in Tropical South America D. Álvarez & G. Poveda 10.3390/rs14112521
- How Are Pine Species Responding to Soil Drought and Climate Change in the Iberian Peninsula? Á. González-Zamora et al. 10.3390/f14081530
- A review of causal analysis methods in geographic research Z. Zou & C. Cheng 10.1016/j.envsoft.2023.105929
- Multifaceted characteristics of aridity changes and causal mechanisms in Chinese drylands Y. Hu et al. 10.1177/03091333221129867
- Causal discovery of drivers of surface ozone variability in Antarctica using a deep learning algorithm P. Kumar et al. 10.1039/D1EM00383F
- Modeling atmospheric aging of small-scale wood combustion emissions: distinguishing causal effects from non-causal associations V. Leinonen et al. 10.1039/D2EA00048B
- Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior L. Hernandez Rodriguez & P. Kumar 10.1063/5.0131468
- Research on delay propagation mechanism of air traffic control system based on causal inference L. Zeng et al. 10.1016/j.trc.2022.103622
- Response of Vegetation to Drought in the Source Region of the Yangtze and Yellow Rivers Based on Causal Analysis J. Lu et al. 10.3390/rs16040630
- Multi-faceted analyses of seasonal trends and drivers of land surface variables in Indo-Gangetic river basins S. Uereyen et al. 10.1016/j.scitotenv.2022.157515
- Browning of vegetation in efficient carbon sink regions of India during the past two decades is driven by climate change and anthropogenic intrusions R. Kashyap et al. 10.1016/j.jenvman.2023.117655
- A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables—A Case Study for Indo-Gangetic River Basins S. Uereyen et al. 10.3390/rs14010197
- A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty quantification C. Zhang et al. 10.1016/j.jenvman.2023.119613
- Technical note: Incorporating expert domain knowledge into causal structure discovery workflows J. Mäkelä et al. 10.5194/bg-19-2095-2022
- Spatially varying relevance of hydrometeorological hazards for vegetation productivity extremes J. Kroll et al. 10.5194/bg-19-477-2022
- On the Potential of Sentinel-2 for Estimating Gross Primary Production D. Pabon-Moreno et al. 10.1109/TGRS.2022.3152272
- Decoupling between ecosystem photosynthesis and transpiration: a last resort against overheating C. Krich et al. 10.1088/1748-9326/ac583e
- Directed causal effect with PCMCI in hyperscanning EEG time series L. Silfwerbrand et al. 10.3389/fnins.2024.1305918
- Causal inference for time series J. Runge et al. 10.1038/s43017-023-00431-y
- Rising water-use efficiency in European grasslands is driven by increased primary production C. Poppe Terán et al. 10.1038/s43247-023-00757-x
- Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage L. Zou et al. 10.1007/s11269-022-03381-0
- Dynamic relationships between gross primary production and energy partitioning in three different ecosystems based on eddy covariance time series analysis V. Cicuéndez et al. 10.3389/ffgc.2023.1017365
- A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections X. Tibau et al. 10.1017/eds.2022.11
- Spectral Ranking of Causal Influence in Complex Systems E. Zalmijn et al. 10.3390/e23030369
- Networks of climate change: connecting causes and consequences P. Holme & J. Rocha 10.1007/s41109-023-00536-9
3 citations as recorded by crossref.
- Earth system data cubes unravel global multivariate dynamics M. Mahecha et al. 10.5194/esd-11-201-2020
- Towards a global understanding of vegetation–climate dynamics at multiple timescales N. Linscheid et al. 10.5194/bg-17-945-2020
- Decoupling between ecosystem photosynthesis and transpiration: a last resort against overheating C. Krich et al. 10.1088/1748-9326/ac583e
Latest update: 29 Jun 2024
Short summary
Causal inference promises new insight into biosphere–atmosphere interactions using time series only. To understand the behaviour of a specific method on such data, we used artificial and observation-based data. The observed structures are very interpretable and reveal certain ecosystem-specific behaviour, as only a few relevant links remain, in contrast to pure correlation techniques. Thus, causal inference allows to us gain well-constrained insights into processes and interactions.
Causal inference promises new insight into biosphere–atmosphere interactions using time series...
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