Articles | Volume 15, issue 5
https://doi.org/10.5194/acp-15-2535-2015
© 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/acp-15-2535-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A science-based use of ensembles of opportunities for assessment and scenario studies
E. Solazzo
European Commission, Joint Research Centre, Institute for Environment and Sustainability, Air and Climate Unit, Ispra, Italy
S. Galmarini
CORRESPONDING AUTHOR
European Commission, Joint Research Centre, Institute for Environment and Sustainability, Air and Climate Unit, Ispra, Italy
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Cited
17 citations as recorded by crossref.
- Model Study of Global Mercury Deposition from Biomass Burning F. De Simone et al. 10.1021/acs.est.5b00969
- Are mercury emissions from satellite electric propulsion an environmental concern?* D. Fourie et al. 10.1088/1748-9326/ab4b75
- Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations C. Maurer et al. 10.1016/j.jenvrad.2022.106968
- The optimization of model ensemble composition and size can enhance the robustness of crop yield projections L. Li et al. 10.1038/s43247-023-01016-9
- Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron E. Chianese et al. 10.1016/j.ecoinf.2018.12.001
- Particulate-phase mercury emissions from biomass burning and impact on resulting deposition: a modelling assessment F. De Simone et al. 10.5194/acp-17-1881-2017
- A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4) O. Clifton et al. 10.5194/acp-23-9911-2023
- Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles A. Maiorano et al. 10.1016/j.fcr.2016.05.001
- Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. 10.5194/acp-18-8727-2018
- Intercomparison of the representations of the atmospheric chemistry of pre-industrial methane and ozone in earth system and other global chemistry-transport models R. Derwent et al. 10.1016/j.atmosenv.2021.118248
- Multimodel ensembles improve predictions of crop–environment–management interactions D. Wallach et al. 10.1111/gcb.14411
- Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations D. Goto et al. 10.5194/acp-20-3589-2020
- On the limit to the accuracy of regional-scale air quality models S. Rao et al. 10.5194/acp-20-1627-2020
- Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3 U. Im et al. 10.5194/acp-18-5967-2018
- Regional effects of atmospheric aerosols on temperature: an evaluation of an ensemble of online coupled models R. Baró et al. 10.5194/acp-17-9677-2017
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- International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations C. Maurer et al. 10.1016/j.jenvrad.2018.01.030
16 citations as recorded by crossref.
- Model Study of Global Mercury Deposition from Biomass Burning F. De Simone et al. 10.1021/acs.est.5b00969
- Are mercury emissions from satellite electric propulsion an environmental concern?* D. Fourie et al. 10.1088/1748-9326/ab4b75
- Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations C. Maurer et al. 10.1016/j.jenvrad.2022.106968
- The optimization of model ensemble composition and size can enhance the robustness of crop yield projections L. Li et al. 10.1038/s43247-023-01016-9
- Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron E. Chianese et al. 10.1016/j.ecoinf.2018.12.001
- Particulate-phase mercury emissions from biomass burning and impact on resulting deposition: a modelling assessment F. De Simone et al. 10.5194/acp-17-1881-2017
- A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4) O. Clifton et al. 10.5194/acp-23-9911-2023
- Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles A. Maiorano et al. 10.1016/j.fcr.2016.05.001
- Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more? S. Galmarini et al. 10.5194/acp-18-8727-2018
- Intercomparison of the representations of the atmospheric chemistry of pre-industrial methane and ozone in earth system and other global chemistry-transport models R. Derwent et al. 10.1016/j.atmosenv.2021.118248
- Multimodel ensembles improve predictions of crop–environment–management interactions D. Wallach et al. 10.1111/gcb.14411
- Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations D. Goto et al. 10.5194/acp-20-3589-2020
- On the limit to the accuracy of regional-scale air quality models S. Rao et al. 10.5194/acp-20-1627-2020
- Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3 U. Im et al. 10.5194/acp-18-5967-2018
- Regional effects of atmospheric aerosols on temperature: an evaluation of an ensemble of online coupled models R. Baró et al. 10.5194/acp-17-9677-2017
- Evaluation and uncertainty estimation of the impact of air quality modelling on crop yields and premature deaths using a multi-model ensemble E. Solazzo et al. 10.1016/j.scitotenv.2018.03.317
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Latest update: 23 Nov 2024
Short summary
Ensembles of results from multiple models require systematic screening according to well-defined and analytical principles described in the paper. The screening guarantees more robust conclusions especially in the case of scenario analysis.
Ensembles of results from multiple models require systematic screening according to well-defined...
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