Articles | Volume 13, issue 4
https://doi.org/10.5194/esd-13-1437-2022
© Author(s) 2022. 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-13-1437-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Oct 2022
Research article |
| 19 Oct 2022
| 19 Oct 2022
Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal timescales – a poor man's initialized prediction system
Barcelona Supercomputing Center, Barcelona, Spain
Department of Geography, University of Montreal, Montreal, Canada
Markus G. Donat
Barcelona Supercomputing Center, Barcelona, Spain
Institució Catalana de Recerca i Estudis Avançats (ICREA),
Barcelona, Spain
Pablo Ortega
Barcelona Supercomputing Center, Barcelona, Spain
Francisco J. Doblas-Reyes
Barcelona Supercomputing Center, Barcelona, Spain
Institució Catalana de Recerca i Estudis Avançats (ICREA),
Barcelona, Spain
Carlos Delgado-Torres
Barcelona Supercomputing Center, Barcelona, Spain
Margarida Samsó
Barcelona Supercomputing Center, Barcelona, Spain
Pierre-Antoine Bretonnière
Barcelona Supercomputing Center, Barcelona, Spain
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Cited
24 citations as recorded by crossref.
- Constraining decadal variability regionally improves near-term projections of hot, cold and dry extremes P. De Luca et al. https://doi.org/10.1088/1748-9326/acf389
- Large potential of performance-based model weighting to improve decadal climate forecast skill V. Verjans et al. https://doi.org/10.1038/s41612-026-01397-6
- Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask J. Rader & E. Barnes https://doi.org/10.1029/2023GL104983
- Clearing the noise to predict the rhythm of the North Atlantic climate R. Alkama et al. https://doi.org/10.1126/sciadv.aed8616
- How Credibly Do CMIP6 Simulations Capture Historical Mean and Extreme Precipitation Changes? M. Donat et al. https://doi.org/10.1029/2022GL102466
- Multi-decadal variation of ENSO forecast skill since the late 1800s J. Lou et al. https://doi.org/10.1038/s41612-023-00417-z
- New Potential to Reduce Uncertainty in Regional Climate Projections by Combining Physical and Socio‐Economic Constraints F. Lehner et al. https://doi.org/10.1029/2023AV000887
- Forecast skill of Indian summer monsoon rainfall decadal climate predictions S. Dwivedi et al. https://doi.org/10.1007/s00704-026-06528-w
- A Novel Gaussian Process Approach for Prediction of University Academic Evaluation D. Yu et al. https://doi.org/10.1109/TCSS.2025.3547904
- Forecasting climate change effects on drought using the decadal climate prediction project in arid and semi-arid regions of southeastern Iran F. Shoja et al. https://doi.org/10.1007/s11069-025-07405-x
- Multi-decadal initialized climate predictions using the EC-Earth3 global climate model R. Mahmood et al. https://doi.org/10.5194/esd-16-1923-2025
- Improving the forecast quality of near-term climate projections by constraining internal variability based on decadal predictions and observations M. Donat et al. https://doi.org/10.1088/2752-5295/ad5463
- AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability J. Landsberg et al. https://doi.org/10.1088/3049-4753/ae4805
- Data-driven seasonal climate predictions via variational inference and transformers L. Palma et al. https://doi.org/10.1038/s41612-026-01320-z
- Constraining the NAO-temperature teleconnection in CMIP6 simulations enables skillful multi-annual predictions of Eurasian winter climate R. Mahmood et al. https://doi.org/10.1088/1748-9326/adde75
- Filtering CMIP6 models in the Euro-Mediterranean based on a circulation patterns approach M. Olmo et al. https://doi.org/10.1016/j.wace.2025.100765
- Assessing and Adapting Flexible Pavement Design to Climate Change in Semi-Arid Nigeria: A Mechanistic-Empirical Evaluation Y. Yerima et al. https://doi.org/10.1061/JPEODX.PVENG-2078
- Supporting food security with multi-annual climate information: Co-production of climate services for the Southern African Development Community C. Delgado-Torres et al. https://doi.org/10.1016/j.scitotenv.2025.179259
- Seamless seasonal to multi-annual predictions of temperature and Standardized Precipitation Index by constraining transient climate model simulations J. Acosta Navarro et al. https://doi.org/10.5194/esd-16-1723-2025
- Seamless climate information from months to multiple years: constraining decadal predictions with seasonal predictions and past observations, and their comparison to multi-annual predictions C. Delgado-Torres et al. https://doi.org/10.5194/esd-17-41-2026
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Constraining decadal climate predictions with seasonal forecasts: a step toward seamless multi-year climate information B. Solaraju-Murali et al. https://doi.org/10.1088/1748-9326/adfd73
- Toward robust fine-scale decadal precipitation forecasts through dynamically consistent subsampling J. Couallier et al. https://doi.org/10.5194/esd-17-1177-2026
- Twelve months at 1.5 °C signals earlier than expected breach of Paris Agreement threshold A. Cannon https://doi.org/10.1038/s41558-025-02247-8
24 citations as recorded by crossref.
- Constraining decadal variability regionally improves near-term projections of hot, cold and dry extremes P. De Luca et al. https://doi.org/10.1088/1748-9326/acf389
- Large potential of performance-based model weighting to improve decadal climate forecast skill V. Verjans et al. https://doi.org/10.1038/s41612-026-01397-6
- Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask J. Rader & E. Barnes https://doi.org/10.1029/2023GL104983
- Clearing the noise to predict the rhythm of the North Atlantic climate R. Alkama et al. https://doi.org/10.1126/sciadv.aed8616
- How Credibly Do CMIP6 Simulations Capture Historical Mean and Extreme Precipitation Changes? M. Donat et al. https://doi.org/10.1029/2022GL102466
- Multi-decadal variation of ENSO forecast skill since the late 1800s J. Lou et al. https://doi.org/10.1038/s41612-023-00417-z
- New Potential to Reduce Uncertainty in Regional Climate Projections by Combining Physical and Socio‐Economic Constraints F. Lehner et al. https://doi.org/10.1029/2023AV000887
- Forecast skill of Indian summer monsoon rainfall decadal climate predictions S. Dwivedi et al. https://doi.org/10.1007/s00704-026-06528-w
- A Novel Gaussian Process Approach for Prediction of University Academic Evaluation D. Yu et al. https://doi.org/10.1109/TCSS.2025.3547904
- Forecasting climate change effects on drought using the decadal climate prediction project in arid and semi-arid regions of southeastern Iran F. Shoja et al. https://doi.org/10.1007/s11069-025-07405-x
- Multi-decadal initialized climate predictions using the EC-Earth3 global climate model R. Mahmood et al. https://doi.org/10.5194/esd-16-1923-2025
- Improving the forecast quality of near-term climate projections by constraining internal variability based on decadal predictions and observations M. Donat et al. https://doi.org/10.1088/2752-5295/ad5463
- AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability J. Landsberg et al. https://doi.org/10.1088/3049-4753/ae4805
- Data-driven seasonal climate predictions via variational inference and transformers L. Palma et al. https://doi.org/10.1038/s41612-026-01320-z
- Constraining the NAO-temperature teleconnection in CMIP6 simulations enables skillful multi-annual predictions of Eurasian winter climate R. Mahmood et al. https://doi.org/10.1088/1748-9326/adde75
- Filtering CMIP6 models in the Euro-Mediterranean based on a circulation patterns approach M. Olmo et al. https://doi.org/10.1016/j.wace.2025.100765
- Assessing and Adapting Flexible Pavement Design to Climate Change in Semi-Arid Nigeria: A Mechanistic-Empirical Evaluation Y. Yerima et al. https://doi.org/10.1061/JPEODX.PVENG-2078
- Supporting food security with multi-annual climate information: Co-production of climate services for the Southern African Development Community C. Delgado-Torres et al. https://doi.org/10.1016/j.scitotenv.2025.179259
- Seamless seasonal to multi-annual predictions of temperature and Standardized Precipitation Index by constraining transient climate model simulations J. Acosta Navarro et al. https://doi.org/10.5194/esd-16-1723-2025
- Seamless climate information from months to multiple years: constraining decadal predictions with seasonal predictions and past observations, and their comparison to multi-annual predictions C. Delgado-Torres et al. https://doi.org/10.5194/esd-17-41-2026
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Constraining decadal climate predictions with seasonal forecasts: a step toward seamless multi-year climate information B. Solaraju-Murali et al. https://doi.org/10.1088/1748-9326/adfd73
- Toward robust fine-scale decadal precipitation forecasts through dynamically consistent subsampling J. Couallier et al. https://doi.org/10.5194/esd-17-1177-2026
- Twelve months at 1.5 °C signals earlier than expected breach of Paris Agreement threshold A. Cannon https://doi.org/10.1038/s41558-025-02247-8
Saved (final revised paper)
Latest update: 12 Sep 2026
Short summary
Near-term climate change projections are strongly affected by the uncertainty from internal climate variability. Here we present a novel approach to reduce such uncertainty by constraining decadal-scale variability in the projections using observations. The constrained ensembles show significant added value over the unconstrained ensemble in predicting global climate 2 decades ahead. We also show the applicability of regional constraints for attributing predictability to certain ocean regions.
Near-term climate change projections are strongly affected by the uncertainty from internal...
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