How Reliable Are Decadal Climate Predictions of Near-Surface Air Temperature?
| Autor: | Francisco J. Doblas-Reyes, Verónica Torralba, Deborah Verfaillie, Núria Pérez-Zanón, Balakrishnan Solaraju-Murali, Markus G. Donat, Simon Wild |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Ambiental, Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Earth and Life Institute [Louvain-La-Neuve] (ELI), Université Catholique de Louvain = Catholic University of Louvain (UCL), Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona Supercomputing Center |
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences media_common.quotation_subject Library science Decadal variability 010502 geochemistry & geophysics Climate prediction 01 natural sciences Climate models Surface air temperature Simulació per ordinador Political science Gratitude media_common.cataloged_instance European union Weatther forecasting -- Mathematical models Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences media_common Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia [Àrees temàtiques de la UPC] Computer simulation Desenvolupament humà i sostenible::Enginyeria ambiental [Àrees temàtiques de la UPC] Climatology Previsió del temps -- Models matemàtics 13. Climate action [SDE]Environmental Sciences Christian ministry Forecast verification/skill Forecasting |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Journal of Climate Journal of Climate, 2020, 34 (2), pp.697-713. ⟨10.1175/JCLI-D-20-0138.1⟩ |
| ISSN: | 1520-0442 0894-8755 |
| Popis: | Decadal climate predictions are being increasingly used by stakeholders interested in the evolution of climate over the coming decade. However, investigating the added value of those initialized decadal predictions over other sources of information typically used by stakeholders generally relies on forecast accuracy, while probabilistic aspects, although crucial to users, are often overlooked. In this study, the quality of the near-surface air temperature from initialized predictions has been assessed in terms of reliability, an essential characteristic of climate simulation ensembles, and compared to the reliability of noninitialized simulations performed with the same model ensembles. Here, reliability is defined as the capability to obtain a true estimate of the forecast uncertainty from the ensemble spread. We show the limited added value of initialization in terms of reliability, the initialized predictions being significantly more reliable than their noninitialized counterparts only for specific regions and the first forecast year. By analyzing reliability for different forecast system ensembles, we further highlight the fact that the combination of models seems to play a more important role than the ensemble size of each individual forecast system. This is due to sampling different model errors related to model physics, numerics, and initialization approaches involved in the multimodel, allowing for a certain level of error compensation. Finally, this study demonstrates that all forecast system ensembles are affected by systematic biases and dispersion errors that affect the reliability. This set of errors makes bias correction and calibration necessary to obtain reliable estimates of forecast probabilities that can be useful to stakeholders. The EUCP project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement 776613. The research leading to these results has also received funding from the Spanish Ministerio de Ciencia, Innovación y Universidades as part of the CLINSA project with funding reference CGL2017-85791-R. MGD is also supported by the Spanish Ministry of Science, Innovation and Universities Grant RYC-2017-22964. BSM acknowledges financial support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement 713673 and from a fellowship of “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/IN17/11620038. SW has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. We acknowledge the use of the s2dverification (Manubens et al. 2018), startR (BSC/CNS and Manubens 2020), SpecsVerification (Siegert 2017), CSTools (Perez-Zanon et al. 2019), ClimProjDiags (BSC/CNS et al. 2020), and boot (Davison and Hinkley 1997; Canty and Ripley 2020) R (R Core Team 2013) software packages. We also thank Nicolau Manubens, An-Chi Ho, Pablo Ortega, Rashed Mahmood, Yohan Ruprich-Robert, and Roberto Bilbao from the BSC for their technical and scientific support. We further express our gratitude to Charles Pelletier from ELI for his careful reading of the manuscript. Finally, we would like to thank Antje Weisheimer (University of Oxford) and two anonymous reviewers for constructive comments that helped us greatly improve this article. |
| Databáze: | OpenAIRE |
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