1. Toward consistent observational constraints in climate predictions and projections
- Author
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Andrew Ballinger, Juliette Mignot, Rashed Mahmood, Didier Swingedouw, James M. Murphy, Ben B. B. Booth, Glen R. Harris, Jason Lowe, Francisco J. Doblas-Reyes, Gabriele C. Hegerl, Lukas Brunner, Antje Weisheimer, Leonard Borchert, Markus G. Donat, Barcelona Supercomputing Center, School of Geosciences [Edinburgh], University of Edinburgh, Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Océan et variabilité du climat (VARCLIM), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Institució Catalana de Recerca i Estudis Avançats (ICREA), University of Alaska [Anchorage], Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), University of Oxford, and European Centre for Medium-Range Weather Forecasts (ECMWF)
- Subjects
model evaluation ,010504 meteorology & atmospheric sciences ,Observational constraints ,Climate change ,010502 geochemistry & geophysics ,01 natural sciences ,Economics ,Regional science ,European commission ,GE1-350 ,Model evaluation ,climate modeling ,0105 earth and related environmental sciences ,Future projections ,[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph] ,Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia [Àrees temàtiques de la UPC] ,Horizon (archaeology) ,Climatologia -- Mètodes de simulació ,climate predictions ,observational constraints ,Environmental sciences ,climate change ,13. Climate action ,Climate modeling ,Climate predictions ,Christian ministry ,Climate model ,Observational study ,future projections ,Climate sciences ,Canvis climàtics - Abstract
Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution. All authors were supported by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613. JM was also supported by the french ANR MOPGA project ARCHANGE and by the EU-H2020 Blue Action (GA 727852) and 4C projects (GA 821003). MGD also received funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. Peer Reviewed "Article signat per 14 autors/es: Gabriele C. Hegerl, Andrew P. Ballinger, Ben B. B. Booth, Leonard F. Borchert, Lukas Brunner, Markus G. Donat, Francisco J. Doblas-Reyes, Glen R. Harris, Jason Lowe, Rashed Mahmood, Juliette Mignot, James M. Murphy, Didier Swingedouw and Antje Weisheimer"
- Published
- 2021