Uncertainty component estimates in transient climate projections
Quantifying model uncertainty and internal variability components in climate projections has been paid a great attention in the recent years. For multiple synthetic ensembles of climate projections, we compare the precision of uncertainty component estimates obtained respectively with the two Analysis of Variance (ANOVA) approaches mostly used in recent works: the popular Single Time approach (STANOVA), based on the data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the available simulation period. We show that the precision of all uncertainty estimates is higher when more members are used, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more precise than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3–5 times smaller than STANOVA ones. Except for STANOVA when less than three members is available, the precision is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the precision is low for QEANOVA to very low for STANOVA. In the most unfavorable configurations (small number of members, large internal variability), large over- or underestimation of uncertainty components is thus very likely. In a number of cases, the uncertainty analysis should thus be preferentially carried out with a time series approach or with a local-time series approach, applied to all predictions available in the temporal neighborhood of the target prediction lead time.
KeywordsUncertainty sources Climate projections ANOVA Internal variability Model uncertainty Scenario uncertainty Precision of estimates
We thank the three anonymous reviewers for their constructive suggestions which helped to significantly improve the content of our manuscript.
BH designed the analysis, developed the local-QEANOVA and the synthetic simulations. JB derived the theoretical expressions for unbiased estimators of uncertainty components and wrote the appendixes. All authors contributed to write the manuscript and discuss results.
- Charlton-Perez AJ, Hawkins E, Eyring V, Cionni I, Bodeker GE, Kinnison DE, Akiyoshi H, Frith SM, Garcia R, Gettelman A, Lamarque JF, Nakamura T, Pawson S, Yamashita Y, Bekki S, Braesicke P, Chipperfield MP, Dhomse S, Marchand M, Mancini E, Morgenstern O, Pitari G, Plummer D, Pyle JA, Rozanov E, Scinocca J, Shibata K, Shepherd TG, Tian W, Waugh DW (2010) The potential to narrow uncertainty in projections of stratospheric ozone over the 21st century. Atmos Chem Phys 10(19):9473–9486CrossRefGoogle Scholar
- Hingray B, Blanchet J (2018) Uncertainty components estimates in transient climate projections. bias of moment-based estimators in the single time and time series approaches. Tech. rep., IGE, Univ-Grenoble Alpes, Grenoble, FR. https://hal.archives-ouvertes.fr/hal-01738218
- Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Deque M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kroener N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14(2):563–578CrossRefGoogle Scholar
- Johns T, Royer JF, Höschel I, Huebener H, Roeckner E, Manzini E, May W, Dufresne JL, Otteå O, Vuuren D, Salas y Melia D, Giorgetta M, Denvil S, Yang S, Fogli P, Körper J, Tjiputra J, Stehfest E, Hewitt C (2011) Climate change under aggressive mitigation: the ensembles multi-model experiment. Clim Dyn 37(9–10):1975–2003CrossRefGoogle Scholar
- Montgomery DC (2012) Design and analysis of experiments, 8th edn. Wiley, HobokenGoogle Scholar