Assessment of a full-field initialised decadal climate prediction system with the CMIP6 version of EC-Earth

Bilbao, Roberto; Wild, S. B.; Ortega, Pablo; Navarro, Juan C. Acosta; Arsouze, Thomas; Bretonnière, Pièrre-Antoine; Caron, Louis-Philippe; Castrillo, Miguel; Cruz-García, Rubén; Cvijanović, Ivana; Doblas‐Reyes, F. J.; Donat, Markus G.; Dutra, Emanuel; Echevarría, Pablo; Ho, An-Chi; Loosveldt-Tomas, Saskia; Moreno-Chamarro, Eduardo; Pérez-Zañón, Núria; Ramos, Arthur; Ruprich‐Robert, Yohan; Sicardi, Valentina; Tourigny, Étienne; Vegas-Regidor, Javier

doi:10.5194/esd-2020-66

Cited by 4 publications

(15 citation statements)

References 37 publications

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“…The MM ensemble is a collection of different forecast systems, each with their own ensemble generation method. For example, the EC-Earth forecast system uses initial perturbations in both the atmosphere and the ocean (Du et al 2012;Ménégoz et al 2018;Bilbao et al 2020).…”

Section: A Forecast Systems and Ensemblesmentioning

confidence: 99%

How Reliable Are Decadal Climate Predictions of Near-Surface Air Temperature?

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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 initialised 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 initialised predictions has been assessed in terms of reliability, an essential characteristic of climate simulation ensembles, and compared to the reliability of non-initialised 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 initialisation in terms of reliability, the initialised predictions being significantly more reliable than their non-initialised counterparts only for specific regions and the first forecast year. By analysing 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 initialisation approaches involved in the multi-model, 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.

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Section: A Forecast Systems and Ensemblesmentioning

confidence: 99%

How Reliable Are Decadal Climate Predictions of Near-Surface Air Temperature?

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“…showing considerably lower trends from forecast years 2-6, the same years in which the initialisation shock manifests locally (Bilbao et al, 2020). In the GSE, the trends in PRED also starts close to the reanalysis ones, overlapping with them up to FY 6, but after that they decrease quickly and show no final sign of recovery.…”

Section: Representation Of Upper Ohc Long-term Trends In the Ec-earth3mentioning

confidence: 86%

“…Another region that exhibits a significant loss in skill with forecast time is the western STNA. Other regions, such as LS and IIS, also experience an initial moderate loss in skill, but it is recovered at the longest forecast years, which suggests that the temporary loss in skill could be related to the effect of initial shocks reported in (Bilbao et al, 2020)).…”

Section: Forecast Quality and Added Value Of Initialisation In North Atlanticmentioning

confidence: 95%

“…In the particular case of the North Atlantic, not all areas are equally predictable. Most decadal prediction systems show high levels of skill for the upper ocean temperatures at Subpolar Gyre latitudes (Matei et al, 2012;Robson et al, 2018;Mignot et al, 2016;Yeager et al, 2012Yeager et al, , 2018Bilbao et al, 2020). On these regions slowly-varying ocean dynamics exerts a prominent influence, but observations also suggest long predictive capacity due to long thermal memory, and therefore persistence (Buckley et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the GSE exhibits lower skill in many prediction systems (e.g. Matei et al, 2012;Mignot et al, 2016;Yeager et al, 2018;Bilbao et al, 2020), which suggest that the region might be inherently less predictable. However, the Gulf Stream region is also known to suffer from important systematic biases in the typical non-eddying model resolutions used for climate prediction (i.e., 1 • x 1 • ), which might hamper predictive skill (Hewitt et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Trends, variability and predictive skill of the ocean heat content in North Atlantic: An analysis with the EC-Earth3 model

Carmo-Costa

Bilbao

Ortega

et al. 2021

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As the global climate is warming, with important regional differences, there is a growing need to (i) better understand whether and how internal variability controls the regional warming trends, and (ii) to identify the regions in which both the trends and the superimposed interannual variability are predictable. In this study we investigate trends, variability and predictive skill of the upper ocean heat content in the North Atlantic basin. This region is a source of decadal variability, in which internal ocean processes can locally modulate the global warming trends and add additional prediction skill. The analysis is focused on the period 1970-2014, and combines the study of an ensemble of ocean reanalyses, with two sets of CMIP6 experiments performed with the Earth system model EC-Earth3: (i) a 10-member historical ensemble; and (ii) an initialized 10-member retrospective decadal prediction system. External forcings are found to be important for the development of the regional trends, but on their own are unable to reproduce the exact geographical pattern. Our results also show that not all regions in the North Atlantic are equally predictable, which is explained by different contributions of the forcings and internal climate variability. While high levels of predictive skill in regions like the Eastern Subpolar North Atlantic, or the Irminger and Iceland Seas are clearly enabled by initialization, with a negligible influence of the external forcings, skill in others areas like the Subtropical North Atlantic, or the Gulf Stream extension mostly comes from the externally forced trends. The Labrador Sea is a particular case where predictive skill has both an external and internal origin. Large observational and modeling uncertainties affect the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain its very poor predictive skill. We would like to acknowledge the financial support from FCT through projects&#160;FCT-UIDB/50019/2020&#160;and&#160;PD/BD/142785/2018.

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Trends, variability and predictive skill of the ocean heat content in North Atlantic: an analysis with the EC-Earth3 model

et al. 2021

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This study investigates linear trends, variability and predictive skill of the upper ocean heat content (OHC) in the North Atlantic basin. This is a region where strong decadal variability superimposes the externally forced trends, introducing important differences in the local warming rates and leading in the case of the Central Subpolar North Atlantic to an overall long-term cooling. Our analysis aims to better understand these regional differences, by investigating how internal and forced variability contribute to local trends, exploring also their role on the local prediction skill. The analysis combines the study of three ocean reanalyses to document the uncertainties related to observations with two sets of CMIP6 experiments performed with the global coupled climate model EC-Earth3: a historical ensemble to characterise the forced signals, and a retrospective decadal prediction system to additionally characterise the contributions from internal climate variability. Our results show that internal variability is essential to understand the spatial pattern of North Atlantic OHC trends, contributing decisively to the local trends and providing high levels of predictive skill in the Eastern Subpolar North Atlantic and the Irminger and Iceland Seas, and to a lesser extent in the Labrador Sea. Skill and trends in other areas like the Subtropical North Atlantic, or the Gulf Stream Extension are mostly externally forced. Large observational and modeling uncertainties affect the trends and interannual variability in the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain the very poor local predictive skill.

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Assessment of a full-field initialised decadal climate prediction system with the CMIP6 version of EC-Earth

Cited by 4 publications

References 37 publications

How Reliable Are Decadal Climate Predictions of Near-Surface Air Temperature?

How Reliable Are Decadal Climate Predictions of Near-Surface Air Temperature?

Trends, variability and predictive skill of the ocean heat content in North Atlantic: An analysis with the EC-Earth3 model

Trends, variability and predictive skill of the ocean heat content in North Atlantic: an analysis with the EC-Earth3 model

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