Presentation and Evaluation of the IPSL‐CM6A‐LR Ensemble of Extended Historical Simulations

Bonnet, Rémy; Boucher, Oliviér; Deshayes, Julie; Gastineau, Guillaume; Hourdin, F.; Mignot, Juliette; Servonnat, Jérôme; Swingedouw, Didier

doi:10.1029/2021ms002565

Cited by 75 publications

(86 citation statements)

References 127 publications

(262 reference statements)

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“…We use a multi‐model framework comprising eight ESM‐based prediction systems, including CanESM5 (Swart et al., 2019), CESM‐DPLE (Yeager et al., 2018), GFDL‐ESM2 (Park et al., 2018), IPSL‐CM6A‐LR (Boucher et al., 2020), MIROC‐ES2L (Watanabe et al., 2020), MPI‐ESM‐LR (Giorgetta et al., 2013), MPI‐ESM1.2‐HR (Mauritsen et al., 2019), and NorCPM1 (Counillon et al., 2016). Details of each prediction system are given in Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

Predictable Variations of the Carbon Sinks and Atmospheric CO₂Growth in a Multi‐Model Framework

Ilyina

Hong-mei

Spring

et al. 2021

Geophysical Research Letters

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Section: Methodsmentioning

confidence: 99%

Predictable Variations of the Carbon Sinks and Atmospheric CO₂Growth in a Multi‐Model Framework

Ilyina

Hong-mei

Spring

et al. 2021

Geophysical Research Letters

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“…The IPSL ensemble of extended historical simulations (IPSL-EHS) is composed of 32 simulations available over the 1850-2059 period [19]. These simulations were performed with the IPSL-CM6A-LR model [25] using the CMIP6 protocol for the historical period (1850-2014) and then extended with the SSP2-4.5 BA-BA−+ scenario onwards [26].…”

Section: Data and Variables Of Interestmentioning

confidence: 99%

“…However, on these time scales, the observed mean state of climate variables can be strongly influenced by the low-frequency internal climate variability. For example, the Atlantic Multidecadal Variability (AMV), which is the leading mode of low-frequency internal climate variability in the North Atlantic ocean [12][13][14], is known to influence the climate over Europe on multidecadal timescale [15], with knock-on effects on the statistics of extreme temperature over the Mediterranean region [16,17] and floods and drought, for example in France [18,19]. Therefore, calibrating a bias adjustment on a simulation that is in the opposite phase of the AMV to the observed one could induce a misleading adjustment compared to a simulation in a phase close to the observed one.…”

Section: Introductionmentioning

confidence: 99%

“…As an illustration a commonly used bias adjustment method, the cumulative distribution function transform (CDF-t) [24] is used here. In order to isolate the influence of internal variability, we used the historical simulations and their extensions into the future performed with the IPSL-CM6A-LR climate model (CM stands for climate model and LR for low resolution) [19]. These simulations have the same external forcing and only differ by their initial conditions and therefore their internal climatic variability.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study

Bonnet

Boucher

Vrac

et al. 2022

Environ. Res.: Climate

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Climate simulations often need to be adjusted before carrying out impact studies at a regional scale. Technically, bias adjustment methods are generally calibrated over the last few decades, in order to benefit from a more comprehensive and accurate observational network. At these timescales, however, the climate state may be influenced by the low-frequency internal climate variability. There is therefore a risk of introducing a bias to the climate projections by bias-adjusting simulations with low-frequency variability in a different phase to that of the observations. In this study, we developed a new pseudo-reality framework using an ensemble of simulations from the IPSL-CM6A-LR climate model in order to assess the impact of the low-frequency internal climate variability of the North Atlantic sea surface temperatures on bias-adjusted projections of mean and extreme surface temperature over Europe. We show that using simulations in a similar phase of the Atlantic Multidecadal Variability reduces the pseudo-biases in temperature projections. Therefore, for models and regions where low frequency internal variability matters, it is recommended to sample relevant climate simulations to be bias adjusted in a model ensemble or alternatively to use a very long reference period when possible.

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“…1) and the known existence of low-frequency variability in AMOC (e.g. Fischer, 2011;Shi and Lohmann, 2016;McKay et al, 2018;Bonnet et al, 2021). The relative role of internal variability is assessed by comparing its strength to the size of the changes in the long-term mean.…”

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confidence: 99%

No changes in overall AMOC strength in interglacial PMIP4 timeslices

Jiang

Brierley

Thornalley

et al. 2022

Preprint

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Abstract. The Atlantic Meridional Overturning Circulation (AMOC) is a key mechanism of poleward heat transport and an important part of the global climate system. How it responded to past changes in forcing, such as experienced during Quaternary interglacials, is an intriguing and open question. Previous modelling studies suggest an enhanced AMOC in the mid-Holocene compared to the pre-industrial period. In earlier simulations from the Palaeoclimate Modelling Intercomparison Project (PMIP), this arose from feedbacks between sea ice and AMOC changes, which were dependent on resolution. Here we present an initial analysis of the recently available PMIP4 simulations. The ensemble mean of the PMIP4 models shows the strength of the AMOC does not markedly change between the midHolocene and piControl experiments or between the lig127k and piControl experiments. Therefore, it appears orbital forcing itself does not alter the overall AMOC. We further investigate the coherency of the forced response in AMOC across the two interglacials, along with the strength of the signal, using eight PMIP4 models which performed both interglacial experiments. Only 2 models show a stronger change with the stronger forcing, but those models disagree on the direction of the change. We propose that the strong signals in these 2 models are caused by a combination of forcing and the internal variability. After investigating the AMOC changes in the interglacials, we further explored the impact of AMOC on the climate system, especially on the changes in the simulated surface temperature and precipitation. After identifying the AMOC’s fingerprint on the surface temperature and rainfall, we demonstrate that only a small percentage of the simulated surface climate changes can be attributed to the AMOC. Proxy records during the two interglacial periods paint a similar picture of minimal changes, which fits nicely with the simulated results. Although the overall AMOC strength shows minimal changes, future work is required to explore whether this occurs through compensating variations in the different components of AMOC (such as Iceland-Scotland overflow water). This lines of evidence caution against interpreting reconstructions of past interglacial climate as being driven by AMOC, outside of abrupt events.

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Presentation and Evaluation of the IPSL‐CM6A‐LR Ensemble of Extended Historical Simulations

Cited by 75 publications

References 127 publications

Predictable Variations of the Carbon Sinks and Atmospheric CO₂Growth in a Multi‐Model Framework

Predictable Variations of the Carbon Sinks and Atmospheric CO₂Growth in a Multi‐Model Framework

Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study

No changes in overall AMOC strength in interglacial PMIP4 timeslices

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Presentation and Evaluation of the IPSL‐CM6A‐LR Ensemble of Extended Historical Simulations

Cited by 75 publications

References 127 publications

Predictable Variations of the Carbon Sinks and Atmospheric CO2Growth in a Multi‐Model Framework

Predictable Variations of the Carbon Sinks and Atmospheric CO2Growth in a Multi‐Model Framework

Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study

No changes in overall AMOC strength in interglacial PMIP4 timeslices

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Predictable Variations of the Carbon Sinks and Atmospheric CO₂Growth in a Multi‐Model Framework

Predictable Variations of the Carbon Sinks and Atmospheric CO₂Growth in a Multi‐Model Framework