the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Parametric Sensitivity and Constraint of Contrail Cirrus Radiative Forcing in the Atmospheric Component of CNRM-CM6-1
Maxime Perini
Laurent Terray
Daniel Cariolle
Saloua Peatier
Marie-Pierre Moine
Abstract. The impact of aviation on climate change due to CO2 emissions no longer needs to be demonstrated. However, the impact of non-CO2 effects such as those from contrails is still subject to large uncertainties. An often neglected source of uncertainty comes from climate model sensitivity to numerical parameters representing subgrid-scale processes. Here we investigate the sensitivity of contrail radiative forcing due parametric uncertainty based on the atmospheric component of the CNRM-CM6-1 coupled model. A perturbed parameter ensemble is generated from the sampling of twenty-two adjustable parameters involved in convection, cloud microphysics and radiative transfer processes. A surrogate model based on multi-linear regression is used to explore the full range of contrail radiative forcing due to parametric uncertainty. Based on an optimization algorithm and a climatological skill score, we find a constrained range of contrail radiative forcing from equally skillful model versions with different sets of parameters. We find a contrail radiative forcing best-estimate of 56 mW.m-2 with a 5–95 % confidence interval of 38–70 mW.m-2. Finally, a sensitivity analysis shows that model parameters controlling contrail's lifetime play a major role in the estimation of contrail radiative forcing.
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Maxime Perini et al.
Status: open (until 26 Dec 2023)
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CC1: 'Comment on egusphere-2023-2478', Sidiki Sanogo, 21 Nov 2023
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Dear Maxime et al.,
You have led a very nice analysis. Congratulations. However, I would like to comment on the analysis presented in figure 4:
You have compared the RHI distributions of ARPEGE, 1°x1° with the RHI distribution of the IAGOS data. IAGOS data are very local measurements, since they are measured every 4s. How do you justify this ? Do you think that these two distributions are comparable ?
Thank you.
Citation: https://doi.org/10.5194/egusphere-2023-2478-CC1
Maxime Perini et al.
Maxime Perini et al.
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