%0 Journal Article %1 rostron2020impact %A Rostron, John W. %A Sexton, David M. H. %A McSweeney, Carol F. %A Yamazaki, Kuniko %A Andrews, Timothy %A Furtado, Kalli %A Ringer, Mark A. %A Tsushima, Yoko %D 2020 %J Climate Dynamics %K clouds colleagues model ukcp %N 3 %P 521--551 %R 10.1007/s00382-020-05281-8 %T The impact of performance filtering on climate feedbacks in a perturbed parameter ensemble %U https://doi.org/10.1007/s00382-020-05281-8 %V 55 %X A key contribution to the latest generation of climate projections for the UK (UKCP18) was a perturbed parameter ensemble (PPE) of global coupled models based on HadGEM3-GC3.05. Together with 13 CMIP5 simulations, this PPE provides users with a dataset that samples modelling uncertainty and is ideal for use in impacts studies. Evaluations of global mean surface temperatures for this PPE have shown twenty-first century warming rates consistently at the top end of the CMIP5 range. Here we investigate one potential contributory factor to this lack of spread: that the methodology to select plausible members from a larger, related PPE of atmosphere-only experiments preferentially ruled out those predicted to have more negative climate feedbacks (i.e. lower climate sensitivities). We confirm that this is indeed the case. We show that performance in extratropical long-wave cloud forcing played a key role in this by constraining ice cloud parameters, which in turn constrained the feedback distribution (though causal links are not established). The relatively weak relationship driving this constraint is shown to arise from stronger relationships for the long-wave and short-wave cloud feedback components, which largely cancel out due to changes in tropical high clouds. Moreover, we show that the strength of these constraints is due to a structural bias in extratropical long-wave cloud forcing across the PPE. We discuss how choices made in the methodology to pick the plausible PPE members may result in an overly strong constraint when there is a structural bias and possible improvements to this methodology for the future.

@article{rostron2020impact, abstract = {A key contribution to the latest generation of climate projections for the UK (UKCP18) was a perturbed parameter ensemble (PPE) of global coupled models based on HadGEM3-GC3.05. Together with 13 CMIP5 simulations, this PPE provides users with a dataset that samples modelling uncertainty and is ideal for use in impacts studies. Evaluations of global mean surface temperatures for this PPE have shown twenty-first century warming rates consistently at the top end of the CMIP5 range. Here we investigate one potential contributory factor to this lack of spread: that the methodology to select plausible members from a larger, related PPE of atmosphere-only experiments preferentially ruled out those predicted to have more negative climate feedbacks (i.e. lower climate sensitivities). We confirm that this is indeed the case. We show that performance in extratropical long-wave cloud forcing played a key role in this by constraining ice cloud parameters, which in turn constrained the feedback distribution (though causal links are not established). The relatively weak relationship driving this constraint is shown to arise from stronger relationships for the long-wave and short-wave cloud feedback components, which largely cancel out due to changes in tropical high clouds. Moreover, we show that the strength of these constraints is due to a structural bias in extratropical long-wave cloud forcing across the PPE. We discuss how choices made in the methodology to pick the plausible PPE members may result in an overly strong constraint when there is a structural bias and possible improvements to this methodology for the future.}, added-at = {2020-07-22T10:09:05.000+0200}, author = {Rostron, John W. and Sexton, David M. H. and McSweeney, Carol F. and Yamazaki, Kuniko and Andrews, Timothy and Furtado, Kalli and Ringer, Mark A. and Tsushima, Yoko}, biburl = {https://www.bibsonomy.org/bibtex/2dc58738ae19b2b45ae42bdc445234a14/pbett}, day = 01, description = {The impact of performance filtering on climate feedbacks in a perturbed parameter ensemble | SpringerLink}, doi = {10.1007/s00382-020-05281-8}, interhash = {3b760bfdfa84b3ad89d957648ac88496}, intrahash = {dc58738ae19b2b45ae42bdc445234a14}, issn = {1432-0894}, journal = {Climate Dynamics}, keywords = {clouds colleagues model ukcp}, month = aug, number = 3, pages = {521--551}, timestamp = {2020-07-22T10:09:35.000+0200}, title = {The impact of performance filtering on climate feedbacks in a perturbed parameter ensemble}, url = {https://doi.org/10.1007/s00382-020-05281-8}, volume = 55, year = 2020 }