@article{fcfb9a4e12a541ce88a03f8f5d0dd359,
title = "Links between internal variability and forced climate feedbacks: The importance of patterns of temperature variability and change",
abstract = "Understanding the relationships between internal variability and forced climate feedbacks is key for using observations to constrain future climate change. Here we probe and interpret the differences in these relationships between the climate change projections provided by the CMIP5 and CMIP6 experiment ensembles. We find that internal variability feedbacks better predict forced feedbacks in CMIP6 relative to CMIP5 by over 50%, and that the increased predictability derives primarily from the slow (>20 years) response to climate change. A key novel result is that the increased predictability is consistent with the higher resemblance between the patterns of internal and forced temperature changes in CMIP6, which suggests temperature pattern effects play a key role in predicting forced climate feedbacks. Despite the increased predictability, emergent constraints provided by observed internal variability are weak and largely unchanged from CMIP5 to CMIP6 due to the shortness of the observational record.",
author = "Davis, {Luke L. B.} and Thompson, {David W. J.} and Maria Rugenstein and Thomas Birner",
note = "Data Availability Statement: The CMIP6 and CMIP5 data used in this study were obtained online via the Earth System Grid Federation (Cinquini et al., 2014). The software used to generate the results in this study are published under three separate repositories: One for calculating CMIP5 and CMIP6 feedbacks (Davis, 2024b), another for estimating observational climate feedbacks (Davis, 2024c), and a third for plotting and post-processing the results (Davis, 2024a). The Zelinka et al. (2020) forcing-feedback data used in Supporting Figure S2 in Supporting Information S1 were obtained from Zelinka (2021). The Huang et al. (2017) radiative kernels used in Supporting Figure S6 in Supporting Information S1 were obtained from Huang (2022). This work benefited from the open-source software packages Climate Data Operators (used to process model data; Schulzweida, 2023); Xarray (used to derive inter-model statistics; Hoyer et al., 2024); and Matplotlib, Cartopy, and Proplot (used to create the figures; Caswell et al., 2021; Elson et al., 2023; Davis, 2021). Research funding: National Science Foundation; Royal Society Wolfson Fellowship; National Aeronautics and Space Administration",
year = "2024",
month = dec,
day = "28",
doi = "10.1029/2024GL112774",
language = "English",
volume = "51",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
number = "24",
}