Abstract
We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.
Highlights
Observations reflect how the real-world climate system responds to changing natural and anthropogenic external forcings, as well as how the system fluctuates due to its own chaotic internal variability
We find that most ensembles, including CanESM2, CanESM5, CSIRO.MK3.6, GFDL-ESM3, IPSL-CM5A, and MIROC6, exceed this range due to too high rank 0 and rank n frequencies that are caused by biases in the forced response beyond what can be attributed to internal variability
We can exploit the power of single model initialcondition large ensembles (SMILEs) experiments to determine whether real-world observations are well distributed within the well-sampled range of climate states simulated by each model
Summary
Observations reflect how the real-world climate system responds to changing natural and anthropogenic external forcings, as well as how the system fluctuates due to its own chaotic internal variability. One example of this is the methodological framework that we apply in our study This framework (Suarez-Gutierrez et al 2018; Maher et al 2019; Suarez-Gutierrez et al 2020a, b) relies on the precise characterization of simulated internal variability in SMILE experiments, which provide welldefined estimates of both the time-evolving forced response and the probability distribution of deviations from this mean state caused by internal variability. Previous studies using evaluation frameworks that consider whole ensemble distributions to evaluate the agreement of several climate models with observations are based on multimodel ensembles such as the Coupled Model Intercomparison Project (CMIP; Taylor et al 2012), which have a limited number of simulations for each model and do not allow for a clean separation between simulated forced response and internal variability (Annan and Hargreaves 2010; Marotzke and Forster 2015). We provide a multi-model comparison of the well-sampled transient internal variability and forced response in SMILEs from ten comprehensive, fully-coupled climate models from both the CMIP5 and CMIP6 generations, and the first multi-model evaluation of how well these models capture the internal variability and forced response in observations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
