On the Estimation of Internal Climate Variability During the Preindustrial Past Millennium. Issue 2 (18th January 2022)

• Record Type: Journal Article
• Authors: Mann, Michael E.; Steinman, Byron A.; Brouillette, Daniel J.; Fernandez, Alejandro; Miller, Sonya K.
• Is Part Of: Geophysical research letters. Volume 49:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-18
• Subjects: Last Millennium -- climate change -- radiative forcing -- paleoclimate; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2021GL096596
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: We use an ensemble of simulations of a coupled model (NCAR Community Earth System Model) driven by natural radiative forcing estimates over the pre‐industrial past millennium to test the efficacy of methods designed to remove forced variability from proxy‐based climate reconstructions and estimate residual internal variability (e.g., a putative "Atlantic Multidecadal Oscillation"). Within the framework of these experiments, the forced component of surface temperature change can be estimated accurately from the ensemble mean, and the internal variability of each of the independent realizations can be accurately assessed by subtracting off that estimate. We show in this case, where the true internal variability is known, that regression‐based methods of removing the forced component from proxy reconstructions will, in the presence of uncertainties in the underlying natural radiative forcing, fail to yield accurate estimates thereof, incorrectly attributing unresolved forced features (and multidecadal spectral peaks associated with them) to internal variability. Plain Language Summary: We use climate model simulations to test methods used in past work to estimate internal climate variability, including the so‐called "Atlantic Multidecadal Oscillation", from proxy‐based paleoclimate reconstructions. We show that uncertainties in the main drivers of pre‐industrial climate variability, including volcanic eruptions and fluctuations in solar output, limit the reliability of those methods. We find, in particular, that they are likely to attribute to "internal" climate variability "forced" features that reflect the response of the climate system to those drivers. Key Points: Climate model ensembles driven by estimated radiative forcing can be used to estimate both forced and internal climate variability Such exercises can yield biased results in the presence of uncertainty in both forcing and response as pertains to the past millennium Ensemble analysis shows a widely used procedure yields biased results for past millennium