Temporal Comparisons Involving Paleoclimate Data Assimilation: Challenges and Remedies

Abstract Paleoclimate reconstructions are increasingly central to climate assessments, placing recent and future variability in a broader historical context. Several estimation methods produce plumes of climate trajectories that practitioners often want to compare to other reconstruction ensembles or to deterministic trajectories produced by other means, such as global climate models. Of particular interest are “offline” data assimilation (DA) methods, which have recently been adapted to paleoclimatology. Offline DA lacks an explicit model connecting time instants, so its ensemble members are not true system trajectories. This obscures quantitative comparisons, particularly when considering the ensemble mean in isolation. We propose several resampling methods to introduce a priori constraints on temporal behavior, as well as a general notion, called plume distance, to carry out quantitative comparisons between collections of climate trajectories (“plumes”). The plume distance provides a norm in the same physical units as the variable of interest (e.g., °C for temperature) and lends itself to assessments of statistical significance. We apply these tools to four paleoclimate comparisons: 1) global mean surface temperature (GMST) in the online and offline versions of the Last Millennium Reanalysis (v2.1); 2) GMST from these two ensembles to simulations of the Paleoclimate Modelling Intercomparison Project past1000 ensemble; 3) Last Millennium Reanalysis, version 2.1 (LMRv2.1), to the PAGES 2k Consortium ensemble of GMST; and 4) the Northern Hemisphere mean surface temperature from LMRv2.1 to the Büntgen et al. ensemble. Results generally show more compatibility between these ensembles than is visually apparent. The proposed methodology is implemented in an open-source Python package, and we discuss the possible applications of the plume distance framework beyond paleoclimatology. Significance Statement Paleoclimate data assimilation is an emerging technique to reconstruct past climate variations. The currently dominant approximation, offline data assimilation, lacks the ability to connect information across time. This work proposes open-source solutions to this problem and applies them to three paleoclimate questions, before discussing broader implications.