Usage (Emile-Geay et al., 2025)#
This page gathers notebooks showing the usage of the pens package [1] as used in the article “Temporal Comparisons Involving Paleoclimate Data Assimilation: Challenges & Remedies” [2], by Julien Emile-Geay, Gregory J. Hakim, Frederi Viens, Feng Zhu and Daniel E. Amrhein.
There are two main uses to these notebooks:
Understand the genesis and properties of a new tool introduced in the paper, called “plume distance”, which is broadly applicable to all manner of ensembles as used in climate science (broadly defined). Indeed, while the article focused on paleoclimate reconstruction ensembles (particularly those generated by “offline” data assimilation methods), the plume distance can help quantify distance between timeseries generated by initial condition ensembles (e.g. like [3]), observational ensembles obtained by [4], or any other kind [5] (e.g. perturbed parameter ensembles).
Reproduce results from the paper, which showcases several useful Python packages to do paleoclimatology in the Cloud, including pens [6], xarray [7], and Pyleoclim [8].
- [Fig. 1] Motivation for the study
- [Fig. 2] Naïve sampling
- [Figs. 3-4] LMRonline
- [Fig. 5] Ensemble resampling
- [Figs. 6-8] Proximity Probability and Plume Distance
- [Fig. 9] Schematic for the Plume Distance
- [Fig. 10] Consistency of the PAGES 2k ensemble
- [Figs. 11-12] LMR v2.1 vs Büntgen et al ensemble
- [Table 1] Proximity of PMIP3 traces to LMR ensembles