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Abstract Paleoclimate reconstructions are increasingly central to climate assessments, placing recent and future variability in a broader historical context. 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 Model Intercomparison Project past1000 ensemble; (3) LMRv2.1 to the PAGES 2k (2019) ensemble of GMST and (4) northern hemisphere mean surface temperature from LMR v2.1 to the Büntgen et al. (2021) 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 possible applications of the plume distance framework beyond paleoclimatology. 

This is the second in a series of two papers that develops a theory of relatively Anosov representations using the original “contracting flow on a bundle” definition of Anosov representations introduced by Labourie and Guichard–Wienhard. In this paper, we focus on building families of examples. 

Abstract Incorporation of ferric iron in mantle silicates stabilizes different crystal structures and changes phase transition conditions, thus impacting seismic wave speeds and discontinuities. In MgSiO3-Fe2O3 mixtures, recent experiments indicate the coexistence of fully oxidized iron-rich (Mg0.5Fe0.53+)(Fe0.53+Si0.5)O3 with Fe-poor silicate (wadsleyite or bridgmanite) and stishovite at 15 to 27 GPa and 1773 to 2000 K, conditions relevant to subducted lithosphere in the Earth’s transition zone and uppermost lower mantle. X-ray diffraction measurements show that (Mg0.5Fe0.53+)(Fe0.53+Si0.5)O3 recovered from these conditions adopts the R3c LiNbO3-type structure, which transforms to the bridgmanite structure again between 18.3 GPa and 24.7 GPa at 300 K. Diffraction observations are used to obtain the equation of state of the LiNbO3-type phase up to 18.3 GPa. These observations combined with multi-anvil experiments suggest that the stable phase of (Mg0.5Fe0.53+)(Fe0.53+Si0.5)O3 is bridgmanite at 15-27 GPa, which transforms on decompression to LiNbO3-type structure. Our calculation revealed that ordering of the ferric ion reduces the kinetic energy barrier of the transition between (Mg0.5Fe0.53+)(Fe0.53+Si0.5)O3 LiNbO3 structure and bridgmanite relative to the MgSiO3 akimotoite-bridgmanite system. Dense Fe3+-rich bridgmanite structure is thus stable at substantially shallower depths than MgSiO3 bridgmanite and would promote subduction. 

Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. Such reconstructions can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and time-consuming, as they involve (i) preprocessing of the proxy records, climate model simulations, and instrumental observations; (ii) application of one or more statistical methods; and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. cfr provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with extensive documentation of the application programming interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the graphical expectation–maximization (GraphEM) algorithm, respectively. 

Abstract Paleoclimate reconstructions are now integral to climate assessments, yet the consequences of using different methodologies and proxy data require rigorous benchmarking. Pseudoproxy experiments (PPEs) provide a tractable and transparent test bed for evaluating climate reconstruction methods and their sensitivity to aspects of real-world proxy networks. Here we develop a dataset that leverages proxy system models (PSMs) for this purpose, which emulates the essential physical, chemical, biological, and geological processes that translate climate signals into proxy records, making these synthetic proxies more relevant to the real world. We apply a suite of PSMs to emulate the widely-used PAGES 2k dataset, including realistic spatiotemporal sampling and error structure. A hierarchical approach allows us to produce many variants of this base dataset, isolating the impact of sampling bias in time and space, representation error, sampling error, and other assumptions. Combining these various experiments produces a rich dataset (“pseudoPAGES2k”) for many applications. As an illustration, we show how to conduct a PPE with this dataset based on emerging climate field reconstruction techniques. 

Externally heated diamond anvil cells provide a stable and uniform thermal environment, making them a versatile device to simultaneously generate high-pressure and high-temperature conditions in various fields of research, such as condensed matter physics, materials science, chemistry, and geosciences. The present study features the Externally Heated Diamond ANvil Cell Experimentation (EH-DANCE) system, a versatile configuration consisting of a diamond anvil cell with a customized microheater for stable resistive heating, bidirectional pressure control facilitated by compression and decompression membranes, and a water-cooled enclosure suitable for vacuum and controlled atmospheres. This integrated system excels with its precise control of both pressure and temperature for mineral and materials science research under extreme conditions. We showcase the capabilities of the system through its successful application in the investigation of the melting temperature and thermal equation of state of high-pressure ice-VII at temperatures up to 1400 K. The system was also used to measure the elastic properties of solid ice-VII and liquid H2O using Brillouin scattering and Raman spectra of carbonates using Raman spectroscopy, highlighting the potential of the EH-DANCE system in high-pressure research.