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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.

 

Long‐term agricultural field experiments (LTFEs) have been conducted for nearly 150 years. Yet lack of coordination means that synthesis across such experiments remains rare, constituting a missed opportunity for deriving general principles of agroecosystem structure and function. Here, we introduce the Diverse Rotations Improve Valuable Ecosystem Services (DRIVES) project, which uses legacy data from North American LTFEs to address research questions about the multifunctionality of agriculture. The DRIVES Project is a network of researchers who have compiled a database of primary (i.e., observations) and secondary (i.e., transformed observations or modeling results) data from participating sites. It comprises 21 LTFEs that evaluate how crop rotational diversity impacts cropping system performance. The Network consists of United States Department of Agriculture, university, and International Maize and Wheat Improvement Center scientists (20 people) who manage and collect primary data from LTFEs and a core team (nine people) who organize the network, curate network data, and synthesize cross‐network findings. As of 2024, the DRIVES Project database contains 495 site‐years of crop yields, daily weather, soil analysis, and management information. The DRIVES database is findable, accessible, interoperable, and reusable, which allows integration with other public datasets. Initial research has focused on how rotational diversity impacts resilience in the face of adverse weather, nutritional quality, and economic feasibility. Our collaborative approach in handling LTFE data has established a model for data organization that facilitates broader synthesis studies. We openly invite other sites to join the DRIVES network and share their data.

 

This work was conducted by the Diverse Rotations Improve Valuable Ecosystem Services (DRIVES) project, based in the USDA-ARS Sustainable Agricultural Systems Lab in Beltsville, MD. The DRIVES team compiled a database of 20-plus long-term cropping systems experiments in North America in order to conduct cross-site research. This repository contains all scripts from our first research paper from the DRIVES database: "Rotational complexity increases cropping system output under poorer growing conditions," published in One Earth (in press). This analysis uses crop yield and experimental design data from the DRIVES database and public data sources for crop prices and inflation. This repository includes limited datasets derived from public sources or lacking connection to site IDs. We do not have permission to share the full primary dataset, but can provide data upon request with permission from site contacts.The scripts show all data setup, analysis, and visualization steps used to investigate how crop rotation diversity (defined by rotation length and the number of species) impacts productivity of whole rotations and component crops under varying growing conditions. We used Bayesian multilevel modeling fit to data from 20 long-term cropping systems datasets in North America (434 site-years, 36,000 observations). Rotation- and crop-level productivity were quantified as dollar output, using price coefficients derived from National Agriculture Statistics Service (NASS) price data (included in repository). Growing condtions were quantified using an Environmental Index calculated from site-year average output. Bayesian multilevel models were implemented using the 'brms' R package, which is a wrapper for Stan. Descriptions of all files are included in README.pdf. 

This paper investigates the dynamics of systemic risk in banking networks by analyzing equilibrium points and stability conditions. The focus is on a model that incorporates interactions among distressed and undistressed banks. The equilibrium points are determined by solving a reduced system of equations, considering both homogeneous and heterogeneous scenarios. Local and global stability analyses reveal conditions under which equilibrium points are stable or unstable. Numerical simulations further illustrate the dynamics of systemic risk, while the theoretical findings offer insights into the behavior of distressed banks under varying conditions. Overall, the model enhances our understanding of systemic financial risk and offers valuable insights for risk management and policymaking in the banking sector.

 

A covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the calibration is enabled by the emulation of the high-fidelity model through the implementation of a reduced basis method (RBM)—a set of dimensionality reduction techniques that can speed up demanding calculations involving partial differential equations by several orders of magnitude. The RBM emulator we build—using only 100 evaluations of the high-fidelity model—is able to accurately reproduce the model calculations in tens of milliseconds on a personal computer, an increase in speed of nearly a factor of 3,300 when compared to the original solver. Besides the analysis of the posterior distribution of parameters, we present model calculations for masses and radii with properly estimated uncertainties. We also analyze the model correlation between the slope of the symmetry energy L and the neutron skin of 48 Ca and 208 Pb. The straightforward implementation and outstanding performance of the RBM makes it an ideal tool for assisting the nuclear theory community in providing reliable estimates with properly quantified uncertainties of physical observables. Such uncertainty quantification tools will become essential given the expected abundance of data from the recently inaugurated and future experimental and observational facilities.