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Included are 100 years of monthly mean ocean model output from CESM1.2 integrations for the Eocene carried out by Adam Aleksinski and Matthew Huber, with critical assistance from Alexandra Jahn, and with assistance and support from Jiang Zhu (NCAR). These simulations were carried out at NCAR. These simulations incorporate results using the standard Eocene Deepmip 1 boundary conditions (Lunt et al, 2017), including the boundary condition datasets (Herold et al., 2014), and were branched off originally from simulations carried out at NCAR by Jiang Zhu (Zhu et al., 2020). The three simulations included here incorporate neodymium in them for the first time and span a range of CO2 and gateway configurations that make it appropriate for the Middle Eocene to late Eocene. The continuation (“SF_SU_55Ma_init-hycont”) experiment run continued from the end of Zhu et al. (2020)’s 3x preindustrial pCO2 (854.1 ppm) experiment, which used DeepMIP compliant geography and bathymetry for simulating the early Eocene. This simulation was run for a total of 4,800 years. Two runs each branched from this SF_SU_55Ma_init-hycont after 1,200 years of runtime, and each ran for 3,600 years after that point. In the Open Drake Passage experimental run (SF_SU_55Ma_open-hycont), the atmospheric pCO2 concentration from the continuation simulation was retained, and the bathymetry of the Drake Passage and Tasman Seaway were both lowered to a depth of 1973 mbsl. In the Halved pCO2 experiment SF_SU_55Ma_cool), the Herold et al original bathymetry was retained, but atmospheric pCO2 was reduced by a factor of half, to 427.05 ppm. The model output is global in extent and is netcdf format, which has been tarred and gzipped, and follows standard conventions for ocean GCMs. The data are on an irregular 'POP' grid. All the necessary information to read and process these data are included in the netcdf metadata. 

Abstract Projections of a sea ice-free Arctic have so far focused on monthly-mean ice-free conditions. We here provide the first projections of when we could see the first ice-free day in the Arctic Ocean, using daily output from multiple CMIP6 models. We find that there is a large range of the projected first ice-free day, from 3 years compared to a 2023-equivalent model state to no ice-free day before the end of the simulations in 2100, depending on the model and forcing scenario used. Using a storyline approach, we then focus on the nine simulations where the first ice-free day occurs within 3–6 years, i.e. potentially before 2030, to understand what could cause such an unlikely but high-impact transition to the first ice-free day. We find that these early ice-free days all occur during a rapid ice loss event and are associated with strong winter and spring warming. 

Long electron spin coherence lifetimes are crucial for high sensitivity and resolution in many pulse electron paramagnetic resonance (EPR) experiments aimed at measuring hyperfine and dipolar couplings, as well as in potential quantum sensing applications of molecular spin qubits. In immobilized systems, methyl groups contribute significantly to electron spin decoherence as a result of methyl torsional quantum tunneling. We examine the electron spin decoherence dynamics of the nitroxide radical 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) in both a methyl-free solvent and a methyl-containing solvent at cryogenic temperature. We model nitroxide and solvent methyl effects on decoherence using cluster correlation expansion (CCE) simulations extended to include methyl tunneling and compare the calculations to experimental data. We show that by using the methyl tunneling frequency as a fit parameter, experimental Hahn echo decays can be reproduced fairly well, allowing structural properties to be investigated in silico. In addition, we examine the Hahn echo of a hypothetical system with an unpaired electron and a single methyl to determine the effect of geometric configuration on methyl-driven electron spin decoherence. The simulations show that a methyl group contributes the most to electron spin decoherence if it is located between 2.5 and 6–7 Å from the electron spin, with its orientation being of secondary importance. 

We consider the problem of \emph{identifying,} from statistics, a distribution of discrete random variables $$X_1 \ldots,X_n$$ that is a mixture of $$k$$ product distributions. The best previous sample complexity for $$n \in O(k)$$ was $$(1/\zeta)^{O(k^2 \log k)}$$ (under a mild separation assumption parameterized by $$\zeta$$). The best known lower bound was $$\exp(\Omega(k))$$. It is known that $$n\geq 2k-1$$ is necessary and sufficient for identification. We show, for any $$n\geq 2k-1$$, how to achieve sample complexity and run-time complexity $$(1/\zeta)^{O(k)}$$. We also extend the known lower bound of $$e^{\Omega(k)}$$ to match our upper bound across a broad range of $$\zeta$$. Our results are obtained by combining (a) a classic method for robust tensor decomposition, (b) a novel way of bounding the condition number of key matrices called Hadamard extensions, by studying their action only on flattened rank-1 tensors. 

Abundant proxy records suggest a profound reorganization of the Atlantic Meridional Overturning Circulation (AMOC) during the Last Glacial Maximum (LGM, ~21,000 y ago), with the North Atlantic Deep Water (NADW) shoaling significantly relative to the present-day (PD) and forming Glacial North Atlantic Intermediate Water (GNAIW). However, almost all previous observational and modeling studies have focused on the zonal mean two-dimensional AMOC feature, while recent progress in the understanding of modern AMOC reveals a more complicated three-dimensional structure, with NADW penetrating from the subpolar North Atlantic to lower latitude through different pathways. Here, combining²³¹Pa/²³⁰Th reconstructions and model simulations, we uncover a significant change in the three-dimensional structure of the glacial AMOC. Specifically, the mid-latitude eastern pathway (EP), located east of the Mid-Atlantic Ridge and transporting about half of the PD NADW from the subpolar gyre to the subtropical gyre, experienced substantial intensification during the LGM. A greater portion of the GNAIW was transported in the eastern basin during the LGM compared to NADW at the PD, resulting in opposite²³¹Pa/²³⁰Th changes between eastern and western basins during the LGM. Furthermore, in contrast to the wind-steering mechanism of EP at PD, the intensified LGM EP was caused primarily by the rim current forced by the basin-scale open-ocean convection over the subpolar North Atlantic. Our results underscore the importance of accounting for three-dimensional oceanographic changes to achieve more accurate reconstructions of past AMOC. 

This dataset contains the daily Arctic sea ice area (SIA) and sea ice extent (SIE) data for all CMIP6 models and the historical period based on the NOAA/NSIDC Climate Data Record (CDR) created for Heuzé and Jahn, The first ice-free day in the Arctic Ocean could occur before 2030, accepted, Nature Communications. This is a derived dataset based on publicly available underlying data: - For the CMIP6 data, the SIA and SIE data included here is based on the daily siconc and siconca CMIP6 model output freely available on the CMIP6 data portals (https://pcmdi.llnl.gov/CMIP6/). These pan-Arctic daily SIA and SIE were calculated north of 30N, on each model's native grid, using each models grid area data (areacello or areacella). SIA was defined as sea ice concentration multiplied by the grid cell area and summed over all grid cells. SIE was defined as the sum of the grid cell area for all grid cells where the sea ice concentration was larger than 0.15. All processed SIA and SIE data is included in this dataset, even if the model was later excluded from the analysis for one reason or another (see Heuzé and Jahn 2024, Methods section). All data included has the same number of days as the underlying model. The historical data spans 1980-2014 and can be found in the CMIP6_historical_data.zip file, and the scenario data spans 2015 to the end of the 21st century simulation, for multiple scenarios (SSPs), and can be found in CMIP6_ssp_data.zip. Files are provided as .zip files to make it easy to download all data at once, as the SIA and SIE data is saved in one file per model and ensemble member, and for the scenario simulations, also per ssp. - For the NOAA/NSIDC Climate Data Record (CDR), the SIA and SIE data included here is based on the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, doi:10.7265/efmz-2t65, Meier et al 2021. The sea ice concentration is multiplied by the grid size of each grid box, for this data, 25x25 kilometers (km) = 625 kilometers squared (km2), and then summed over the full domain. In doing that, we include the interpolated data in the pole hole as included in the sea ice concentration data, but exclude all land/coastal grid points (i.e., values > 2.5 in the underlying data). As the filename indicates, we removed all leap year data from this data (dropped every Feb 29th) so that all years have 365 days. Note that while the file name says this data is for 19790101 to 20231231, it does indeed include 1978 as first year (so 1978-01-01-2023-12-31), with daily data starting on 1978-10-25 (nan before then). We did not change the name of the data file to still allow all archived scripts using this datafile to run. Scripts that work on this data associated with Heuzé and Jahn (2024) can be found at: https://zenodo.org/records/14008665, doi:10.5281/zenodo.14006059 References: Meier, W. N., F. Fetterer, A. K. Windnagel, and S. Stewart. 2021. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. Boulder, Colorado, USA. NSIDC: National Snow and Ice Data Center https://doi.org/10.7265/efmz-2t65 

Attention filters sensory inputs to enhance task-relevant information. It is guided by an “attentional template” that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks. 

Transportation emissions of aromatic hydrocarbons such as xylenes evolve in the atmosphere due to radical oxidation and can form a variety of products, including secondary organic aerosol (SOA).