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Improving our ability to understand and predict the dynamics of the terrestrial carbon cycle remains a pressing challenge despite a rapidly growing volume and diversity of Earth Observation data. State data assimilation represents a path forward via an iterative cycle of making process-based forecasts and then statistically reconciling these forecasts against numerous ground-based and remotely-sensed data constraints into a “reanalysis” data product that provides full spatiotemporal carbon budgets with robust uncertainty accounting. Here we report on an >100x expansion of the PEcAn+SIPNET reanalysis from 500 sites CONUS, 25 ensemble members, and 2 data constraints to 6400 sites across North America, 100 ensemble members, and 5 data constraints: GEDI and Landtrendr AGB, MODIS LAI, SoilGrids Soil C, and SMAP soil moisture. We also report on an ensemble-based machine learning (ML) downscaling to a 1km product that preserves spatial, temporal, and across-variable covariances and demonstrate the impacts of these covariances on uncertainty accounting (Fig. 1). Synergistically, we use the same ML models to assess what climate, vegetation, and soil variables explain the spatiotemporal variability in different C pools and fluxes. In addition, we review a wide range of ongoing validation activities, comparing the outputs of the reanalysis against withheld data from: Ameriflux and NEON NEE and LE; USFS Forest Inventory biomass, biomass increment, tree rings, soil C, and litter; and NEON soil C and soil respiration. Finally, we touch on ML analyses to diagnose and correct systematic biases and emulator-based recalibration efforts. 

A concise strategy to prepare polycyclic heteroaromatics involving a deaminative contraction cascade is detailed. 

Abstract Ferromanganese concretions commonly occur in shallow‐water coastal regions worldwide. In the Baltic Sea, they can record information about past and present underwater environments and could be a potential source for critical raw materials. We report on their microstructural characteristics and magnetic properties and link them to their formation mechanisms and environmental significance. Microstructural investigations from nano‐ and micro‐computed tomography, electron microscopy, and micro‐X‐ray fluorescence elemental mapping reveal diverse growth patterns within concretions of different morphologies. Alternating Fe‐ and Mn‐rich growth bands indicate fluctuating redox conditions during formation. Bullet‐shaped magnetofossils, produced by magnetotactic bacteria, are present, which suggests the influence of bacterial activity on concretion formation. Spheroidal concretions, which occur in deeper and more tranquil environments, have enhanced microbial biomineralization and magnetofossil preservation. Conversely, crusts and discoidal concretions from shallower and more energetic environments contain fewer magnetofossils and have a greater detrital content. Our results provide insights into concretion formation mechanisms and highlight the importance of diagenetic processes, oxygen availability, and bacterial activity in the Baltic Sea. 

SUMMARY Greigite is a sensitive environmental indicator and occurs commonly in nature as magnetostatically interacting framboids. Until now only the magnetic response of isolated non-interacting greigite particles have been modelled micromagnetically. We present here hysteresis and first-order reversal curve (FORC) simulations for framboidal greigite (Fe3S4), and compare results to those for isolated particles of a similar size. We demonstrate that these magnetostatic interactions alter significantly the framboid FORC response compared to isolated particles, which makes the magnetic response similar to that of much larger (multidomain) grains. We also demonstrate that framboidal signals plot in different regions of a FORC diagram, which facilitates differentiation between framboidal and isolated grain signals. Given that large greigite crystals are rarely observed in microscopy studies of natural samples, we suggest that identification of multidomain-like FORC signals in samples known to contain abundant greigite could be interpreted as evidence for framboidal greigite. 

SUMMARY Quasi-linear field-dependence of remanence provides the foundation for sedimentary relative palaeointensity studies that have been widely used to understand past geomagnetic field behaviour and to date sedimentary sequences. Flocculation models are often called upon to explain this field dependence and the lower palaeomagnetic recording efficiency of sediments. Several recent studies have demonstrated that magnetic-mineral inclusions embedded within larger non-magnetic host silicates are abundant in sedimentary records, and that they can potentially provide another simple explanation for the quasi-linear field dependence. In order to understand how magnetic inclusion-rich detrital particles acquire sedimentary remanence, we carried out depositional remanent magnetization (DRM) experiments on controlled magnetic inclusion-bearing silicate particles (10–50 μm in size) prepared from gabbro and mid-ocean ridge basalt samples. Deposition experiments confirm that the studied large silicate host particles with magnetic mineral inclusions can acquire a DRM with accurate recording of declination. We observe a silicate size-dependent inclination shallowing, whereby larger silicate grains exhibit less inclination shallowing. The studied sized silicate samples do not have distinct populations of spherical or platy particles, so the observed size-dependence inclination shallowing could be explained by a ‘rolling ball’ model whereby larger silicate particles rotate less after depositional settling. We also observe non-linear field-dependent DRM acquisition in Earth-like magnetic fields with DRM behaviour depending strongly on silicate particle size, which could be explained by variable magnetic moments and silicate sizes. Our results provide direct evidence for a potentially widespread mechanism that could contribute to the observed variable recording efficiency and inclination shallowing of sedimentary remanences. 

Abstract In theory, the same sea-ice models could be used for both research and operations, but in practice, differences in scientific and software requirements and computational and human resources complicate the matter. Although sea-ice modeling tools developed for climate studies and other research applications produce output of interest to operational forecast users, such as ice motion, convergence, and internal ice pressure, the relevant spatial and temporal scales may not be sufficiently resolved. For instance, sea-ice research codes are typically run with horizontal resolution of more than 3 km, while mariners need information on scales less than 300 m. Certain sea-ice processes and coupled feedbacks that are critical to simulating the Earth system may not be relevant on these scales; and therefore, the most important model upgrades for improving sea-ice predictions might be made in the atmosphere and ocean components of coupled models or in their coupling mechanisms, rather than in the sea-ice model itself. This paper discusses some of the challenges in applying sea-ice modeling tools developed for research purposes for operational forecasting on short time scales, and highlights promising new directions in sea-ice modeling.