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Abstract Deciduous tree cover is expected to increase in North American boreal forests with climate warming and wildfire. This shift in composition has the potential to generate biophysical cooling via increased land surface albedo. Here we use Landsat-derived maps of continuous tree canopy cover and deciduous fractional composition to assess albedo change over recent decades. We find, on average, a small net decrease in deciduous fraction from 2000 to 2015 across boreal North America and from 1992 to 2015 across Canada, despite extensive fire disturbance that locally increased deciduous vegetation. We further find near-neutral net biophysical change in radiative forcing associated with albedo when aggregated across the domain. Thus, while there have been widespread changes in forest composition over the past several decades, the net changes in composition and associated post-fire radiative forcing have not induced systematic negative feedbacks to climate warming over the spatial and temporal scope of our study. 

Abstract Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage. 

ABSTRACT We investigate the strong-lensing cluster Abell 370 (A370) using a wide Integral Field Unit (IFU) spectroscopic mosaic from the Multi-Unit Spectroscopic Explorer (MUSE). IFU spectroscopy provides significant insight into the structure and mass content of galaxy clusters, yet IFU-based cluster studies focus almost exclusively on the central Einstein-radius region. Covering over 14 arcmin2, the new MUSE mosaic extends significantly beyond the A370 Einstein radius, providing, for the first time, a detailed look at the cluster outskirts. Combining these data with wide-field, multi-band Hubble Space Telescope (HST) imaging from the BUFFALO project, we analyse the distribution of objects within the cluster and along the line of sight. Identifying 416 cluster galaxies, we use kinematics to trace the radial mass profile of the halo, providing a mass estimate independent from the lens model. We also measure radially averaged properties of the cluster members, tracking their evolution as a function of infall. Thanks to the high spatial resolution of our data, we identify six cluster members acting as galaxy–galaxy lenses, which constrain localized mass distributions beyond the Einstein radius. Finally, taking advantage of MUSE’s 3D capabilities, we detect and analyse multiple spatially extended overdensities outside of the cluster that influence lensing-derived halo mass estimates. We stress that much of this work is only possible thanks to the robust, extended IFU coverage, highlighting its importance even in less optically dense cluster regions. Overall, this work showcases the power of combining HST + MUSE, and serves as the initial step towards a larger and wider program targeting several clusters. 

Abstract Arctic warming can influence tundra ecosystem function with consequences for climate feedbacks, wildlife and human communities. Yet ecological change across the Arctic tundra biome remains poorly quantified due to field measurement limitations and reliance on coarse-resolution satellite data. Here, we assess decadal changes in Arctic tundra greenness using time series from the 30 m resolution Landsat satellites. From 1985 to 2016 tundra greenness increased (greening) at ~37.3% of sampling sites and decreased (browning) at ~4.7% of sampling sites. Greening occurred most often at warm sampling sites with increased summer air temperature, soil temperature, and soil moisture, while browning occurred most often at cold sampling sites that cooled and dried. Tundra greenness was positively correlated with graminoid, shrub, and ecosystem productivity measured at field sites. Our results support the hypothesis that summer warming stimulated plant productivity across much, but not all, of the Arctic tundra biome during recent decades.