Forest responses to climate change are highly uncertain, but critical for forecasting and managing forest carbon dynamics. Tree-ring time series data provide annually resolved growth responses to climate, but often lack the stand-level information needed to scale growth up to carbon uptake. In contrast, the U. S. Forest Service Forest Inventory and Analysis (FIA) Program is an exceptional spatial network to estimate forest carbon, but lacks the annual resolution needed to determine how tree growth and carbon uptake respond to interannual climate variation. Tree rings sampled from national inventories thus provide a unique opportunity to include climate sensitivity in forecasts of tree- and plot-level aboveground biomass. We used a Bayesian state-space model to fuse tree-ring time series sourced in the U. S. national forest inventory with decadal measurements of bole diameter from > 900 ponderosa pine across the interior western U.S., and estimated the effects of climate, stand density, tree size, and their two-way interactions on tree growth. We estimated tree diameter and annual diameter increment for all trees in each inventory plot, then scaled to aboveground tree- and plot-level biomass estimates using allometric scaling. Fusion of forest inventory data with tree-ring growth advances forecasting of tree growth and plot-level biomass across space and time– first, it provides empirically-constrained forecasts of how climate change influences tree growth and aboveground biomass over time, including the uncertainty surrounding this response, and second, it provides a framework to quantify how tree- and site-level factors (i.e. tree size, tree density, plot conditions) drive spatial variation in forest carbon dynamics.