Forest responses to future climate change are highly uncertain, but critical for forecasting and managing forest carbon dynamics. The US Forest Service Forest Inventory and Analysis (FIA) program currently provides decadal estimates of standing forest Carbon stocks across space, but lacks detail about how annual climate variation affects Carbon uptake. Tree-ring time series data can fill this gap, providing annually-resolved growth responses to climate, a key response for understanding forest Carbon response to near-term climate change. We applied principles of ecological forecasting (data fusion, model validation with incoming data, and uncertainty quantification) to combine these two data sources, tree-ring time series and repeat forest inventory measurements, in order to quantify uncertainties around forest carbon, and improve forecasts. We use a Bayesian state-space model to fuse annual tree growth measurements from >1000 ponderosa pine tree-ring time series and repeat measurements of tree diameters in the intermountain west US. This modeling approach yields estimates of annual tree growth and diameter from 1965-2018, forecasts of tree growth from 2018-2100,  and allows us to parse the ecological drivers of tree growth in the past and in future forecasts.  Forest aboveground biomass and biomass increment are then estimated by applying a Bayesian allometric scaling model, and stochastic mortality processes to the posterior diameter estimates of all trees in >650 FIA plots. Quantification of uncertainty around these forecasts identifies paths towards model improvement. Fusion of forest inventory data with tree ring growth advances forecasting of forest carbon in two ways – first, it provides empirically-constrained forecasts of how climate change will influence both tree-level and stand scale biomass over time, including the uncertainty surrounding this response. Second, this data fusion approach, and the routine remeasurement of FIA forest plots sets the stage for an iterative forecasting system of forest carbon, and expansion of this approach across the US FIA network.

Place:   Math, 402