Legacies of model initialization on predictions of future ecosystem dynamics in California's Sierra Nevada: insights...

Accurate estimates of aboveground vegetation structure are essential for making reliable predictions of terrestrial ecosystem responses to climate change. However, traditional small-scale ground-based inventory methods cannot easily be scaled up to comprehensive, large-scale estimates of ecosystem structure. We assimilate remotely-sensed Light Detection and Ranging measurements of vegetation structure and corresponding imaging-spectrometry-derived estimates of canopy composition into the ecosystem demography (ED2.2) terrestrial biosphere model across an elevational transect in California's Sierra Nevada. We then used the model to assess: how incorporating observed ecosystem structure and composition influences predictions of ecosystem change over the coming century as compared to simulations initialized with long-term potential vegetation; and how ecosystems are predicted to respond differently to future climate change. Our analyses show multi-decadal impacts of initialization on predictions of ecosystem composition and structure, emphasizing long-term legacies of climate and disturbance history in predictions of ecosystem responses to climate change that are not captured when models are initialized with outputs from long-term historical simulations. The remote sensing-initialized simulations predict increases in aboveground biomass and leaf area index, and pronounced elevation-dependent changes in canopy composition. The differences among initialization methods, climate scenarios, and elevational gradients have important implications for improving ecosystem modeling and informing land management strategies.