PS 22-18 - Combining nutrient competition, dynamic vegetation, and parameter calibration to improve boreal forest responses to changing climate

Background/Question/Methods

While climate change is impacting all parts of the globe, boreal forests are experiencing disproportionally higher rates of temperature increase, thus drastically impacting one of the largest biomes in the world. Changes in high-latitude forests have strong implications to the regional water and carbon cycling, and shifts in canopy cover (i.e., abundance and shifts between evergreen and deciduous species) will alter albedo, ecosystem productivity, and surface and canopy water fluxes. For example, high latitude warming may increase nutrient availability via a deepening of the soil active layer. Warming also induces permafrost degradation and loss, leading to strong interactions that alter the hydrology, and soil biological and physical processes. These climate-related interactions will affect plant competitive interactions, survival, and ultimately community distribution and carbon storage.

Results/Conclusions

In this study we explore how vegetation dynamics will be affected by changes in soil nutrient supply and plant nutrient uptake strategy due to shifts in PFTs (e.g., faster resource acquisition in deciduous plants), and shifts in carbon allocation. To be able to accurately predict these complex ecological processes we are using the demographic vegetation model FATES (Functionally-Assembled Terrestrial Ecosystem Simulator) that is coupled to the land surface model ELMv1. We present here the newly implemented representation of nutrient competition, acquisition, and extensible approach of nutrient and carbon allocation within plants. This work has successfully coupled the interactions of nutrients between soil biogeochemistry in ELM and plant productivity and carbon in FATES, with improved model hypothesis testing for plant’s nutrient storage capacity. With the inclusion of nutrient cycling the productivity and biomass storage was significantly reduced for the simulated boreal forests. After an uncertainty quantification using neural network surrogates from large ensembles, this sensitivity analysis revealed that model parameters related to carbon storage and leaf economics had the largest sensitivity. We then applied a Bayesian inference approach to calibrate model parameters against observational datasets, and greatly improved model predicts and representation of these vulnerable forests in an Earth System Model.