Constraining boreal carbon allocation and turnover by assimilating forest growth dynamics in a differentiable framework

Abstract. Terrestrial biosphere models (TBMs) exhibit substantial uncertainty in simulating the land carbon sink in particular at interannual to decadal time scales, partly because parameters that govern carbon allocation and biomass turnover rates are weakly constrained. Typical model-data fusion approaches, which rely heavily on high-frequency (minutes to days) observations related to fluxes (e.g., gross primary production and leaf area index), often struggle to constrain the slow turnover processes that govern long-term biomass accumulation over multiple years. Here, we employ DifferLand, a JAX-based differentiable TBM, to jointly assimilate satellite-derived boreal forest biomass growth trajectories and high-frequency observations. Calibrations using only high-frequency fluxes reproduce short-term dynamics but yield large biases in mature forest biomass (RMSE = 138.7 Mg ha^-1), while incorporating a single-year biomass stock constraint only partially reduces the error (RMSE = 87.5 Mg ha^-1) and provides limited constraints on the allocation patterns. In contrast, incorporating our biomass growth curves reduce the biomass RMSE to 11.3 Mg ha^-1 (a 91.9 % reduction) without degrading fits to high-frequency fluxes. Our findings reveal that while a single-year biomass stock data provides some constraints on biomass residence times, the full growth trajectory is essential to simultaneously constrain carbon allocation and turnover. The retrieved parameters indicate that boreal forests sustain biomass primarily through longer wood carbon residence times (74.1 % lower wood turnover rates) rather than higher allocation to wood, compared to calibrations using only high-frequency flux observations. Attribution analyses further show that climate conditions are the dominant driver of wood turnover, with a sharp increase when the temperature of the coldest month exceeds -20 °C. Our study demonstrates the importance of assimilating slow ecological trajectories to improve long-term predictions of carbon storage and highlights the potential acceleration of the sensitivity of the boreal carbon sink to warming under future climate change.