Atmospheric mass balance analyses suggest terrestrial carbon (C) storage is increasing, partially abating the atmospheric CO2 growth rate. Yet, the continued strength of this critical ecosystem service to humanity remains highly uncertain and the subject of intense scientific debate. Some evidence suggests that terrestrial C sink strength will persist due to the continued enhancement of vegetation photosynthesis by rising atmospheric CO2 (i.e., ‘CO2 fertilization’). Other lines of evidence indicate that the CO2 fertilization effect may already be largely limited by other factors including emergent nutrient constrains and intensifying aridity.
Results/Conclusions
Here we focus on exploring the opportunities and challenges of utilizing satellite remote sensing observations to constrain estimates of terrestrial CO2 fertilization. As part of this effort, we demonstrate a global-scale framework for assimilation of multiple satellite data streams including photosynthetic capacity – derived from satellite estimates of leaf area index (LAI) – and aboveground biomass – derived from satellite estimates of vegetation optical depth (VOD). We implement an Ensemble Adjustment Kalman Filter (EAKF) using the Data Assimilation Test Bed (DART), which is coupled with the Community Land Model. We show that we can successfully assimilate these independent data streams separately or in combination at the global scale, and we illustrate how this influences model projections, model error, and forecast horizons. We further quantify the potential for non-additive reductions in forecast error when annual photosynthetic capacity and biomass estimates are assimilated in combination. Global scale assimilation of satellite observation to help constrain Earth system model C dynamics offers a promising method that could i) reconcile current divergent theories on CO2 fertilization effects, and ii) enable more accurate forecasts of C cycle – climate feedbacks.