Combining Eddy Covariance Towers, Field Measurements, and the MEMS 2 Ecosystem Model Improves Confidence in the Climate Impacts of Bioenergy With Carbon Capture and Storage

Abstract

Carbon dioxide removal technologies such as bioenergy with carbon capture and storage (BECCS) are required if the effects of climate change are to be reversed over the next century. However, BECCS demands extensive land use change that may create positive or negative radiative forcing impacts upstream of the BECCS facility through changes to in situ greenhouse gas fluxes and land surface albedo. When quantifying these upstream climate impacts, even at a single site, different methods can give different estimates. Here we show how three common methods for estimating the net ecosystem carbon balance of bioenergy crops established on former grassland or former cropland can differ in their central estimates and uncertainty. We place these net ecosystem carbon balance forcings in the context of associated radiative forcings from changes to soil N₂O and CH₄ fluxes, land surface albedo, embedded fossil fuel use, and geologically stored carbon. Results from long term eddy covariance measurements, a soil and plant carbon inventory, and the MEMS 2 process-based ecosystem model all agree that establishing perennials such as switchgrass or mixed prairie on former cropland resulted in net negative radiative forcing (i.e., global cooling) of −26.5 to −39.6 fW m⁻² over 100 years. Establishing these perennials on former grassland sites had similar climate mitigation impacts of −19.3 to −42.5 fW m⁻². However, the largest climate mitigation came from establishing corn for BECCS on former cropland or grassland, with radiative forcings from −38.4 to −50.5 fW m⁻², due to its higher plant productivity and therefore more geologically stored carbon. Our results highlight the strengths and limitations of each method for quantifying the field scale climate impacts of BECCS and show that utilizing multiple methods can increase confidence in the final radiative forcing estimates.