Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic-Boreal Region of North America

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25 & DEG; gridded LULCC data (i.e., LULCCFLUS and LULCCDemeter, respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between LULCCFLUS and LULCCDemeter, and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations. Land use and land cover change (LULCC) affects the carbon cycle in ecosystems. To predict future LULCC and carbon cycle changes, scientists use spatial downscaling methods to create detailed LULCC maps. However, different methods can lead to different results and can impact carbon cycle projections. Our study found that using different spatial downscaling methods can contribute to a large portion of the uncertainty in future projections of LULCC and carbon cycle over the Arctic-Boreal region. It is important to consider these uncertainties when studying future changes in land use and carbon cycle. We identified a traditionally ignored source of uncertainty in model projected carbon cycle from the future land use and land cover change (LULCC) dataSpatial downscaling is a necessary step for generating gridded LULCC data, but different downscaling methods may lead to results with large spatial differencesThe impacts of using different spatial downscaling methods are more than 79% of the contributions of future LULCC to carbon cycle projections in 2100

Automated summary

Land use and land cover change (LULCC) affects the carbon cycle in ecosystems. To predict future LULCC and carbon cycle changes, scientists use spatial downscaling methods to create detailed LULCC maps. However, different methods can lead to different results and can impact carbon cycle projections. Our study found that using different spatial downscaling methods can contribute to a large portion of the uncertainty in future projections of LULCC and carbon cycle over the Arctic-Boreal region.