Downscaling of Long-Term Global Scenarios to Regions with a Forest Sector Model

How the future of the global forest sink depends on timber demand, forest management, and carbon policies

Deforestation has contributed significantly to net greenhouse gas emissions, but slowing deforestation, regrowing forests and other ecosystem processes have made forests a net sink. Deforestation will still influence future carbon fluxes, but the role of forest growth through aging, management, and other silvicultural inputs on future carbon fluxes are critically important but not always recognized by bookkeeping and integrated assessment models. When projecting the future, it is vital to capture how management processes affect carbon storage in ecosystems and wood products. This study uses multiple global forest sector models to project forest carbon impacts across 81 shared socioeconomic (SSP) and climate mitigation pathway scenarios. We illustrate the importance of modeling management decisions in existing forests in response to changing demands for land resources, wood products and carbon. Although the models vary in key attributes, there is general agreement across a majority of scenarios that the global forest sector could remain a carbon sink in the future, sequestering 1.2-5.8 GtCO2e/yr over the next century. Carbon fluxes in the baseline scenarios that exclude climate mitigation policy ranged from -0.8 to 4.9 GtCO2e/yr, highlighting the strong influence of SSPs on forest sector model estimates. Improved forest management can jointly increase carbon stocks and harvests without expanding forest area, suggesting that carbon fluxes from managed forests systems deserve more careful consideration by the climate policy community.

Global simulation of fine resolution land use/cover change and estimation of aboveground biomass carbon under the shared socioeconomic pathways

Land use change driven by human activities plays a critical role in the terrestrial carbon budget through habitat loss and vegetation change. Despite the projections of the global population and economic growth under the framework of the Shared Socioeconomic Pathways (SSPs), little is known of land use/cover change (LUCC) at a fine spatial resolution and how carbon pools respond to LUCC under different SSPs. This study projected the future global LUCC with 1 km spatial resolution and a 10-year time step from 2010 to 2100 and then explored its direct impacts on aboveground biomass carbon (AGB) under SSPs. Scenario SSP3 yields the highest global cropland expansion, among which approximately 48% and 46% is expected to be located in the current forest land and grassland, respectively. Scenario SSP1 has the largest forest expansion and is mainly converted from grassland (54%) and cropland (30%). Due to the spatial change in land use/cover, global AGB loss is expected to reach approximately 3.422 Pg C in 2100 under scenario SSP3 and increases by approximately 0.587 Pg C under scenario SSP1. Africa is expected to lose 30% of AGB under the scenario SSP3. Aboveground biomass in Asia will fix 0.774 Pg C to reverse the AGB loss in 2100 under scenario SSP1. The global carbon loss estimated by the land use products with 10 km and 25 km resolution are less than that with 1 km by 1.5% (ranging from -11.2% in Africa to +34.0% in Oceania) and 2.9% (ranging from -11.8% in Africa to +24.0% in Oceania), respectively. These findings suggest that sufficient spatial details in the existing SSP scenario projections could reduce the uncertainties of AGB assessment, and reasonable land use development and management is a key measure to mitigate the negative impacts of LUCC on the biomass carbon pool.

A Tool for Long-Term Forest Stand Projections of Swedish Forests

The analysis of forest management strategies at landscape and regional levels forms a vital part of finding viable directions that will satisfy the many services expected of forests. This article describes the structure and content of a stand simulator, GAYA, which has been adapted to Swedish conditions. The main advantage of the GAYA implementation compared to other resources is that it generates a large number of management programmes within a limited time frame. This is valuable in cases where the management programmes appear as activities in linear programming (LP) problems. Two methods that are engaged in the projections, a climate change response function and a soil carbon model, are designed to complement other methods, offering transparency and computational effectiveness. GAYA is benchmarked against projections from the Heureka system for a large set of National Forest Inventory (NFI) plots. The long-term increment for the entire NFI set is smaller for GAYA compared with Heureka, which can be attributed to different approaches for modelling the establishment of new forests. The carbon pool belonging to living trees shows the same trend when correlated to standing volume. The soil carbon pool of GAYA increases with increased standing volume, while Heureka maintains the same amount over the 100-year projection period.