Report on new Forest Management Practices (FMP) in forest models and implications for land cover change parametrisation in climate models

Response of both forest models (i.e., PICUS and 3D-CMCC-FEM) on forest properties is stronger for alternative management options, than for different climate forcings, such as temperature, precipitation or CO2 concentrations. This is a crucial finding highlighting the importance of forest management in mitigating climate change impacts, reinforcing the role of adaptive strategies in forest conservation. No tree harvesting (FM0) and extensive tree harvesting (FM4, that is, continuous harvesting at low intensity) results in accumulating tree carbon, leaf area index and other forest characteristics related to stand density and tree size. While the removal of trees through different FMP (i.e., FM2, FM3, FM7 and FM8) leads to an evident reduction in tree carbon at the stand level stocks, compared to FM0, aggregating multiple forest stands into a mosaic the overall impact is buffered, but still detectable. This is because the loss in one stand, especially associated to final harvest at the end of rotation period, is compensated by growth in others. Still the effects of forest management remains detectable for aggregated stands and thus forests across larger scales. This finding emphasizes the importance of considering forest management impacts on smallscale (forest stands), that combined determine the properties of forests on larger scales (i.e., regions, forest enterprises, countries), when implementing forest management in climate or land-surface models. The differences between PICUS and 3D-CMCC-FEM in estimating potential carbon stocks without interventions stem from variations in biomass allometries and varying selfthinning, and mortality processes. These differences create uncertainty in model outputs, which can be addressed by validating the models with reference satellite data (e.g. net primary production, gross primary production, LAI, evapotranspiration) and ground observations. Examining uncertainty is a crucial next step to enhance the consistency of forest models and ensure they closely align with real present conditions. Forest and climate models will be linked using simulation results derived from selected FMP. Forest simulation outputs will provide critical data, such as LAI, canopy height, and other forest structure metrics, which can be applied in climate (RegCM and REMO-iMOVE) and land-surface (JULES) models. First, this data can be integrated to update and refine the land cover input datasets, allowing for more accurate representations of current forest conditions. Otherwise, the data can be used to adjust the model’s parameterization by incorporating a new PFT. Both approaches ensure that the FMP influences the model representation of surface fluxes, as well as other key surface parameters within climate model grid cells. As a result, these changes influence landatmosphere interactions, affecting local and regional climatic processes. Including FMP in climate models enhances the accuracy of future climate predictions by capturing the mutual relationship between forest dynamics and climate under varying management practices. For more information, please visit the project website: https://optforeu.eu/