Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9710): implementation and evaluation of simulations for Europe

Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to projecting the development of forest carbon sink capacity in a future climate. Forest management capabilities are important to include in dynamic vegetation models to account for the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the implementation of a forest management module containing even-age and clear-cut and uneven-age and continuous-cover management alternatives in the dynamic vegetation model LPJ-GUESS. Different age and species structure initialisation strategies and harvest alternatives are introduced. The model is applied at stand and European scales. Different management alternatives are applied in simulations of European beech (Fagus sylvaticus) and Norway spruce (Picea abies) even-aged monoculture stands in central Europe and evaluated against above-ground standing stem volume and harvested volume data from long-term experimental plots. At the European scale, an automated thinning and clear-cut strategy is applied. Modelled carbon stocks and fluxes are evaluated against reported data at the continent and country levels. Including wood harvest in regrowth forests increases the simulated total European carbon sink by 32 % in 1991–2015 and improves the fit to the reported European carbon sink, growing stock, and net annual increment (NAI). Growing stock (156 m3 ha−1) and NAI (5.4 m3 ha1 yr1) densities in 2010 are close to reported values, while the carbon sink density in 2000–2007 (0.085 kg C m−2 yr1) equates to 63 % of reported values, most likely reflecting uncertainties in carbon fluxes from soil given the unaccounted for forest land-use history in the simulations. The fit of modelled and reported values for individual European countries varies, but NAI is generally closer to reported values when including wood harvest in simulations.

Bookkeeping estimates of the net land-use change flux – a sensitivity study with the CMIP6 land-use dataset

The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (Bookkeeping of Land Use Emissions, BLUE) based on a LULCC dataset including uncertainty estimates (the Land-Use Harmonization 2 (LUH2) dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low land-use estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux, while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %; references are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than that from LULCC uncertainty, and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr−1 at an estimate of 1.7 PgC yr−1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period, the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor of 3) than the other two factors for the net LULCC flux in 2014, and historical LULCC uncertainty is negligible for estimates of future scenarios.

Simulated biogeochemical effects of idealized land cover and land management changes

<p>Land cover and land management (LCLM) changes are important sources and sinks for anthropogenic CO<sub>2</sub> fluxes. Current earth system models (ESMs) are capable to simulate the globally most sensitive LCLM changes (strong effects or large spatial extent in the earth system) such as de- and afforestation, wood harvest and irrigation, however, a comprehensive analysis between these ESMs is still absent. The present study aims to quantify the biogeochemical effects of forest cover changes, wood harvesting and irrigation of croplands on the global carbon cycle.</p><p> </p><p>Therefore, we conducted coupled atmosphere-ocean-land experiments of idealized global deforestation with and without cropland irrigation as well as global re-/afforestation with and without wood harvest over a 150-year period under present day solar and trace gas forcing. All experiments were simulated by three different ESMs (MPI-ESM, EC-EARTH and CESM) to quantify inter-model uncertainty and potentially uncover specific model biases. The analysis focuses on the transient response of land carbon fluxes and pools after an abrupt LCLM practice change, in order to track the emergence of signals that could potentially mitigate climate change. Additionally, we want to unravel model differences concerning the temporal dynamics of LCLM change effects. Since greenhouse gases (GHG) concentration is kept constant at present-day level, the climate changes here arise from the biogeophysical effects of LCLM changes. We use a checkerboard approach to separate local and non-local components of the climate changes as proposed by Winckler et al., 2017, i.e. we separate the changes in climate induced locally by the LCLM changes from those induced remotely by advection and changes in atmospheric circulation.</p><p> </p><p>First results with the MPI-ESM show that immediate global deforestation starting from present-day land-use distribution causes a 824 GtC loss of the total land carbon pool throughout the simulation period of 150 years, about 46% of which stem from tropical regions (17°S–17°N). Land carbon stocks are not balanced until the end of the simulation, which indicates that the land will continue to emit CO<sub>2</sub> to the atmosphere and a long-term commitment by deforestation for climate change. Non-local effects lead to a loss of 26 GtC from land, again with largest losses found for the tropical regions. Even though it is a small part compared to the total loss (local plus non-local effect), it reveals potentially substantial consequences that LCLM changes at large scale can have unintendedly on other regions, including remote pristine ones, through biogeophysical climate change.</p>

Carbon Life Cycle Assessment on California-Specific Wood Products Industries: Do Data Backup General Default Values for Wood Harvest and Processing?

Carbon life cycle assessments (C LCA) play a major role in greenhouse gas (GHG)-related forest management analytics for wood products and consist of several steps along a forest to disposal path. Yet, input values for wood product C LCAs frequently rely on potentially outdated generic datasets for wood product outputs and mill efficiencies. Assumptions regarding sawmill efficiencies and sawmill-specific wood product outputs have a direct and significant impact on wood product C LCAs because these variables affect the net carbon footprint of the finished product. The goal of this analysis was to evaluate how well standard wood product C LCA inputs and assumptions for the two initial wood products LCA steps (i) forest operations and (ii) wood processing represent the current state of the wood processing industry in California. We found that sawmill efficiencies and wood product outputs both support and deviate from lookup tables currently used in publications supporting the climate-forest policy dialogue. We recommend further analysis to resolve the major discrepancies in the carbon fraction stored in durable wood products and production-related emissions to improve C LCA metrics and advance forest-related climate policy discussions in California and elsewhere.

Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9333): Implementation and evaluation of simulations for Europe

Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to making projections of the development of forest carbon sink capacity in a future climate. Forest management capabilities in dynamic vegetation models are important to include the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the introduction of a forest management module in the dynamic vegetation model LPJ-GUESS. Different age- and species-structure setup strategies and harvest alternatives are introduced. The model is used to represent current European forests and an automated harvest strategy is applied. Modelled carbon stocks and fluxes are evaluated against observed data at the continent and country levels. Including wood harvest in simulations increases the total European carbon sink by 32 % in 1991–2015 and improves the fit to the reported European carbon sink, growing stock and net annual increment (NAI). Growing stock (156 m3 ha−1) and NAI (5.4 m3 ha−1 y−1) densities in 2010 are close to reported values, while the carbon sink density in 2000–2007 (0.085 kgC m−2 y−1) is 63 % of reported values. The fit of modelled values and observations for individual European countries vary, but NAI is generally closer to observations when including wood harvest in simulations.

Recent increase in European forest harvests as based on area estimates (Ceccherini et al. 2020a) not confirmed in the French case

• Key message A recent paper by Ceccherini et al.(2020a) reported an abrupt increase of 30% in the French harvested forest area in 2016–2018 compared to 2004–2015. A re-analysis of their data rather led us to conclude that, when accounting for the singular effect of storm Klaus, the rate of change in harvested area depended on the change year used to separate the two periods to compare. Moreover, the comparison with data on harvested volumes from different sources brought contrasted results depending on the source. Therefore, it cannot be concluded that wood harvest increased in France in 2016–2018 compared to 2004–2015. The discrepancy between Ceccherini et al.’s data and other data on harvested volumes points out the difficulty of reconciling different approaches to estimate wood harvest at a country level.

Net land-use change carbon flux estimates and sensitivities – An assessment with a bookkeeping model based on CMIP6 forcing

The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (BLUE) based on a LULCC dataset including uncertainty estimates (the LUH2 dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low land- use estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %, reference are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than from LULCC uncertainty and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr−1 at an estimate of 1.7 PgC yr−1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor three) than the other two factors for the net LULCC flux in 2014 and historical LULCC uncertainty is negligible for estimates of future scenarios.

GHG displacement factors of harvested wood products: the myth of substitution

A common idea is that substituting wood for fossil fuels and energy intensive materials is a better strategy in mitigating climate change than storing more carbon in forests. This opinion remains highly questionable for at least two reasons. Firstly, the carbon footprints of wood-products are underestimated as far as the “biomass carbon neutrality” assumption is involved in their determination, as it is often the case. When taking into account the forest carbon dynamics consecutive to wood harvest, and the limited lifetime of products, these carbon footprints are time-dependent and their presumed values under the carbon neutrality assumption are achieved only in steady-state conditions. Secondly, even if carbon footprints are correctly assessed, the benefit of substitutions is overestimated when all or parts of the wood products are supposed to replace non-wood products whatever the market conditions. Indeed, substitutions are effective only if an increase in wood product consumption implies verifiably a global reduction in non-wood productions. When these flaws in the evaluation of wood substitution effects are avoided, one must conclude that increased harvesting and wood utilization may be counter-productive for climate change mitigation objectives, especially when wood is used as a fuel.

Modeling of Logging Industry Dynamics Under the Global Climate Change: the Evidence from Siberian Regions

As a part of global economy, forestry experiences influence of diverse factors and global climate change in particular, which can affect forestry directly or indirectly via changes in qualitative and quantitative assessment of forest growing stocks, and in positive or negative manner. Climate change effects on Russian forestry are still poorly studied and call for more attention in policy-making. One of the direct impacts is shortening of winter logging season duration, which may result in decreasing wood harvests. Using the data on logging volumes in Krasnoyarsk Krai and Irkutsk Oblast and estimated duration of winter logging season on meteorological stations for the retrospective period of 1966-2018, eight ARDL models were evaluated. The modeling results supported the idea of dependence of harvested wood volumes on winter logging season duration across all considered meteorological stations. To reduce negative impacts on logging industry in terms of wood harvest reduction, adaptation activities in forestry are sorely needed