4 results
Search Results
Now showing 1 - 4 of 4
- ItemUnderstanding the uncertainty in global forest carbon turnover(Katlenburg-Lindau [u.a.] : Copernicus, 2020) Pugh, Thomas A.M.; Rademacher, Tim; Shafer, Sarah L.; Steinkamp, Jörg; Barichivich, Jonathan; Beckage, Brian; Haverd, Vanessa; Harper, Anna; Heinke, Jens; Nishina, Kazuya; Rammig, Anja; Sato, Hisashi; Arneth, Almut; Hantson, Stijn; Hickler, Thomas; Kautz, Markus; Quesada, Benjamin; Smith, Benjamin; Thonicke, KirstenThe length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985-2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth. © 2020 SPIE. All rights reserved.
- ItemDescription and evaluation of the process-based forest model 4C v2.2 at four European forest sites(Katlenburg-Lindau : Copernicus, 2020) Lasch-Born, Petra; Suckow, Felicitas; Reyer, Christopher P. O.; Gutsch, Martin; Kollas, Chris; Badeck, Franz-Werner; Bugmann, Harald K. M.; Grote, Rüdiger; Fürstenau, Cornelia; Lindner, Marcus; Schaber, JörgThe process-based model 4C (FORESEE) has been developed over the past 20 years to study climate impacts on forests and is now freely available as an open-source tool. The objective of this paper is to provide a comprehensive description of this 4C version (v2.2) for scientific users of the model and to present an evaluation of 4C at four different forest sites across Europe. The evaluation focuses on forest growth as well as carbon (net ecosystem exchange, gross primary production), water (actual evapotranspiration, soil water content), and heat fluxes (soil temperature) using data from the PROFOUND database. We applied different evaluation metrics and compared the daily, monthly, and annual variability of observed and simulated values. The ability to reproduce forest growth (stem diameter and biomass) differs from site to site and is best for a pine stand in Germany (Peitz, model efficiency ME=0.98). 4C is able to reproduce soil temperature at different depths in Sorø and Hyytiälä with good accuracy (for all soil depths ME > 0.8). The dynamics in simulating carbon and water fluxes are well captured on daily and monthly timescales (0.51 < ME < 0.983) but less so on an annual timescale (ME < 0). This model–data mismatch is possibly due to the accumulation of errors because of processes that are missing or represented in a very general way in 4C but not with enough specific detail to cover strong, site-specific dependencies such as ground vegetation growth. These processes need to be further elaborated to improve the projections of climate change on forests. We conclude that, despite shortcomings, 4C is widely applicable, reliable, and therefore ready to be released to the scientific community to use and further develop the model.
- ItemSelf-amplified Amazon forest loss due to vegetation-atmosphere feedbacks(London : Nature Publishing Group, 2017) Zemp, D.C.; Schleussner, C.-F.; Barbosa, H.M.J.; Hirota, M.; Montade, V.; Sampaio, G.; Staal, A.; Wang-Erlandsson, L.; Rammig, A.Reduced rainfall increases the risk of forest dieback, while in return forest loss might intensify regional droughts. The consequences of this vegetation-atmosphere feedback for the stability of the Amazon forest are still unclear. Here we show that the risk of self-amplified Amazon forest loss increases nonlinearly with dry-season intensification. We apply a novel complex-network approach, in which Amazon forest patches are linked by observation-based atmospheric water fluxes. Our results suggest that the risk of self-amplified forest loss is reduced with increasing heterogeneity in the response of forest patches to reduced rainfall. Under dry-season Amazonian rainfall reductions, comparable to Last Glacial Maximum conditions, additional forest loss due to self-amplified effects occurs in 10-13% of the Amazon basin. Although our findings do not indicate that the projected rainfall changes for the end of the twenty-first century will lead to complete Amazon dieback, they suggest that frequent extreme drought events have the potential to destabilize large parts of the Amazon forest.
- ItemVariation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models(Hoboken, NJ : Blackwell Publishing Ltd, 2016) Johnson, M.O.; Galbraith, D.; Gloor, M.; De Deurwaerder, H.; Guimberteau, M.; Rammig, A.; Thonicke, K.; Verbeeck, H.; von Randow, C.; Monteagudo, A.; Phillips, O.L.; Brienen, R.J.W.; Feldpausch, T.R.; Lopez Gonzalez, G.; Fauset, S.; Quesada, C.A.; Christoffersen, B.; Ciais, P.; Sampaio, G.; Kruijt, B.; Meir, P.; Moorcroft, P.; Zhang, K.; Alvarez-Davila, E.; Alves de Oliveira, A.; Amaral, I.; Andrade, A.; Aragao, L.E.O.C.; Araujo-Murakami, A.; Arets, E.J.M.M.; Arroyo, L.; Aymard, G.A.; Baraloto, C.; Barroso, J.; Bonal, D.; Boot, R.; Camargo, J.; Chave, J.; Cogollo, A.; Cornejo Valverde, F.; Lola da Costa, A.C.; Di Fiore, A.; Ferreira, L.; Higuchi, N.; Honorio, E.N.; Killeen, T.J.; Laurance, S.G.; Laurance, W.F.; Licona, J.; Lovejoy, T.; Malhi, Y.; Marimon, B.; Marimon, B.H. Jr.; Matos, D.C.L.; Mendoza, C.; Neill, D.A.; Pardo, G.; Peña-Claros, M.; Pitman, N.C.A.; Poorter, L.; Prieto, A.; Ramirez-Angulo, H.; Roopsind, A.; Rudas, A.; Salomao, R.P.; Silveira, M.; Stropp, J.; ter Steege, H.; Terborgh, J.; Thomas, R.; Toledo, M.; Torres-Lezama, A.; van der Heijden, G.M.F.; Vasquez, R.; Guimarães Vieira, I.C.; Vilanova, E.; Vos, V.A.; Baker, T.R.