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, Kirsten

The 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.

2018, Kim, HyeJin, Rosa, Isabel M. D., Alkemade, Rob, Leadley, Paul, Hurtt, George, Popp, Alexander, van Vuuren, Detlef P., Anthoni, Peter, Arneth, Almut, Baisero, Daniele, Caton, Emma, Chaplin-Kramer, Rebecca, Chini, Louise, De Palma, Adriana, Di Fulvio, Fulvio, Di Marco, Moreno, Espinoza, Felipe, Ferrier, Simon, Fujimori, Shinichiro, Gonzalez, Ricardo E., Gueguen, Maya, Guerra, Carlos, Harfoot, Mike, Harwood, Thomas D., Hasegawa, Tomoko, Haverd, Vanessa, Havlík, Petr, Hellweg, Stefanie, Hill, Samantha L. L., Hirata, Akiko, Hoskins, Andrew J., Janse, Jan H., Jetz, Walter, Johnson, Justin A., Krause, Andreas, Leclère, David, Martins, Ines S., Matsui, Tetsuya, Merow, Cory, Obersteiner, Michael, Ohashi, Haruka, Poulter, Benjamin, Purvis, Andy, Quesada, Benjamin, Rondinini, Carlo, Schipper, Aafke M., Sharp, Richard, Takahashi, Kiyoshi, Thuiller, Wilfried, Titeux, Nicolas, Visconti, Piero, Ware, Christopher, Wolf, Florian, Pereira, Henrique M.

To support the assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the IPBES Expert Group on Scenarios and Models is carrying out an intercomparison of biodiversity and ecosystem services models using harmonized scenarios (BES-SIM). The goals of BES-SIM are (1) to project the global impacts of land-use and climate change on biodiversity and ecosystem services (i.e., nature's contributions to people) over the coming decades, compared to the 20th century, using a set of common metrics at multiple scales, and (2) to identify model uncertainties and research gaps through the comparisons of projected biodiversity and ecosystem services across models. BES-SIM uses three scenarios combining specific Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs)-SSP1xRCP2.6, SSP3xRCP6.0, SSP5xRCP8.6-to explore a wide range of land-use change and climate change futures. This paper describes the rationale for scenario selection, the process of harmonizing input data for land use, based on the second phase of the Land Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem services models used, the core simulations carried out, the harmonization of the model output metrics, and the treatment of uncertainty. The results of this collaborative modeling project will support the ongoing global assessment of IPBES, strengthen ties between IPBES and the Intergovernmental Panel on Climate Change (IPCC) scenarios and modeling processes, advise the Convention on Biological Diversity (CBD) on its development of a post-2020 strategic plans and conservation goals, and inform the development of a new generation of nature-centred scenarios.

2020, Li, Wei, Ciais, Philippe, Stehfest, Elke, van Vuuren, Detlef, Popp, Alexander, Arneth, Almut, Di Fulvio, Fulvio, Doelma, Jonathan, Humpenöder, Florian, Harper, Anna B., Park, Taejin, Makowski, David, Havlik, Petr, Obersteiner, Michael, Wang, Jingmeng, Krause, Andreas, Liu, Wenfeng

Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieve negative emissions (together with CCS carbon capture and storage). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0:5 0:5 spatial resolution. We also provide a combined "best bioenergy crop" yield map by selecting one of the five crop types with the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 tDMha1 yr1 (DM dry matter). High yields mainly occur in the Amazon region and southeastern Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are 50% higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models or to identify the most suitable lands for future bioenergy crop plantations. The 0:5 0:5 global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from https://doi.org/10.5281/zenodo.3274254 (Li, 2019). © 2019 Cambridge University Press. All rights reserved.