3 results
Search Results
Now showing 1 - 3 of 3
- ItemA protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios(Katlenburg-Lindau : Copernicus, 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.
- ItemParameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble(San Francisco, California, US : PLOS, 2019) Folberth, Christian; Elliott, Joshua; Müller, Christoph; Balkovič, Juraj; Chryssanthacopoulos, James; Izaurralde, Roberto C.; Jones, Curtis D.; Khabarov, Nikolay; Liu, Wenfeng; Reddy, Ashwan; Schmid, Erwin; Skalský, Rastislav; Yang, Hong; Arneth, Almut; Ciais, Philippe; Deryng, Delphine; Lawrence, Peter J.; Olin, Stefan; Pugh, Thomas A.M.; Ruane, Alex C.; Wang, XuhuiGlobal gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
- ItemThe Global Gridded Crop Model Intercomparison phase 1 simulation dataset(London : Nature Publ. Group, 2019) Müller, Christoph; Elliott, Joshua; Kelly, David; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Hoek, Steven; Izaurralde, Roberto C.; Jones, Curtis D.; Khabarov, Nikolay; Lawrence, Peter; Liu, Wenfeng; Olin, Stefan; Pugh, Thomas A. M.; Reddy, Ashwan; Rosenzweig, Cynthia; Ruane, Alex C.; Sakurai, Gen; Schmid, Erwin; Skalsky, Rastislav; Wang, Xuhui; de Wit, Allard; Yang, HongThe Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s).