In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios errors become significant only in some marginal lands where crops are not currently grown. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values.Ĭlimatological-mean yields are a central metric in climate change impact analysis we show here that they can be captured without relying on interannual variations. Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide ( CO 2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. Franke Received: – Discussion started: – Revised: – Accepted: – Published: Franke Hide author detailsĬorrespondence: James A. 20 Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, UKĬorrespondence: James A.19 Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK.18 School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK.17 Earth Institute Center for Climate Systems Research, Columbia University, New York, NY, USA.16 Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.15 Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.14 EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.13 Department of Statistics, University of Chicago, Chicago, IL, USA.12 Texas Agrilife Research and Extension, Texas A&M University, Temple, TX, USA.11 Department of Geographical Sciences, University of Maryland, College Park, MD, USA.10 Department of Geography, Ludwig-Maximilians-Universität München, Munich,.9 Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria.7 Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium.6 Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA.5 Center for Climate Systems Research, Columbia University, New York, NY 10025, USA.4 NASA Goddard Institute for Space Studies, New York, NY, USA.3 Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam,.2 Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of.1 Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA.
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