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  The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Franke, J., Müller, C., Elliott, J., Ruane, A. C., Jägermeyr, J., Snyder, A., Dury, M., Fallon, P., Folberth, C., François, L., Hank, T., Izaurralde, R. C., Jacquemin, I., Jones, C., Li, M., Liu, W., Olin, S., Phillips, M., Pugh, T. A., Reddy, A., Williams, K., Wang, Z., Zabel, F., Moyer, E. (in press): The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0). - Geoscientific Model Development.

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 Urheber:
Franke, James1, Autor
Müller, Christoph2, Autor              
Elliott, Joshu1, Autor
Ruane, Alex C.1, Autor
Jägermeyr, Jonas2, Autor              
Snyder, Abigail1, Autor
Dury, Marie1, Autor
Fallon, Pete1, Autor
Folberth, Christian1, Autor
François, Louis1, Autor
Hank, Tobias1, Autor
Izaurralde, R. Cesar1, Autor
Jacquemin, Ingrid1, Autor
Jones, Curtis1, Autor
Li, Michelle1, Autor
Liu, Wenfeng1, Autor
Olin, Stefan1, Autor
Phillips, Meridel1, Autor
Pugh, Thomas A.M.1, Autor
Reddy, Ashwan1, Autor
Williams, Karina1, AutorWang, Ziwei1, AutorZabel, Florian1, AutorMoyer, Elisabeth1, Autor mehr..
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

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 Zusammenfassung: Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. 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 II. The GGCMI Phase II 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 (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). 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 dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

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 Datum: 2020-07-07
 Publikationsstatus: Angenommen
 Seiten: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: PIKDOMAIN: RD2 - Climate Resilience
MDB-ID: 2894
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Titel: Geoscientific Model Development
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals185