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Strong regional inf...
Strong regional influence of climatic forcing datasets on global crop model ensembles
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- Ruane, Alex C. (författare)
- NASA Goddard Institute for Space Studies
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- Phillips, Meridel (författare)
- NASA Goddard Institute for Space Studies,Columbia University
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- Müller, Christoph (författare)
- Potsdam Institute for Climate Impact Research
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- Elliott, Joshua (författare)
- University of Chicago
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- Jägermeyr, Jonas (författare)
- University of Chicago,NASA Goddard Institute for Space Studies
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- Arneth, Almut (författare)
- Karlsruhe Institute of Technology
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- Balkovic, Juraj (författare)
- Comenius University,International Institute for Applied Systems Analysis
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- Deryng, Delphine (författare)
- Humboldt University of Berlin
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- Folberth, Christian (författare)
- International Institute for Applied Systems Analysis
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- Iizumi, Toshichika (författare)
- National Agriculture and Food Research Organization (NARO)
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- Izaurralde, Roberto C. (författare)
- University of Maryland
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- Khabarov, Nikolay (författare)
- International Institute for Applied Systems Analysis
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- Lawrence, Peter (författare)
- National Center for Atmospheric Research
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- Liu, Wenfeng (författare)
- China Agricultural University
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- Olin, Stefan (författare)
- Lund University,Lunds universitet,BECC: Biodiversity and Ecosystem services in a Changing Climate,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,MERGE: ModElling the Regional and Global Earth system,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science
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- Pugh, Thomas A.M. (författare)
- Lund University,Lunds universitet,BECC: Biodiversity and Ecosystem services in a Changing Climate,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,MERGE: ModElling the Regional and Global Earth system,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science,University of Birmingham
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- Rosenzweig, Cynthia (författare)
- NASA Goddard Institute for Space Studies
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- Sakurai, Gen (författare)
- National Agriculture and Food Research Organization (NARO)
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- Schmid, Erwin (författare)
- University of Natural Resources and Life Sciences, Vienna
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- Sultan, Benjamin (författare)
- Espace pour le Développement (ESPACE-DEV)
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- Wang, Xuhui (författare)
- Peking University,University of Paris-Saclay
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- de Wit, Allard (författare)
- Wageningen University
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- Yang, Hong (författare)
- University of Basel,Eawag: Swiss Federal Institute of Aquatic Science and Technology
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(creator_code:org_t)
- Elsevier BV, 2021
- 2021
- Engelska.
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 300
- Relaterad länk:
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http://dx.doi.org/10...
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https://hal.archives...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.
Ämnesord
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Klimatforskning (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Climate Research (hsv//eng)
Nyckelord
- Agricultural Model Intercomparison and Improvement Project (AgMIP)
- Agroclimate
- Climate Impacts
- Climatic Forcing Datasets
- Crop production
- Global Gridded Crop Model Intercomparison (GGCMI)
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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Ruane, Alex C.
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Phillips, Meride ...
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Müller, Christop ...
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Elliott, Joshua
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Jägermeyr, Jonas
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Arneth, Almut
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Balkovic, Juraj
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Deryng, Delphine
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Folberth, Christ ...
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Iizumi, Toshichi ...
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Izaurralde, Robe ...
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Khabarov, Nikola ...
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Lawrence, Peter
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Liu, Wenfeng
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Olin, Stefan
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Pugh, Thomas A.M ...
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Rosenzweig, Cynt ...
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Sakurai, Gen
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Schmid, Erwin
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Sultan, Benjamin
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Wang, Xuhui
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de Wit, Allard
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Yang, Hong
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- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Geovetenskap och ...
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och Klimatforskning
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Agricultural and ...
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Lunds universitet