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Stock M, Pieters O, De Swaef T, wyffels F. Plant science in the age of simulation intelligence. FRONTIERS IN PLANT SCIENCE 2024; 14:1299208. [PMID: 38293629 PMCID: PMC10824965 DOI: 10.3389/fpls.2023.1299208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/07/2023] [Indexed: 02/01/2024]
Abstract
Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as "simulation intelligence", has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.
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Affiliation(s)
- Michiel Stock
- KERMIT and Biobix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Olivier Pieters
- IDLAB-AIRO, Ghent University, imec, Ghent, Belgium
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
| | - Tom De Swaef
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
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Chang Y, Latham J, Licht M, Wang L. A data-driven crop model for maize yield prediction. Commun Biol 2023; 6:439. [PMID: 37085696 PMCID: PMC10121691 DOI: 10.1038/s42003-023-04833-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 04/10/2023] [Indexed: 04/23/2023] Open
Abstract
Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The proposed model tracks the daily biomass accumulation process during the maize growing season and uses daily produced biomass to estimate the final grain yield. Computational studies using crop yield, field location, genotype and corresponding environmental data were conducted in the US Corn Belt region from 1981 to 2020. The results suggest that the proposed model can achieve an accurate prediction performance with a 7.16% relative root-mean-square-error of average yield in 2020 and provide scientifically explainable results. The model also demonstrates its ability to detect and separate interactions between genotypic parameters and environmental variables. Additionally, this study demonstrates the potential value of the proposed model in helping farmers achieve higher yields by optimizing seed selection.
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Affiliation(s)
- Yanbin Chang
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, 2529 Union Drive, Ames, 50011, IA, USA
| | - Jeremy Latham
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, 2529 Union Drive, Ames, 50011, IA, USA
| | - Mark Licht
- Department of Agronomy, Iowa State University, 716 Farm House Lane, Ames, 50011, IA, USA
| | - Lizhi Wang
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, 2529 Union Drive, Ames, 50011, IA, USA.
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Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain. Sci Rep 2022; 12:4049. [PMID: 35260727 PMCID: PMC8904498 DOI: 10.1038/s41598-022-08056-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 02/21/2022] [Indexed: 12/02/2022] Open
Abstract
This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer’s allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014–2017). By considering combinations of allocation strategies, the adjusted R2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale.
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Franke JA, Müller C, Minoli S, Elliott J, Folberth C, Gardner C, Hank T, Izaurralde RC, Jägermeyr J, Jones CD, Liu W, Olin S, Pugh TAM, Ruane AC, Stephens H, Zabel F, Moyer EJ. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change. GLOBAL CHANGE BIOLOGY 2022; 28:167-181. [PMID: 34478595 DOI: 10.1111/gcb.15868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/04/2021] [Indexed: 06/13/2023]
Abstract
Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.
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Affiliation(s)
- James A Franke
- Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
| | - Christoph Müller
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Sara Minoli
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Joshua Elliott
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
| | - Christian Folberth
- Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Charles Gardner
- Program on Global Environment, University of Chicago, Chicago, Illinois, USA
| | - Tobias Hank
- Ludwig-Maximilians-Universitat Munchen (LMU), Munich, Germany
| | | | - Jonas Jägermeyr
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- NASA Goddard Institute for Space Studies, New York City, New York, USA
- Center for Climate Systems Research, Columbia University, New York City, New York, USA
| | - Curtis D Jones
- Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
| | - Wenfeng Liu
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China
| | - Stefan Olin
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
| | - Thomas A M Pugh
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
| | - Alex C Ruane
- NASA Goddard Institute for Space Studies, New York City, New York, USA
| | - Haynes Stephens
- Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
| | - Florian Zabel
- Ludwig-Maximilians-Universitat Munchen (LMU), Munich, Germany
| | - Elisabeth J Moyer
- Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
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Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India. REMOTE SENSING 2021. [DOI: 10.3390/rs13122379] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurate and spatially explicit yield information is required to ensure farmers’ income and food security at local and national levels. Current approaches based on crop cutting experiments are expensive and usually too late for timely income stabilization measures like crop insurances. We, therefore, utilized a Gradient Boosted Regression (GBR), a machine learning technique, to estimate rice yields at ~500 m spatial resolution for rice-producing areas in India with potential application for near real-time estimates. We used resampled intermediate resolution (~5 km) images of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and observed yields at the district level in India for calibrating GBR models. These GBRs were then used to downscale district yields to 500 m resolution. Downscaled yields were re-aggregated for validation against out-of-sample district yields not used for model training and an additional independent data set of block-level (below district-level) yields. Our downscaled and re-aggregated yields agree well with reported district-level observations from 2003 to 2015 (r = 0.85 & MAE = 0.15 t/ha). The model performance improved further when estimating separate models for different rice cropping densities (up to r = 0.93). An additional out-of-sample validation for the years 2016 and 2017, proved successful with r = 0.84 and r = 0.77, respectively. Simulated yield accuracy was higher in water-limited, rainfed agricultural systems. We conclude that this downscaling approach of rice yield estimation using GBR is feasible across India and may complement current approaches for timely rice yield estimation required by insurance companies and government agencies.
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Jha PK, Araya A, Stewart ZP, Faye A, Traore H, Middendorf BJ, Prasad PVV. Projecting potential impact of COVID-19 on major cereal crops in Senegal and Burkina Faso using crop simulation models. AGRICULTURAL SYSTEMS 2021; 190:103107. [PMID: 33623181 PMCID: PMC7893291 DOI: 10.1016/j.agsy.2021.103107] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/04/2021] [Accepted: 02/09/2021] [Indexed: 05/08/2023]
Abstract
CONTEXT The rapid emergence of COVID-19 could have direct and indirect impacts on food production systems and livelihoods of farmers. From the farming perspective, disruption of critical input availability, supply chains and labor, influence crop management. Disruptions to food systems can affect (a) planting area; and (b) crop yields. OBJECTIVES To quantify the impacts of COVID-19 on major cereal crop's production and their cascading impact on national economy and related policies. METHODS We used the calibrated crop simulation model (DSSAT suite) to project the impact of potential changes in planting area and grain yield of four major cereal crops (i.e., rice, maize, sorghum, and millet) in Senegal and Burkina Faso in terms of yield, total production, crop value and contribution to agricultural gross domestic product (GDP). Appropriate data (i.e., weather, soil, crop, and management practices) for the specific agroecological zones were used as an input in the model. RESULTS AND CONCLUSIONS The simulated yields for 2020 were then used to estimate crop production at country scale for the matrix of different scenarios of planting area and yield change (-15, -10, -5, 0, +5, +10%). Depending on the scenario, changes in total production of four cereals combined at country levels varied from 1.47 M tons to 2.47 M tons in Senegal and 4.51 M tons to 7.52 M tons in Burkina Faso. The economic value of all four cereals under different scenarios ranged from $771 Million (M) to $1292 M in Senegal and from $1251 M to $2098 M in Burkina Faso. These estimated total crop values under different scenarios were compared with total agricultural GDP of the country (in 2019 terms which was $3995 M in Senegal and $3957 M in Burkina Faso) to assess the economic impact of the pandemic on major cereal grain production. Based on the scenarios, the impact on total agricultural GDP can change -7% to +6% in Senegal and - 8% to +9% in Burkina Faso. SIGNIFICANCE Results obtained from this modeling exercise will be valuable to policymakers and end-to-end value chain practitioners to prepare and develop appropriate policies to cope or manage the impact of COVID-19 on food systems.
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Affiliation(s)
- P K Jha
- Sustainable Intensification Innovation Lab, Kansas State University, Manhattan, Kansas, USA
| | - A Araya
- Sustainable Intensification Innovation Lab, Kansas State University, Manhattan, Kansas, USA
- Department of Agronomy, Kansas State University, Manhattan, Kansas, USA
| | - Z P Stewart
- Sustainable Intensification Innovation Lab, Kansas State University, Manhattan, Kansas, USA
- Department of Agronomy, Kansas State University, Manhattan, Kansas, USA
| | - A Faye
- Senegalese Institute of Agricultural Research, Dakar, Senegal
| | - H Traore
- Institute of Environment and Agricultural Research, Ouagadougou, Burkina Faso
| | - B J Middendorf
- Sustainable Intensification Innovation Lab, Kansas State University, Manhattan, Kansas, USA
| | - P V V Prasad
- Sustainable Intensification Innovation Lab, Kansas State University, Manhattan, Kansas, USA
- Department of Agronomy, Kansas State University, Manhattan, Kansas, USA
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Balkovič J, Madaras M, Skalský R, Folberth C, Smatanová M, Schmid E, van der Velde M, Kraxner F, Obersteiner M. Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020; 274:111206. [PMID: 32818829 DOI: 10.1016/j.jenvman.2020.111206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/08/2020] [Accepted: 08/05/2020] [Indexed: 06/11/2023]
Abstract
Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1-0.5 Mg C ha-1 y-1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5-1.5 Mg C ha-1 y-1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.
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Affiliation(s)
- Juraj Balkovič
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovičova 6, 842 15, Bratislava, Slovak Republic.
| | - Mikuláš Madaras
- Crop Research Institute, Division of Crop Management Systems, Drnovská 507/73, 161 06, Praha 6 - Ruzyně, Czech Republic.
| | - Rastislav Skalský
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; National Agricultural and Food Centre, Soil Science and Conservation Research Institute, Trenčianska 55, 821 09, Bratislava, Slovak Republic.
| | - Christian Folberth
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria.
| | - Michaela Smatanová
- Central Institute for Supervising and Testing in Agriculture, Hroznová 63/2, 656 06, Brno, Czech Republic.
| | - Erwin Schmid
- Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, 1180, Vienna, Austria.
| | | | - Florian Kraxner
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria.
| | - Michael Obersteiner
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; Environmental Change Institute, University of Oxford, South Parks Road, Oxford, OX1 3QY, United Kingdom.
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Jägermeyr J, Robock A, Elliott J, Müller C, Xia L, Khabarov N, Folberth C, Schmid E, Liu W, Zabel F, Rabin SS, Puma MJ, Heslin A, Franke J, Foster I, Asseng S, Bardeen CG, Toon OB, Rosenzweig C. A regional nuclear conflict would compromise global food security. Proc Natl Acad Sci U S A 2020; 117:7071-7081. [PMID: 32179678 PMCID: PMC7132296 DOI: 10.1073/pnas.1919049117] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.
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Affiliation(s)
- Jonas Jägermeyr
- Department of Computer Science, University of Chicago, Chicago, IL 60637;
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, New York, NY 10025
- Climate Resilience, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14473 Potsdam, Germany
| | - Alan Robock
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901
| | - Joshua Elliott
- Department of Computer Science, University of Chicago, Chicago, IL 60637
| | - Christoph Müller
- Climate Resilience, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14473 Potsdam, Germany
| | - Lili Xia
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901
| | - Nikolay Khabarov
- Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria
| | - Christian Folberth
- Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria
| | - Erwin Schmid
- Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, 1180 Vienna, Austria
| | - Wenfeng Liu
- Laboratoire des Sciences du Climat et de l'Environnement, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- Department Systems Analysis, Integrated Assessment and Modeling, Swiss Federal Institute of Aquatic Science and Technology, 8600 Duebendorf, Switzerland
| | - Florian Zabel
- Department of Geography, Ludwig-Maximilians-Universität München, 80333 Munich, Germany
| | - Sam S Rabin
- Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
| | - Michael J Puma
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, New York, NY 10025
- Center for Climate Systems Research, Columbia University, New York, NY 10025
| | - Alison Heslin
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, New York, NY 10025
- Center for Climate Systems Research, Columbia University, New York, NY 10025
| | - James Franke
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637
| | - Ian Foster
- Department of Computer Science, University of Chicago, Chicago, IL 60637
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439
| | - Senthold Asseng
- Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL 32611
| | - Charles G Bardeen
- Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO 80305
- Department of Atmospheric and Oceanic Sciences, Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303
| | - Owen B Toon
- Department of Atmospheric and Oceanic Sciences, Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303
| | - Cynthia Rosenzweig
- Goddard Institute for Space Studies, National Aeronautics and Space Administration, New York, NY 10025
- Center for Climate Systems Research, Columbia University, New York, NY 10025
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