scholarly journals The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0)

2020 ◽  
Vol 13 (5) ◽  
pp. 2315-2336 ◽  
Author(s):  
James A. Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jägermeyr ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Model ◽  
Assessment Tools ◽  
Nonlinear Dependence ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Improvement Project ◽  
Crop Models ◽  
Growing Season Length ◽  
Temperature Water

Abstract. Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

scholarly journals The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0)

2019 ◽  
Author(s):  
James Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jagermeyr ◽  
...  
Keyword(s):  
Climate Change ◽  
Phase Ii ◽  
Crop Model ◽  
Assessment Tools ◽  
Nonlinear Dependence ◽  
Model Intercomparison ◽  
Improvement Project ◽  
Crop Models ◽  
Growing Season Length ◽  
Temperature Water

Abstract. Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase II experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase II experimental protocol and its simulation data archive. Twelve crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (``CTWN'') for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions, but is largest in high-latitude regions where crops may be grown in the future.

Comprehensive global climate impact assessment for crop yields

2020 ◽  
Author(s):  
Christoph Müller ◽  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Crop Yields ◽  
Global Climate ◽  
Model Development ◽  
Climate Impact ◽  
Crop Model ◽  
Model Intercomparison ◽  
Crop Models ◽  
Climate Impact Assessment

&lt;p&gt;Climate change impacts on agriculture are subject to large uncertainties from a variety of sources. One of the most important sources of uncertainty is the uncertainty in the realization of climate change itself. In the absence of clear climate mitigation strategies and substantial uncertainties on population growth, economic development, technology and lifestyles, a very broad set of greenhouse gas emission scenarios has been developed to inform climate modeling. Climate models often differ in the spatial patterns of projected changes in particular with respect to changes in precipitation. The Coupled Model Intercomparison Project (CMIP5, CMIP6) provides a broad range of future climate change projections.&lt;/p&gt;&lt;p&gt;Crop models are often applied at selected sites or with global coverage, as in the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Global crop model applications have been shown to have some skill, but also add additional uncertainty, given that many processes cannot be calibrated properly for the lack of suitable reference data and because management information is largely absent (M&amp;#252;ller et al., 2017).&lt;/p&gt;&lt;p&gt;However, already the computational power required to compute the comprehensive set of climate projections prohibits such applications. Instead, typically, small and largely random selections of climate scenarios are used to project impacts, such as agricultural crop yields. McSweeney and Jones (2016) find that a selection of 5 climate models as often applied, is insufficient to cover the range of projections in all regions.&lt;/p&gt;&lt;p&gt;Here we present initial results of a comprehensive global climate impact assessment for crop yields that explores the full range of the CMIP6 climate projection archive. For this, we use a set of 9 global gridded crop model emulators (Franke et al., 2019b) that were trained on a very large systematic input sensitivity analysis with up to 1404 global-coverage, 31-year simulation data sets per crop and crop model (Franke et al., 2019a). The training domain includes variations in atmospheric carbon dioxide (CO2) concentrations (4 levels from 360 ppm to 810 ppm), air temperature (7 levels from -1 to +6&amp;#176;C), water supply (8 levels from -50 to +30% and full irrigation), nitrogen fertilization (3 levels from 10 to 200 kgN/ha) and adaptation (2 levels: none and regained growing seasons) and thus represents an unprecedented rich data base for emulator training. The emulators, in form of grid-cell specific regression models with 27 coefficients, are computationally light-weight and can thus be applied to the full CMIP6 data archive.&lt;/p&gt;&lt;p&gt;We here present first results from this analysis, breaking down the different sources of uncertainty (emission concentration pathways, climate model, crop model). Results will help to interpret crop model simulations in general: the unstructured reduction of the uncertainty space from selecting a small number of climate scenarios by e.g. first availability and/or individual crop models has so far hampered to quantify the uncertainty in crop model projections.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Franke (2019a) Geoscientific Model Development Discuss, 2019:1-30.&lt;/p&gt;&lt;p&gt;Franke (2019b)&amp;#160; Geoscientific Model Development, submitted&lt;/p&gt;&lt;p&gt;McSweeney &amp; Jones, (2016) Climate Services, 1:24-29.&lt;/p&gt;&lt;p&gt;M&amp;#252;ller (2017) Geoscientific Model Development, 10:1403-1422.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

The Global Gridded Crop Model Intercomparison – an AgMIP activity

2021 ◽  
Author(s):  
Christoph Müller ◽  
Jonas Jägermeyr ◽  
the GGCMI team
Keyword(s):  
Crop Yields ◽  
Crop Model ◽  
Large Set ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Output Data ◽  
Phase 3 ◽  
Area Index ◽  
Improvement Project ◽  
Entry Points

&lt;p&gt;The Global Gridded Crop Model Intercomparison was founded in 2012 as a joint activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP) and the InterSectoral Model Intercomparison Project (ISIMIP). Over these 10 years, GGCMI has attracted contributions from many international crop modeling groups and has generated large global agricultural data sets in different model simulation phases. Input data comprise gridded management data for agricultural systems that can be used in combination with climate data that are provided by ISIMIP. Annual output data include crop yields and other variables of plants and soil status for irrigated and purely rainfed production systems for different field crops at 0.5 degree spatial resolution, covering the whole land surface, where crop production is feasible. All data are made publicly available. While Phase 1 of GGCMI was focused on the historical period&lt;sup&gt;[1,2]&lt;/sup&gt;, aiming at model evaluation&lt;sup&gt;[3]&lt;/sup&gt;, Phase 2 generated an unprecedented large data set of systematic disturbances along the CO2 (C), Temperature (T), Water (W) and Nitrogen (N) dimension&lt;sup&gt;[4]&lt;/sup&gt;. A major outcome of Phase 2 is a very large set of emulators&lt;sup&gt;[5]&lt;/sup&gt; that allows for lightweight, flexible and comprehensive crop yield projections and analyses. With analyses of Phase 2 still forming, Phase 3 was started in collaboration with ISIMIP&amp;#8217;s Phase 3, providing new future projections for a range of CMIP6 climate change projections and different management scenarios. Crop models do not only provide outputs on crop yields but also on various processes, such as evapotranspiration, leaf area index, phenology and soil dynamics that allow for broader analyses. GGCMI is a collaborative effort and always open to new contributors. Given the amount and complexity of in- and output data, we welcome proposals for new studies and data analyses. In this presentation we&amp;#8217;re providing an overview of the GGCMI activities and exemplify possible entry points for collaboration.&lt;/p&gt;&lt;p&gt;&lt;sup&gt;[1] http://dx.doi.org/10.5194/gmd-8-261-2015&lt;/sup&gt;&lt;/p&gt;&lt;p&gt;&lt;sup&gt;[2] http://dx.doi.org/10.1038/s41597-019-0023-8&lt;/sup&gt;&lt;/p&gt;&lt;p&gt;&lt;sup&gt;[3] http://dx.doi.org/10.5194/gmd-10-1403-2017&lt;/sup&gt;&lt;/p&gt;&lt;p&gt;&lt;sup&gt;[4] http://dx.doi.org/10.5194/gmd-13-2315-2020&lt;/sup&gt;&lt;/p&gt;&lt;p&gt;&lt;sup&gt;[5] http://dx.doi.org/10.5194/gmd-13-3995-2020&lt;/sup&gt;&lt;/p&gt;

scholarly journals The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)

2020 ◽  
Vol 13 (9) ◽  
pp. 3995-4018
Author(s):  
James A. Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jägermeyr ◽  
...  
Keyword(s):  
Crop Yields ◽  
Crop Model ◽  
Climate Impacts ◽  
Future Climate ◽  
Yield Response ◽  
Phase 2 ◽  
Crop Models ◽  
Growing Seasons ◽  
Nitrogen Inputs ◽  
Temperature Water

Abstract. 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 2. 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 (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 of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. 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. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the 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.

scholarly journals Prediction of Maize Yield Response to Climate Change with Climate and Crop Model Uncertainties

2015 ◽  
Vol 54 (4) ◽  
pp. 785-794 ◽  
Author(s):  
Yi Zhang ◽  
Yanxia Zhao ◽  
Sining Chen ◽  
Jianping Guo ◽  
Enli Wang
Keyword(s):  
Climate Change ◽  
Crop Yields ◽  
Maize Yield ◽  
Crop Model ◽  
Global Climate Models ◽  
Yield Response ◽  
Yield Reduction ◽  
Climate Projections ◽  
Crop Models ◽  
Climate Change Uncertainty

AbstractProjections of climate change impacts on crop yields are subject to uncertainties, and quantification of such uncertainty is essential for the effective use of the projection results for adaptation and mitigation purposes. This work analyzes the uncertainties in maize yield predictions using two crop models together with three climate projections downscaled with one regional climate model nested with three global climate models under the A1B emission scenario in northeast China (NEC). Projections were evaluated for the Zhuanghe agrometeorological station in NEC for the 2021–50 period, taking 1971–2000 as the baseline period. The results indicated a yield reduction of 13% during 2021–50, with 95% probability intervals of (−41%, +12%) relative to 1971–2000. Variance decomposition of the yield projections showed that uncertainty in the projections caused by climate and crop models is likely to change with prediction period, and climate change uncertainty generally had a larger impact on projections than did crop model uncertainty during the 2021–50 period. In addition, downscaled climate projections had significant bias that can introduce significant uncertainties in yield projections. Therefore, they have to be bias corrected before use.

scholarly journals The GGCMI phase II emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)

2020 ◽  
Author(s):  
James Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jägermeyr ◽  
...  
Keyword(s):  
Phase Ii ◽  
Crop Yields ◽  
Crop Model ◽  
Climate Impacts ◽  
Future Climate ◽  
Yield Response ◽  
Crop Models ◽  
Growing Seasons ◽  
Nitrogen Inputs ◽  
Temperature Water

Abstract. 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.

Same soil - different climate: crop model inter-comparison with lysimeter data of translocated monoliths

2021 ◽  
Author(s):  
Jannis Groh ◽  
Horst H. Gerke ◽  
Keyword(s):  
Climate Change ◽  
Model Performance ◽  
Predictive Ability ◽  
Crop Model ◽  
Climatic Conditions ◽  
Climate Change Scenarios ◽  
Soil Ecosystem ◽  
Site Specific ◽  
Climate Conditions ◽  
Crop Models

&lt;p&gt;Crop model comparisons have mostly been carried out to test predictive ability under previous climate conditions and for soils of the same location. However, the ability of individual agricultural models to predict the effects of changes in climatic conditions on soil-ecosystems beyond the range of site-specific variability is unknown. The objective of this study was to test the predictive ability of agroecosystem models using weighable lysimeter data for the same soil under changing climatic conditions and to compare simulated plant growth and soil-ecosystem response to climate change between these models. To achieve this, data from the TERENO-SOILCan lysimeters-network for a soil-ecosystem at the original site (Dedelow) and data from the lysimeters with Dedelow soil monoliths transferred to Bad Lauchst&amp;#228;dt and Selhausen were analysed. The transfer of the soils took place to a drier and warmer location (Bad Lauchst&amp;#228;dt) and to a warmer and wetter location (Selhausen) compared to the original location of the soils in Dedelow with the same crop rotation. After model calibration for data from the original Dedelow site, crop growth and soil water balances of transferred Dedelow soil monoliths were predicted using the site-specific boundary conditions and compared with the observations at Selhausen and Bad Lauchst&amp;#228;dt. The overall simulation output of the models was separated into a plant-related part, ecosystem-productivity (grain yield, biomass, LAI) and an environmental part, ecosystem-fluxes (evapotranspiration, net-drainage, soil moisture). The results showed that when the soil was transferred to a drier region, the agronomic part of the crop models predicted well, and when the soil was moved to wetter regions, the environmental flow part of the models seemed to predict better. The results suggest that accounting for climate change scenarios, more consideration of soil properties and testing model performance for conditions outside the calibrated range and site-specific variability will help improve the models.&lt;/p&gt;

scholarly journals Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

2017 ◽  
Vol 10 (4) ◽  
pp. 1403-1422 ◽  
Author(s):  
Christoph Müller ◽  
Joshua Elliott ◽  
James Chryssanthacopoulos ◽  
Almut Arneth ◽  
Juraj Balkovic ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Data ◽  
Model Performance ◽  
Global Scale ◽  
Crop Model ◽  
Yield Variability ◽  
Crop Modeling ◽  
Model Intercomparison ◽  
Crop Models ◽  
Simulation Results

Abstract. Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

scholarly journals The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0)

2015 ◽  
Vol 8 (2) ◽  
pp. 261-277 ◽  
Author(s):  
J. Elliott ◽  
C. Müller ◽  
D. Deryng ◽  
J. Chryssanthacopoulos ◽  
K. J. Boote ◽  
...  
Keyword(s):  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Phase 1 ◽  
Climate Impact ◽  
Crop Model ◽  
Surface Fluxes ◽  
Historical Period ◽  
Model Intercomparison ◽  
Improvement Project

Abstract. We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.

scholarly journals The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: scientific objectives and experimental design

2016 ◽  
Vol 12 (3) ◽  
pp. 663-675 ◽  
Author(s):  
Alan M. Haywood ◽  
Harry J. Dowsett ◽  
Aisling M. Dolan ◽  
David Rowley ◽  
Ayako Abe-Ouchi ◽  
...  
Keyword(s):  
Climate Change ◽  
Experimental Design ◽  
Boundary Conditions ◽  
Surface Topography ◽  
Phase 1 ◽  
Future Climate Change ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Ice Surface ◽  
Intercomparison Project

Abstract. The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2. Following on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data–model comparison. Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land–ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO2 are emerging on orbital timescales, and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models. Finally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.

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