The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

McDermid, Sonali P.; Ruane, Alexander C.; Rosenzweig, Cynthia; Hudson, Nicholas I.; Morales, Monica D.; Agalawatte, Prabodha; Ahmad, Shakeel; Ahuja, Lajpat R.; Amien, Istiqlal; Anapalli, Saseendran S.; Anothai, Jakarat; Asseng, Senthold; Biggs, J. S.; Bert, Federico; Bertuzzi, Patrick; Bhatia, Virender Singh; Bindi, Marco; Broad, Ian; Cammarano, Davide; Carretero, Ramiro; Chattha, Ashfaq Ahmad; Chung, Uran; Debats, Stephanie; Deligios, Paola A.; Sanctis, Giacomo De; Dhliwayo, Thanda; Dumont, Benjamin; Estes, Lyndon; Ewert, Frank; Ferrise, Roberto; Gaiser, Thomas; García, Guillermo; Gbegbelegbe, Sika; Geethalakshmi, V.; Gérardeaux, Edward; Goldberg, R. A.; Grant, Brian; Guevara, Edgardo; Hickman, Jonathan; Hoffmann, Holger; Huan-Ping, Huang; Hussain, Jasmin; Justino, Flávio; Karunaratne, Asha S.; Koehler, Ann‐Kristin; Kouakou, Patrice Koffi; Kumar, Soora Naresh; Arunachalam, Lakshmanan; Lieffering, Mark; Lin, Xiaomao; Luo, Qunying; Magrin, Graciela O.; Mancini, Marco; Marin, Fábio Ricardo; Marta, Anna Dalla; Masutomi, Yuji; Mavromatis, T.; McLean, Greg; Meira, Santiago; Mohanty, M.; Moriondo, Marco; Nasim, Wajid; Negm, Lamyaa; Orlando, Francesca; Orlandini, Simone; Öztürk, Işık; Pinto, Helena Maria Soares; Podesta, Guillermo; Qi, Zhiming; Ramarohetra, Johanna; Rahman, Muhammad Habib ur; Raynal, Hélène; Rodríguez, Gabriel; Rötter, Reimund P.; Sharda, Vaishali; Lu, Shicheng; Smith, Ward; Snow, Val; Soltani, Afshin; Srinivas, K.; Sultan, Benjamin; Swain, Dillip Kumar; Tao, Fulu; Tesfaye, Kindie; Travasso, Maria; Trombi, Giacomo; Topaj, Alex; Vanuytrecht, Eline; Viscarra, Federico E.; Wajid, Syed Aftab; Wang, Enli; Wang, Hong; Wang, Jing; Wijekoon, Erandika; Lee, Byun-Woo; Yang, Xiaoguang; Young, Ban Ho; Yun, Jin I.; Zhao, Zhigan; Zubair, Lareef

doi:10.1142/9781783265640_0008

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…These levels were chosen as per Rosenzweig et al . () to represent the range of potential 21 st ‐century climate changes and to enable cross‐crop comparisons with other model intercomparison studies (e.g., Mcdermid et al ., ). A full 5 × 5 × 3 factorial combination of these modified 30‐year weather datasets was conducted at each site (i.e.…”

Section: Methodsmentioning

confidence: 97%

“…These consisted of five different levels of C (360, 450, 540, 630, and 720 ppm), five levels of T (À3, 0, +3, +6, or + 9°C below/above baseline 24-h maximum/minimum air temperatures), and three levels of W (À30%, 0, and +30% decreases/increases in daily precipitation from baseline). These levels were chosen as per Rosenzweig et al (2013) to represent the range of potential 21 st -century climate changes and to enable cross-crop comparisons with other model intercomparison studies (e.g., Mcdermid et al, 2015). A full 5 9 5 9 3 factorial combination of these modified 30-year weather datasets was conducted at each site (i.e.…”

Section: Simulation Protocolsmentioning

confidence: 99%

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A potato model intercomparison across varying climates and productivity levels

Fleisher

Condori

Quiroz

et al. 2016

Global Change Biology

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109

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A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

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Section: Methodsmentioning

confidence: 97%

Section: Simulation Protocolsmentioning

confidence: 99%

A potato model intercomparison across varying climates and productivity levels

Fleisher

Condori

Quiroz

et al. 2016

Global Change Biology

Self Cite

109

View full text Add to dashboard Cite

show abstract

“…The delta approach was the most common climate scenario generation methodology used in the White et al (2011) review of agricultural impact models. Many impact sector models also respond strongly to mean temperature and rainfall shifts, allowing for the development of simple but effective emulators (e.g., Ruane et al 2014;McDermid et al 2015a;Howden and Crimp 2005).…”

Section: Introductionmentioning

confidence: 99%

Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment

2017

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We present the Representative Temperature and Precipitation (T&P) GCM Subsetting Approach developed within the Agricultural Model Intercomparison and Improvement Project (AgMIP) to select a practical subset of global climate models (GCMs) for regional integrated assessment of climate impacts when resource limitations do not permit the full ensemble of GCMs to be evaluated given the need to also focus on impacts sector and economics models. Subsetting inherently leads to a loss of information but can free up resources to explore important uncertainties in the integrated assessment that would otherwise be prohibitive. The Representative T&P GCM Subsetting Approach identifies five individual GCMs that capture a profile of the full ensemble of temperature and precipitation change within the growing season while maintaining information about the probability that basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry) are projected in the full GCM ensemble. We demonstrate the selection methodology for maize impacts in Ames, Iowa, and discuss limitations and situations when additional information may be required to select representative GCMs. We then classify 29 GCMs over all land areas to identify regions and seasons with characteristic diagonal skewness related to surface moisture as well as extreme skewness connected to snow-albedo feedbacks and GCM uncertainty. Finally, we employ this basic approach to recognize that GCM projections demonstrate coherence across space, time, and greenhouse gas concentration pathway. The Representative T&P GCM Subsetting Approach provides a quantitative basis for the determination of useful GCM subsets, provides a practical and coherent approach where previous assessments selected solely on availability of scenarios, and may be extended for application to a range of scales and sectoral impacts.

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“…The Agricultural Model Intercomparison and Improvement Project (AgMIP) (Rosenzweig et al, 2013) took steps to begin addressing these issues with the Coordinated Climate-Crop Modeling Project (C3MP), a modeling study specifically designed to, among other things, provide the data necessary to develop a flexible and dynamic crop yield emulator (Ruane et al, 2014;McDermid et al, 2015). C3MP invited point-based crop modelers from across the AgMIP community to simulate their calibrated agricultural system's response to 99 sensitivity tests in which 1980-2009 baseline climate data were modified to synthesize changes in mean carbon dioxide concentration ([CO 2 ]), temperature, and precipitation.…”

Section: Introductionmentioning

confidence: 99%

A crop yield change emulator for use in GCAM and similar models: Persephone v1.0

et al. 2019

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Abstract. Future changes in Earth system state will impact agricultural yields and, through these changed yields, can have profound impacts on the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields but are often too computationally intensive to dynamically couple into global multi-sector economic models, such as the Global Change Assessment Model (GCAM) and other similar-in-scale models. Yet, generalizing a faster site-specific crop model's results to be used globally will introduce inaccuracies, and the question of which model to use is unclear given the wide variation in yield response across crop models. To examine the feedback loop among socioeconomics, Earth system changes, and crop yield changes, rapidly generated yield responses with some quantification of crop response uncertainty are desirable. The Persephone v1.0 response functions presented in this work are based on the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Climate-Crop Modeling Project (C3MP) sensitivity test data set and are focused on providing GCAM and similar models with a tractable number of rapid to evaluate dynamic yield response functions corresponding to a range of the yield response sensitivities seen in the C3MP data set. With the Persephone response functions, a new variety of agricultural impact experiments will be open to GCAM and other economic models: for example, examining the economic impacts of a multi-year drought in a key agricultural region and how economic changes in response to the drought can, in turn, impact the drought.

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The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Cited by 15 publications

References 20 publications

A potato model intercomparison across varying climates and productivity levels

A potato model intercomparison across varying climates and productivity levels

Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment

A crop yield change emulator for use in GCAM and similar models: Persephone v1.0

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