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A Hindcast Experiment Using The Gcam 3.0 Agriculture And Land-Use Module

Author

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  • KATHERINE CALVIN

    (Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland, College Park, MD 20742, USA)

  • MARSHALL WISE

    (Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland, College Park, MD 20742, USA)

  • PAGE KYLE

    (Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland, College Park, MD 20742, USA)

  • LEON CLARKE

    (Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland, College Park, MD 20742, USA)

  • JAE EDMONDS

    (Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland, College Park, MD 20742, USA)

Abstract

We report results of a “hindcast” experiment focusing on the agricultural and land-use component of the Global Change Assessment Model (GCAM). We initialize GCAM to reproduce observed agriculture and land use in 1990 and forecast agriculture and land use patterns on one-year time steps to 2010. We report overall model performance for nine crops in 14 regions. We report areas where the hindcast is in relatively good agreement with observations and areas where the correspondence is poorer. We find that when given observed crop yields as input data, producers in GCAM implicitly have perfect foresight for yields leading to over compensation for year-to-year yield variation. We explore a simple model in which planting decisions are based on expectations but production depends on actual yields and find that this addresses the implicit perfect foresight problem. Second, while existing policies are implicitly calibrated into IAMs, changes in those policies over the period of analysis can have a dramatic effect on the fidelity of model output. Third, we demonstrate that IAMs can employ techniques similar to those used by the climate modeling community to evaluate model skill. We find that hindcasting has the potential to yield substantial benefits to the IAM community.

Suggested Citation

  • Katherine Calvin & Marshall Wise & Page Kyle & Leon Clarke & Jae Edmonds, 2017. "A Hindcast Experiment Using The Gcam 3.0 Agriculture And Land-Use Module," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 1-21, February.
    • Handle: RePEc:wsi:ccexxx:v:08:y:2017:i:01:n:s2010007817500051
    DOI: 10.1142/S2010007817500051
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    References listed on IDEAS

    as
    1. Son H. Kim, Jae Edmonds, Josh Lurz, Steven J. Smith, and Marshall Wise, 2006. "The objECTS Framework for integrated Assessment: Hybrid Modeling of Transportation," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 63-92.
    2. Congressional Budget Office, 2014. "The Renewable Fuel Standard: Issues for 2014 and Beyond," Reports 45477, Congressional Budget Office.
    3. Marshall Wise & Kate Calvin & Page Kyle & Patrick Luckow & Jae Edmonds, 2014. "Economic And Physical Modeling Of Land Use In Gcam 3.0 And An Application To Agricultural Productivity, Land, And Terrestrial Carbon," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 1-22.
    4. Congressional Budget Office, 2014. "The Renewable Fuel Standard: Issues for 2014 and Beyond," Reports 45477, Congressional Budget Office.
    5. Congressional Budget Office, 2014. "The Renewable Fuel Standard: Issues for 2014 and Beyond," Reports 45477, Congressional Budget Office.
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    Cited by:

    1. Glotin, David & Bourgeois, Cyril & Giraudet, Louis-Gaëtan & Quirion, Philippe, 2019. "Prediction is difficult, even when it's about the past: A hindcast experiment using Res-IRF, an integrated energy-economy model," Energy Economics, Elsevier, vol. 84(S1).
    2. Zhao, Xin & Calvin, Katherine & Patel, Pralit & Abigail, Snyder & Wise, Marshall & Waldhoff, Stephanie & Hejazi, Mohamad & Edmonds, James, 2021. "Impacts of interannual climate and biophysical variability on global agriculture markets," Conference papers 333245, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    3. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).
    4. Shi, Wenjing & Ou, Yang & Smith, Steven J. & Ledna, Catherine M. & Nolte, Christopher G. & Loughlin, Daniel H., 2017. "Projecting state-level air pollutant emissions using an integrated assessment model: GCAM-USA," Applied Energy, Elsevier, vol. 208(C), pages 511-521.
    5. Zhao, Xin & Calvin, Katherine V. & Wise, Marshall A. & Iyer, Gokul, 2021. "The role of global agricultural market integration in multiregional economic modeling: Using hindcast experiments to validate an Armington model," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 1-17.

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