The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, temperature, water, and nitrogen (version 1.0)

James Franke ,Christian Folberth,Tobias Hank,Jonas Jägermeyr,Christoph Müller,Ashwan Reddy,Thomas A M Pugh,Louis François,Ingrid Jacquemin,Ziwei Wang,Florian Zabel,Marie Dury,Abigail Snyder,Meridel Phillips,Alex C Ruane,Stefan Olin,Wenfeng Liu,Joshua Elliott,Michelle Li,Pete Falloon ,Curtis D Jones ,R C Izaurralde ,K D Williams ,E J Moyer

doi:10.5194/gmd-13-3995-2020

Abstract

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.

Highlights

Improving our understanding of the impacts of future climate change on crop yields is critical for global food security in the 21st century
We describe a set of globally gridded crop model emulators developed from the new parameter-sweep dataset of the Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 effort
The Global Gridded Model Intercomparison Project (GGCMI) Phase 2 simulations are described in detail in Franke et al (2020a), but we summarize briefly here

Summary

Introduction

Improving our understanding of the impacts of future climate change on crop yields is critical for global food security in the 21st century. Statistical crop models can only capture crop responses under the range of current conditions but have several advantages: they implicitly include management and behavioral practices that are difficult to model explicitly, and they are typically simple analytical expressions that are implemented by downstream impact modelers. Both types of models are routinely used, and comparative studies have concluded that when done carefully, both approaches can provide similar yield estimates Both types of models are routinely used, and comparative studies have concluded that when done carefully, both approaches can provide similar yield estimates (e.g., Lobell and Burke, 2010; Moore et al, 2017; Roberts et al, 2017; Zhao et al, 2017; B. Liu et al, 2016)

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Results

Conclusion