Submitted as: model description paper
06 Jun 2023
Submitted as: model description paper |  | 06 Jun 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water and nitrogen perturbations

Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen

Abstract. Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replaces the models per se, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with machine learning algorithm to reproduce the response of year-to-year variation of four crop yield to CO2 (C), temperature (T), water (W) and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2 experiment. The emulators were able to explain more than 92 % variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CTWN perturbations, the emulators could well reproduce the year-to-year variation of crop yield over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future, and serving as a lightweight tool of multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulation of crop yield under climate change.

Weihang Liu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-74', Anonymous Referee #1, 07 Jul 2023
  • RC2: 'Comment on gmd-2023-74', Anonymous Referee #2, 01 Aug 2023
  • AC1: 'Comment on gmd-2023-74', Tao Ye, 21 Aug 2023

Weihang Liu et al.

Weihang Liu et al.


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Short summary
We develop a machine learning based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a lightweight way.