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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Short summary
Crop growth in land surface models normally requires high temporal resolution climate data, but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. Our work provides an alternative way to use the monthly climate for crop yield projections. Such approach could be easily adopted by other crop models.
Preprints
https://doi.org/10.5194/gmd-2020-186
https://doi.org/10.5194/gmd-2020-186

Submitted as: model evaluation paper 24 Aug 2020

Submitted as: model evaluation paper | 24 Aug 2020

Review status: a revised version of this preprint is currently under review for the journal GMD.

Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections

Yaqiong Lu1,2 and Xianyu Yang3 Yaqiong Lu and Xianyu Yang
  • 1Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610040, China
  • 2National Center for Atmospheric Research, Boulder, CO 80305, USA
  • 3Chengdu University of Information Technology, Chengdu, 610225, China

Abstract. Crop growth in land surface models normally requires high temporal resolution climate data (3-hourly or 6-hourly), but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 °C warming, 2.0 °C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, and crop yields were underestimated by 5–8 % across the three scenarios (1.5 °C, 2.0 °C, and RCP4.5) for the global average, and 28–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

Yaqiong Lu and Xianyu Yang

 
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Yaqiong Lu and Xianyu Yang

Yaqiong Lu and Xianyu Yang

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Short summary
Crop growth in land surface models normally requires high temporal resolution climate data, but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. Our work provides an alternative way to use the monthly climate for crop yield projections. Such approach could be easily adopted by other crop models.
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