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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 12
Geosci. Model Dev., 8, 3987–3997, 2015
https://doi.org/10.5194/gmd-8-3987-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: JULES-crop: a parameterisation of crops in the JULES land...

Special issue: Joint UK Land Environment Simulator (JULES) – configurations,...

Geosci. Model Dev., 8, 3987–3997, 2015
https://doi.org/10.5194/gmd-8-3987-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 15 Dec 2015

Development and technical paper | 15 Dec 2015

Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecasts

K. E. Williams and P. D. Falloon K. E. Williams and P. D. Falloon
  • Met Office Hadley Centre, FitzRoy Road, Exeter, Devon EX1 3PB, UK

Abstract. JULES-crop is a parametrisation of crops in the Joint UK Land Environment Simulator (JULES). We investigate the sources of the interannual variability in the modelled maize yield, using global runs driven by reanalysis data, with a view to understanding the impact of various approximations in the driving data and initialisation. The standard forcing data set for JULES consists of a combination of meteorological variables describing precipitation, radiation, temperature, pressure, specific humidity and wind, at subdaily time resolution. We find that the main characteristics of the modelled yield can be reproduced with a subset of these variables and using daily forcing, with internal disaggregation to the model time step. This has implications in particular for the use of the model with seasonal forcing data, which may not have been provided at subdaily resolution for all required driving variables. We also investigate the effect on annual yield of initialising the model with climatology on the sowing date. This approximation has the potential to considerably simplify the use of the model with seasonal forecasts, since obtaining observations or reanalysis output for all the initialisation variables required by JULES for the start date of the seasonal forecast would present significant practical challenges.

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