Abstract. The JULES-crop model (Osborne et al., 2015) is a parametrisation of crops within the Joint UK Land Environment Simulator (JULES), which aims to simulate both the impact of weather and climate on crop productivity and the impact of croplands on weather and climate. In this evaluation paper, observations of maize at three FLUXNET sites in Nebraska (US-Ne1, US-Ne2 and US-Ne3) are used to test model assumptions and make appropriate input parameter choices. JULES runs are performed for the irrigated sites (US-Ne1 and US-Ne2) both with the crop model switched off (prescribing leaf area index (LAI) and canopy height) and with the crop model switched on. These are compared against GPP and carbon pool FLUXNET observations. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties.Copyright statement. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other.

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Abstract. The Land Variational Ensemble Data Assimilation Framework (LAVENDAR) implements the method of four-dimensional ensemble variational (4D-En-Var) data assimilation (DA) for land surface models. Four-dimensional ensemble variational data assimilation negates the often costly calculation of a model adjoint required by traditional variational techniques (such as 4D-Var) for optimizing parameters or state variables over a time window of observations. In this paper we present the first application of LAVENDAR, implementing the framework with the Joint UK Land Environment Simulator (JULES) land surface model. We show that the system can recover seven parameters controlling crop behaviour in a set of twin experiments. We run the same experiments at the Mead continuous maize FLUXNET site in Nebraska, USA, to show the technique working with real data. We find that the system accurately captures observations of leaf area index, canopy height and gross primary productivity after assimilation and improves posterior estimates of the amount of harvestable material from the maize crop by 74 %. LAVENDAR requires no modification to the model that it is being used with and is hence able to keep up to date with model releases more easily than other DA methods.

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