These configurations are intended for use by the JULES development and research community (they have been academically published and also approved for use by the relevant JULES module leaders), but are only maintained on an ad hoc basis rather than routinely (and are not guaranteed bit-comparable between JULES versions). If you are aware of a widely-used configuration that is not included here, then please send me the details (see email address below) - either in Rose suite format or as a set of namelists (along with a version number of the version of JULES for which they are appropriate):

Configuration Description

Source / Suite ID

Global Water Resources Reanalysis 2 (WRR2)

Intended to be the best JULES hydrology options for GB under the UKEP project (Martínez-de la Torre et al. 2019) and its extrapolation for global applications under the earth2observe project (Fink & Martínez-de la Torre 2017). The main difference with other configurations is the use of a new development to include a terrain slope dependency in the saturation excess surface runoff production scheme (see jules_hydrology and jules_pdm namelists). This development has been part of the trunk since JULESvn4.9.



Alberto Martínez de la Torre's suite for use with JULESvn4.5 and WRR2 data (downscaled from 0.5deg WFDEI data): u-ai199.

(Note: This suite uses 3-hourly driving data, with 'nb' temporal interpolation flag SWdown and LWdown, and 1 hour timestep (as it is set up for daily and monthly outputs).

UK CHESS-LAND Configuration

(CHESS = Climate, Hydrological and Ecological research Support System)

For use with CHESS data over UK (minus Shetland and N. Ireland) at 1 km resolution with prescribed vegetation.

Developed by Alberto Martínez de la Torre from WRR2 (the hydrology options are as for WRR2, but the spatial resolution is different and the C4 grass PFT has been replaced with crop land cover).

Suite u-au394.

See Blyth et al. (2019)

FLUXNET Configuration

For use with Fluxnet2015 data at single- or multiple-sites.

Please note that if you use Fluxnet data then it's required to register with them to let them know that you are using their data (even if you actually download the data from elsewhere).

See suite u-al752 developed by Karina Williams (UKMO), Anna Harper (Univ. Exeter), Patrick McGuire (Univ. Reading) and Carolina Duran-Rojas (Univ. Exeter). Also used in Patrick M's tutorial here. As of Jan 2019 this suite includes 73 sites (including the JULES Golden Sites).

Alternatively, see u-ap091 developed by Heather Rumbold from GL6 (with local (gap filled) atmospheric forcing + IGBP land cover + HWSD soils).

JULES Golden Sites A new initiative 2018 to identify a set of global sites with high-quality observational data that can be used to drive JULES and evaluate the output. See here. Also see the FLUXNET Configuration above.
ESM-SnowMIP configuration

by Cécile Ménard. Adapted from JULES-C and the snow configuration for plot-scale simulations with observed meteorological forcing. Used with JULES 4.8 in July 2017.

Namelists: JULES Snow NML zip file

Details: Notes Snow configuration

Snow configuration by John Edwards, described here. u-ai433, u-ah563
JULES-Impacts See Kate Halladay's and Richard Betts's 2017 presentation and also Anna Harper's 2016 presentation.  
JULES-ML (Managed Land) See Anna Harper's and Andy Wiltshire's 2016 presentation.  
JULES-Fire For use with the INFERNO fire model and also mentioned here.  

JULES-Crop configurations and others with >=9 PFTs

(n.b. configurations with >5 PFTs (PFT = Plant Functional Type) are not just for crop modelling: Harper's papers introduced more PFTs in order to have a generally better characterisation of vegetation in JULES as well)


Examples of C3 crops are wheat, rice, barley, soybean.

Examples of C4 crops are maize, sugarcane, sorghum.

For 9 PFTs see the main wiki, but also here and Harper et al. (2016:Table 2) and Harper et al. (2018), e.g. JULES-C2.

For 13 PFTs (generally the same PFTs as for 9 PFT configs, with the addition of C3 pasture, C3 crop, C4 pasture and C4 crop): see "Evaluation of JULES vegetation phenology", e.g. suite u-ad340

Configurations using 17 PFTs seem to be experimental either for compliance with CABLE which has used 17 PFTs for many years (see e.g. here) or for fire-vegetation interactions (see e.g. here or here), however I have not been able to find a standard suite for JULES with 17 PFTs (as of May 2024).

For Toby's efforts with oil palm plantation PFTs see here.

Crop models in JULES: Currently, JULES has FOUR different ways of representing crops in its simulations:

     1. Use >5 natural PFTs (e.g. 9 PFTs left). Standard JULES runs have 5 Plant Functional Types (PFTs), including two that are designed to simulate grasslands, which may be used approximately to represent croplands (within the known uncertainty of applying a biome parameterisation to represent a monoculture). Generally, temperate and tropical grassland parameterisations should not be too different from C3 and C4 crop parameterisations, respectively. This option allows you to prescribe canopy height and LAI and couple to the UM if required.

     2. JULES-Crop (Leung et al. 2020), activated by putting ncpft>0 (see here). If you activate this, you must also put l_triffid=FALSE and l_trif_crop=FALSE. This option has the advantage of allowing a more precise characterisation of the crop growing cycle, but has the disadvantage that you cannot do a run coupled to the UM.

     3. TRIFFID-Crop, activated by making l_triffid=TRUE and l_trif_crop=TRUE (see here). This option treats some of the standard PFTs as crops (specified by crop_io; but n.b. these are not technically 'crop PFTs'). Having TRIFFID (look in the reference list on this page for HCTN24) on allows carbon cycle quantities to be calculated more reliably, but has the disadvantage that you can’t prescribe canopy height or LAI.

     4. JULES-opticrop. See here.

Please note that inclusion in these tables is not intended to mean a configuration is necessarily 'recommended' for your particular use of JULES: these are configurations that have been tried and tested in particular situations and therefore should be consistent and accurate in those contexts, but you will have to assess for yourself whether it is suitable for your application of JULES. For example, in many operational configurations intended for the UM configurations are optimised for best results across ocean and sea ice surfaces as well as land surface, which means that they may not be as optimal as other configurations on land-only runs.
For benchmarking tests for some of these configurations, see the evaluation page. Finally, many other configurations are in use in the community (on the Rosie Go database), but these are not intended for wide, general use.