The JULES Community
The JULES model is available cost-free to any researcher for non-commercial use, and this has led to a large and diverse community from all over the globe. Although the majority of researchers using JULES are based in the U.K., requests for the JULES code have come from places as diverse as China, India, New Zealand and South America.
Here's how to get started as a JULES user:
- The first step is to request access to the code here. Registration is free and open to all for non-commercial use (registration is required just to keep track of who is using JULES, not to restrict usage). When you register, you will automatically be set up with a Met Office Science Repository Service (MOSRS) login (may take a few days), which will allow you to access the JULES TRAC.
- Next, bookmark the main JULES manual pages and the online manual for the version of JULES you'll be using (I have the namelist section bookmarked - for JULESvn4.8 this is here - because I continually use the search box there to look up what particular model variables mean).
- Now do at least one of the tutorials on our training page, for example Toby's JULES From Scratch.
- JULES general interest: jules*A*T*lists.reading.ac.uk (subscribe here, although this might happen automatically when you register for MOSRS)
- JULES users: jules-users*A*T*lists.reading.ac.uk (subscribe here, although this might happen automatically when you register for MOSRS)
- JULES support: jules-support*A*T*metoffice.gov.uk for all technical issues with JULES, Rose, Cylc or FCM, however please check the documentation and tutorials on the JULES TRAC and JULES manual pages before emailing a support request.
- Finally, there are also the Met Office JULES Announce and JULES General Discussions yammer groups (email Kerry Smout-Day for an invitation).
In common with all land surface models, JULES needs three elements to perform a simulation: driving data, ancillary/prescribed data and control files. In general it's up to you to find and acquire these, but there's a lot of help available. Here are some links to help you select the most appropriate data sources for your project:
These are met. data or climate data, which can either be obtained from standard open-access datasets (see table below; usually in NetCDF format) or from a met. station (as in e.g. the Loobos example or studies like Marthews et al. 2012). Basically, JULES requires data on Radiation (downward SW and LW), Precipitation (rainfall and snowfall), Temperature, Specific humidity and Wind speed (more specifically, the variables listed here must be available, though not necessarily at every timestep: JULES can interpolate/disaggregate data from e.g. 3 hourly to hourly - see here). JULES-appropriate sources include:
|Data source||Extent||Resolution||Time Range||Comments|
|CHESS||U.K. (excl. Shetland & N. Ireland)||1 km||1961-2015||2016 article|
|WATCH Forcing data ERA Interim (WFDEI)||Global (excl. Antarctica)||0.5°||1979-2012||Weedon et al. (2014), ReadMe|
|CRU-NCEP v4||Global||0.5°||1901-2012||Viovy & Ciais (2011)|
|Princeton||Global||1.0° / 0.5° / 0.25°||1948-2010||Sheffield et al. (2006)|
|GSWP3||Global||0.5°||1850-2010||Website under construction: can't yet download data|
|CHELSA||Global||30 arc-sec||1979-2013||Based on ERA Interim|
(n.b. there are many other data products and alternative sources, e.g. see the links here)
Spatial datasets, e.g. soil properties (hydraulic and thermal parameter values), land-cover (frac; vegetation and non-vegetation) and DEM-derived gridded flow-directions and accumulated areas. If these have been provided as time-varying ancillaries then they are called prescribed data by JULES (other ancillary data are constant). For non-point runs, usually all in NetCDF format. JULES runs require these (non-time-varying) ancillary fields and these (time-varying) prescribed data fields, usually at the same resolution as your driving data:
|Driving data is for:||Source for land cover fraction (frac)||Leaf area index (LAI)|
|A point run||Use site data||Use site data||Recommended values are in the standard science configurations (see below)||If soil data are available, use pedotransfer functions (see e.g. Marthews et al. 2014)|
|A gridded run||See IGBP or USGS||See GLCF||See TRY|
n.b. there are MANY other sources: these are just some starter suggestions (if anyone wants to contribute to this page by expanding this table then pls email me! - Toby, May 2017).
The UK Met Office Central Ancillary Program (CAP): The Met Office Unified Model (UM) uses the CAP for creating ancillary data files on appropriate grids for use with the UM (see User Guides here (requires password) or here or [very old] docs #70 and #73 here). The related tool Xancil is an application written by Jeff Cole for creating UM ancillary files from netCDF input files. As of 2015, CAP was running on ARCHER too.
However, there were several critical weaknesses recognised in the CAP system, described in this position paper by Keir Bovis in 2012, and the TIAN program was initiated to produce a replacement, which will be the new ANTS system (due for public release in 2017, whereupon it will begin to replace the CAP).
There is no central repository for JULES ancillaries. In the absence of this, ancillaries for JULES runs are difficult to come by and usually assembled for each individual project from one or other of the sources above (including use of some converted UM ancillaries). With the Rosie Go system (see below) the situation has improved: it's now possible to download Rose suites from projects similar to your own, look inside to see what ancillary files have been used and then email the suite owner to seek permission to use those files. Before ANTS becomes widely available, this is probably the most direct way to acquire the ancillary and prescribed data files you may need.
Currently JULES's control files must be in the form of either (i) a set of Fortran namelist files (model parameter input .nml files) or (ii) a Rose suite (a suite is effectively a bundle of control files, including the same namelists as used for (i) ).
- ROSE SUITES: You get these through the Rosie Go system (part of your Rose installation; see here for how to do this and here for background). The idea is that you DON'T assemble your own suite from scratch, but search on Rosie Go for a job/configuration that is similar to what you want to do (e.g. try searching for "UKV"), download one of the available suites (this is free) and modify its options and settings appropriately for use on your system.
- NAMELIST FILES: Sets of namelists are not usually distributed around the JULES community any more (except as part of Rose suites), but if you are working from a set of these then you can either (i) run JULES without Rose (see here) or (ii) use the create_rose_app to convert them into a Rose suite and use that to run JULES with Rose (see here). The one location where namelist files are still shared rather than Rose suites is the configurations repo.
The namelists JULES requires for (i) are described on the JULES Manuals pages (select your version of JULES and find section "The JULES namelist files") and this is the best source for details of both these kinds of control files, because both the Rose suite and the namelist files are composed of the same namelists.
Gridded runs from Land Surface Models (LSMs) in general can output data in 2D or 1D NetCDF files and you usually need to use different software to visualise/analyse these two types of gridded output. However, please be aware that JULES can only give you 1D output from gridded runs (whether the driving data are 1D or 2D). This means that you either need to use analysis software that can deal with 1D files or you need to anticipate a post-processing step to convert your 1D JULES outputs into 2D files (for example, Alberto Martínez post-processed 1D output to generate the 2D results of the EU earth2observe project downloadable from the WCI Portal).
Data visualisation tools include:
- For 2D NetCDF output: On UNIX/Linux people seem to use GrADS (see also here and here), Xconv, Iris (see also Cube Browser and Thea) or Ncview. On Windows you can use Panoply (very quick, but limited) or a GIS package like QGIS (see also here; has a lot of advantages if you want to compare JULES output to other raster or vector data, e.g. to calculate land surface fluxes over an irregular area like a river catchment).
- For 1D 'land axis' NetCDF output: On UNIX/Linux you can use GrADS. You can display a 1D NetCDF file in GrADS if you can generate a .pdef file that correctly describes the grid of the datafile you have. See here for how to construct a pdef file for gridded output. Another option on UNIX/linux is xmgrace/grace.
(see presentation by Emma Robinson on Training and the data visualisation examples there). Post-processing options are almost universally personally-written scripts (because the variety in grids and masks used for LSM simulations is so wide that a universal post-processor is extremely difficult to code up): here are [*** NOT YET FINISHED ***].
See here for evaluation / benchmarking.