The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations Article Published Version Creative Commons: Attribution 4.0 (CC-BY) Open Access Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K. and Zerroukat, M. (2019) The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geoscientific Model Development, 12 (5). pp. 1909-1963. ISSN 1991-9603 doi: https://doi.org/10.5194/gmd-12-1909- 2019 Available at http://centaur.reading.ac.uk/83821/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.5194/gmd-12-1909-2019 Publisher: EGU All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online Geosci. Model Dev., 12, 1909–1963, 2019 https://doi.org/10.5194/gmd-12-1909-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations David Walters1, Anthony J. Baran1,2, Ian Boutle1, Malcolm Brooks1, Paul Earnshaw1, John Edwards1, Kalli Furtado1, Peter Hill3, Adrian Lock1, James Manners1, Cyril Morcrette1, Jane Mulcahy1, Claudio Sanchez1, Chris Smith1, Rachel Stratton1, Warren Tennant1, Lorenzo Tomassini1, Kwinten Van Weverberg1, Simon Vosper1, Martin Willett1, Jo Browse4, Andrew Bushell1, Kenneth Carslaw7, Mohit Dalvi1, Richard Essery5, Nicola Gedney6, Steven Hardiman1, Ben Johnson1, Colin Johnson1, Andy Jones1, Colin Jones8, Graham Mann7,8, Sean Milton1, Heather Rumbold1, Alistair Sellar1, Masashi Ujiie9, Michael Whitall1, Keith Williams1, and Mohamed Zerroukat1 1Met Office, FitzRoy Road, Exeter, EX1 3PB, UK 2School of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, AL10 9AB, UK 3Department of Meteorology, University of Reading, Reading, RG6 6BB, UK 4Centre for Geography, Society and the Environment, University of Exeter – Penryn Campus, Cornwall, TR10 9EZ, UK 5School of Geosciences, University of Edinburgh, Edinburgh, EH8 9XP, UK 6Met Office, Joint Centre for Hydrometeorological Research, Maclean Building, Wallingford, OX10 8BB, UK 7School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK 8National Centre for Atmospheric Science, University of Leeds, Leeds, LS2 9JT, UK 9Numerical Prediction Division, Japan Meteorological Agency, 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan Correspondence: David Walters (david.walters@metoffice.gov.uk) Received: 15 November 2017 – Discussion started: 28 November 2017 Revised: 15 February 2018 – Accepted: 20 March 2018 – Published: 14 May 2019 Abstract. We describe Global Atmosphere 7.0 and Global In addition, we describe the GA7.1 branch configura- Land 7.0 (GA7.0/GL7.0), the latest science configurations of tion, which reduces an overly negative anthropogenic aerosol the Met Office Unified Model (UM) and the Joint UK Land effective radiative forcing (ERF) in GA7.0 whilst main- Environment Simulator (JULES) land surface model devel- taining the quality of simulations of the present-day cli- oped for use across weather and climate timescales. GA7.0 mate. GA7.1/GL7.0 will form the physical atmosphere/land and GL7.0 include incremental developments and targeted component in the HadGEM3–GC3.1 and UKESM1 climate improvements that, between them, address four critical er- model submissions to the CMIP6. rors identified in previous configurations: excessive precipi- tation biases over India, warm and moist biases in the tropical tropopause layer (TTL), a source of energy non-conservation in the advection scheme and excessive surface radiation bi- 1 Introduction ases over the Southern Ocean. They also include two new parametrisations, namely the UK Chemistry and Aerosol In this paper, we document the Global Atmosphere 7.0 con- (UKCA) GLOMAP-mode (Global Model of Aerosol Pro- figuration (GA7.0) of the Met Office Unified Model (UM; cesses) aerosol scheme and the JULES multi-layer snow Brown et al., 2012) and the Global Land 7.0 configura- scheme, which improve the fidelity of the simulation and tion (GL7.0) of the Joint UK Land Environment Simulator were required for inclusion in the Global Atmosphere/Global (JULES) land surface model (Best et al., 2011; Clark et al., Land configurations ahead of the 6th Coupled Model Inter- 2011). These are the latest iterations in the line of GA/GL comparison Project (CMIP6). configurations developed for use in global atmosphere/land and coupled modelling systems across weather and climate Published by Copernicus Publications on behalf of the European Geosciences Union. 1910 D. Walters et al.: UM GA7.0/GA7.1 and JULES GL7.0 configurations timescales. This development is a continual process made so for consistency with that documentation, we list the trac up of small incremental changes to parameters and options ticket numbers (denoted by trac’s # character) along with within existing parametrisation schemes, the implementation these descriptions. Section4 includes an assessment of the of new schemes and options, and less frequent major changes configuration’s performance in global weather prediction and to the structure of the model and the framework on which it atmosphere/land-only climate simulations. This illustrates is built. The Global Atmosphere 6.0 configuration (GA6.0; the reduction of the critical model errors noted above, and Walters et al., 2017) fell into the latter category, as it included highlights some improvements in simple weather prediction a once-in-a-decade replacement of the model’s dynamical tests, but suggests that improvements are needed in the inter- core. To allow the configuration developers to concentrate action between the model and its data assimilation before im- on that change, the inclusion of other changes was limited plementation for operational forecasting. In Sect.5 we briefly to those that were known to be necessary alongside the dy- describe GA7.1, which is based on the GA7.0 “trunk” config- namical core, or to significantly improve system performance uration but includes a minimal set of changes for addressing measures, so as to make the dynamical core implementation the excessive aerosol forcing discussed in Sect. 4.5. As a re- easier. For this reason, GA7 sees the inclusion of a number sult of this work, GA7.1 and GL7.0 are suitable for use as the of bottom-up developments to the atmospheric parametrisa- physical atmosphere and land components in the HadGEM3– tion schemes developed over several years that improve the GC3.1 and UKESM1 climate models that will be submitted fidelity and internal consistency of the model. These include to the CMIP6. an improved treatment of gaseous absorption in the radiation scheme, improvements to the treatment of warm rain and ice cloud, and an improvement to the numerics in the model’s 2 Global Atmosphere 7.0 and Global Land 7.0 convection scheme. It also includes a number of top-down developments motivated by the findings of process evalu- 2.1 Dynamical formulation and discretisation ation groups (PEGs), which are tasked with understanding The UM’s ENDGame dynamical core uses a semi-implicit the root causes of model error. These changes include fur- semi-Lagrangian formulation to solve the non-hydrostatic, ther developments in the model’s microphysics and incre- fully compressible deep-atmosphere equations of mo- mental improvements to our implementation of the dynami- tion (Wood et al., 2014). The primary atmospheric prog- cal core. In combination with the bottom-up developments nostics are the three-dimensional wind components, virtual discussed previously, these lead to large reductions in our dry potential temperature, Exner pressure and dry density, four critical model errors, namely rainfall deficits over India whilst moist prognostics such as the mass mixing ratio of during the South Asian monsoon, temperature and humid- water vapour and prognostic cloud fields as well as other ity biases in the tropical tropopause layer (TTL), deficiencies atmospheric loadings are advected as free tracers. These in the model’s numerical conservation and surface flux bi- prognostic fields are discretised horizontally onto a regu- ases over the Southern Ocean. Finally, GA7 and GL7 include lar longitude–latitude grid with Arakawa C-grid stagger- new parametrisation schemes, which increase the complex- ing (Arakawa and Lamb, 1977), whilst the vertical discreti- ity and fidelity of the model and introduce new functionality sation utilises a Charney–Phillips staggering (Charney and that was deemed necessary for the next generation climate Phillips, 1953) using terrain-following hybrid height coor- modelling systems in which they will be used and which will dinates. The discretised equations are solved using a nested form the UK’s contribution to the 6th Coupled Model Inter- iterative approach centred about solving a linear Helmholtz comparison Project (CMIP6; Eyring et al., 2015). These new equation. By convention, global configurations are defined capabilities include a multi-moment modal representation of on 2× N longitudinal columns and 1:5× N latitudinal rows prognostic tropospheric aerosols, a multi-layer snow scheme of grid points for scalar variables, with the meridional wind and a seamless stochastic physics package, which will over- variable held at the north and south poles and scalar and see the inclusion of stochastic physics terms in production zonal wind variables first stored half a grid length away from UM climate simulations for the first time.