We describe Global Atmosphere 7.0 and Global Land 7.0 (GA7.0/GL7.0), the latest science configurations of the Met Office Unified Model (UM) and the Joint UK Land Environment Simulator (JULES) land surface model developed for use across weather and climate timescales. GA7.0 and GL7.0 include incremental developments and targeted improvements that, between them, address four critical errors identified in previous configurations: excessive precipitation 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 biases over the Southern Ocean. They also include two new parametrisations, namely the UK Chemistry and Aerosol (UKCA) GLOMAP-mode (Global Model of Aerosol Processes) aerosol scheme and the JULES multi-layer snow scheme, which improve the fidelity of the simulation and were required for inclusion in the Global Atmosphere/Global Land configurations ahead of the 6th Coupled Model Intercomparison Project (CMIP6). In addition, we describe the GA7.1 branch configuration, which reduces an overly negative anthropogenic aerosol effective radiative forcing (ERF) in GA7.0 whilst maintaining the quality of simulations of the present-day climate. GA7.1/GL7.0 will form the physical atmosphere/land component in the HadGEM3–GC3.1 and UKESM1 climate model submissions to the CMIP6.

We describe Global Atmosphere 6.0 and Global Land 6.0 (GA6.0/GL6.0): the latest science configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment) dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model's physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe GA6.1/GL6.1, which includes a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global numerical weather prediction, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.

The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing.

The Southern Ocean is a pivotal component of the global climate system yet it is poorly represented in climate models, with significant biases in upper-ocean temperatures, clouds and winds. Combining Atmospheric and Coupled Model Inter-comparison Project (AMIP5/CMIP5) simulations, with observations and equilibrium heat budget theory, we show that across the CMIP5 ensemble variations in sea surface temperature biases in the 40–60°S Southern Ocean are primarily caused by AMIP5 atmospheric model net surface flux bias variations, linked to cloud-related short-wave errors. Equilibration of the biases involves local coupled sea surface temperature bias feedbacks onto the surface heat flux components. In combination with wind feedbacks, these biases adversely modify upper-ocean thermal structure. Most AMIP5 atmospheric models that exhibit small net heat flux biases appear to achieve this through compensating errors. We demonstrate that targeted developments to cloud-related parameterisations provide a route to better represent the Southern Ocean in climate models and projections.

The importance of probabilistic weather predictions and climate projections is growing. One of the key elements of the former is stochastic physics, schemes that perturb some uncertain processes in a general circulation model (GCM), such as physical parametrizations or diffusion. They help to increase the ensemble dispersion of ensemble prediction systems (EPS) and in some cases improve certain atmospheric processes by noise‐induced drifts. We have developed a new configuration of stochastic physics schemes for the Met Office Unified Model (MetUM). It consists of an improved Stochastic Kinetic Energy Backscatter v2 (SKEB2), plus the Stochastic Perturbation of Tendencies (SPT).The improvements to SKEB2 remove spurious physical artefacts, e.g. a spurious wave caused by low‐wave‐number perturbations, and improve the resolution sensitivity of the scheme. The SPT produces a larger ensemble spread in the Tropics than present schemes, but its impact on long‐term climate budgets makes the use of conservation constraints for water vapour and energy essential.The new configuration produces a higher impact in the Tropics, increasing the ensemble spread and improving some long‐standing climate biases in areas of excessive convection, whilst minimizing the negative impact on tropical processes like tropical convective waves.

There is still no consensus on the mechanisms that modify precipitation over and around cities, especially for those located in the tropics where convective processes primarily drive rainfall. Here we contribute to the ongoing discussion about the urban‐associated precipitation by investigating the urban effect on the diurnal cycle of rainfall over Singapore. We use the urban version of the numerical weather prediction system of the Meteorological Service Singapore (hereafter called uSINGV) at a 300 m horizontal resolution to simulate the rainfall conditions over Singapore and its surroundings during the inter‐monsoon period between 2010 and 2014. Two simulations with different land surface conditions are conducted: one with urban areas (i.e. present conditions) and one without urban areas. uSINGV is shown to perform well for rainfall when compared to observations. Comparison between simulations reveals that the urban area is responsible for the formation of a rainfall “hot spot” over Singapore and Johor Bahru, located at the southern tip of the Malay Peninsula, and the urban effect is accountable for 20–30% of total rainfall during late afternoons and evenings, highlighting a strong urban effect on localized rainfall over a tropical city. Enhancement of convection due to the urban heat island effect, increased frictional convergence due to buildings' drag, the seaward shift of the sea‐breeze front, and the increased inflow of boundary‐layer moisture by the stronger sea breeze are suggested as most probable reasons for the increased rainfall in the urban area.