Abstract. 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.

Abstract. 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.

Abstract. We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family memCorrespondence to: G. M. Martin (gill.martin@metoffice.gov.uk) bers are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.

Cloud fraction, liquid and ice water contents derived from long-term radar, lidar, and microwave radiometer data are systematically compared to models to quantify and improve their performance.

[1] The African Monsoon Multidisciplinary Analysis (AMMA) is a major international campaign investigating far-reaching aspects of the African monsoon, climate and the hydrological cycle. A special observing period was established for the dry season (SOP0) with a focus on aerosol and radiation measurements. SOP0 took place during January and February 2006 and involved several ground-based measurement sites across west Africa. These were augmented by aircraft measurements made by the Facility for Airborne Atmospheric Measurements (FAAM) aircraft during the Dust and Biomass-burning Experiment (DABEX), measurements from an ultralight aircraft, and dedicated modeling efforts. We provide an overview of these measurement and modeling studies together with an analysis of the meteorological conditions that determined the aerosol transport and link the results together to provide a balanced synthesis. The biomass burning aerosol was significantly more absorbing than that measured in other areas and, unlike industrial areas, the ratio of excess carbon monoxide to organic carbon was invariant, which may be owing to interaction between the organic carbon and mineral dust aerosol. The mineral dust aerosol in situ filter measurements close to Niamey reveals very little absorption, while other measurements and remote sensing inversions suggest significantly more absorption. The influence of both mineral dust and biomass burning aerosol on the radiation budget is significant throughout the period, implying that meteorological models should include their radiative effects for accurate weather forecasts and climate simulations. Generally, the operational meteorological models that simulate the production and transport of mineral dust show skill at lead times of 5 days or more. Climate models that need to accurately simulate the vertical profiles of both anthropogenic and natural aerosols to accurately represent the direct and indirect effects of aerosols appear to do a reasonable job, although the magnitude of the aerosol scattering is strongly dependent upon the emission data set.