The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present‐day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2‐AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2‐AO, many aspects of the present‐day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top‐of‐atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.

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.

[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.

Practical experience in developing and using the U.K. Met Office Unified Model for both weather and climate prediction provides lessons about both the benefits and challenges of seamless prediction.T he concept of a unified or seamless framework for weather and climate prediction has attracted a lot of attention in the last few years (Hurrell et al. 2009;Brunet et al. 2010;Shapiro et al. 2010;Nobre et al. 2010;Hazeleger et al. 2010;Senior et al. 2011). Traditionally the weather and climate prediction problems have been seen as different disciplines. Numerical weather prediction (NWP) is crucially dependent on defining an accurate initial state and running at the highest possible resolutions, while climate prediction has sought to incorporate the full complexity of the Earth system in order to accurately capture long time-scale variations and feedbacks determining the current climate and potential climate change. Unifying modeling and prediction across time scales stems from a recognition that the evolution of the weather and climate are linked by the same physical processes in the atmosphereocean-land-cryosphere system operating across multiple space and time scales. In addition, there is an increasing requirement to include Earth system complexity in NWP models (e.g., atmospheric chemistry for air quality predictions) and growing evidence that improvements to the resolution and initialization of coupled climate models are required to accurately capture important modes of atmospheric and oceanic variability on monthly to decadal time scales (e.g., Scaife et al. 2011).What does seamless prediction look like in practice? The aim of this paper is to discuss the Met Office experiences over the last 25 years as we have moved toward a fully unified framework for our global and regional atmospheric, land, and ocean prediction systems, highlighting the clear benefits but also the potential drawbacks and pitfalls encountered along the way. We will also discuss the current status of our unified prediction systems and vision for the future. historiCAl deVelopMent of the Met offiCe WeAther And CliMAte Models. Phase 1 (1960-90): Separate NWP and climate models. As in most other modeling centers, the Met Office initial development of numerical models for weather forecasting and climate was entirely 1865 december 2012 AmerIcAN meTeOrOLOGIcAL SOcIeTY |

[1] Measurements of the top-of-the-atmosphere outgoing longwave radiation (OLR) for July 2003 from Meteosat-7 are used to assess the performance of the numerical weather prediction version of the Met Office Unified Model. A significant difference is found over desert regions of northern Africa where the model emits too much OLR by up to 35 Wm À2 in the monthly mean. By cloud-screening the data we find an error of up to 50 Wm À2 associated with cloud-free areas, which suggests an error in the model surface temperature, surface emissivity, or atmospheric transmission. By building up a physical model of the radiative properties of mineral dust based on in situ, and surface-based and satellite remote sensing observations we show that the most plausible explanation for the discrepancy in OLR is due to the neglect of mineral dust in the model. The calculations suggest that mineral dust can exert a longwave radiative forcing by as much as 50 Wm À2 in the monthly mean for 1200 UTC in cloud-free regions, which accounts for the discrepancy between the model and the Meteosat-7 observations. This suggests that inclusion of the radiative effects of mineral dust will lead to a significant improvement in the radiation balance of numerical weather prediction models with subsequent improvements in performance.

Abstract. 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.