The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations

Walters, David; Brooks, M. E.; Boutle, Ian; Melvin, Thomas; Stratton, Rachel; Wells, Helen; Williams, Keith L.; Wood, Nigel; Allen, Thomas J.; Bushell, Andrew C.; Copsey, Dan; Earnshaw, Paul; Edwards, John M.; Groß, Markus; Hardiman, Steven C.; Harris, Chris; Heming, Julian; Klingaman, Nicholas P.; Levine, Richard A.; Manners, James; Martin, Gill; Milton, Sean; Mittermaier, Marion; Morcrette, C. J.; Riddick, Thomas; Roberts, Malcolm; Sánchez, Claudio; Selwood, Paul; Stirling, Alison; Smith, Chris; Suri, Dan; Tennant, Warren; Vidale, Pier Luigi; Wilkinson, Jonathan; Willett, Martin; Woolnough, Steven J.; Xavier, Prince

doi:10.5194/gmd-2016-194

Cited by 113 publications

(161 citation statements)

References 75 publications

(98 reference statements)

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“…11d). All three of these regions exhibit positive rainfall biases in the MetUM-GA6 configuration's climatology (Walters et al, 2016). In contrast, the western African region (Fig.…”

Section: Looking Across Timescalesmentioning

confidence: 93%

“…We use the MetUM Global Atmosphere version 6.0 (MetUM-GA6; Walters et al, 2016;Williams et al, 2015), which is an updated version of the MetUM-GA3 (Walters et al, 2011) configuration analysed by Klingaman et al (2017), with a different dynamical core (ENDGAME; Wood et al, 2014 orographic gravity-wave drag representation (Vosper et al, 2009), and several changes to the convective parametrization (see Walters et al (2011Walters et al ( , 2016 for details). MetUM-GA6 includes a 25 % increase to the rates of mixing entrainment and detrainment for diagnosed deep convection relative to MetUM-GA3, implemented to improve the representation of tropical sub-seasonal variability following Klingaman and Woolnough (2014).…”

Section: Model Descriptionmentioning

confidence: 99%

“…We then use spectra averaged over particular regions to illustrate the characteristics of this model configuration. The regions were chosen based on typical climatological bias regions illustrated in Walters et al (2016): wet bias regions of the equatorial Indian Ocean, southern China and the western Pacific, and the western Africa dry bias region.…”

Section: Spectral Characteristicsmentioning

confidence: 99%

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Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

2017

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Abstract. This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellitederived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through subdaily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ∼ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ∼ 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

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“…11d). All three of these regions exhibit positive rainfall biases in the MetUM-GA6 configuration's climatology (Walters et al, 2016). In contrast, the western African region (Fig.…”

Section: Looking Across Timescalesmentioning

confidence: 93%

Section: Model Descriptionmentioning

confidence: 99%

Section: Spectral Characteristicsmentioning

confidence: 99%

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Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

2017

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“…This is based on the second Global Coupled configuration (GC2) of the HadGEM3 global climate model, described in Williams et al (2015). HadGEM3-GC2 uses the GA6.0 configuration of the Met Office Unified Model (UM, version 8.4) as its atmospheric component, on an N216 grid 1 (a horizontal resolution of 0.83 • in longitude and 0.55 • in latitude) and 85 vertical levels reaching a height of 85 km near the mesopause (Walters et al 2016). This is coupled to the GL6.0 configuration of the JULES land surface model (Best et al 2011), the GO5.0 configuration of the NEMO ocean model with a 0.25 • nominal resolution and 75 vertical levels (version 3.4, Megann et al 2014;Madec 2008), and the GSI6.0 configuration of the CICE sea ice model (version 4.1, Rae et al 2015;Hunke and Lipscomb 2010).…”

Section: A Data Setsmentioning

confidence: 99%

Skill and Reliability of Seasonal Forecasts for the Chinese Energy Sector

Bett

Thornton

Lockwood

et al. 2017

Journal of Applied Meteorology and Climatology

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We assess the skill and reliability of forecasts of winter and summer temperature, wind speed and irradiance over China, using the GloSea5 seasonal forecast system. Skill in such forecasts is important for the future development of seasonal climate services for the energy sector, allowing better estimates of forthcoming demand and renewable electricity supply. We find that although overall the skill from the direct model output is patchy, some high-skill regions of interest to the energy sector can be identified. In particular, winter mean wind speed is skilfully forecast around the coast of the South China Sea, related to skilful forecasts of the El Niño-Southern Oscillation. Such information could improve seasonal estimates of offshore wind power generation. Similarly, forecasts of winter irradiance have good skill in eastern central China, with possible use for solar power estimation. Much of China shows skill in summer temperatures, which derives from an upward trend. However, the region around Beijing retains this skill even when detrended. This temperature skill could be helpful in managing summer energy demand. While both the strengths and limitations of our results will need to be considered when developing seasonal climate services in the future, the outlook for such service development in China is promising. This document is c Crown

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“…The Met Office Unified Model Global Atmosphere configuration 6.0 is used in the present study [Walters et al, 2016]. The model dynamical core employs a semiimplicit, semi-Lagrangian formulation to solve nonhydrostatic, fully compressible equations of deep-atmospheric motion [Davies et al, 2005].…”

Section: A7 Met Office Unified Model (Um)mentioning

confidence: 99%

The “Grey Zone” cold air outbreak global model intercomparison: A cross evaluation using large‐eddy simulations

Tomassini

Field

Honnert

et al. 2017

J Adv Model Earth Syst

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A stratocumulus‐to‐cumulus transition as observed in a cold air outbreak over the North Atlantic Ocean is compared in global climate and numerical weather prediction models and a large‐eddy simulation model as part of the Working Group on Numerical Experimentation “Grey Zone” project. The focus of the project is to investigate to what degree current convection and boundary layer parameterizations behave in a scale‐adaptive manner in situations where the model resolution approaches the scale of convection. Global model simulations were performed at a wide range of resolutions, with convective parameterizations turned on and off. The models successfully simulate the transition between the observed boundary layer structures, from a well‐mixed stratocumulus to a deeper, partly decoupled cumulus boundary layer. There are indications that surface fluxes are generally underestimated. The amount of both cloud liquid water and cloud ice, and likely precipitation, are under‐predicted, suggesting deficiencies in the strength of vertical mixing in shear‐dominated boundary layers. But also regulation by precipitation and mixed‐phase cloud microphysical processes play an important role in the case. With convection parameterizations switched on, the profiles of atmospheric liquid water and cloud ice are essentially resolution‐insensitive. This, however, does not imply that convection parameterizations are scale‐aware. Even at the highest resolutions considered here, simulations with convective parameterizations do not converge toward the results of convection‐off experiments. Convection and boundary layer parameterizations strongly interact, suggesting the need for a unified treatment of convective and turbulent mixing when addressing scale‐adaptivity.

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The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations

Cited by 113 publications

References 75 publications

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

Skill and Reliability of Seasonal Forecasts for the Chinese Energy Sector

The “Grey Zone” cold air outbreak global model intercomparison: A cross evaluation using large‐eddy simulations

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