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

Walters, David; Boutle, Ian; Brooks, M. E.; Melvin, Thomas; Stratton, Rachel; Vosper, Simon; Wells, Helen; Williams, Keith D.; 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 C.; 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-10-1487-2017

Cited by 402 publications

(332 citation statements)

References 90 publications

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“…This experiment was conducted with the HadGEM3-GA6.0 configuration of the Met Office Unified Model (Walters et al, 2017) and is described in Sect. 5.2.…”

Section: Model Ensemblementioning

confidence: 99%

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Schiemann

Vidale

Shaffrey

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Limited spatial resolution is one of the factors that may hamper applications of global climate models (GCMs), in particular over Europe with its complex coastline and orography. In this study, the representation of European mean and extreme precipitation is evaluated in simulations with an atmospheric GCM (AGCM) at different resolutions between about 135 and 25 km grid spacing in the mid-latitudes. The continent-wide root-mean-square error in mean precipitation in the 25 km model is about 25 % smaller than in the 135 km model in winter. Clear improvements are also seen in autumn and spring, whereas the model's sensitivity to resolution is very small in summer. Extreme precipitation is evaluated by estimating generalised extreme value distributions (GEVs) of daily precipitation aggregated over river basins whose surface area is greater than 50 000 km 2 . GEV location and scale parameters are measures of the typical magnitude and of the interannual variability of extremes, respectively. Median model biases in both these parameters are around 10 % in summer and around 20 % in the other seasons. For some river basins, however, these biases can be much larger and take values between 50 % and 100 %. Extreme precipitation is better simulated in the 25 km model, especially during autumn when the median GEV parameter biases are more than halved, and in the North European Plains, from the Loire in the west to the Vistula in the east. A sensitivity experiment is conducted showing that these resolution sensitivities in both mean and extreme precipitation are in many areas primarily due to the increase in resolution of the model orography. The findings of this study illustrate the improved capability of a global high-resolution model in simulating European mean and extreme precipitation.

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“…This experiment was conducted with the HadGEM3-GA6.0 configuration of the Met Office Unified Model (Walters et al, 2017) and is described in Sect. 5.2.…”

Section: Model Ensemblementioning

confidence: 99%

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Schiemann

Vidale

Shaffrey

et al. 2018

Hydrol. Earth Syst. Sci.

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“…Two sets of simulations in the MetOffice Unified Model (UM) vn10.3 based on GA6 (Walters et al, 2017) were created to test the sensitivity of cloud adjustment to changes in CDNC to model resolution in an idealized aquaplanet setting. The simulations were performed in a GCM-surrogate setting and a convection-permitting setting.…”

Section: Aquaplanetmentioning

confidence: 99%

Aerosol midlatitude cyclone indirect effects in observations and high-resolution simulations

et al. 2018

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Abstract. Aerosol-cloud interactions are a major source of uncertainty in inferring the climate sensitivity from the observational record of temperature. The adjustment of clouds to aerosol is a poorly constrained aspect of these aerosolcloud interactions. Here, we examine the response of midlatitude cyclone cloud properties to a change in cloud droplet number concentration (CDNC). Idealized experiments in high-resolution, convection-permitting global aquaplanet simulations with constant CDNC are compared to 13 years of remote-sensing observations. Observations and idealized aquaplanet simulations agree that increased warm conveyor belt (WCB) moisture flux into cyclones is consistent with higher cyclone liquid water path (CLWP). When CDNC is increased a larger LWP is needed to give the same rain rate. The LWP adjusts to allow the rain rate to be equal to the moisture flux into the cyclone along the WCB. This results in an increased CLWP for higher CDNC at a fixed WCB moisture flux in both observations and simulations. If observed cyclones in the top and bottom tercile of CDNC are contrasted it is found that they have not only higher CLWP but also cloud cover and albedo. The difference in cyclone albedo between the cyclones in the top and bottom third of CDNC is observed by CERES to be between 0.018 and 0.032, which is consistent with a 4.6-8.3 Wm −2 in-cyclone enhancement in upwelling shortwave when scaled by annualmean insolation. Based on a regression model to observed cyclone properties, roughly 60 % of the observed variability in CLWP can be explained by CDNC and WCB moisture flux.

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“…With finer resolution, more orography is resolved explicitly and less is parameterised. Please refer to Walters et al (2017) for further details on physical parameterisations and their dependence on resolution.…”

Section: Metum Simulationsmentioning

confidence: 99%

“…Figure 1 shows the grid-point mean orographic height at each resolution. As described in Walters et al (2017), subgrid orographic boundary layer drag, flow blocking and gravity wave drag are parameterised in the MetUM. With finer resolution, more orography is resolved explicitly and less is parameterised.…”

Section: Metum Simulationsmentioning

confidence: 99%

“…We refer to these as A96 and A216, respectively. They use the Global Atmosphere configuration 6.0 of the MetUM (GA6; Walters et al, 2017), driven by monthly mean SSTs from the Reynolds product (Reynolds et al, 2007) and varying solar, greenhouse gas and aerosol forcings as observed in 1982-2008. The coupled simulations use the Global Coupled configuration 2.0 of the MetUM (GC2; Williams et al, 2015). We analyse two GC2 simulations at N96 and N216 resolutions in the atmosphere, referred to as C96 and C216, that are initialised with present-day ocean data (EN3; Ingleby and Huddleston, 2007) and spun-up sea-ice and land surface conditions.…”

Section: Metum Simulationsmentioning

confidence: 99%

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Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

et al. 2018

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Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyse the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ∼ 200, 90 and 40 km in the zonal direction at the equator, respectively) are analysed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China but improve with finer resolution and coupling. Empirical orthogonal teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal mean time series. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

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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 402 publications

References 90 publications

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Aerosol midlatitude cyclone indirect effects in observations and high-resolution simulations

Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

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