The Met Office Global Coupled model 2.0 (GC2) configuration

Williams, Keith D.; Harris, Chris; Bodas‐Salcedo, Alejandro; Camp, Joanne; Comer, Ruth; Copsey, Dan; Fereday, David; Graham, Tim; Hill, Richard S.; Hinton, T.; Hyder, Patrick; Ineson, Sarah; Masato, Giacomo; Milton, Sean; Roberts, Malcolm; Rowell, David P.; Sánchez, Claudio; Shelly, Ann; Sinha, Bablu; Walters, David; West, Alex; Woollings, Tim; Xavier, Prince

doi:10.5194/gmd-8-1509-2015

Cited by 271 publications

(229 citation statements)

References 69 publications

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“…This is in contrast to other regions, such as the high-latitude North Atlantic, that has a strong year round warming bias. These biases are broadly consistent with known errors associated with the UK Met Office Unified Model (Williams et al, 2015), which is employed as the atmospheric model in ACCESS-ESM1. Our SST response is also broadly consistent with other ESMs, such as HadGEM2 (Martin et al, 2011), which also use the UK Met Office Unified Model.…”

Section: Sea Surface Temperature and Mixed Layer Depthsupporting

confidence: 58%

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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Abstract. Over the last decade many climate models have evolved into Earth system models (ESMs), which are able to simulate both physical and biogeochemical processes through the inclusion of additional components such as the carbon cycle. The Australian Community Climate and Earth System Simulator (ACCESS) has been recently extended to include land and ocean carbon cycle components in its ACCESS-ESM1 version. A detailed description of ACCESS-ESM1 components including results from pre-industrial simulations is provided in Part 1. Here, we focus on the evaluation of ACCESS-ESM1 over the historical period in terms of its capability to reproduce climate and carbon-related variables. Comparisons are performed with observations, if available, but also with other ESMs to highlight common weaknesses. We find that climate variables controlling the exchange of carbon are well reproduced. However, the aerosol forcing in ACCESS-ESM1 is somewhat larger than in other models, which leads to an overly strong cooling response in the land from about 1960 onwards. The land carbon cycle is evaluated for two scenarios: running with a prescribed leaf area index (LAI) and running with a prognostic LAI. We overestimate the seasonal mean (1.7 vs. 1.4) and peak amplitude (2.0 vs. 1.8) of the prognostic LAI at the global scale, which is common amongst CMIP5 ESMs. However, the prognostic LAI is our preferred choice, because it allows for the vegetation feedback through the coupling between LAI and the leaf carbon pool. Our globally integrated land-atmosphere flux over the historical period is 98 PgC for prescribed LAI and 137 PgC for prognostic LAI, which is in line with estimates of land use emissions (ACCESS-ESM1 does not include land use change).The integrated ocean-atmosphere flux is 83 PgC, which is in agreement with a recent estimate of 82 PgC from the Global Carbon Project for the period 1959-2005. The seasonal cycle of simulated atmospheric CO 2 is close to the observed seasonal cycle (up to 1 ppm difference for the station at Mace Head and up to 2 ppm for the station at Mauna Loa), but shows a larger amplitude (up to 6 ppm) in the high northern latitudes. Overall, ACCESS-ESM1 performs well over the historical period, making it a useful tool to explore the change in land and oceanic carbon uptake in the future.

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Section: Sea Surface Temperature and Mixed Layer Depthsupporting

confidence: 58%

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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“…The current generation coupled climate model Hadley Centre Global Environmental Model version 3 global configuration version 2 (HadGEM3-GC2) (Williams et al, 2015) was able to sustain anAMOC off state for 450 years (Mecking et al, 2016).A historical simulation with the same set-up as the CMIP5 models will be used to compare to the CMIP5 models. This model configuration is also used in the operational seasonal and decadal forecast systems of the Met Office (GloSea5 and DEPRESYS3).…”

Section: Models and Datamentioning

confidence: 99%

The effect of model bias on Atlantic freshwater transport and implications for AMOC bi-stability

Mecking¹,

Drijfhout²,

Jackson³

et al. 2017

Tellus A: Dynamic Meteorology and Oceanography

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Evidence from paleo-proxy records suggests that the Atlantic Meridional Overturning Circulation (AMOC) can be in both an AMOC on state, the AMOC as we observe it today, and an AMOC off state, where the AMOC becomes extremely weak or even collapses. The freshwater transport due to the AMOC (M ov ) at 34°S in the Atlantic has often been used as an indicator for bi-stability, with a positive M ov suggesting a monostable AMOC and a negative M ov suggesting a bi-stable AMOC. Often studies have shown that the sign of the divergence of the M ov might be a good indicator of AMOC bi-stability. In this study we investigate how model bias affects the sign of M ov across all latitudes in the Atlantic basin, through a detailed analysis of the Coupled Model Inter-Comparison Project 5 (CMIP5) model ensemble. M ov , in the CMIP5 models is generally too positive in the southern Atlantic due to a salinity bias, while in the subtropical North Atlantic the values of M ov are influenced by a combination of velocity and salinity biases. We compare these results to observations, reanalysis products and Hadley Centre Global Environmental Model version 3 global configuration version 2, a current generation coupled model which exhibits a stable AMOC off state, and discuss the differences that can lead to the possibility of a bi-stable AMOC as opposed to a monostable AMOC.

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“…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%

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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The Met Office Global Coupled model 2.0 (GC2) configuration

Cited by 271 publications

References 69 publications

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

The effect of model bias on Atlantic freshwater transport and implications for AMOC bi-stability

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

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