The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling

Déqué, Michel; Dreveton, C.; Braun, Alain; Cariolle, D.

doi:10.1007/bf00208992

Cited by 511 publications

(244 citation statements)

References 35 publications

Supporting

Mentioning

230

Contrasting

Unclassified

Order By: Relevance

“…ARPEGE, the atmosphere component of the Bergen Climate Model version 2 (BCM2; Otterå et al 2009) was developed at METEO-FRANCE (Déqué et al 1994). The ARPEGE is run with a truncation at wave number 63 (TL63), a time step of 1800 s, 31 vertical levels rang-ing from the surface to 0.01 hPa and a horizontal resolution of approximately 2.8°.…”

Section: Methodsmentioning

confidence: 99%

Atmospheric response to the autumn sea-ice free Arctic and its detectability

et al. 2015

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Atmospheric response to the autumn sea-ice free Arctic and its detectability

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The atmospheric component is ARPEGE-CLIMATv5.2, which derives from ARPEGE (action de recherche petite echelle grande echelle, which means research project on small and large scales, Déqué et al 1994). ARPEGE-CLIMATv5.2 is a spectral model using a resolution of about 1.4°i n longitude and latitude, while vertically there are 31 levels.…”

Section: Cnrmcm51mentioning

confidence: 99%

Skill assessment of three earth system models with common marine biogeochemistry

et al. 2012

View full text Add to dashboard Cite

We have assessed the ability of a common ocean biogeochemical model, PISCES, to match relevant modern data fields across a range of ocean circulation fields from three distinct Earth system models: IPSL-CM4-LOOP, IPSL-CM5A-LR and CNRM-CM5.1. The first of these Earth system models has contributed to the IPCC 4th assessment report, while the latter two are contributing to the ongoing IPCC 5th assessment report. These models differ with respect to their atmospheric component, ocean subgrid-scale physics and resolution. The simulated vertical distribution of biogeochemical tracers suffer from biases in ocean circulation and a poor representation of the sinking fluxes of matter. Nevertheless, differences between upper and deep ocean model skills significantly point to changes in the underlying model representations of ocean circulation. IPSL-CM5A-LR and CNRM-CM5.1 poorly represent deep-ocean circulation compared to IPSL-CM4-LOOP degrading the vertical distribution of biogeochemical tracers. However, their representations of surface wind, wind stress, mixed-layer depth and geostrophic circulations (e.g., Antarctic Circumpolar Current) have been improved compared to IPSL-CM4-LOOP. These improvements result in a better representation of large-scale structure of biogeochemical fields in the upper ocean. In particular, a deepening of 20-40 m of the summer mixed-layer depth allows to capture the 0-0.5 lgChl L -1 concentrations class of surface chlorophyll in the Southern Ocean. Further improvements in the representation of the ocean mixedlayer and deep-ocean ventilation are needed for the next generations of models development to better simulate marine biogeochemistry. In order to better constrain ocean dynamics, we suggest that biogeochemical or passive tracer modules should be used routinely for both model development and model intercomparisons.

show abstract

“…These models are based on established physical laws and have proven fidelity for assessing changes to global quantities (Randall et al 2007;Anderson et al 2004;Collins et al 2004;Déqué et al 1994;Flato et al 2013;Pope et al 2000;Roeckner et al 2003). However, GCMs typically are of too a coarse resolution to directly infer climatology of high-impact weather at local scales and it is common to downscale over regions of interest using statistical techniques or nested regional climate models (RCMs).…”

Section: Introductionmentioning

confidence: 99%

Bias corrections of global models for regional climate simulations of high-impact weather

et al. 2013

View full text Add to dashboard Cite

All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the sixhourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.

show abstract

The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling

Cited by 511 publications

References 35 publications

Atmospheric response to the autumn sea-ice free Arctic and its detectability

Atmospheric response to the autumn sea-ice free Arctic and its detectability

Skill assessment of three earth system models with common marine biogeochemistry

Bias corrections of global models for regional climate simulations of high-impact weather

Contact Info

Product

Resources

About