Sensitivity of a regional climate model to the resolution of the lateral boundary conditions

Denis, B.; Laprise, René; Caya, Daniel

doi:10.1007/s00382-002-0264-6

Cited by 126 publications

(109 citation statements)

References 18 publications

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“…Due to the high computational resources required for performing high-resolution global simulations for long periods, the vast majority of previous BB studies (e.g. Denis et al 2002bDenis et al , 2003Antic et al 2004;Dimitrijevic and Laprise 2005;Herceg et al 2006;Diaconescu et al 2007;Køltzow et al 2008;Leduc and Laprise 2009;Leduc et al 2011;Rapaić et al 2011) have used a ''poor-man'' version of BB experiment, generating the BB simulation by running an RCM over a large domain instead of a global model. An exception is the study of Radu et al (2008) who used the full version of the BB experiment with ALADIN regional and ARPEGE global models.…”

Section: Model Description and Experimental Set-upmentioning

confidence: 99%

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Can added value be expected in RCM-simulated large scales?

Diaconescu

Laprise

2013

Clim Dyn

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Nested Limited-Area Models require driving data to define their lateral boundary conditions (LBC). The optimal choice of domain size and the repercussions of LBC errors on Regional Climate Model (RCM) simulations are important issues in dynamical downscaling work. The main objective of this paper is to investigate the effect of domain size, particularly on the larger scales, and to question whether an RCM, when run over very large domains, can actually improve the large scales compared to those of the driving data. This study is performed with a detailed atmospheric model in its global and regional configurations, using the ''Imperfect Big-Brother'' (IBB) protocol. The ERA-Interim reanalyses and five global simulations are used to drive RCM simulations for five winter seasons, on four domain sizes centred over the North American continent. Three variables are investigated: precipitation, specific humidity and zonal wind component. The results following the IBB protocol show that, when an RCM is driven by perfect LBC, its skill at reproducing the large scales decreases with increasing the domain of integration, but the errors remain small even for very large domains. On the other hand, when driven by LBC that contain errors, RCMs can bring some reduction of errors in large scales when very large domains are used. The improvement is found especially in the amplitude of patterns of both the stationary and the intra-seasonal transient components. When large errors are present in the LBC, however, these are only partly corrected by the RCM.Although results showed that an RCM can have some skill at improving imperfect large scales supplied as driving LBC, the main added value of an RCM is provided by its small scales and its skill to simulate extreme events, particularly for precipitation. Under the IBB protocol all RCM simulations were fairly skilful at reproducing small scales statistics, although the skill decreased with increasing LBC errors. Coarse-resolution model simulations have difficulties in simulating heavy precipitation events, and as a result their precipitation distributions are systematically shifted toward smaller intensity. Under the IBB protocol, all RCM simulations have distributions very similar to the reference field, being little affected by LBC errors, and no significant differences were found between the small scales statistics and the precipitation distributions obtained over different RCM domains.

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Section: Model Description and Experimental Set-upmentioning

confidence: 99%

“…Jones et al 1995;Christensen et al 1997;Machenhauer et al 1998;Frei et al 2003;Rinke et al 2006;Jacob et al 2007), or under the idealised Big-Brother Experiment (e.g. Denis et al 2002bDenis et al , 2003Antic et al 2004;Dimitrijevic and Laprise 2005). However, in most applications, the LBC are not perfect, especially in the case they come from GCM simulations.…”

mentioning

confidence: 99%

Can added value be expected in RCM-simulated large scales?

Diaconescu

Laprise

2013

Clim Dyn

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“…We simulated the January climate because this is a significant peak fire month in Australia. Lateral boundary conditions for RAMS were updated every 12-h which were found to be acceptable for simulations at a grid spacings of around 45 km by Denis et al (2003) and were shown by Peel et al (2005) to result in a good simulation of the current climate over Australia.…”

Section: Regional Climate Model Configurationmentioning

confidence: 99%

The impact of climate change on the risk of forest and grassland fires in Australia

2007

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We explore the impact of future climate change on the risk of forest and grassland fires over Australia in January using a high resolution regional climate model, driven at the boundaries by data from a transitory coupled climate model. Two future emission scenarios (relatively high and relatively low) are used for 2050 and 2100 and four realizations for each time period and each emission scenario are run. Results show a consistent increase in regional-scale fire risk over Australia driven principally by warming and reductions in relative humidity in all simulations, under all emission scenarios and at all time periods. We calculate the probability density function for the fire risk for a single point in New South Wales and show that the probability of extreme fire risk increases by around 25% compared to the present day in 2050 under both relatively low and relatively high emissions, and that this increases by a further 20% under the relatively low emission scenario by 2100. The increase in the probability of extreme fire risk increases dramatically under the high emission scenario by 2100. Our results are broadly in-line with earlier analyses despite our use of a significantly different methodology and we therefore conclude that the likelihood of a significant increase in fire risk over Australia resulting from climate change is very high. While there is already substantial investment in fire-related management in Australia, our results indicate that this investment is likely to have to increase to maintain the present fire-related losses in Australia.

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“…LAMs also present specific issues like sensitivity to the size of the domain of simulation, to the boundary conditions, and to the frequency of update of boundary conditions (Bhaskaran et al, 1996;Seth and Giorgi, 1998;Noguer et al, 1998;Denis et al, 2002aDenis et al, , 2003. For longterm simulations, Lo et al(2008) showed that continuous runs can produce a very low score when the simulations are compared to observations and that simulations reinitialized periodically have better results than continuous runs.…”

Section: Introductionmentioning

confidence: 99%

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

Omrani

Drobinski

Dubos

2011

Quart J Royal Meteoro Soc

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In this work, we consider the effect of indiscriminate nudging time on an idealized high-resolution global model (GM) and limited-area model (LAM) simulations. The model used is a two-layer quasi-geostrophic model on the beta-plane.The effect of nudging is studied as a function of the predictability time, following a 'Big Brother' experimental approach: a high-resolution 'global' model is used to generate a 'reference run'. These fields are filtered afterwards to remove small scales and provide the coarse-resolution fields which are used to drive the high-resolution GM and the LAM. Comparison of the reference fields and the high-resolution runs over the same region allows the estimation of the ability of the high-resolution GM and LAM to regenerate the removed small scales. This fully nonlinear set-up mimics the configuration used for regional high-resolution atmospheric modelling.For the high-resolution GM, the results show that the behaviour of the nudged model depends primarily on the ratio of the nudging time to the predictability time. When the nudging time is very small compared to the predictability time, the model reproduces the large scale used to drive the model. On the other hand, if the nudging time is close to or larger than the predictability time, the nudging effect is weak and both large and small scales are poorly reproduced compared to the reference fields. The best result is obtained with a nudging time close to half the predictability time. This technique clearly improves the model capacity to reproduce the reference fields.For the high-resolution LAM, our results show that for a sufficiently small domain the simulation is largely controlled by the lateral boundary conditions (LBCs) and is quasi-insensitive to nudging. However, if the domain size exceeds a few Rossby radii, the high-resolution LAM becomes sensitive to initial conditions and the control exerted by LBCs becomes insufficient to prevent a divergence from the driving fields. Although the reconstructed fine scales are significantly damped, they are surprisingly well correlated to their reference values in a deterministic sense, not a statistical sense. Copyright c 2011 Royal Meteorological Society Key Words: downscaling; uncertainties; large/small-scale perturbations; potential vorticity

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Sensitivity of a regional climate model to the resolution of the lateral boundary conditions

Cited by 126 publications

References 18 publications

Can added value be expected in RCM-simulated large scales?

Can added value be expected in RCM-simulated large scales?

The impact of climate change on the risk of forest and grassland fires in Australia

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

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