Challenging some tenets of Regional Climate Modelling

Laprise, René; Elía, Ramón de; Caya, Daniel; Biner, S.B.; Lucas‐Picher, Philippe; Diaconescu, Emilia Paula; Leduc, Martin; Alexandru, Adelina; Šeparović, Leo

doi:10.1007/s00703-008-0292-9

Cited by 201 publications

(179 citation statements)

References 72 publications

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“…It also is possible that statistical downscaling methods developed on past climate might not hold true under climate change conditions. An alternative, widely-used approach is to nest a RCM within GCM boundary conditions (Laprise et al 2008;Bender et al 2010;Knutson et al 2007Knutson et al , 2008Walsh et al 2004;Done et al 2013). Because of their smaller domain, RCMs can operate at higher resolution than GCMs to enable simulation of much finer scale features, which are required for assessment of many extreme weather phenomena.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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“…The choices of integration domains and nesting techniques are free parameters of RCMs. The optimal integration domain depends on the particular situation, although there are some general recommendations that can facilitate user's judgment (e.g., Laprise et al 2008). For example, Leduc and Laprise (2008) showed that the use of a too small domain could result in the simulations being deficient in fine-scale variance.…”

Section: Introductionmentioning

confidence: 99%

“…The application of these methods to reduce internal variability noise requires better understanding of the ways they might alter the signal of RCM sensitivity to modification, e.g., by suppressing its magnitude. Too small domains are generally non-recommended for climate simulations and sensitivity studies because of the spurious effects of the proximity of the lateral boundaries, fine-scale variance deficiency and lack of continental-scale interactions and feedback among the RCM variables (e.g., Jones et al 1995;Seth and Giorgi 1998;Laprise et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Impact of spectral nudging and domain size in studies of RCM response to parameter modification

2011

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The paper aims at finding an RCM configuration that facilitates studies devoted to quantifying RCM response to parameter modification. When using short integration times, the response of the time-averaged variables to RCM modification tend to be blurred by the noise originating in the lack of predictability of the instantaneous atmospheric states. Two ways of enhancing the signal-tonoise ratio are studied in this work: spectral nudging and reduction of the computational domain size. The approach followed consists in the analysis of the sensitivity of RCMsimulated seasonal averages to perturbations of two parameters controlling deep convection and stratiform condensation, perturbed one at a time. Sensitivity is analyzed within different simulation configurations obtained by varying domain size and using the spectral nudging option. For each combination of these factors multiple members of identical simulations that differ exclusively in initial conditions are also generated to provide robust estimates of the sensitivities (the signal) and sample the noise. Results show that the noise magnitude is decreased both by reduction of domain size and the spectral nudging. However, the reduction of domain size alters some sensitivity signals. When spectral nudging is used significant alterations of the signal are not found.

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“…Secondly, it is necessary to show 4 that RCM adds small-scale features absent in the GCM driving fields and that these features agree with observations and with high-resolution GCM fields. Laprise et al (2007) provide a summary of studies related to this issue. On the opinion of Laprise et al (2007) the consensus on the first point is not yet reached within the RCMs community.…”

Section: Introductionmentioning

confidence: 99%

“…Laprise et al (2007) provide a summary of studies related to this issue. On the opinion of Laprise et al (2007) the consensus on the first point is not yet reached within the RCMs community. It is not clear if the large scales of GCM are unaffected, improved or degraded by RCMs.…”

Section: Introductionmentioning

confidence: 99%

Climate version of the ETA regional forecast model

Pisnichenko

Тарасова

2009

Theor Appl Climatol

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The regional climate model prepared from Eta WS (workstation) forecast model has been of the investigation of a consistency between the output fields of the Eta model and HadAM3P are presented. The geopotential, temperature and wind fields of both models were analysed.For the evaluation of the likeness of these two models outputs, there were used Fourier analysis of time series, consistency index, constituted from linear regression coefficients, time mean and space mean models' arithmetic difference and root mean square difference, dispersion analysis, and some others characteristics. This investigation demonstrates that there are not significant differences in behaviour and spatial arragement of large-scale structures of the two models.Also, the regional model characteristics do not have considerable positive or negative trend during integration time in relation to the global model characteristics. From the total analysis we can affirm that in the description of large-scale climate structures these two models are in consistency.

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Challenging some tenets of Regional Climate Modelling

Cited by 201 publications

References 72 publications

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

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

Impact of spectral nudging and domain size in studies of RCM response to parameter modification

Climate version of the ETA regional forecast model

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