24 September 2010

Conference on Advances in the Atmospheric and Oceanic

Sciences: A celebration of the 50th Anniversary of McGill's

Department of Atmospheric and Oceanic Science

Regional Climate Modelling

with nested limited-area models:

Validation of the technique with the

Big-Brother Experiment protocol

Outline:

Climate modelling framework

Dynamical downscaling concept

Validation issue: Big-Brother Experiment

Some results

René Laprise

Director, ESCER Centre

Université du QuébecàMontréal (UQAM)

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Coupled Global Climate Models

• The most sophisticated tool to… – Investigate the processes responsible for the maintenance of the

dynamical equilibrium of the climate system

– Make projections of anticipated climate changes associated with anthropogenic effects (such as emissions of greenhouse gases and aerosols, changes in land-surface use, etc.)

• High computational cost of climate simulations– Long (centuries to millennia) simulations

– Ensemble simulations for statistical significance

– Computing cost is proportional to x-n, (3 < n < 4)

• Limitations– Coarse resolution

• Results in numerical truncation

• Limits the physical processes that can be explicitly resolved

• Rely heavily on parameterisation for subgrid-scale processes (moist convection, clouds, etc.)

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Nested Regional Climate Model

• A pragmatic approach to reduce computing cost of high-resolution climate models– High resolution applied over only a subset of the

globe

– Low-resolution GCM simulation used to define the lateral (and ocean surface) boundary conditions of RCM

• Dynamical downscalingansatz:– “Driven by large-scale fields at LBC, an RCM

generates fine scales that are dynamically consistent with these”

– A kind of “Magnifying glass”

GCM

On a winter day in a GCM simulation…900-hPa Specific Humidity

GCM (T32, 450-km) 45-km RCM (driven by GCM)

GCM

RCM

Dynamical downscaling

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Verification of high-res. climate simulations is complicated by

– Data availability: Few dense observational climate network

– Interpretation: Station data Vs grid point statistics

– Model errors: Several RCM errors are common with GCM, e.g.

• Numerical approximation & limited resolution (x, y, z, t)

• Parameterisation of subgrid-scale physical effects

• Prescription of geophysical fields

Model errors that are specific to nested RCM:

– Limited-area computational domain

– Nesting technique

– Resolution jump between RCM and its nesting data

– Update frequency of the lateral boundary conditions (LBC)

– Imperfections in LBC data

In order to focus on specific errors, excluding the common ones:

The Big-Brother Experimental protocol

RCM validation issue

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The driving data:

Renalyses or GCM« Big-Brother »

RCM Simulation

Filtering

small scales

[transition

1080-2160 km]

« Little-Brother »

RCM Simulation

Verification

against BB

The Big-BrotherExperiment

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BB &LBs domains

Model:

• CRCM_3.6.1

• 45 km true at 60°N

20 members of LB

Started 24 h apart

2 LB Domains:

106 x 106 grid points

190 x 190 grid points

Simulation period:

August-October 1999

Validation period:

September-October 1999

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BB

mL_16

R*=96%

G*=124%

mL_11

R*=78%

G*=91%

mS_18

R*=93%

G*=53%

mS_8

R*=89%

G*=52%

BB

mS_7

R*=83%

G*=97%

R*=87%

mS_18 G*=91% mL_3

R*=79%

G*=113%

mL_2

R*=65%

G*=128%

Stationary component of precipitation

Large scales Small scales

Best

Worst

Small

domain Large

domain

Small

domain Large

domain

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Transient-eddy standard deviation of precipitation

BB

mS_11

R*=91%

G’=59%

mS_12

R*=86%

G’=61%

R*=55%

mL_12 G’=123%

R*=86%

G’=117%mL_16

BB

mL_4

R*=72%

G’=124%

mL_12

R*=58%

G’=122%

mS_18

R*=76%

G’=63%

R*=66%

mS_15 G’=67%

Best

Worst

Small

domain Large

domain

Small

domain Large

domain

Large scales Small scales

Taylor diagrams of precipitation

small-scale transient-eddy component

Fine scales are variance-

deficient on smalldomains

IV islarger for the large domain

Small LB domains

Large LB domains

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General Conclusions

• Dynamical Downscaling with RCM does “work”

– Development of fine scales in a high-resolution RCM driven by low-resolution data at LBC

– The fine scales are the main potential added value of an RCM

– The full development of the fine scales require the use of fairly large regional domains (order 200x200 for a resolution jump of the order of 10x)

– Large domains result in weak control of large scales by LBC

– Large-scale (spectral) nudging can be an effective means of maintaining control by driving fields (not shown)

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Denis, B., J. Côté and R. Laprise, 2002: Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using discrete cosine transforms (DFT).Mon. Wea. Rev. 130(7), 1812-1829.

Denis, B., R. Laprise, D. Caya and J. Côté, 2002: Downscaling ability of one-way-nested regional climate models: The Big-Brother experiment.Clim. Dyn. 18, 627-646.

Denis, B., R. Laprise and D. Caya, 2003: Sensitivity of a Regional Climate Model to the spatial resolution and temporal updating frequency of the lateral boundary conditions.Clim. Dyn. 20, 107-126.

de Elía, R., R. Laprise and B. Denis, 2002: Forecasting skill limits of nested, limited-area models: A perfect-model approach.Mon. Wea. Rev. 130, 2006-2023.

Antic, S., R. Laprise, B. Denis and R. de Elía, 2005: Testing the downscaling ability of a one-way nested Regional Climate Model in regions of complex topography.Clim. Dyn. 23, 473-493.

Dimitrijevic, M., and R. Laprise, 2005: Validation of the nesting technique in an RCM and sensitivity tests to the resolution of the lateral boundary conditions during summer.Clim. Dyn. 25, 555-580.

Diaconescu, E. P., R. Laprise and L. Sushama, 2007: The impact of lateral boundary data errors on the simulated climate of a nested Regional Climate Model.Clim. Dyn. 28(4), 333-350.

Alexandru, A., R. de Elía and R. Laprise, 2007: Internal variability in regional climate downscaling at the seasonal time scale.Mon. Wea. Rev. 135(9), 3221-3238.

Lucas-Picher, Ph., D. Caya, R. de Elíaand R. Laprise, 2008: Investigation of regional climate models’ internal variability with a ten-member ensemble of ten years over a large domain. Clim. Dyn.Clim. Dyn. 31, 927-940 .

Lucas-Picher, Ph., D. Caya, S. Biner and R. Laprise, 2008: Quantification of the lateral boundary forcing in a Regional Climate Model using an ageing tracer. Mon. Wea. Rev. 136, 4980-4996.

Šeparović, L., R. de Elía and R. Laprise, 2008: Reproducible and irreproducible components in ensemble simulations of a Regional Climate Model.Clim. Dyn.136(12), 4942–4961.

Laprise, R., R. de Elía, D. Caya, S. Biner, Ph. Lucas-Picher, E. P. Diaconescu, M. Leduc, A. Alexandru and L. Šeparović, 2008: Challenging some tenets of Regional Climate Modelling. Meteor. Atmos. Phys. 100, Special Issue on Regional Climate Studies,3-22.

Alexandru, A., R. de Elía, R. Laprise, L. Šeparovićand S. Biner, 2009: Influence of Large-Scale Nudging on ensemble simulations with a regional climate model. Mon. Wea. Rev. 137(5), 1668-1688.

Leduc, M., and R. Laprise, 2009: Regional Climate Model sensitivity to domain size. Clim. Dyn.32(6), 833-854.

Laprise, R., D. Kornic, M. Rapaić, L. Šeparović, M. Leduc, O. Nikiema, A. Di Luca, E. Diaconescu, A. Alexandru, Ph. Lucas-Picher, R. de Elía, D. Caya and S. Biner, 2010 : Considerations of domain size and large-scale driving for nested Regional Climate Models: Impact on internal variability and skill at developing small-scale details. In: Proceedings of the MilutinMilankovitch Anniversary Symposium, Belgrade, 22-25 September 2009.

Rapaić, M., M. Leduc and R. Laprise: Evaluation of internalvariability and estimation of the downscalingability of the Canadian RCM for differentdomainsizes over the North Atlantic regionusing the Big-BrotherExperimentalapproach. Clim. Dyn. (submitted).