Regional Climate Modelling with nested limited-area models ... · Regional Climate Modelling with...
Transcript of Regional Climate Modelling with nested limited-area models ... · Regional Climate Modelling with...
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).