Advanced uncertainty evaluation of climate models and their future climate projections H Järvinen, P Räisänen , M Laine, J Tamminen, P Ollinaho Finnish Meteorological Institute A Ilin, E Oja Aalto University School of Science and Technology , Finland A Solonen, H Haario Lappeenranta University of Technology, Finland
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Advanced uncertainty evaluation of climate models and their future climate projections
Advanced uncertainty evaluation of climate models and their future climate projections H Järvinen, P Räisänen , M Laine, J Tamminen, P Ollinaho Finnish Meteorological Institute A Ilin, E Oja Aalto University School of Science and Technology , Finland A Solonen, H Haario - PowerPoint PPT Presentation
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Advanced uncertainty evaluation of climate models and their future climate
projections
H Järvinen, P Räisänen, M Laine, J Tamminen, P OllinahoFinnish Meteorological Institute
A Ilin, E OjaAalto University School of Science and Technology , Finland
A Solonen, H HaarioLappeenranta University of Technology, Finland
Closure parameters• Appear in physical parameterization schemes where
some unresolved variables are expressed by predefined parameters rather than being explicitly modelled
• Span a low-dimensional non-linear estimation problem
• Currently: best expert knowledge is used to specify the optimal closure parameter values, based on observations, process studies, model simulations, etc.
• Important when:
(1) Fine-tuning climate models to the present climate
(2) Replacing parameterization schemes with new ones
2/19
3/19
Markov chain Monte Carlo (MCMC) • Consecutive model simulations while updating the
model parameters by Monte Carlo sampling
• Proposal step (parameter values drawn from a proposal distribution)
• Acceptance step (evaluate the objective function and accept/reject the proposal)
• “A random walk” in the parameter space (a Markov chain) and exploration of the Bayesian posterior distribution
• Not optimization ... Instead, a full multi-dimensional parameter probability distribution is recovered
4/19
MH (non-adaptive)
AM
DRAM (adaptive)
ECHAM5 closure parameters
CAULOC = influencing the autoconversion of cloud droplets (rain formation, stratiform clouds)
CMFCTOP = relative cloud mass flux at level above non-buoyancy (in cumulus mass flux
scheme)
CPRCON = a coefficient for determining conversion from cloud water to rain (in convective
clouds)
ENTRSCV = entrainment rate for shallow convection5/19
ECHAM5 simulations
• Markov chain in the 4-parameter space
• One year simulation with the T21L19 ECHAM5 model repeated many times with perturbed parameters
• Several objective function were tested
• All formulations: Top-of-Atmosphere (ToA) net radiative flux
6/19
7/19
cost 2
2
modelGLOBAL
obs
obsFF
Global-annual mean net flux in ECHAM5
Global-annual mean net flux in CERES data (0.9 W m-2)
Interannual standard deviationIn ERA40 reanalysis (0.53 Wm-2)
2
2obs,model,
12
1t121
costZONAL
yt
ytyt
yy
FFw
Monthly zonal-mean values
Interannual std. dev. of monthly zonal means
8/19
cost 2
2
modelGLOBAL
obs
obsFF
Global-annual mean net flux in ECHAM5
Global-annual mean net flux in CERES data (0.9 W m-2)
Interannual standard deviationIn ERA40 reanalysis (0.53 Wm-2)
2
2obs,model,
12
1t121
costZONAL
yt
ytyt
yy
FFw
Monthly zonal-mean values
Interannual std. dev. of monthly zonal means
Small cost function implies model to be close to CERES data
- global annual-mean net radiation
- annual cycle of zonal mean net radiation
9/19
Longwave Shortwave
CERES observations
Global annual mean ToA radiative flux
Net
• cost =costGLOBAL + costZONAL
10/19
Longwave Shortwave
Default model
Net
• cost =costGLOBAL + costZONAL
11/19
Longwave Shortwave
The cost function only included net ToA radiation…
both the LW and SW biases decreased
Net
= default value
T42L31 :: Cloud ice particles, SW scattering
14/19
CAULOC CMFCTOP CPRCON ENTRSCV
CPRCON ENTRSCVCMFCTOP ZASICCAULOC ZINPAR ZINHOMI
Uncertainty of future climate projections (principle)
• Climate sensitivity :: Change in Tsurf due to 2 × CO2
• Sample from the closure parameter posterior PDF’s
• Perform a climate sensitivity run with each model
• Result: a proper PDF of climate sensitivity
- conditional on the selected closure parameters and cost function
15/19
Practical problem: at T21L19, ECHAM5 is hypersensitive!
16/19
Warming 8.9 Kwhen model crashes!
Warming 9.6 Kwhen model crashes!!
Glo
bal-m
ean
tem
pera
ture
(K
)
Conclusions (so far)
17/19
Can we use MCMC for parameter estimation in climate models?
Yes, we can! But …
2) It is computationally expensive - chain lengths of > 1000 model runs are needed
1) Choice of the cost function is critical
Means to fight the computational expense• Adaptive MCMC
• parallel MCMC chains ( reduced wallclock time)
• re-use of chains (off-line tests of new cost functions through ”importance sampling”)