Jean-Philippe Boulanger*, Fernando Martinez** and Enrique C. Segura**
description
Transcript of Jean-Philippe Boulanger*, Fernando Martinez** and Enrique C. Segura**
Projection of future climate change conditions using IPCC
simulations, neural networks and Bayesian statistics.
Temperature and Precipitation mean state and seasonal cycle
in South America.
Projection of future climate change conditions using IPCC
simulations, neural networks and Bayesian statistics.
Temperature and Precipitation mean state and seasonal cycle
in South America.
Jean-Philippe Boulanger*, Fernando Martinez** and Enrique C. Segura**
*LOCEAN, UMR CNRS/IRD/UPMC, Tour 45-55/Etage 4/Case 100, UPMC, 4 Place Jussieu, 75252 Paris Cedex 05, France. Presently at Departamento de Ciencias de la Atmosfera y los Oceanos, University of Buenos Aires, Argentina
**Departamento de Ciencias la Computación, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Argentina
Methodology: Training phaseMethodology: Training phase20
th c
entu
ry m
odel
sim
ula
tion
s 20th cen
tury ob
servations
Transfer Function
Methodology: Projection phaseMethodology: Projection phase21
st c
entu
ry m
odel
sim
ula
tion
s 21sr cen
tury p
rojection
Transfer Function
?
Neural Networks and Bayesian statisticsNeural Networks and Bayesian statistics
Error on the weights
€
exp(−Ew ) =α
2π
⎛
⎝ ⎜
⎞
⎠ ⎟W / 2
exp −α
2wi
2
i=1
W
∑ ⎛
⎝ ⎜
⎞
⎠ ⎟
Model-Data fit error
€
exp(−ED ) =β
2π
⎛
⎝ ⎜
⎞
⎠ ⎟N / 2
exp −β
2y(xn ,w) − tn{ }
2
n=1
N
∑ ⎛
⎝ ⎜
⎞
⎠ ⎟
Two classes of error:
€
ln p(D | α ,β ) = −αEw − βED −1
2ln A +
W
2lnα +
N
2ln β −
W + N
2ln(2π )
Neural Networks and Bayesian statisticsNeural Networks and Bayesian statistics
• The evidence procedure: a maximization problem
• Generalizing the hyperparameter concept:• Define one hyperparameter for each entry neuron• Compute a Model Weight index as:
€
MWIi = (1/α i) / (1/α i)i
∑
Why using neural networks?Why using neural networks?
• The major critics to Bayesian methods is the subjective choice of the prior distribution
• Neural networks optimized by Bayesian methods define prior distributions linked to the NN architecture, not to the field or models under study (more objective)
• Most methods are based on a linear combination of model outputs (indices)
• NN optimized by Bayesian methods offer a non-linear combination of model spatial outputs (maps)
• The major limitation of NN is their reduced skill for extrapolation
What results do we expect?What results do we expect?
• A set of MWIs potentially generalized to a set of linear combination weights. • They indicate how much each model contributes to the model
mix. • To a certain extent, they should indicate a model skill (with
caution).
• A non-linear transfer function if the NN has skills for extrapolation
• A universal method based on objective definitions of the prior and likelihood distributions
Observations and modelsObservations and models
• Observations: • CRU data interpolated onto a 2.5°x2.5° grid
• Models: Model Name andInstitute
Ocean model Atmosphere model Land model Ice model
ipsl_cm4IPSL
OPA8.12x2L31
LMDZ.3- 96x72x19 ORCHIDEE1.3 LIM
cnrm_cm3Météo-France
OPA8.12x2L31
Arpege-Climat v3(T42L45, cy 22b+)
TRIP Gelato 3.10
mpi_echam5MPI
(1x1L41) ECHAM5 (T63L32) ECHAM5
ukmo_hadcm3UKMO
1.25 x 1.25 2.5 x 3.75 MOSES1
ncar_ccsm3_0NCAR
POP1.4.3, gx1v3 CAM3.0, T85L26 CLM3.0, gx1v3 CSIM5.0,T85
gfdl_cm2_1GFDL
OM3.1(mom4p1p7_om3p5,tripolar360x200L50)
AM2.1 (am2p13fv,M45L24)
LM2 SIS
miroc3_2_medresMIROC
COCO3.3256x192 L44
AGCM5.7b, T42 L20 MATSIRO T42 COCO3.3,256x192L44
Temperature analysisTemperature analysis
Too warm
Temperature analysisTemperature analysis
Too zonal
Mean Temperature MWIsMean Temperature MWIs
IPSL CNRM MPI UKMO NCAR GFDL MIROC
Four season MWIsFour season MWIs
IPSL CNRM MPI UKMO NCAR GFDL MIROC
IPSL CNRM MPI UKMO NCAR GFDL MIROC
IPSL CNRM MPI UKMO NCAR GFDL MIROC
IPSL CNRM MPI UKMO NCAR GFDL MIROC
MLP validationMLP validation
MLP Validation for extrapolationMLP Validation for extrapolation
SRES A2
SRES A1B
SRES B1
SRES A2SRES A1BSRES B1
SSA:South of 30°S
NSA:North of 30°S
LPB:La Plata Basin
SRES A2
SRES A1B
SRES B1
MWIs
SRES A2
SRES A1B
SRES B1
General Conclusions-MethodGeneral Conclusions-Method
• NN optimized by Bayesian method may allow:• Computing MWIs representative of model skills• Evaluating an optimal linear combination of climate models• Defining an objective prior distrbution independent on the
problem under study
• BUT their skill in extrapolating is case-dependent and leads to very different behavior and conclusions.
• Further analysis are required, with a larger ensemble of models and over different regions of the world (more regional approach).
Temperature: conclusions-1Temperature: conclusions-1
• MLP optimized by Bayesian methods lead to estimate the optimal set of weights for combining linearly IPCC climate models
• MLP skill for extrapolation is low due to the common and large trend of temperature among models, which make future values be out of present climate data distribution
• MLP projection allows deriving a level of confidence in the projection, which summarizes linear model combination error and model dispersion (or disagreement in future changes)
Temperature: conclusions-2Temperature: conclusions-2
• Large temperature increase over most of the continent with a seasonal cycle modulation• SRES A2 displays the largest warming. • SRES A1B projects a warming 80% that of SRES A2 in late 21st
century. • SRES B1 reaches about 60% of SRES A2 warming. All display the
same patterns.
• NSA: about 4°C increase with larger amplitudes over the Chilean and Peruvian coasts, the central Amazons and the Colombia-Venezuela-Guiana region. Amplitude of the seasonal cycle would be reduced.
• SSA: about 3°C increase, but the penalizing function is close to zero in the southern tip. Amplitude of the seasonal cycle would increase
Precipitation-ConclusionsPrecipitation-Conclusions
• In SRES A2, annual mean precipitation would decrease over Colombia-Venezuela-Guyana as well as part of the Amazons and the Chilean coasts, while it would increase at the equator on the Pacific side and between 20°S and 35°S along the Atlantic coasts.
• In the northern part of South America, precipitation increases in summer and decreases in winter. During austral summer, the South American Monsoon would be weaker. Nordeste would receive less precipitation in austral summer and fall, but more precipitation in winter and spring.
• Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes.
General Conclusions-ResultsGeneral Conclusions-Results• In a much warmer climate as the one projected, it is
likely that changes in winter conditions may increase the risk of development of Dengue southward of its actual position.
• This is only one example of potential climate impact on society, there is no doubt that other diseases as well as crop yields may also be affected in such conditions.
• The study of such impacts in South America is under analysis in the framework of the European CLARIS Project (http://www.claris-eu.org).
• Our results must be compared to other methods to evaluate whether they all converge toward similar projections.
Sensitivity to MLP architectureSensitivity to MLP architecture
Multi-model ensemble and Bayesian methods
Multi-model ensemble and Bayesian methods
• Optimally combine models based on their skills in simulating present climate conditions
• Works by Giorgio et al. (2001), Giorgi and Mearns (2002) and Tebaldi et al. (2004) based on Bayesian statistics offers an interesting method to project climate indices
€
p(λ i | Mn{ }n=1,N
) =p(M = Data | λ i) p(λ i)
p(M = Data)PriorLikelihood
Neural NetworksNeural Networks
Multi-layer Perceptron
Neural NetworksNeural Networks
Multi-layer Perceptron
Neural Networks and Bayesian statisticsNeural Networks and Bayesian statistics
€
p(w | D) =p(D | w)p(w)
p(D)Density of parameters for a given data set D
Prior Distribution
€
p(w) =α
2π
⎛
⎝ ⎜
⎞
⎠ ⎟W / 2
exp −α
2wi
2
i =1
W
∑ ⎛
⎝ ⎜
⎞
⎠ ⎟
Likelihood
€
p(D | w) =β
2π
⎛
⎝ ⎜
⎞
⎠ ⎟N / 2
exp −β
2y(xn ,w)− tn{ }
2
n=1
N
∑ ⎛
⎝ ⎜
⎞
⎠ ⎟
Prior
Likelihood
Neural Networks and Bayesian statisticsNeural Networks and Bayesian statistics
€
p(w | D) = p(w,α ,β | D)dαdβ∫∫p(w | D) ≈ p(w |α MP ,β MP ,D) p(α ,β | D)dαdβ∫∫
Prior Distribution
€
p(w) =α
2π
⎛
⎝ ⎜
⎞
⎠ ⎟W / 2
exp −α
2wi
2
i =1
W
∑ ⎛
⎝ ⎜
⎞
⎠ ⎟
Model-Data fit error
€
p(D | w) =β
2π
⎛
⎝ ⎜
⎞
⎠ ⎟N / 2
exp −β
2y(xn ,w)− tn{ }
2
n=1
N
∑ ⎛
⎝ ⎜
⎞
⎠ ⎟
Posterior Distribution as a function of the hyperparameters
To optimize
Four seasonsFour seasons
DJF
MAM
JJA
SON
Precipitation: A different fieldPrecipitation: A different field
• Climate models poorly simulate the precipitation fields (mean and variability)
• Climate models strongly disagree in simulating future changes
BUT
• Most of the climate models project future mean precipitation values similar or close to the range of present climate distribution
What can we expect?What can we expect?
• MWIs are unlikely to be useful to create an optimal linear combination.
BUT• MWIs may represent the model skills in
simulating the large scale structures.
• The MLPs may actually be used directly to project climate change model outputs optimally combining the models and correcting the model biases.