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2nd ACRE Workshop1st - 3rd April, 2009

O’Reilly’s Rainforest Retreat, Lamington National Park, Queensland, Australia

Downscaling the historical reanalyses

Antonio S. Cofiñoantonio.cofino@unican.es

www.meteo.unican.es

Santander Meteorology &

Data Mining Group

GCMs

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GCMGlobal

PredictionsEmission Scenarios

Why Downscaling Methods?

AEMET

Interpolated Temp (20 km)

SCALE NEEDEDFOR IMPACT STUDIES

ECHAM5/MPI-OM (200 km)

TYPICAL SCALE OFGCMs

REALWORLD

Climatology (1961-90)

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Downscaling for Calibration

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GCMGlobal

PredictionsEmission Scenarios

Downscaling Methodologies

Climatology (1961-90)

A2

Statistical Downscaling techniques

are based on empirical models fitted to data using historical records.

Y = f (X;)

Historical Records

The form and parameters of the model depend of the different tecniques used.

A2

RCMA2 B2Dynamical Downscaling

runs regional climate models in reduced domains with boundary conditions given by the GCMs.

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Climate: Multi-Model, Multi-Scenario

Partners Model Atmosphere Resolution Ocean Resol.

UKMO

METO-HC

HadGem1 HadGam1 1.25x1.875° L38 HadGom1 0.33-1° L40

IPSL IPSL-CM4 LMDZ-4 2.5x3.75° L19 OPA8.1 0.5-2° L31

MPI ECHAM5/MPI-OM

ECHAM5 T63 L31 MPI-OM 1.5° L40

FUB EGMAM ECHAM4-MA T30 L39 HOPE-G 0.5-2.8° L20

CNRM CNRM-CM3 ARPEGE V3 T63 L45 OPA8 0.5-2° L31

NERSC ARPEGE V3-MICOM-OASIS

ARPEGE V3 T63 L31 NERSC Modified MICOM2.8 1.5° L35

DMI ECHAM5/MPI-OM

ECHAM5 T63 L31 MPI-OM 1.5° L40

UiO OSLO CTM2 OSLO CTM2 T21 L60 ---

GCM Global Predictions

Emission Scenarios

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Seasonal: Multi-Model, Multi-Analysis

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http://www.ensembles-eu.org

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Dynamical Downscaling: RCMs

Based on numerical models solving, at “high” temporal and spatial resolution, the primitive equations of the atmosphere.

Usually the low resolution outputs from GCMs are used as Boundary Conditions and Initial Conditions for one-way nesting of a Local Area Model.

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Dynamical Downscaling: RCMs

• The results from RCMs are critically dependent on decisions about:

– Spatial ant temporal resolution– The size and the position of the domain– The parameterizations of physical process– The Boundary Conditions – The Initial Conditions -> Internal variability

Fernandez et al. (2007) J Geoph Res 112:D04101 “Sensitivity of MM5 to physical parameter ...”Jones et al. (1995) Q J R Meteorol Soc 121:1413 “Simulation of climate change over Europe ...”Vukicevic & Errico (1990) Mon Wea Rev 118:1460 “The influence of artificial and physical ...”Fernández (2004) PhD Diss. UPV/EHU “Statistical and dynamical downscaling models ...”GonzálezRouco et al. (2001) J Clim 14:964 “Quality Control and Homogeneity of Precip ...”Colle et al. (2000) Wea Forecasting 15:730 “MM5 precipitation verification over the Pacific ...”

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ENSEMBLES. The SDS Portal

http://www.meteo.unican.es/ensembles

Y = f (X;)

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Data Access Portal

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Advantages Shorcomings

Linear Regression Very simpleEasy to interpret

Linear assumption

Spatially inconsistent

Selection of predictors

Neural Networks Nonlinear

“Universal” interpolator

Complex blackbox-like

Optimization required

Selection of predictors

Analogs Nonlinear

Spatial consistency

Algorithmic. No model.

Difficult to interpret

Weather Typing Nonlinear

Easy to interpret

Spatial consistency

Adaptations for EPS

Algorithmic & Generative

Loss of variance

Problem with borders (for deterministic forecasts)

Statistical Downscaling: Methods

• Transfer-Function Approaches (generative)

• Non-Generative Algorithmic Methods

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Gridded Atmospheric Patterns for day n

Predictands: precip., etc.for day n

Yn(T(1ooo mb),..., T(500 mb); Z(1ooo mb),..., Z(500 mb); .......;

H(1ooo mb),..., H(500 mb)) = Xn

Linear Regression:

Yn= a Xn+ b

a

x

y

x1 x2 x3 x4 x5

Linear Regression Logistic Regression

Suitable for probabilistic forecast with a simple modification:

Logistic Regression

P(precip>10mm)

Yn= F(a Xn+ b)

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EOF 1 EOF 2

EOF 3 EOF 4

Selection of Predictors

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Linear & Logistic Regression

Wind Speed [0,)

P(Wind Speed > 50km/h) [0,1]

Observations from 1977- 2002.

ERA40 over 27 grid points for the same period

(60% for trainning and 40% for validation)

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Artificial Neural Networks

Artificial Neural Networks are inspired in the structure and functioning of the brain, which is a collection of interconnected neurons (the simplest computing elements performing information processing):

Each neuron consists of a cell body, that contains a cell nucleus. There are number of fibers, called dendrites, and a single long fiber called axon branching out from the cell body.The axon connects one neuron to others (through the dendrites). The connecting junction is called synapse.

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Functioning of a “Neuron”

• The synapses releases chemical transmitter substances.• The chemical substances enter the dendrite, raising or lowering the

electrical potential of the cell body.• When the potential reaches a threshold, an electric pulse or action

potential is sent down to the axon affecting other neurons.(Therefore, there is a nonlinear activation).

• Excitatory and inhibitory synapses.

nonlinear activation function

neuron potential: mixed input of

neighboring neurons

weights (+ or -, excitatory or inhibitory)

(threshold)

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cxe1

1)x(f

The neural activity (output) is given by a no linear function.

Gradient descent

InputsOutputs

1. Init the neural weight with random values2. Select the input and output data and train it3. Compute the error associate with the output 4. Compute the error associate with the hidden neurons

5. Compute

and update the neural weight according to these values

Multilayer Perceptron (Feed-forward)

x h y

hi

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Regression vs. Neural Networks

Wind Speed [0,)Observations from 1977- 2002.

ERA40 over 27 grid points for the same period

60% for trainning and 40% for validation

•Model 1: Regression

•Model 2: Neural Network

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PC1

PC

2

The probabilistic local prediction is obtained from the relative frequency of snow occurrence (binary variable) in the analog set or cluster.

Analog set

WeatherType

(cluster)

Analogs & Weather Typing

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Selection of the Atmospheric Pattern

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• Logistic Regression

• Neural Network 10 PCs:5:1

• Analogs, k-NN (k=50)

Model 1

P(12 )

Model 1

Model 2

P=(P(06),P(12),P(18),P(24),P(30))

Model 2

Comparison of Techniques: Wind

P(Wind Speed > 50km/h) [0,1]

Observations from 1977- 2002.

ERA40 over 27 grid points for the same period

60% for trainning and 40% for validation

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Probabilistic Weather Typing

Pforecast (precip > u) = Ck P(precip > u | Ck) Pforecast(Ck)

The application to an EPS requires applying the method to each of the ensemble members:

x1x2x3x4x5...

Prob(x)Mean(x)

Aggregation of results

WeatherType

(cluster)

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Precipitation

Perú

Do

wn

scal

ing

nee

ded

!

Seasonal precip. during DJF 1997/98 at Morropón: 1300 mm, Sausal: 360 mm.

Observations:

Predictions (DEMETER):

50 Km

Analysis and Downscaling Multi-Model Seasonal Forecasts in Peru using Self-Organizing Maps by J. M. Gutiérrez, R. Cano, A. S. Cofiño, and C. Sordo, Tellus 57A, 435-447

(2005).

Validation of Regional Projections

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U = Percentile 80

U= Percentile 90

Probabilistic: P(precip>u)=* Numeric Forecast: Precip = *

Skill of the Downscaling Method

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Daily-Scale Regional Projection

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Downscaling & Extreme Indicators

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Conclusions

• End-User applications require downscaled data: spatial, temporal and parameters

• 2 approaches:– Dynamical: parameterization tuning, high costs in terms of computer

resources, can provide downscaled data where no observations are available,…..

– Statistical: based on “past” observations, difficult to give a physical meaning, predictor selection issues, calibration of GCM, can provide non-linear relationships between predictors and predictands,…

• Used for Climate change scenarios, Seasonal forecasting, Weather forecasting…and of course re-analysis applications

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Questions?

antonio.cofino@unican.es

http://www.meteo.unican.es