Transcript ENSEMBLES statistical downscaling portal

ACRE Workshop, June 23-25, 2008. Zurich
http://www.mdm.unican.es
The ENSEMBLES Statistical
Downscaling Web Portal.
End2End Tool for Regional Projection
Antonio S. Cofiño
D. San-Martín, J.M. Gutiérrez, C. Sordo, J.
Fernández, D. Frías, M.A. Rodríguez, S.
Herrera, R. Ancell, M. Pons, B. Orfila, E. Díez
Meteorology &
Data Mining
Santander Group
Motivation
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There are many projects around the world producing global (GCM) and
regional (RCM) simulations of climate change.
Many of these projects involve end-uses from impact sectors ...
However, it is still difficult for end-users to access the stored simulations and
to post-process them to be suitable for their own models: daily resolution,
interpolation to prescribed locations, etc.
There is a need of friendly interactive tools so users can easily
run interpolation/downscaling jobs on their own data using the
existing downscaling techniques and simulation datasets (AR4,
Prudence, ENSEMBLES, ...).
Statistical Downscaling (SD). WHY?
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Typical resolution
of climate
change GCMs.
ECHAM5/MPI-OM (200 km)
There is a gap between
the coarse-resolution
outputs available from
GCMs and the regional
needs of the end-users.
Surface variables:
• Mean precipitation
• Precip. 90th percentile
• Consecutive dry days
• Number of heavy events
Typical resolution
of Seasonal
GCMs.
Even if they
work at
seasonal or
climate change scales, end-users
normally need daily values
interpolated (downscaled) to the
local points or grids of interest.
Statistical Downscaling (SD). HOW?
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Emission Scenarios
Dynamical Downscaling runs
regional climate models in
reduced domains with boundary
conditions given by the GCMs.
Global
Predictions
GCM
RCM
A2
Historical Records
B2
A2
Climatology (1961-90)
Y = f (X;)
The form and
parameters of the
model depend of
Statistical Downscaling is based on the different
empirical models fitted to data using tecniques used.
historical records.
A2
SD. Transfer Functions
Precipitaion,
temperatures,
etc.
(T(1ooo mb),..., T(500 mb);
Z(1ooo mb),..., Z(500 mb);
.......;
Yn
Linear Regression: TMaxn = a+b T850n
Neural Networks:
Local Records of Tmax
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H(1ooo mb),..., H(500 mb)) = Xn
TMaxn = f (T850n ),
Neural Net.
Linear
model
850mb
GCM outputs (closest grid point)
Neural networks are non-parametric
models inspired in the brain.
SD. Weather Types (e.g. analogs)
Analog set
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PC2
frequency
mean
PC1
Weather
Type
(cluster)
Pforecast (precip > u) = SCk P(precip > u | Ck) Pforecast(Ck)
Skill of Statistical Downscaling
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The variability of the results obtained using different types of downscaling models in
some studies suggests the convenience of using as much statistical downscaling
methods as possible when developing climate-change projections at the local scale.
For some indices and
seasons, the spread is very
small (e.g. pav in JJA) but
for others it is much larger
(e.g. pnl90 in DJF).
Importantly, for each index
the variability among
models is of the same order
of magnitude as the
variability between the two
scenarios.
DOWNSCALING HEAVY PRECIPITATION
OVER THE UNITED KINGDOM:
A COMPARISON OF DYNAMICAL AND
STATISTICAL METHODS AND
THEIR FUTURE SCENARIOS
(HAYLOCK ET AL. 2006)
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www.meteo.unican.es/ensembles
30 users from 20 partners
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Computing Infraestructure
Data Availability. Observations
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• ECA (European Climate Assessment & Dataset project). Daily
datasets of precipitation, temperature, pressure, humidity, cloud cover,
sunshine and snow depth since 1900 over networks of 100-1000
stations.
• Ensembles 50km gridded daily
observation records of precipitation
and surface temperature. 1950-2006.
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Data Availability. GCMs
Data Availability. Reanalysis & S2D
Data available for the European region:
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• NCEP/NCAR Reanalysis1. 1948-2007
• ERA40 ECMWF: 1957-2002
• JRA25 Japanese Reanalysis: 1979-2004
A smaller worldwide dataset is also available
Available for Europe
• DEMETER. Multi-model seasonal prediction experiment
including seven models ran for six months four times a year
using 9 different perturbed initial conditions (9 members).
• ENSEMBLES Stream 1. Check the help in the portal for updated
information about this dataset.
Data Availability. ACC
Daily worldwide datasets obtained
from different sources:
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• CERA
• IPCC data centre (PCMDI)
• Local Providers.
• PCMDI_CGCM3. Canadian Centre for Climate Modelling and
Analysis, including 20th century (from 1951 to 2000) and scenarios
A1B, B1 (periods 2046-2065 and 2081-2100).
• CERA_MPI-ECHAM5, including 20th century data (1961-2000)
and scenarios A1B, B1, and A2 (2001-2100).
• CNRM-CM3 (local provider), including 20th century (1961-2000)
and scenarios A1B, B1, and A2 (2001-2100).
We will continue including datasets as they become available.
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Data Access Portal
60,Potential Vorticity,PV
129,Geopotential,Z
130,Temperature,T
131,U velocity,U
132,V velocity,V
133,Specific humidity,Q
136,Total Column Water,TCW
137,Total Column Water Vapour,TCW
138,Relative vorticity,VO
142,Large Scale Precipitation,LSP
143,Convective Precipitation,CP
151,MSLP,MSL
155,Divergence,D
157,Relative humidity,R
165,10m E-Wind Component,10U
166,10m N-Wind Component,10V
167,2m Temperature,2T
168,2m Dew Point,2D
1000, 925, 850, 700, 500, 300 mb
00, 06, 12, 18 , 24 UTC
1.125ºx1.125º resolution
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Data Access: s2d & acc
Statistical Downscaling Portal
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Problem: Local climate change prediction for
Madrid (Spain): maximum temperature
Goal: Provide daily local values for the summer
season june-august 2010-2040 in a suitable format
(e.g., text file, or Excel file).
Predictors
(T(1ooo mb),..., T(500 mb);
Regional zone
Local zone
Z(1ooo mb),..., Z(500 mb);
H(1ooo mb),..., H(500 mb))
Xn
Downscaling
Model
Regres, CCA, …
Yn = WT Xn
Predictands
Precipitation
Temperature
Yn
This is the structure followed in the portal’s design:
predictors + predictand + downscaling method.
Demo... My Home
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The “My Home”
tab allows the
user to explore:
1.
The zones
(pre-defined
regions).
2.
The profile
with the
account
information.
3.
The status
of the jobs:
queued,
running,
finished.
Demo... Predictors
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A simple zone with
a single predictor
parameter:
T850mb
was created.
New zones can be
easily defined by
clicking in the “new
zone” button.
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Demo... predictand
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Demo... Downscaling Method
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Demo... Validation
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Demo... Regional Projection
Demo...Time to Compute
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Scheduling the job
Five minutes later ...
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Climate Change Scenarios
Max. Temp
Seasonal validation (RSA)
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Precip
Tmax
... windows of opportunity with ENSO
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Teleconnections with ENSO may bring some seasonal
predictability to the extra-tropics and, thus, some window
of opportunity for operational seasonal forecast.
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... windows of opportunity with ENSO
Current Status
• Support for users in ENSEMBLES project.
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• Data access for Reanalysis, Seasonal2Decadal and
Climate Change models.
• Users can use common observations datasets or
upload their own data for downscaling.
• Data access control based on user authentication and
authorization.
• User can choose predictors, predictands and transfer
function to be used in the downscaling process.
• Quality assessment of the downscaling.
• Download the downscaled data.
Future actions
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• Start to open the tool to wider community outside
ENSEMBLES project and Europe.
• Data access and storage: towards remote accessing of
datasets based on OPeNDAP. Reanalysis, S2D & ACC
simulations.
• Incorporate more statistical downscaling tools.
• Ongoing work on geographically distributed computing
and storage based on GRID technologies (EGEE,
EELA,…).
Thank you !!!
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[email protected]