Transcript Document

ESA Climate Change Initiative
Climate Modelling User Group
CMUG
www.cci-cmug.org
Overview
• What are the issues for climate
modelling?
• What is the role of CMUG?
• What is the added value of CMUG?
(requirements, errors, data formats, exploitation,
lessons learnt)
• Some examples..
Slide 2
Uses of satellite data for climate
• To ascertain decadal and longer term changes in the climate
• Detection & attribution of observed variations to natural and
anthropogenic forcings
• Evaluate the physical processes most relevant to reducing
uncertainty in climate prediction
• To develop, constrain and validate climate models thus gaining
confidence in projections of future change
• Input or comparison to reanalyses (e.g. ERA-CLIM, EURO4M)
• Seasonal and decadal model initialisation (ocean, land surface,
stratosphere)
• To identify biases in current and past in situ measurements (e.g.
radiosondes, buoys)
Slide 3
Issues for climate modelling
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Higher resolution (horiz, vertical, time)
Regional climate prediction (e.g. UKCP)
More physical processes
Seasonal to decadal prediction
Use of reanalyses for climate
Seamless prediction - weather prediction to climate
change using same model
• Metrics developed to evaluate models – CCI
datasets can help here
• The way we use observational data is evolving
Slide 4
Model resolutions are
increasing…
E.g. the new Met Office
model, HadGEM3, will
have a horizontal
resolution of ~ 60 km and
85 vertical levels
Issues for climate modelling
•
•
•
•
•
•
Higher resolution (horiz, vertical, time)
Regional climate prediction (e.g. UKCP)
More physical processes
Seasonal to decadal prediction
Use of reanalyses for climate
Seamless prediction - weather prediction to climate
change using same model
• Metrics developed to evaluate models – CCI
datasets can help here
• The way we use observational data is evolving
Slide 6
Climate models are becoming
increasingly complex…
A fully coupled Earth
System Model includes:
• Atmosphere, ocean, seaice, land surface
• Land ecosystems:
vegetation, soils
• Ocean ecosystems:
plankton
• Aerosols: sulphate, black
carbon, organic carbon,
dust, sea salt
W. Collins et al., 2008
• Tropospheric chemistry:
ozone, methane, oxidants
Issues for climate modelling
•
•
•
•
•
•
Higher resolution (horiz, vertical, time)
Regional climate prediction (e.g. UKCP)
More physical processes
Seasonal to decadal prediction
Use of reanalyses for climate
Seamless prediction - weather prediction to climate
change using same model
• Metrics developed to evaluate models – CCI
datasets can help here
• The way we use observational data is evolving
Slide 8
Decadal prediction:Global mean
surface temperature anomaly
Observations
Forecast
Forecast from 2007
D. Smith et al., Science 2007
© Crown copyright Met Office
Requires data for both
initialisation and
verification of forecasts.
Issues for climate modelling
•
•
•
•
•
•
Higher resolution (horiz, vertical, time)
Regional climate prediction (e.g. UKCP)
More physical processes
Seasonal to decadal prediction
Use of reanalyses for climate (ERA-CLIM)
Seamless prediction - weather prediction to climate
change using same model
• Metrics developed to evaluate models – CCI
datasets can help here
• The way we use observational data is evolving
Slide 10
The way we use data for
model evaluation is evolving
Model
CloudSat
• Forward modelling of measured quantities
(radiances, radar reflectivities) rather than highlevel products
from A. Bodas-Salcedo
et al., 2009
• Increased focus on using observations to
investigate physical processes in greater detail
• Aim is to improve the representation of these
processes in climate models
CMUG Consortium
Met Office Hadley Centre
HadGEM, FOAM, HadISST
Roger Saunders Mark Ringer Paul Van Der Linden
ECMWF
IFS, ERA, MACC
Dick Dee
David Tan
MPI-Meteorology
ECHAM, JSBACH
Alex Loew Silvia Kloster Stefan Kinne
Slide 13
MétéoFrance
Arpege, MOCAGE, CNRM-CM, Mercator
Serge Planton Thierry Phulpin
ESA CCI projects
CMUG Consortium and models
Sea-level
Sea surface
temperature
Ocean Colour
Glaciers and ice caps
Climate
Modellers
Land Cover
Fire disturbance
Cloud properties
Ozone
Aerosols
Met Office Hadley Centre
Climate Modelling
NWP
HadGEM3, FOAM, HadSST
Greenhouse Gases
ECMWF
Reanalyses
NWP
IFS (ERA-Interim)
MACC
MPI-Hamburg
Climate Modelling
ECHAM6, JSBACH
Reanalyses
Slide 14
MétéoFrance
Climate Modelling
NWP
Arpege, MERCATOR
CNRM-CM, MOCAGE
Main Activities of CMUG
1. Refining of scientific requirements derived from
GCOS for climate modellers.
2. Provide technical feedback to CCI projects
3. Assess the global satellite climate data records
(CDRs) produced from the 10 CCI consortia
4. Look specifically at required consistencies across
ECVs from a user viewpoint.
5. Promote and report on the use of the CCI
datasets by climate modellers
6. Interact with related climate modelling and
reanalysis initiatives.
Slide 15
Main Activities of CMUG
1. Refining of scientific requirements derived from
GCOS for climate modellers.
2. Provide technical feedback to CCI projects
3. Provide reanalysis data to CCI projects
4. Assess the global satellite climate data records
(CDRs) produced from the 10 CCI consortia
5. Look specifically at required consistencies across
ECVs from a user viewpoint.
6. Promote and report on the use of the CCI
datasets by modellers
7. Interact with related climate modelling and
reanalysis initiatives.
Slide 16
Implications for requirements
• The new ECV datasets must have added value over existing
ones and future proof for model evolutions
• Start from GCOS Tables as much has been done there
• Be clear about applications for specific dataset as this drives
the required accuracy:
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–
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Climate monitoring
Change detection
Evaluate processes in model
Model validation
Assimilation
high stability, precision and accuracy
high stability, precision
high precision and accuracy
high stability, precision
high precision
• Datasets must be globally complete (spatially and
temporally)
• Uncertainty estimates are as important as product itself for
all applications. Correlation of errors in space/time also
important
Slide 17
Lessons learnt from past
• Recognise move of modellers to using lower level of
products (e.g. level 1 radiances). This is especially true for
reanalyses (N.B. Importance of GSICS and CLARREO)
• It took more than 15 years to get ISCCP cloud and ATSR
SST datasets used for climate
• Observation simulators are important for some satellite
products to compare apples with apples (e.g. clouds ..)
• Good statistical summaries of TCDRs help
• CCI projects should provide colocated datasets
• Essential to include error characteristics
• Easy access to data and simple format to read
Slide 18
Observation simulators
Observation
simulator
• Ensures comparison of equivalent model
variable with observations
• This was the key for use of ISCCP clouds
• Note additional source of error from simulator in
comparisons
Slide 19
COSP
CFMIP Observational Simulator Package
COSP
STATS
OBSERVATIONS
STATS
Altitude (km)
MODEL WORLD
CloudSat
CALIPSO
ISCCP
MISR
MODIS
Radar Reflectivity
COMMON GROUND
Slide 20
Users are being consulted
On-line questionnaire is available at:
http://survey.euro.confirmit.com/wix/p416267727.aspx until 4th July.
• To date replies from about 25 respondents
• Also meeting at EGU General Assembly to gather inputs
• IS-ENES to provide input to questionnaire and help analyse results
• BADC providing advice on data format issues
Climate modelling centres consulted:
Hadley, UEA,MPI, IPSL, MF CNRM, Rossby Centre, GFDL, GISS,
NCAR, JPL, JAMSTEC, NCEO, MRI-JMA, CMA, CAWCR, NCMWRF,
KMA, ….
Reanalysis centres:
ECMWF, JMA, NCEP, GMAO, CIRES
Slide 21
Speaking the same language
• Definition of variables has in
the past been top-down
• New communities are more
bottom up via internet fora
• We need to bridge the gap
between EO data providers
and climate modellers
• CMOR NetCDF is an example
from the climate world
Slide 22
Satellite
world
Climate
world
Data Format Issues
(inputs from EGU meeting)
• Access: FTP, Web browser, OpenDAP,..
• Level of processing:
– Level 1 (swath) for model assessment (N.B. needs model observation
operator ideally in COSP)
– Level 2 (swath) for model process studies and inferring trends
– Level 3 (gridded) for generic model evaluation
• Format: CF compliant NetCDF (but what about swath data?)
• Projection: Lat/Long preferred
• Tools for reading: Dataset producers should provide
these
• Consistency for all products produced in CCI
Slide 23
Main Activities of CMUG
1. Refining of scientific requirements derived from
GCOS for climate modellers.
2. Provide technical feedback to CCI projects
3. Provide reanalysis data to CCI projects
4. Assess the global satellite climate data records
(CDRs) produced from the 10 CCI consortia
5. Look specifically at required consistencies across
ECVs from a user viewpoint.
6. Promote and report on the use of the CCI
datasets by modellers
7. Interact with related climate modelling and
reanalysis initiatives.
Slide 24
Examples of CMUG input
• Ensure CDRs proposed are useful for
climate or reanalysis aplications
• Ensure proposed datasets are consistent
with requirements
• Provide a consistent framework for
specification of errors
• Assess the need for observation
simulators or other tools for exploitation
Slide 25
Error characterisation of CDRs
• An estimate of the errors for each CDR produced is
essential for use in climate applications
• There are several types of errors
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Precision
Accuracy
Stability
Representativeness
See next slide for definitions
• The importance of specifying each depends on the
application
• Errors should be specified on a FOV basis. Aggregated
error estimates are not sufficient
• Single sensor products are simpler than merged products
• Error correlations are also important to document
Slide 26
Errors associated with CDRs
• Accuracy is the measure of the non-random, systematic error, or
bias, that defines the offset between the measured value and the true
value that constitutes the SI absolute standard
• Precision is the measure of reproducibility or repeatability of the
measurement without reference to an international standard so that
precision is a measure of the random and not the systematic error.
Suitable averaging of the random error can improve the precision of
the measurement but does not establish the systematic error of the
observation.
• Stability is a term often invoked with respect to long-term records
when no absolute standard is available to quantitatively establish the
systematic error - the bias defining the time-dependent (or instrumentdependent) difference between the observed quantity and the true
value.
• Representativity is important when comparing with or assimilating in
models. Measurements are typically averaged over different
horizontal and vertical scales compared to model fields. If the
measurements are smaller scale than the model it is important. The
sampling strategy can also affect this term.
Slide 27
Main Activities of CMUG
1. Refining of scientific requirements derived from
GCOS for climate modellers.
2. Provide technical feedback to CCI projects
3. Provide reanalysis data to CCI projects
4. Assess the global satellite climate data records
(CDRs) produced from the 10 CCI consortia
5. Look specifically at required consistencies across
ECVs from a user viewpoint.
6. Promote and report on the use of the CCI
datasets by modellers
7. Interact with related climate modelling and
reanalysis initiatives.
Slide 28
CMUG specific assessments
?
?
Slide 29
Integrated view of ECVs
1.
2.
3.
4.
5.
Through ensuring common input datasets are used for
CDR creation and in some cases common preprocessing (e.g. geolocation, land/sea mask, cloud detection)
Through comparisons of CDRs for different ECVs (e.g.
SST, sea-level, sea-ice and ocean colour)
Through comparisons of CDRs with model fields (e.g.
GHG and Ozone CDRs and MACC model profiles/total
column amounts) CMUG will be involved in
development of observation simulators for some ECVs
Pre-cursors of ECVs will be used for preparation.
Through studying teleconnections (e.g. El-Nino SST
shows consistent impact on cloud fields, fires).
Through assimilation of CDRs and to assess impact on
analyses and predictions (e.g. SST in ERA-Interim)
Slide 30
Landcover
•
Land cover product
ECV landcover will provide land
cover information, but no land
surface parameters associated
with it.
Model parameters
Surface paramters per grid cell and PFT: e.g.
-Albedo
-Background albedo
-LAI
-faPAR
-Forest ratio
-Soil parameter
-Roughness length
Slide 32
Landcover
•
ECV landcover will provide land
cover information, but no land
surface parameters associated
with it.
•
Objective
Land cover product
– Generation of a consistent land
surface parameter data set to be
used in climate models
• Variable in time (no pure
climatology)
• Additional information about
variability of surface parameters
per PFT at the model grid scale
– Evaluate the impact of the new
surface parameter set on climate
model simulations using ECHAM6
– Provision of the data set to the
climate modelling community for
assessment in their models
Slide 33
Model parameters
Surface paramters per grid cell and PFT: e.g.
-Albedo
-Background albedo
-LAI
-faPAR
-Forest ratio
-Soil parameter
-Roughness length
Data used in ERA-Interim
Understanding the effect of changes in the
observing systems is key to understanding
reanalysis quality
Slide 34
Total ozone content over the period
2000-2005
(Tesseydre et al, 2007)
NIWA
climatology
MOCAGEClimat
Use of ISCCP to evaluate models
Low level cloud: CTP < 680 hPa
ISCCP
HadGEM1
Thick
“Stratus”
OD > 23
Medium
“Stratocu”
3<OD<23
Martin et al. (2006)
Slide 36
HadCM3
Main Activities of CMUG
1. Refining of scientific requirements derived from
GCOS for climate modellers.
2. Provide technical feedback to CCI projects
3. Provide reanalysis data to CCI projects
4. Assess the global satellite climate data records
(CDRs) produced from the 10 CCI consortia
5. Look specifically at required consistencies across
ECVs from a user viewpoint.
6. Promote and report on the use of the CCI
datasets by modellers
7. Interact with related climate modelling and
reanalysis initiatives.
Slide 37
Related Activities
1.
2.
3.
4.
5.
6.
7.
GCOS and GSICS
EU (IS-ENES, EUGENE, ..)
EUMETSAT (CM-SAF) and SCOPE-CM
NOAA-NASA initiatives (e.g. JPL CMIP5)
WCRP Observation and Assimilation Panel
Reanalyses (ERA-CLIM, MACC) + EURO4M
Coupled Model Intercomparison Project and
follow-on activities
8. IPCC AR-5 and AR-6
Slide 38
Proposed CMIP5 model runs
CCI datasets could
start to be used in the
evaluation of these
results
AR-5
Slide 39
Summary
• CMUG has now started to support the
ESA CCI.
• We are seeking input from the climate
modelling and reanalysis communities.
• It is crucial the products produced are ‘fit
for purpose’ otherwise this will be a lost
opportunity (and wasted money).
• If interested please get in touch via
[email protected]
Slide 40
Any questions?
www.cci-cmug.org
[email protected]
Slide 41