1. ESA Climate Change Initiative Climate Modelling User Group CMUG www
ESA Climate Change Initiative Climate Modelling User Group CMUG

2. 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..

3. 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)

4. 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

5. Model resolutions are increasing…
E.g. the new Met Office model, HadGEM3, will have a horizontal resolution of ~ 60 km and 85 vertical levels

6. 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

7. Climate models are becoming increasingly complex…
A fully coupled Earth System Model includes:
Atmosphere, ocean, sea- ice, land surface
Land ecosystems: vegetation, soils
Ocean ecosystems: plankton
Aerosols: sulphate, black carbon, organic carbon, dust, sea salt
Tropospheric chemistry: ozone, methane, oxidants
W. Collins et al., 2008

8. 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

9. Decadal prediction:Global mean surface temperature anomaly
Observations
Forecast
Forecast from 2007
Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris and J. M. Murphy, 2007, Improved surface temperature prediction for the coming decade from a global climate model, Science, 317, , doi: /science
Thames Barrier was designed to cope with tidal levels that were anticipated by A new Thames Barrier is being planned and this one will have to last into next century. They’ll need to know about uncertainty in predicted climate change to assess the risks and make a good decision.
What is the measure of uncertainty?
Requires data for both initialisation and verification of forecasts.
D. Smith et al., Science 2007
© Crown copyright Met Office

10. 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

11. The way we use data for model evaluation is evolving
CloudSat
Forward modelling of measured quantities (radiances, radar reflectivities) rather than high-level products
Increased focus on using observations to investigate physical processes in greater detail
Aim is to improve the representation of these processes in climate models
from A. Bodas-Salcedo et al., 2009

12. Objectives of CMUG Support integration within the CCI programme
Through requirements and user assessment from the Climate Modelling Community
Through feedback from a “climate system” perspective
Foster the exploitation of Global Satellite Data Products within the Climate Modelling Community
By promoting the use of CCI data sets to climate modellers
By building partnership and links with existing research organisations, networks and scientific bodies of the Climate Modelling Community.
Assess quality and impact of individual/combined Global Satellite Data Products in Climate Model and Data Assimilation context
By assessing suitability of products for climate applications (e.g. climate modelling, decadal prediction, reanalysis, etc)
By quantifying their incremental value on model performance in an objective manner.

13. Met Office Hadley Centre
CMUG Consortium
Met Office Hadley Centre
HadGEM, FOAM, HadISST
Roger Saunders
Mark Ringer
Paul Van Der Linden
ECMWF
IFS, ERA, MACC
MPI-Meteorology
ECHAM, JSBACH
MétéoFrance
Arpege, MOCAGE, CNRM-CM, Mercator
Dick Dee
David Tan
Alex Loew
Silvia Kloster
Stefan Kinne
Serge Planton
Thierry Phulpin

14. CMUG Consortium and models
ESA CCI projects
Sea-level
Sea surface temperature
Ocean Colour
Glaciers and ice caps
Land Cover
Fire disturbance
Cloud properties
Ozone
Aerosols
Greenhouse Gases
Climate
Modellers
Reanalyses

15. Main Activities of CMUG
Refining of scientific requirements derived from GCOS for climate modellers.
Provide technical feedback to CCI projects
Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia
Look specifically at required consistencies across ECVs from a user viewpoint.
Promote and report on the use of the CCI datasets by climate modellers
Interact with related climate modelling and reanalysis initiatives.

16. Main Activities of CMUG
Refining of scientific requirements derived from GCOS for climate modellers.
Provide technical feedback to CCI projects
Provide reanalysis data to CCI projects
Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia
Look specifically at required consistencies across ECVs from a user viewpoint.
Promote and report on the use of the CCI datasets by modellers
Interact with related climate modelling and reanalysis initiatives.

17. 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:
Climate monitoring high stability, precision and accuracy
Change detection high stability, precision
Evaluate processes in model high precision and accuracy
Model validation high stability, precision
Assimilation 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

18. 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

19. Observation simulators
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

20. COSP CFMIP Observational Simulator Package
CloudSat
CALIPSO
ISCCP
MISR
MODIS
STATS
MODEL WORLD
OBSERVATIONS
Altitude (km)
STATS
Radar Reflectivity
COMMON GROUND

21. Users are being consulted
On-line questionnaire is available at:
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
IS-ENES=Infrastructure for European Network for Earth System Modelling EU FP7 project
18 partners 10 countries

22. 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
Satellite Climate
world world
CMOR=Climate Model Output Rewriter
CMOR was not designed to serve as an all-purpose writer of CF-compliant netCDF files, but simply to reduce the effort required to prepare and manage MIP model output. Although MIPs encourage systematic analysis of results across models, this is only easy to do if the model output is written in a common format with files structured similarly and with sufficient metadata uniformly stored according to a common standard. Individual modeling groups store their data in different ways, but if a group can read its own data, then it should easily be able to transform the data, using CMOR, into the common format required by the MIPs. The adoption of CMOR as a standard code for exchanging climate data will facilitate participation in MIPs because after learning how to satisfy the output requirements of one MIP, it will be easy to prepare output for other MIPs.

23. 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

24. Main Activities of CMUG
Refining of scientific requirements derived from GCOS for climate modellers.
Provide technical feedback to CCI projects
Provide reanalysis data to CCI projects
Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia
Look specifically at required consistencies across ECVs from a user viewpoint.
Promote and report on the use of the CCI datasets by modellers
Interact with related climate modelling and reanalysis initiatives.

25. 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

26. 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
Precision
Accuracy
Stability
Representativeness
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
See next slide for definitions

27. 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 instrument-dependent) 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.

28. Main Activities of CMUG
Refining of scientific requirements derived from GCOS for climate modellers.
Provide technical feedback to CCI projects
Provide reanalysis data to CCI projects
Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia
Look specifically at required consistencies across ECVs from a user viewpoint.
Promote and report on the use of the CCI datasets by modellers
Interact with related climate modelling and reanalysis initiatives.

29. CMUG specific assessments? ?

30. Integrated view of ECVs
Through ensuring common input datasets are used for CDR creation and in some cases common pre-processing (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)

31. Integrated view (TBD) ?

32. 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
How far we can go will depend on the data we will get
Best exploit high resolution observational data

33. Landcover
Land cover product
ECV landcover will provide land cover information, but no land surface parameters associated with it.
Objective
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
Model parameters
Surface paramters per grid cell and PFT: e.g.
Albedo
Background albedo
LAI
faPAR
Forest ratio
Soil parameter
Roughness length
How far we can go will depend on the data we will get
Best exploit high resolution observational data

34. Data used in ERA-Interim
Understanding the effect of changes in the observing systems is key to understanding reanalysis quality 34

35. Total ozone content over the period 2000-2005 (Tesseydre et al, 2007)
NIWA climatology
MOCAGE-Climat
Evolution des moyennes zonales du contenu total en ozone de l’atmosphère entre 2000 et Le modèle est intégré en T21 L60. Les données climatologiques sont issues du NIWA (New Zealand). Il s’agit du résultat d ’une assimilation de données combinant 4 instruments TOMS (Total Ozone Mapping Spectrometer), 3 restitutions différentes d’instruments GOME (Global Ozone Monitoring Experiment), et des données d’instruments SBUV (Solar Backscatter Ultra-Violet). Cette climatologie couvre la période avec une résolution de 1,25° en longitude et 1 ° en latitude.
On note les maxima de concentration aux hautes latitudes à la fin de l’hiver et l’apparition du « trou » d’ozone au printemps, début de l’été (octobre-novembre dans l’HS.
Le modèle sur-estime l’accumulation d’ozone à la fin de l’hiver dans la basse à moyenne statosphère en raison notamment d’une circulation de Brewer-Dobdon trop rapide. Il n’y a pas de biais dans l’UTLS.
The variability of the ozone column, both spatially and temporarily, is satisfactory. However, because the Brewer-Dobson circulation is too fast, too much ozone is accumulated in the lower to mid-stratosphere at the end of winter. Ozone in the UTLS region does not show any systematic bias. In the troposphere better agreement with ozone sonde measurements is obtained at mid and high latitudes than in the tropics and differences with observations are the lowest in summer (Tesseydre et al, 2007).

36. Use of ISCCP to evaluate models Low level cloud: CTP < 680 hPa
ISCCP HadGEM HadCM3
Thick “Stratus”
OD > 23
Medium “Stratocu”
3<OD<23
Martin et al. (2006)‏

37. Main Activities of CMUG
Refining of scientific requirements derived from GCOS for climate modellers.
Provide technical feedback to CCI projects
Provide reanalysis data to CCI projects
Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia
Look specifically at required consistencies across ECVs from a user viewpoint.
Promote and report on the use of the CCI datasets by modellers
Interact with related climate modelling and reanalysis initiatives.

38. Related Activities 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
IPCC AR-5 and AR-6

39. Proposed CMIP5 model runs
CCI datasets could
start to be used in the
evaluation of these
results
AR-5

40. 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

41. Any questions? www.cci-cmug.org cmug@metoffice.gov.uk

42. Definition of terms
Essential Climate Variables are defined as geophysical variables required to characterise the state of the atmosphere and surface and its variability over decadal and longer time periods.
Climate Data Records are a long time series dataset e.g. from several consecutive satellite instruments, where the same processing has been applied to all the data and overlap periods have been used to remove any biases so it can be used for climate monitoring. Note also FCDRs (level 1b) and TCDRs (products).
Global Satellite Data Product is used for any generic satellite product that is already available (e.g. for NWP assimilation) but has not been reprocessed with climate applications in mind. However some satellite products although not suitable for climate trends can be valuable for studying processes in climate models (e.g. ISCCP).
There are some data sets that are produced for climate applications but not necessarily for climate monitoring, of which ISCCP is a good example. ISCCP makes a good point, namely that a data set that is very useful for model development and evaluation doesn’t necessarily have to be capable of detecting /quantifying trends. We need to ensure that in pursuing the latter people don’t lose sight of the former.

43. GCOS ECVs Ocean Atmosphere Land Sea-Surface Temperature
Precipitation
Earth Radiation Budget
Upper-air Temperature
Upper-air Wind
Surface Wind Speed and Direction
Water Vapour
Cloud Properties
Carbon Dioxide
Methane
Other GHGs
Ozone (tropospheric)
Ozone (stratospheric)
Aerosol Properties
Snow Cover (Extent, Snow Water Equivalent)
Glaciers and Ice Caps
Permafrost and seasonally -frozen ground
River Discharge
Lake levels
Albedo
Land Cover
fAPAR
Leaf Area Index
Biomass
Fire Disturbance
Soil Moisture (surface and root zone)
Sea-Surface Temperature
Sea Level
Sea Ice
Sea State
Ocean Salinity
Ocean Colour
The individual consortium proposals for each ECV are now starting work.
It will be important to ensure that different ECVs are consistent with others (e.g. clouds and aerosols) and the satellite datasets produced are of use to climate modellers.